AD-A012 709

RADAR LAND CLUTTER MEASUREMENTS AT FREQUENCIES OF

9.5, 16, 35, AND 95 GHz

N. C. Currie, et al

Georgia Institute of Technology

Prepared for:

Frankford Arsenal

2 April 1975

DISTRIUTED BY:

National Technical Inforration Service11. S. DEPARTMENT OF COMMERCE

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216059 .

TECMiICAL REPORT NO. 3

. •RAD)AR LAND C1UTTER MEASUREMENTSAT FREQUENCIES OF $.6,14,36, andI 96 Hz

0

EES/OiT PROJECT A-1486

trmpard forUNITED STATES ARMYFRANKFORD ARSENALPHILADELPHIA, PA. 19137

UNDER DDCCONTRACT DAAA 26-73-C-0256 -D

N. C. Coris, F. 8. Dyer, and R. 0. HeymA

2 April 1375!I

ENGINEERING EXPERIMENT STATIONGeorgia Institnte of TechmelogyAtlaruts, G otogim 30332.

R*P odu,,c bf.7

NATIONAL TECHNICALINFORMATION SERVICE

U S Depw'."e,.# of Con-nerceSprh-gf,•ld VA 22151

Approvtd for publin roleugW. . . . . .:.. ' .-Hi .tio . .U.Ui ited .

Unclassified* '" CL ASS.iyiCATIO% OF THIS PAGE (When T.WeEa &tewod)

REPORT DOCUMENTATION PAGEREDISRTOS BEFORL: COMPLETINC. FORMJ*RL;'.)RT NUMBER 2. GVT ACCESSION NO. 3. RKCIPIENT*S CATALOG NLJMDER

4 T T, E Fand $Sabtelee) S. TYPE OF REPORT & PERIOD COVERED

Radar Land Clutter Measurements at Frequencies TcnclRprof 9.5, 16. 35 atid 95 (:i~z. Technical______Report___

6. PERFORMING ORG. REPORT NUMBER

____ ___ ___ ___ ___ ___ ____ ___ ___ ___ ___ ___ A-1485-OO 3AtT.40Rfuj S. CONTRACT OR GRANT NUMUIERIA,

N. C. Currie, F. B. Dyer, anid R. D. Hayes DAAA25-73-C-0256

4 OFFIGRORMING ORGANIZATION NAME AND ADDRESS 110. P040GRAM ELEMENT. PROJECT. TASK~

Sensor Systems Division, Engineering Experiment AE&OKUI UBR

Station, Georgia Institute of Technology Mlod, P-00004Atlanta, Georgia 30332

7 -=POLLING OFFICE NAME AND ADDRESS .RPOTAT

Frankford Arsenal 2 April 1975United States Army 13. NUMBER OF PAGES A

-.1hl.adelphia, Pennsylvania 19137 -" Ado'''4 TICRING AGENCY NAME & AODRESS'uf different from ControlIling Office) 15. SECURITY CL -ASS. (fo this report)

Unclassi fiedIS&. OECLASSIFICATIO4q DOWNGRADING

SCHEDULE

-* ý,W8B 'i(nN STATEMENT '-il liu, Repu,at,

..V70.auTtON STATEMENT tot th. abstract ejilcied in Block 20, it different Iran- Report)

r1W.rEMkN-ARY NOTES

Georgia Tech Project A-1485-003

w;vRP)S~ C~intinue *e,P rspre vide if ner e- arv and identify bi* block number)

Radar SpectramBackscatter CorrelationLand ClutterMillimeter

* A 6,- aA T " Wirniw in,, rr~ar8,- xide It nieeIx.8tv and identifi by block number)

A series of measurements of radar backscatter from foliage aiud other naturalobjects have been made at fraquencies of 9.5, 16.5, 35, and 95 G~z. The geometrof the experiments and the equipment were chosen such as to provide data usefulLo the equipment designer in the choice of operating frequency for his parti-cular mission. Amplitude statistics for both horizontal and vertical polariza-t ions were obtained. Non-coherent spectral measurements and correlation pro-perties were investigated in detail as functions of frequen:y, incident angle,

* -and windspeed. Limited comparisons are made between previous measurements and

DD - 1473 EO-'10ON oF I NOV 11% IS OBSOLETE UnclassifipdSECURITY CLASSIFICATION OF THIS PAGE ll,.', Ow.,

UnclassifiedSECURITY CLASSIFICATION OF THI4S PAGE(U,.u,,D.Vs. Emre..)

20. continued

-w' analyses and the current experimental rMsults, and certain properties of theresults are discussed in relation to the phenomenology of land clutter. Ex-tensive sumaries of the data obtained are included as a part of the report.

. . . . . . . .. . .. ......SECURITY CLASSIFICATION OF THIS PA;lGr4'h: , jW"., ,•t,

ENGINEERING EXPERIMENT STATIONGeorgia Institute of Technology

Atlanta, Georgia 30332

RADAR LAND CLUTTER MEASUREMENTS ATFREQUENCIES OF 9.5, 16, 35, and 95 GHz

Technical Report No. 3EES/GIT Project A-1485

byN. C. Currie, F. B. Dyer, and R. D. 'iayos

Prepared for

United States ArmyFrankford Arsenal

Philadelphia, Pennsylvania 19137

underContract DAAA 25-73-C-0256

2 April 1975

Contract DAAA-25-73-C-0256 A-1485-TR-3Frankford Arsenal Engineering Experiment StationUnited States Army Georgia Institute of TechnologyPhiladelphia, Pennsylvania 19137 Atlanta, Georgia 30332

RADAR LAND CLUTTER MEASUREMENTS ATFREQUENCIES OF 9.5, 16, 35, and 95 GHz

by

N. C. Currie, F. B. Dyer, and R. D. Hayes

ABSTRACT

A series of measurements of radar backscatter from foliage and other

natuival objects have been made at frequencies of 9.5, 16.5, 35, and 95 C.Hz.

The geometry of the experimants and the equipmant were chosen so " to

provide data useful to the equipment designer in the choice of operating

frequency for hir particular mission. Amplitude statistics for both horixontal

and vertical polarizations were obtained. Noncoherent spectral measurements

and correlation properties were iznvestigated in detail as functions of frequency,

incident angle, &ad windspeed. Limited comparisons are made between previcus

measurements and analyses and the current experimantal results, and certain

properties of the results are discussed in relation to the phenomenology of

land clutter. Extensive summaries of the data obtained are included as a

part of the report.

tii

ACKNOWLEDGMENTS

In addition to the authors, several individuals within the U.S.

Government and P' Georgia rech have provided assiutance in completing the

studies described herein. Mr. Joe Miller, Superintendent of Kennesaw

National Battlefield Park, and his staff were very understanding in allowing

the use of Kennesaw Mountain as a test site, and were very helpful in several

instances where equipment problems arose during the tests. Mr. Conrad Shore

of Frankford Arsenal, U.S. Army, provided valuable guidance and technical

support throughout the program. At Georgia Tech HMssrs. William Dunn,

Fred Durham, Sam Formby, and Is. Eller Robertson participated in the data

analysis or aidcd in the report preparation associated with this work. Our

appreciation is due Mr. Stave Zehner who provided valuable insight in the

interpretation of the data, especially the spectral distributions. lo. Ann

Evans and Ms. Darenda Rakestraw were responsible for final preparation and

typing of the report.

Preceding page blank

V

TABLE OF CONTENTS

I, INTRODUCTION e . . . . . . . .0 .0 . . . . 1

A. Background*. e ... .0, 1B. Description of Radar Field Measurements ... .. 3

lo Radar Teat Site . . . . . . . . . . . . . . 32. Description of Test Radars ........ 5. 53. Data Gathering Equipment. ... 11

4. AMeasurement Procedure . . . . . . . . . . . . . 16

A. DATA ANALYSIS . .. . . . . . . . . . . 19

1. Data Reduction Facility . .9. .@ . . . . .a 192. Data Analysis Procedure ..... _... o. a 21

a. Pulse-Height Amplitudo Distributions . e * o 21b. Frequency Spectra a & . a a. . . . . . . . 22c. Auto- and Cross-Correlation Functions . * . 24

B. Summary of Results • . . . * a 0 0 0 0. * a 0 0 o 261. Interpretation of the Data . . . . . . . . *. 262. Average Backscatter Data a* * . . * @ . . . . . 343. Amplitude Fluctuations . . . . . . . . . . . . . 424. Spectral Distributions . . . . . , . . . . . . . 535. Correlation Functions .0 . . . . 0 .0. 62

III. CONCLUSIONS AND RECOM NDATIONS . . . . . . . . . . . . 73

IV. REFERENCES . . . . . . . . . ..... . . . . . . . . 77

V. APPENDICES . s . 0 .. . . .. o . * 0. . . 79

A. Selected Spectral Distributions . . . . . .... 81B. Auto-correlation Functions for Selected Data

Runs e.. . . . . . . .o *.... 91

Il

..- .

LIST OF FIGURES

Figure Pg

1. Field of view from the top of the radar vanlocatrd at test site 1 near the summit ofKennesaw Mountain ........ .......... .. . 4

2. Field of view from the radar van located attest site 2 overlooking Little Kennesaw Mountain* .... 4

3. View of radar van in position for data gatheringat test site 1 on Kennesaw Mountain . a .. a .... 6

4. Close-up view of the radar antennas located ontop of the radar van. .* aa...... . . .a a 6

5. View of the operating console inonide the radarvan showing the "A and B-scope" displays andthe data acquisition instrumentation * o . a. . . . . . . 12

6. Close-up of the radar transmitter controls andthe rf calibration equipment ... o u . . 12

7. Simplified block diagram of equipment configurationfor measurement of received power from targets andclutter . , - .,. . . . . . . .a . . . . .a . . . 13

8. View of one of the radar corner reflectors used forcalibration mounted on a 16 ft. mast . . .. . . . .* . 15

9. Truck mounted wind speed-and-direction instrumantationdeployed in a large grassy field. . 15

10. View if the Sensor Systems Division's PDP-8/F buseddata reduction facility . . . . . . . . . 9 e 9 e 20

11. Time history of the recorded backscatter from deciduoustrees; 95 GHz, vertical polarization. . a .. . . . .. . 23

12. Time history of the recorded backscatter from deciduoustrees; 9.5 G11z, vertical polarization . .. ... . . 23

13. Calculated area for the "footprint" on the ground foreach of the radars used in the measurement program asa function of range . ... ...... ... ...... 30

Preceding page blankix

List of Figures (continued)

Figure Pa_

14. View of deciduous tree area from teat site L showing3 dD antenna pattern fox the 9.5 GQz radar (c~rcle)superimpoaed on trees. Depression angle is 8 ,

range is 1.1 km. .a • • • .. .. .. • • • • a a • • 31

13. View of pine tree area from test site 1 showing3 dB antenna pattern for the 9.5 'Az radar (circl 8 )superimposed on the trees. Depression angle is 8range is 1.1 km ..... ... . . . . . . ...... . . 31

16. View of a large field from test site I showing the3 dB antenna pattern for the 9.5 GHz antenna (circle)superimposed on the field. Depression angle is 30;range is 4J1 km.. .. ... . . .. . . . . .a .0. . . . 33

17. View of a deciduous tree area on Little KennesawMountain from test site 2 showing the 3 dB antennapattern for the 9.5 GHz antenna (circle) superimposedon the trees. Depression angle is 0.40; range is0.5km . Q . . . .*. . . . .e * * . . . . . 33

18. Comparison of the average backscatter per umit areameasured in the summer and in the fall; 9.5 GHz . . . . . . 35

19. Comparison of the average backscatter per unit areameasured in the summer and in the fall; 35 GHz . a a . . 36

2G. Comparison of the average backscatter per unit areameasured in the summer and in the fall; 95 GHz . . . . , . 37

21. Comparison of the average backscatter per unit areafor vertical and horizontal polarizations; 9.5 GHz. ... 38

22. Comparison of tte average backscatter per unit areafor vertical and horizontal polarizations; 16.5 0Iz . . . . 39

23. Compartson of the average backscatter per unit areafor vertical and horizontal polarizations; 35 GHz . . . o . 40

24, Comparison of the average backscatter per unit areafor vertical and horizontal polarizations; 95 GIHz . 41

25. Cumulative probability distribution of the receivedpower from deciduous trees; 9.5 GHz, vertical polar-ization, and 4.10 depression angle .. . . . . . 43

. ....

List of Figures (conLinued)

Figure Page

26. Cumulative probability distribution of the receivedpower from deciduous trees; 16.5 GHz, vertical polari-zation, and 4.10 depression angle . . . . ........ 44

27. Cumulative probability distribution of the receivedpower from deciduous trees; 35 GHz vertical polari-zation, and 4.10 depression angle ............. 45

28. Cumulative probability distribution of the receivedpower from deciduous trees; 95 GHz, vertical polari-zation, and 4.10 depression angle . . . . . . . . . . * . 46

29. Probability density function for the measured standarddeviations of the amplitude distributions as a functionof tree type; 9.5 GHz. .. * . ..0. . .. .0. . . . . .. . 49

30. Probability density functions for the measured standarddeviations of the amplitude distributions as a functionof tree type; 16.5 GHz . . . 49

31. Probability density functions for the measured standarddeviations of the amplitude distributions as a functioncf tree type; 35 GlI!z . ... ... ..... ... .. . . . 50

32. Probability density functions for the measured standarddeviations of the amplitude distributions as a functionof tree type; 95 GHz .0. . . .. ..0. . . .. . . . . . 50

33. Probability density functions for the measured standarddeviations of the amplitude distributions as a functionof polarization; 9.5 Gl0z . . . o & . * . * . . .*. . . . 51

34. Probability density functions for the measured standarddeviations of the amplitnde di3tributions as a functionof polarization; 16.5 GHz. .*. . . . . . . .e . . . . . . 51

35. Probability density functions for the measured standarddeviations of the amplitude distributions as a functionof polarization; 35 GHz . . . .*. . .o . . . . . . . o. 52

36. Probability density functions for the measured standarddeviations of the amplitude distributions as a functionof polarization; 95 GHz .................. 52

37. Normalized frequency spectrum of the return from deciduoustrees for two ranges of wind speed; 9.5 GHz, verticalpolarization (linear receiver) ............... 54

xi

---. l

List of Figures (continued)

Figure Page

38. Normalized frequency spectrum of the return from deciduoustrees for two ranges of wind speed; 16.5 GHz, verticalpolarization (linear receiver) . . . ,. . . *. . . . . . . . 55

39. Normalized frequency spectrum of the return from deciduoustrees for two ranges of wind speed; 35 Giz, verticalpolarization (linear receiver) . . . . . . . * .e. . . . . . 56

40. Normalized frequency spectrum of the return from deciduoustrees for two ranges of wind speed; 95 GHz, verticalpolarization (linear receiver) . .. . . . . . . . . . . . . 57

41. Normalized frequency spectrum of a log receiver for thereturn from deciduous trees for two ranges of wind speed;95 Gl0z, vertical polarization . .*. . . . . .a. . . . . . . 58

42. Normalized frequency spectrum of a log receiver for thereturn from deciduous trees for two ranges of wind speed;16.5 GHz, vertical polarization . . . . . .0. . . . . . . . 59

43. Normalized frequency spectrum of a log receiver for thereturn from deciduous trees for two ranges of wind speed;35 Gliz, vertical polarization. . . . . . .a. . . . . . .a * 60

44. Normalized frequency spectrum of a log receiver for thereturn from deciduous trees for two ranges of wind speed;93 Giz, vertical polarization . . . ........ . . . . 61

45. Iecorrelation time versus frequency and wind speed forreturn from deciduous trees .. . . . . . . . . . . . . . . 64

46. Normalized cross-correlation function for return fromdeciduous trees for 9.5 GHz and 16.5 GHz (9.5 x 16.5);2 mph wind speed . . . . . & 0 * . 0 0 0 * * 0 0 0 .0 * . 65

47. Normalized cross-correlation function for return fromdeciduous trees for 9.5 GHz and 16.5 GHz (9.5 x 16.5);8 imph wind speed . . . .. . . . . . . . . . . .. . . . . . 66

48. Normalized cross-correlation function for return fromdeciduous trees for 9.5 GHz and 35 GHz (9.5 x 35);

2 mph wind speed . . . . . . ............. .. 67

xii

List of Figures

Figure Pagc

49. Normalized cross-correlation function for r':-urn fromdeciduous trees for 9.5 GHz and 35 GHz (9.5 x 35);8 mph wind speed . . . . . . . . . . . ... . .. . . .. 68

50. Normalized cross-correlation function for return fromdeciduous trees for £.' GHz and 95 GHz (9.5 x 95);2 mph wind speed . . . . . . . ... . . . . . . .. . . 69

51. Normalized cross-correlation function for return fromdeciduous trees for 9.5 GHz and 95 Gliz (9.5 x 95'1;8 mph wind speed •. ........... ,....... 70

A-Io Example of the frequency spectrum of the return fromdeciduous trees; 9.5 GHz, vertical polarization,and 5 mph wind speed (linear receiver) . . .... . .... 82

A-2. Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 9.5 GHzvertical polariation, and 5 mph wind speed . . . . .... 82

A-3. Example of frequency power spectrum of the return fromdeciduous trees; 16.5 GHz, vertical polarization and5 mph wind speed (linear receiver) . . . . . . . . . . . . 83

A-4. Example of the frequency spectrum from a logarithmicreceiver of the. return from deciduous trees; 16.5 GHz,vertical polarization, and 5 mph wind speed . . . ..... 83

A-5. Example of the frequency spectrum of the return from deciduoustrees; 35 GHz, vertical polarization, and 5 mph wind speed(linear receiver) . .............. .... 84

A-6. Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 35 GHz,vertical polarization, and 5 mph wind speed . .. . . 84

A-7. Example of the frequency spectrum of the return fromdeciduous trees; 95 GHz, vertical polarization, and5 mph wind speed (linear receiver) . . . . . . . . 85

xiii

List of Figures (continued)

Figure Pa•

A-8. Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 95 GHzvertical polarization, and 5 mph wind speed . . . . . . . 85

A-9. Example of the frequency spectrum of the return fromdeciduous trees; 9.5 GHz, vertical polarization,and 10 mph wind speed (linear receiver) . .. .. . .* . . * 86

A-10. Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 9.5 GHz,vertical polarization, and 10 mph wind speed ...... . 86

A-Il. Example of the frequency spectrum of the return fromdeciduous trees; 16.5 GHz, vertical polarization,and 10 mph wind speed (linear receiver) . . * .. . . 87

A-12. Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 16.5 GHz,vertical polarization, and 10 mph wind speed . . . *. 87

A\-13. Example of the frequency spectrum of the return fromdeciduous trees; 35 GHz, vertical polar' tatioan,and10 mph wind speed (linear receiver) . . . . . a .... 88

A-14 Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 35 Gl~z,vertical polarization, and 10 mph wind speed....... 88

A- I. Example of frequency spectrum of the return fromdeciduous trees; 95 GHz, vertical polarizationand10 mph wind speed(linear receiver) . . . .... . 89

A-l6. Example of the frequency spectrum from a logarithmicreceiver of the return from deciduous trees; 95 GHz,vertical polarization, and 10 mph wind speed ....... 89

B-I. Normalized auto-correlation function for the returnfrom deciduous trees; 945 GHz, 2 mph wind speed ...... 92

B-2 Normalized auto-correlation function for the returnfrom deciduous trees; 16.5 GHz, 2 mph wind speed .W. * 1

xiv

- - - - -. .....~f .'

List of Figures (continued)

Fi&ure Page

B-3. Normalized auto-correlation function for the returnfrom deciduous trees; 35 GHz, 2 mph windspeed . . . . . . 4

B-4. Normalized auto-correlation function for the returnfrom deciduous trees; 95 GHz, 2 mph windspeed . . . • . . 5

B-5. Normalized auto-correlation function for the returnfrom deciduous trees; 9.5 GHz, 5 mph windspeed... .... 96

B-6. Normalized auto-correlation function for the returnfrom deciduous trees; 16.5 GHz, 5 mph windspeed 0. ... . , 97

B-7. Normalized auto-correlation function for the returnfrom deciduous trees; 35 GHz, 5 mph windspeed . we..... 98

B-8. Normalized auto-correlation function for the returnfrom deciduous trees; 95 GHz. 5 mph windspeed .... . . . 99

B-9. Normalized auto-correlation function for the returnfrom deciduous trees; 9.5 GHz, 8 mph windspeed. * * . * .0100

B-10. Normalized auto-correlation function for the returnfrom deciduous trees; 16.5 GHz, 8 mph windspeed . . . . * .101

B-l1. Normalized auto-correlation function for the retirnfrom deciduous trees; 35 GHz, 8 mph windspeed ... * * ... a102

B-12. Normalized auto-correlation function Ior the returnfrom deciduous trees; 95 GHz, 8 mpb windspeed .... . . o03

B-13. Normalized auto-correlation function for the returnfrom deciduous trees; 9.5 GHz, 10 mph windspeed . ... * .0 *104

B-14 Normalized auto-correlation function for the returnfrom deciduous trees; 16.5 GHz, 10 mph windspeed. . . . . .105

B-15. Normalized auto-correlation function for the returnfrom deciduous trees; 35 GHz, 10 mph windspeed . . . 1.06

B-16. Normalized auto-correlation function for the returnfrom deciduous trees; 95 Ghz, 10 mph windspeed . . . . . . .107

xv

S: •• '••""•ir• " •'I• •'I•!•l • i~jI~iJ'i:•'m• ~q- |'"! •'•"• ....... •• •i •'• •'• -

LIST OF TABLES

Table

1. Parameters of Georgia Tech GT-I Experimental Radar * * ... 7

2. Parameters of Georgia Tech GT-J Experimental Radar. * * * 8

3. Parameters of Georgia Tech GT--K Experimental Radar . . . * 9

4. Parameters of Georgia Tech GT-M Experimental Radar. . . . 10

5. Summary of the Standard Deviation for VcriousClasses of Clutter. 0 a a a .. . .. 0 . . . .. . 48

Preceding page blankxvii

I. INTRODUCTION

This report summarizes the results of a measurement program to

determine the characteristics of radar backscatter from land clutter

at millimeter wavelengths which was conducted at Kennesaw National

Battlefield Monument, Georgia, over the period June to October 1975.

Emphasis in the report is placed on determining the backscatter pro-

perties of trees and other vegetation for sumer and fall foliage con-

ditions and for grazing angles between 10 and 250.

A. Background

Under Contract DAAA25-73-C-0256 with the U. S. Army, Frankford

Arsenal, the Engineering Experiment Station (EES) at Georgia Tech

undertook to develop suitable mathematical models of radar target

and clutter characteristics to allow computer simulation of fire-

control radar systems in the millimeter frequency region. As a first

step in this program, a literature search was conducted to determine

what data were available at the millimeter wavelengths on clutter

and target characteristics which could affect system performance. A

summary of these data and the empirical models which were developed

were presented in Technical Report No. 1.

The results :.f that study brought to light a number of deficiencies

in the available data. It was determined that (1) few data were available

on the radar cross-section (RCS) characteristics of ground clutter above

X-band, particularly below 100 grazing angle; (2) only limited data were

available above X-band to describe the spectral and polarization behavior

of ground clutter; (3) very limited data were available on the effects of

atmospheric conditions, especially precipitation, in the millimeter region;

and (4) there was a scarcity of data on the radar cross-section properties

of hard targets at these frequencies. EES then proposed to Frankford

Arsenal a program of investigation designed to fill in the gaps in available

! -.I

clutter dpta in the millimeter region, to be followed by an analysis program

Limed at tying the data results together in a unified model for millimeter

radar systems.

Frankford Arsenal, in response to the proposed program, undertook to

iund specific studies of the backscatter from precipitation and from land

clu.ter at millimeter frequencies.

Ballistic Research Laboratories was funded in part by Frankford to

conduct an experiment to measure backscatter from rain at millimeter frequencies

at McCoy A.F.B., Florida; and EEU was tasked, in a modification (Mod. P-00002)

to the original contract, to assist in experiment planning and to act -- on-

site observers during the actual tests. In addition, under this program,

EES constructed and delivered to BRL a range-gated boxcar sampler unit which

would allow recording of backscatter data on magnetic tape during the tests.

'lie services and activities performed were detailed in a letter report atthe end of the McCoy tests [2].

After the termination of the rain backscatter radar field tests, EES

was tasked by Frankford Arsenal (Mod. P-00003) to reduce and analyze the

data uontained on the magnetic tapes in order to obtain amplitude distri-

bution characteristics and spectral and correlation propert.es of the

radar backscatter from rain which could not be determinea by the photo-

graphic methods of data reduction tsed by BRL. Later on, in another

addition to the contract (Mod. P-00005), EES was tasked to complete the analysis

of rain drop-size distribution tapes that were recorded during the Mc~oy tests

and which were partially reduced by the Illinois Water Survey. The results of

the analysis of the radar backscatter and rain drop-size magnetic tapes are

summarized in Technical Report No. 2 on the contract [3].

In addition to the rain backscattei study, Frankford Arsenal funded a

mcasurement program designed to provide certain of the data nceded to correct

the deficiencies in understanding of land clutter in the wlllimeter region.

Specifically EES, under contract Mod. P-00004, undertook a measurement program

to determine the backscattet properties from trees and other vegetation for

.stimmer and fall foliage conditions and for small depression angles as a fuinict ion

2

.............................................",

. . . . . . . . . . . . . . . . . . . . . . . . . . ._.

of transmitted frequency, polarization, and transmitted-received polarization

sense. As in the case of the rain backscatter study, data on the received

signals from land clutter were recorded on magnetic tape so that the amplitude

distribution properties and spectral and correlation characteristics could be

determined in addition to the average backscatter characteristics. This report

will suimmarize the results of that land clutter measurements program.

B. Description of Radar Field Measurements

1. Radar Test Site

In order to obtain the desired measurements a test site was required

which would allow look angles of 100 or more down to grazing and yet provide

an area in which the M-109 van that houses the test radars and data-gathering

equipment could be positioned so as to provide an unobstructed view of trec-

covered areas. Furthermore, due to the limited funds available for the measure-

ment program, the test site had to be located within commuting distance of

EES,

A test site which met all of the necessary conditions was located at

Kennesaw Mountain situated in the Kennesaw Mountain National Monument about

20 miles northwest of Atlanta, Georgia. This area contains numerous over-

looks where the trees on the mountainside have been cut back to allow a

clear view of the surrounding countryside. Two such overlooks were selected

as test sites for the measurement program. Site 1 was located near the

summit of the mountain and provided look angles from 100 to 1 0 *Areas of

both coniferous and deciduous trees as well as large grassy fields were visible

from this site. Site 2 was chosen because it faced nearby Little Kennesaw

Mountain. Little Kennesaw is heavily wooded and has a slope of approximately

15 0. By recording radar returns from its slopes, depression angles of as much

as 250 could be simulated (except for the fact that the trees on its slopes

stand vertically). Figures 1 and 2 show the fields of view from the two test

* sites. Site 1 faced due north whilc Site 2 faced southwest.

3

Figure 1. Field Qf view from the top of the radar vanlocated at test site 1 near the summit ofKennesaw Mountain.

Figure 2. Field of view from the radar van located attest site 2 overlooking ,i,.tle Kenne.3ad Mountain.

2. Description of Test Radars

The four test radars used for this experiment were motmnted on a

single test vehicle along with integrated controls and data acquisition

equipment as shown in Figure 3. The 9.5 GHz, 16.5 GHz, and 35 GHz radars

are permanently mounted to the test vehicle and have removable antennas

on the roof while the receivers and controls are inside the vehicle. The

95 GHz radar has the antenna and receiver integrated into one package (to

minimize waveguide losses) and sits on a platform on the van roof which can

be positioned in both azimuth and elevation. The parameters of the four

radars are given in Tables 1 through 4. Although the X-band and 1'-band

systems can tune over a range of frequencies, they were set for these tests

at 9.5 and 16.5 GHz, respectively.

While differing in detail, the 9.5, 16.5, and 35 Gllz radars are similar,

They are all short-pulse systems and are dual-polarized; that is, they

_Leceive both horizontal and vertical polarizations simultaneously while

transmitting either horizontal or vertical polarization. These three systems

may be operated in either the scanning or nonscanning mode. The 9.5 GHz

radar, has both a 3 ft-diameter scanning parabolic cylinder and a 5-ft diameter

nonscanning dish, both of which were used for these experiments. Each dish can be

boresighted by means of a removable rifle scope. All three of these systems

incorporate logariLhmic receivers of wide dynamic range (approximately 80 dB)

to permit accurate measurement of returns from targets having a widely varying

signal strength. Each system incorporates provisions for injection of known

calibration signals into both parallel- and cross-polarized channels. A common

prf reference of 2000 Hz was used for all systems during the tests described

he re.

The 95 Glz system is somewhat different from the other three systems

in that the antenna is a nons.zanning Cassegrain type and all controls are

mounted in one package contaiirng antenna, Lransmitter, and receiver.

Tho antenna can also be I oresighted by means of a rifJe scope, as can

'lhe other antennas. The radar does not currently have dual-polarized

capahility, although either vertical or horizontal polarization is available.

The antenna sJ.•,es of the four radars resulted in beamwidths rL'nging be-

5

M MIS I.. . . I...I.- MOM ~.

I.A

Figure 3. View of' radar van in position f~or data gatheringat test site 1 on Kenrnesaw Mountain.

Figure 4. Close-up view of the radar antennas locat-aion top of the radar van.

6

TABLE 1

PARAMETERS OF GEORGIA TECH GT-I EXPERIMENTAL RADAR

Parameter Description

Frequency 8.5-9.6 GHz

Peak Power 40 kW

Pulse Width 50 ns

PRF 0-4000 pps

Antenna Type Nonscanning Paraboloid

Azimuth Beamwidth 1.50

Elevation Beamwidth 1.65°

Antenna Gain Vertical Polarization 41.4 dB

Horizontal Polarization 41.6 dil

Antenna Type Scanning Parabolic Cylinder

Scan Rate 0-100 RPM

Azimuth Beamwidth 2°

Elevation Beanmidth 5

Antenna Gain Vertical Polarization 30 dB

Horizontal Polarization 31 dB

Polarization H or V transmitted

H and V received simultaneousiy

IF Center Frequency 60 MHz

IF Bandwidth 20 MHz

IF Response Logarithmic (linear available)

Noise Figure 12 dB

Dynamic Range 80 dB

Display Type A-scope

7

TABLE 2

PARAMETERS OF CEORGIA TECH GT-J EXPERIMENTAL RADAR

Para-meter Description

Frequency 16-17 GHz

Peak Power 50 kW

Pulse Width 50 ns

PRF 0 - 4000 pps

Antenna Type Scanning Paraboloid

Scau Rate 0-120 rpm

Azimuth 3eamwidth 1.50

Elevation Beaimidth 1.5 °

Antenna Gain Vertical Polarization 41.5 dB

Horizontal Polarization 41.4 dB

Polarization H or V transmitted

H and V received simultaneously

IF Certer Frequency 60 Mlz

IF Bandwidth 20 MHz

IF Recponse Logarithmic (linear available)

Noise Figure 13 dB

Dynamic Range 70 dB

Display Type A-scope, B-scope, PPI

8

TABLE 3

PAMA1ETERS OF GEORGIA TECH GT--K EXPERIMENTAL RADAR

Parameter DescriptionFrequency 35 GHzPeak Power 40 kW1ulse %idth 50 ns

0-4000 ppsAntenna Type Scanning ParaboloidScan Rate 0-120 rpmAzimuth Beanwidth IElevation Beamwidth 10Antenna Gain Vertical Polarization 43 dL

Horizontal Polarizatiorý 43 dBPolar4.zation H or V transmitted

H and V received simultaneouslyIF Center Frequency 60 MHzIF Bandwiith 20 11HzIF Response LogarithmicNoise Figure 14 dBDynamic Range 70 dBDisplay Type A-scope, B-scope, PPI

9

TABLE 4PARAMETERS OF GEORGIA TECH GT-M EXPERIMENTAL RADAR

Parameter DescriptionFrequency 95 GHi (Noni)Peak Power 6 kWPulse Width 50 ns or 10 ns

PRF 0-4000 ppsAntenna Type Paraboloid (Cassegrain)Azimuth Beamwidth .700

Elevation Beamwidth .650Antenna Gain Vertical Polarization 46.5 eB

Horizontal Polarization 46.3 dBPolarization H or VIF Center Frequency 60 MHz or 160 MHz

IF Bandwidth 20 MHz or 100 MHzIF Response Logarithmic jlinear availab'>)Noise Figure 15 dBDynamic Range 70 dBDisplay Type A-scope

10

. ....M ~ .....

*tween 1.5 0 for the 9.5 G~z radar (2.0 0 with the scanning parabolic cylinder)

to 0.70 for the 95 GI-z radar, a factor of only two to one. This allows returns

from clutter to be measured at the four frequencies for approximately tha same

size clutter patches so that meaningful comparisons can be made. Figure 4 gives

a close-up view of the four antennas on the top of the radar van.

3. Data Gathering Equipment

For purposes of data gathering, the RF from the 9.5, 16.5, and 35 G1~z

radars and the IF from the 95 GHz radar are piped from the roof of the radar

van to logarithmic [F amplifiers located in the operating console. The re-

sulting logarithmic videos are fed through video lirne drivers so that various

displays and equipment can be connected to the appropriate video outputs without

loading the video signals. Also located at the console are a six-channel narrow-

aperture sampler which stretches the video samples at a particular time

so that it can be recorded on an fmn tape recorder, two A-scope displays, a B-

scope display (for displaying scanning data), a frequency counter, and the timer

control unit for the radars. Figu:e 5 gives a view of the radar console.

The radar transmitter controls and rf signal generators (for calibration

purposes) for the 9.5, 16.5, and 35 GHz systems are located in a rack to the

left of the operating-console as shown in Figure 6. Figure 7 gives a simplified

block diagram of the equipment configuration of one of the radar systems for

calibration and measurement of received power from targets and clutter. For

calibration purposes a signal generator pulse is injected into the receiver

input through a coupler with the transmitter off, and the power level of the

signal. generator is stepped from 0 dBm to the receiver noise level in equal

steps (usually 5 or 10 dB) while the receiver video response is sampledo

stretched, and recorded on magnetic tape.

When measuring the return from targets (nons canning), the antennas

are boresighted on the targets, and an adjustable range gate is used to

vary the range at which the received signal is sampled and recorded.

View of the operating console inside the radar--in showing the "A and B-scope" displays and'he data acquisition instrumentation.

'I

A Close-up of the radar transmitter contivlz;and the rf calibration equipment.

Ai

12

MW. . . . .

0.

V4J

t~ 00

i-H

0

'-4 r>-4

N 13

I - ~V

No signal generator is available for calibration purposes with the 95

G&iz radar so that calibration is achieved by boresighting the radar antenna

on a corner reflector of known radar cross-st %ion, varying a precision RF

attenuator in the received power path until receiver noise is reached, and

recording the resulting transfer function on magnetic tape. This procedure

relates the received signals directly to effective radar cross-sections

which is the quantity to be measured. This method can be made to yield

rather high accuracy if sufficient attention is directed to mapping the effects

of multipath and other site-dependent factors.

For the 9.5, 1.6.5, and 35 GHz radars, the calibration of received power must

be related to RCS, the desired quantity to be measured. This is achieved by

solving the radar equation and carefully measuring the transmitted power,

antenna gain, and waveguide losses.

The RCS of a target as a function of received power for a given

radar system is given by: [4]

2(m2 Pr _(4,n) 3R 4Pt G (i)

where: Pr is the received power in milliwatts

Pt is the transmitted power in milliwatts

G is the antenna gain

X is the wavelength in meters

R is the range in meters

As a further check on the calculations, measurements were performed on several

corner reflectors of known RCS. The configuration shown in Figure 8 is an

example of the technique used, and shows a 22-inch corner reflector deployed

on a 16-ft mast.

14

II

VJ

Figure 8. View of one of the radar norner reflectorsA usedfor calibratlo:; mounted on a 16 ft. mast.

r.:

4''1

Figure 9. Truck mounted wind speed-and-direction instru-

mentation deployed in a large grassy field.

15

It was desired to attempt to correlate wind speed and direction with

the amplitude and spectral properties of the clutter returns being recorded.

fowever, wind measurements made at the radar van could not be expected to

3imulate wind conditions several km away. Accordingly, a portable anemometer

was mounted in the back of a pick-up truck as shown in Figure 9 in order to

allow measurement of wind conditions near each clutter patch under test.

Attempts were made to position the truck downwind from the area tested

whenever the appropriate access was available. For those few areas where

the truck could not be so positioned, representative measurements were made

in the general area. Citizen band radios were used to inform personnel in

the pickup truck when data were being recorded, and wind speed and direction

were recorded at 30-second intervals during these periods.

4. Measurement Procedure

The measurement procedure followed in taking land clutter backscatter

dat-, typically consisted of the following steps:

(i) After a suitable warm-up period, a calibration was performed onthe 9.5, 16.5, and 35 GHz radars utilizing signal generatorsto input known power levels to the radar receivers as statedpreviously.

(2) The four radars were boresighted on one of several corner re-flectors located in a cleared area and the received power levelswere recorded. A 95 GHz calibration was generated by c~iangingthe rf attenuator in 5 dB steps until the receiver noise levelwas reached as explained above.

(3) The pick-up truck was dispatched to a position downwind fromthe clutter patch to be measured.

(4) A characteristic target such as an unusually shaped tree wasused to align the boresight position (using the rifle scopes)of each antenna so that all the antennas were pointing at thesame clutter patch. The depression angle to the boresight targetfrom the top of the radar van was measured with a theodolite.

(:) The returns from the clut'ter patch were recorded simultaneouslyfor several minutes for both horizontal and vertical polarizations.

(6) The antennas were repositioned to a new clutter area, and the

measurement proccdure was repeated.

16

(7) Calibrations were recorded at the beginning and end of eachmagnetic tape as a check against drift in the radars or therecording equipment.

(8) From time to time A-scope photos were taken to document thevideo chiracteristics.

The data obtained as a result of the tests repreoent the amplitude

fluctuations of the power received by each of the radars from a specific

clutter patch defined by the radar pulse lengths and the azimuthal antenna

beamidths.

The recorded signals were processed by computer to yield average values

for the received power, the distribution shapes of the fluctuations, and the

spectral properties.

* I

I I

17

x.kW

II. DATA ANALYSIS

A. Data Analysis Techniques

The data obtained from the radar backscatter measurements consisted

mainly of fm magnetic tape recordings of amplitude fluctuations of the

received signals from land clutter. Strip chart recordings, A-scope

photographs, and other miscellaneous data such as log sheets of wind-

speed and direction, provided background information to supplement the

magnetic tapes, but were not analyzed directly.

1. Data-Reduction Facility

The PDP-8/F based data-reduction facility of the Sensor Systems

Division of EES was used to process all the magnetic tapes from the radar

backscatter tests. Figure 10 gives a view of the basic computer components.

These include the following: (1) An analog signal-conditioner unit which pro-

vides variable gain and offset to allow the interface 3f varied types of

signals to the data-reduction facility. (2) A Fabri-Tek Model 1072 Instrument

Computer which serves as an A/D and D/A interface, and also computes real-

time pulse-height distributions and cross-correlation functions. The D/A

output from the Fabri-Tek computer can be displayed on a CRT display or can

be plotted on an x-y plotter. (3) A PDP-8/F computer which can exchange in-

formation directly wiLh the Fabri-Tek computer. (4) A teletype. (5) A

Sykes Compucorp Digital Cassette Recorder for program development and storage.

The PDP-8/F conLains 16K of memory, of which 8K is magnetic core.

An extended version of F IOCALTA has been developed for use with the PDP-8/F

and is designated FOCL/F [15. This language is interactive and greatly

facilitates program correction and modification. Also available is a

machine language software package for calculating fast Fourier transforms

(PVT), and a set of software commands for Fabri-Tek control. These two

machine language software packages along with the extended FOCAITM

software make this system a very powerful and flexible data-reduction

facility.

Preceding page blank19

L ± ', ,-LM~~..za.slal.~ ~elk.a.~a~.- a~~.

LL. *tl

(nOW

>

20)

2. Data Analysis Procedure

The types of analyses which are normally obtained from the data facil-

ity include: (1) pulse-height amplitude (PHA) distributions, (2) frequency

spectra, and (3) auto- and cross-correlation functions. The methods for

obtaining these three classes of results are sufficiently different as to

require entirely different FOCALTh programs for their calculation. Taken

as a whole, results from these three types of analysis have been used tocharacterize the recorded data.

a. Pulse-Height Amplitude Distributions

Pulse-height amplitude distributions calculated by the data facility

are displayed in two forms: as probability density plots and as probability

distribution functions. The probability density plots are generated

from data time histories from the magnetic records. The analyzer samples

the input analog time history, AID converts the samples, determines into

which of 1024 amplitude bins the sample belongs, and incrtainits a storad

variable corresponding to~ the number of samples which havie fallen in that

amplitude window. When repeated a large number of times, this process

generates a voltage amplitude distribution which is then calibrated and

divided by the total number of samples to achieve a normalized probability

density function.

The voltage amplitude distributions are calibrated by reference to i:.

known comb (amplitude) signal. The peak of the distribution for each voltý:i8e

step in the calibration comb is assigned the dB value corresponding to that

calibration step. The PHA program in the PDP-8/1F then does a cubic f ie to

the calibration and generates a table relating dB value and amplitude b*Lk

number. The cubic fit program was developed to "linearize" nonuniform

calibration steps so that the output density functions can be plotted on a

linear scale.

The probability distributions are calculated by point-'by-point

numerical integration of the probability density functions. The func-

tional values of these distributions are then multiplied by a nonlinear

transfer function so that they can be plotted on probability paper.

21

The resultant probability distributions are useful in determining

the median values of the distributions and also their shapes. in addition,

for certain classes of functions, the average values can be determined

from the distributions using a simple formule [6].

b. Frequency Spectra

The frequency spectra data-reduction program uses the fast Fourier

transform subroutines available for the PDP-8/F to transform input time

histories to the frequency domain. The program has several options

which allow the spectral data to be presented in different forms

including: (1) voltage amplitude in dB versus frequency, (2) normalized

voltage amplitude in dB versus frequency, and (3) normalized power

spectral density in dB versus frequency.

The voltage amplitude program in the PDP-8/F computer processes an

input time history which has been sampled, A/D converted, and stored by

the Fabri-Tek. The program computes the fast Fourier transform for that

time history, and calculates the square root of the sum of the squares

of the real and imaginary parts of each element in the transform. Since

a time histor-C typically represents the voltage out of a logarithmic

receiver, which is proportional Lo the logarithm of the received power,

the amplitude of the spectrum corresponding to the time history is

measured in dB relative tu a milliwatt (dBm). Thus, to calibrate the

spectral amplitude in dB, a calibration is stored in memory which relates

dB values to input voltage amplitudes. and each time history is converted

to dB values as it is read in. Since the Fourier transform is a linear

process, the spe.trum amplitude will be proportional to dB if the time

history is calibrated in dBm and the proportionality constant is set

equal to one in the program.

Figures 1i and 12 give typical time histories for 95 GHz and 9.5

GlIz. The vertical scales represent radar cross-section in units of dBsm.

(Received power can be related to cross-section i1 the appiopriate

radar constants are known.) A time history iu limited in duration to the

"number of Fabri-Tek ummory bits times the sample period. For this cae,

22

..

-3

0

o -18 -1

-23 ..

0 25 50 75 100 125 150 175 200 225 250

Elapsed Time (msec)

Figure 11. Time history of the recorded backscatter from deciduoustrees; 95 GHz, vertical polarization.

"• -25

30 P

35-35

S-40S0 25 50 75 100 125 150 175 200 225 250

Elapsed Time (msec)

Figure 12. Time history of the recorded backscatter from deciduoustrees;9.5 GHz, vertical polarization.

23

the sample rate equals the prf used in taking the data (2000 Hz), so that

a time history is given by: 1/2000 x 1024 - 0.512 seconds. To achieve the

equivalent of a longer time history, the Fourier transforms of 8 adjacent

time histories are averaged together. The actual number of time histories

averaged is :irbitrary; however, previous experience has shown that eight to

ten are sufficient to establish a reasonable short term average.

In calculating the FFT, the dc cerm has been set to zero because its

amplitude is normally so much larger than the rest of the spectrum that

dynamic range problems are encountered in the computer due to the form of

the 1FT algorithm. Thus, the zero frequency point is zeroed in all the plots.

However, this dc point can still be determined independently trom the

amplitude distribution functiuns.

A second method of displaying the log amplitude spectrum is to

normalize the spectrum by dividing all the elements by the peak element

value. The spectrum is then plotted in dB relative to the peak voltage

amplirude, vhich is normally the lowest frequency point. This type of

plot is very useful for comparing frequency roll-off characteristics of

spectral plots with different amplitudes.

Althoigh the data recorded on the magnetic tapes represent the

voltage otitput from a logarithmic receiver, it was deemed desirable

to plot the equivalent spectrum for a linear receiver. Therefore a modifica-

tion was developed to the spectrum program which allowed the "delogging" of

the input data prior to calculation of the Fourier transform. Mien nor-

malized, this program results in the calculation of the normalized linear

frequency spectrum. [7] For certain classes of functions, to which all

the data processed here belong, this power spectrum is equivalent to the

power spectral density. The unnormalized calibrated power spectrum

cannot be determined by this method since the dc term is thrown away, as

previously described.

c. Auto- and Cross-Correlation Functions

The auto-correlation functions and cross-correlation functions forinput time histories are determined by two different methods in the data

analysis. Auto-correlation functions are computed from the inverse

transform of the magnitude of the Fourier transform squared in the PDP-8/F

24

I,

computer, while cross-correlation functions are measured using an SD-75

plug-in in the Fabri-Tek computer. The SD-75 plug-in samples two separate

input signals, A/D converts them, and calculates partial products betweea

samples as a function of time to generate the correlation function between

the signals. This computation is given by

k

•AB (nAt) kl ' A(ti) B(t + nAT), n= 0, 1,2...,k-1 (2)

i=A

where k = aumber of discrete points for which the correlation

function i3 to be determined,

kAi total sample length,

A(ti) = ampJitude of first function sampled at time ti,

B(ti) - amplitude of second function sampled at time ti.

This method could have been used to generate auto-correlation

functions by connecting the same signal to both plug-in inputs, but this

was not done because of several limitations in the plug-in. First, the

set-up and calibration of the auto-correlation plug-in is difficult, and

requires considerable manual handling of the data. In addition, due to

the limited memory available in the Fabri-Tek, the lowest frequency that

that can be accurately measured is approximately one-tenth the total

sampling period while +he highest frequency is, from the sampling theorem,

about one-half the sampling rate. Thus to measure the auto-correlation

function of a signal with 1000-liz bandwidth, the sample rate would be

2000 1lz and the lowest frequency wo'uld be:

SI Sa-l R t. .2 000) .; 1.95 Hz. (3)low Total Mmory 1024 Mmhory

Bi ts

25

Utilizing the fast Fourier transform in the PDP-R/F to perform auto-

correlation calculations solves the problems inherent in using the SD-75

plug-in in the Fabri-Tek, except for the lower frequency limitation. Since

the Fabri-Tek memory is used to store the time histories, the same low

frequency limitations apply. However, a partial solution to this problem

is to calculate the auto-correlation function us±ng a lower sample rate

to pick up lower frequencies at the expense of higher frequencies. A

family of curves can thus be generated at different sampling rates which

describe the auto-correlation function over any desired band of frequencies.

In the case of cross-correlation function computation, the current FFT

program used in the PDP-8/F computer Is not suitable, and the SD-75 plug-in in

the Fabri-Tek must be used even with its limitations. Calibration is

obtained with the plug-in by using signals with known correlation func-

tions, such as a sine wave or Gaussian iioise0 The results from an unknown

signal are then compared with those o: the known signals.

B. Summary of Results

1. Interpretation of the Data

The goal of this test program was to obtain information on the pro-

perties of backscatter from land clutter at frequencies above 10 GHz and at

the lower grazing angles which would be useful to the radar system designer.

In particular, the experiment was structured to allow direct determination of

tlhe transmitted frequency dependence of the backscatter to aid in system fre-

quency choice. To achieve this goal, dependence of the test results on the

measurement system parameters must be reduced as much as practical, so that

the data can be applied to the general case. The radar system factors which

affect the magnitude of the received power from a radar cell include:

(1) range, (2) antenna beamwidths, (3) transmitted pulse length, (4) trans-

mitter power, (5) antenna gain, (6) system losses, and (7) transmitted

frequency. The most commonly accepted way to present data on reflectivity

26

of surfaces so as to remove the dependence on the above parameters has0been to calculate the quantity a (Lhe radar cross-section per unit

area). The received power from a given surface area can be related to

the return from an equivalent metal plate of given area by using the

radar equation as discussed in Section I-B. This equivalent reflectivity

area is divided by the actual area illuminated by the "footprint" of the

radar beam on the ground to obtain a°, the reflectivity ratio.

The solid angle within a radar beam, assuming a Gaussian antenna

pattern*, is given by: [8]

ao ea e (4)

b 4

where:

Ob = solid angle of the beam,

o a azimuth 3 dB beamwidth in radians,a

o - elevation 3 dB beamwidth in radians.e

For large depression angles, the area of the "footprint" of the beam

on the ground is given by:

2 1Ab -- " x 0 R x ()b x a e sinE (5)

where:

o = azimuth beamwidth in radians,a

o = elevation beamwidth in radians,

R = slant range in meters,

E depression angle in radians,

*The antenna patterns obtained from measurements made on the antenna range atGeorgia Tech show these antenna to have a A fac-field distribution. A

Gaussian distribution is an excellent approximatlon to a A- di;trihitLonwithin the half power beamwidth.

27

For a pulsed radar at low depression angles the area illuminated on

the surface is a function of the azimuth beamwidth and the pulse length, and

is given by:

ORa TC 1AT =- X- X -

,2 2 cos E

where: 0 = antenna beamwidth in radians,a

R = range in meters,

S= pulse length in seconds,

C = speed of light in meters/second,

E - depression angle (cos E 1 1 for small angles).

The angle of transition from Equation 5 to Equation 6 is given by:

1ý2 a R

Tan ET = TC R

where:

0 = is elevation beamwidth in radians,e

R = range in meters,

¶ = pulse length in seconds,

C = speed of light in meters/second. 4

This transition angle ET is equal to 510 for the 9.5 GHz and 16.5 GHz

radars, 39 for the 35 GHz radars, and 30 for the 95 GHz radar at a

range of 5C0 meters (the minimum range at which data were recorded), and

becomes larger as the range increases. Since the largest depression0

angle encountered during the tests was 25 , Equation 6 was used to

calculate the area of the beam "footprint" on the surface for each of the

radars in order to determine ao, (It should be noted that a maximum

depression angle of 25 was encountered when the radars were pointed at0Little Kennesaw Mountain which has a slope of approximately 15°.)

28

F.

Ji-i

Figure 13 gives the area calculated for the "footprint" of the beams

for each of the test radars as a function of range. Since land clutter is

typically nonuniform in nature, it is desirable that the beam areas of all

test radars be as nearly equal as pocsible, so that the same areas are

illuminated. This allows any difference in the reflectivity characteristicsof the clutter to be attributed to frequency effects alone. The differences

in beam areas among the four test radars are no more than 6 dB and should

therefore allow direct comparison of the results.

One problem with obtaining measurements at extremely low angles (less

than two degrees) is that the area illuminated by the beam baeomes quite

large due to the long ranges typically encountered. Thus the likelihood of

having uniform clutter in the radar cell is greatly reduced, and the chances

of having man-made targets of large radar cross-section present in the radar

cells are increased. Also the signal-to-noise ratio becomes a problem at

long ranges, particularly for the relatively low powered radars used in these

tests. For these reaonas, no data are presented below 20 depression angle.

One of the primary differences between millimeter radars and those of

lower frequency bands is the much amaller beamwidth for an antenna of given

size, which results in a much greater angular resolution. Generally

short pulse lengths (50--100 nanosec) are also utilized in order to enhance

the resolution. The effect of this increased resolution on returns from land

tlutter is to make the clutter appear much less uniform than for lower

resolution radars, since ii many cases individual trees or other relatively

large stationary targets are individuaily resolved.

This is also true for the case of the radars used in the test program.

I'ig,.ires 14 and 15 give views of patches of deciduous and pine tU ,es on which

in*'asurements were made at a range of about 1.1 km. The circles on the figurc

giv. the coverage of the 9.5 GIIz radar beam (3 dB points) which had the largest

I,., IdIith of the test radars (1.5 degrees). Tt is obvious thaL at this short 4roge, at most only two or three trees are included in the beam "footprint"

29

.. . ... . . . .. . . .. . . . .. . .. . .... .. B . . ........... . . ... ...

T P 1*441- 0

-4 ... .. ...' '4

-W44

o 0 4

iŽI9 I

30

- - -.-

Figur:e 14. View of a deciduous tree area from test; site 1 showing Lhe 3dBantenna pattern for the 9.5 GHz radar (circle) superimposed onthe trees. The depression angle is 8'; the range is 1.1 km.

je ''-.carz .~.. .....

' - Figure 15. View of a pine tree area from test site 1 showing the 3dB 'antenna pattern for the 9.5 GHz radar (circle) superimposedon the trees. The depression angle is 8°; the range is 1.1 km.

p

' 31*1 ,

[A

at one time. In addition, the vertical coverage is limited by the trans-

mitted pulse length rather than the elevation beamwidth, so that the

"footprint" is even smaller than shown in the figures. This fact helps

to account for the spread in the backscatter data (of about 10 dB) for

return from different tree patches at about the same range and look angle.

However, since these measurements simulate the results that would be ob-

tained for an operational high resolution radar, they are thus a valid

measure of the problem facing the system designer in specifying the

performance of such a system.

At longer ranges a number of trees are included in the cell so that

the return should be more uniform. Figure 16 shows the 3 dB beam pattern

on a large field at a range of about 4 km. The antenna pattern just

fills the field vertically, alth:ough once again the vertical extent. of the

beam is limited by the pulse length rather than the vertical beamwidth.

Data taken at Site 2 on the side of Little Kennesaw Mountain were in-

tended to simulate higher angles than could be obtained at Site 1, since

the angular slope of the mountain adds to the effective depression angle

with respect to the ground surface from the radar. However due to the short

range involved (11, 0.5 km) and the narrow beamwidths and pulse lengths of the

test radars, individual trees were resolved on the mountainside. Figure 17

shows the 3 dB pattern on che mountain for the 9.5 GHz radar. Since the

trees on a mountainside grow vertically, the slope of the mountain becomes

unimportant in its effects on the backscatter. For this reason the data

taken on the side of Little Kennesaw Mountain are listed as a special case;

however, they are compared directly with the other data in those situations

where it is appropriate. For wider beamwidths and pulse lengths than used

here, the extrapolation of the mountainside data to the case of data on flat

ground at a higher look angle could be made correctly on a more general

b asis.

Four types of land clutter were studied based on their availability

to the test sites. These include: pine trees, deciduous trees, mixed pine

32

Figure 16. View of a large field from test site 1 showing the 3dB antennapattern for the 9.5 GHz radar (cIrcle) superimposed on thefield. The depression angle is 30; the range is 4.1 km.

Figure 17. View of the deciduous tree area on Little Kennesaw Mountainfrom test site 2 showing the 3dB antenna pattern for the 9.5GHz radar (circle) superimposed on the trees. The depressionangle is 0.40; the range is 0.5 kim.

and deciduous trees and a grassy field. For the reasons given above, it

was not possible to distinguish types of clutter at low angles because of

the large antenna beam areas which resulted. However, for depression

angles between 50 and 100 it was possible 4ot only to illuminate uniform

clutter patches but to explicitly identify the scetterers so that differences

in the return characteristics could be determined fairly accurately.

2. Average Backscatter Data

The measured backscatter per unit area for the various types of ground

clutter studied are given in Figures 18 through 24. Figures 18 through 20

compare data measured during the summer with data obtained in the fall at

9.5 GHz, 35 GHz, and 95 GHz (no 16.5 GHz data were taken in the summer).

Figures 21 through 24 compare data obtained for vertical and horizontal

polarizations at 9.5 GHz, 16.5 GHz, 35 GHz, and 95 GHz.

No marked differences between the summer and fall data appeared in

general; however, at 35 GHz, the summer data were slightly higher than those

taken in the fall. Review of the available data suggests that this trend

does not appear to hold at 95 GHz. (No summer data were obtained at this

frequency for small depression angles because of equipment problems.)

It should be noted that all of the data plotted at or above 150

depression angle were measured on the side of Little Kennesaw Mountain at

Test Site 2. The angles plotted were obtained by the addition of the

actual depression angles from the radars to the average slope of Little

Kennesaw which is 150 to obtain the equivalent depression angles. As

discussed in the previous section (i.e., the relatively small antenna beam

patterns on the mountainside) and because the trees grow vertically, the

measurement geometry is not exactly equivalent to a true, larger depression

angle. That the data appear to be a few dB lower than might be expected

for the plateau region tends to confirm this conclusion.

The polarization dependence of the data is shown in Figures 21-24. In

general vertical polarization appears to give 3-5 dB higher backscatter than

34

,4 4:

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MIL.

horizontal polarization; however, the polarization dependence in backscatter

is less than the scatter in the data (about 12 dB average.) The average value

of the backscaLter from trees was approximately independent of angle above

about 50 depression angle, and equaled -27 dB at 9.5 GHz, - 18 dB at 16.5 GHz

and 35 GHz, and -16 dB for 95 GHz. The average value for the grassy field

was -37 dB at 9.5 GHz, -27 dB at 16.5 GHz and 35 GHz; no data on the field

were obtained at 95 GHz* because the range was too great. The aveazge values

of the return from the rocky area were -33 dB at 9.5 GHz, -24 dB at 16.5 GHz,

-20 dB at 35 GHz and 95 GHz.

The data appear to have a change in slope between 3 and 5" depression

angle where a0 drops off rapidly. This has been reported by other experi-

menters at frequencies of X-band and below, and is thus consistent with previous

results although the break is not as pronounced as previously reported. This

is probably due to the extremely short pulse lengths used for this experiment

which result in the illuminated beam area at low grazing angles being limited

by the pulse length rather than the vertical beamwidth. Thus the equivalent

illuminated ground area does not increase as fast at low angles as would

otherwise be the case.

3. AEplitude Fluctuations

As discussed in a previous section, the methods of recording data on

received signals from targets during these tests utilizing a range-gated

sampler and a magnetic tape recorder, allowed the statistics of signal

fluctuations to be computed with the data facility. Thus the cumulative

probabilities of the amplitude fluctuations of the return from a radar

cell defined by the antenna azimuth beamwidth and the transmitted pulse

length were obtained.

Figures 25 through 28 give typical cumulative distributions for the re-

turns from deciduous trees at 9.5, 16,5, 35, and 95 GHz. The most important

conclusion to be drawn from the figures is that all the distributions are

nearly log normal. (Log normal plots as a straight line on these figures.)

While this was not true for every run made, it was true for the majority of

42

0.99990

0.99980 T

0.999500.99900

0.99800

0.99500

0.99000

0.98000

0.95000111t

0.90000

~1 0.80000

-~ 0. 70000

0. 60000

0.450000

.~ 0.340000

0.20000

c-i C. 10000

0.05000

0.02000

0. JO

0.005001N

0.002000 0.00100

0.00050

0.000100.000105

0. 00001

-12 -22 -32 -42 -52 -62

Received Power (dflm)

Figure 25. Cumulative probability distribution of the receivedpower from deciduous trees; 9.5 GHz, horizontalpolarization, and 4.1' depression angle.

43

0. 99990

0. 999S0

0.999500. 99900 -t-

0. 99800 ,

0.99500

0.990000.9800o --

U 0. 95000 -U)

<w O. 90000

UO. 800000-C

0.70000

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0.001000.00050

0.00020

0.00010 I H0. 00005(.000020. 00001

-6 -16 -26 -36 -46 -0

Rejoived Power (d Bm)

Figure 26. CumulativeI prohability distribution of thereceived power from deciduous trees; 16.5 Gliz.huriznLtZ JIl 1 )0ariza Litr, aind 4.1u depression aug I.

44

0.99990

0.99980

0.99950

0.99900

0.99800

0.99500

0.99000

0.98000

0.95000 I0.90000

0.80000

0.70000

0.60000- 0.50000

0.40000U0.30000

3 0.30000

0.20000

- 0.05000

0.02000

0.01000

0.00500

0.00200

0.00100

0.00020 :

0.00005

0.000020.00001 4 4

-37 -47 -57 -67 -77 -87

Received Power (dBm)

Figure 27. Cumulative probability distribution of thereceived power from deciduous trees; 35 GHz,horizontal polarization, and 4.10 depression angle.

45

S . . .. . - . , -.,•&d '/. - ' " . • .2_• i• •..,•, ,• :• •

0.99990 -0. 99980

0.99950 - .0.99900T

0. 99800 *0.99500

C?0.98000 -4-I1'

0.95000 1cIN

0.950000

0.90000 if I l

1!. 0.700000.600

0.70000

0.640000

U. 50000

4- 0.402000 j40.300500L

0.002000.200100

0. 0050O

0.010.0200050.010002

0. 00001-.0 17 2 37-7-5

Reeied05er(dm

0.igure 284uuaiepoa-it itiuino h

Iec'ie poITro eidoj"tes05.00010znalplaiaioan j

deprssio angle.-H 146

v ~ ~ ~ ~ 0 0' 0' 00 2' .Ol.-S .h 2.M4 1A.1A ~ i~~ A hiIJU~~

the data. This.. of course, allows the data to be modeled very easily when

computing target-to-clutter ratios for radar system design.

If the clutter distributions are log normal, then one can talk about

the widths of the distributions in terms of the standard deviations of

the distributions. Table 5 gives the averages of the calculated standard

deviations for each type of clutter measured during the tests. As can be

seen from the table the standard deviations for trees increase slightly

with transmitted frequency, although for pine trees the standard deviations

appear much larger at 95 GHz than at the other frequencies. The standard

deviations appear independent of frequency and are much lower for the grassy

field and rocky area than for trees, which is to be expected since these

areas have a relatively conctant return. The standard deviations for trees

range about 3-5 dB higher than for a corner reflector, which represents a

large stationary target.Figures 29 through 32 give the probability densities for the standard

deviations for deciduous trees, pine trees, and mixed trees at 9.5, 16.5,

35, and 95 G1-z. Few differences in the frequency of occurrence of a given

value of the standard deviation exist for a given tree type except at 95 Gllz.

At 95 (;Hz both the deciduous and pine tree distributions are bimodal,

although the values from deciduous trees appear to have a wider spread over

Lhe range of 2 dBi to 12 dB.

The large standard deviations (greater than 6 dB) appear to correlate

well with wind speeds greater than 10 mph and in a direction parallel to

line-of-sight from radar to target area. This trend probably exists at the

lower frequencies but did not show up in these data.

Figures 33 through 36 show the polarization dependence of the probability

density functions for the standard deviations. As can be seen, there is little

polarization dependence although vertical appears to have slightly higher values.

47

TABLE 5.

SUMMARY OF THE STANDARD DEVIATION

FOR VARIOUS CLASSES OF CLUTTER

Frequency 9.5 GHz 16.5 GHz 35 GHz 95 GHz

Clutter Type Polarization Average Value of Standard Deviation (dB)

Deciduous Trees- Vertical 3.9 - 4.7 -

Suunmmer Horizontal 4.0 - 4.0 5.4

Average 4.0 - 4.3 5.4

Dheciduous Trees - Vertical 3.9 4.2 4.4 6.4

Fall Horizontal 3.9 4.3 4.3 5.3

Average 3.9 4.2 4.3 5.0

Pine Trees Vertical 3.5 3.7 3.7 6.8

Horizontal 3.3 3.8 4.2 6.3

Average 3.4 3.7 3.9 6.5

Mixed Trees- Vertical 4.3 - 4.0 -

Stimer Horizontal 4.6 - 4.2 -

Average 4.4 - 4.1 -

Njixed Trees - Vertical 4.1 4.1 4.7 6.3

Fail Horizontal 4.5 4.3 4.6 5.0

Average 4.4 4.2 4.6 5.4

(rassy Field VerLical 1.5 - 1.7 2.0

Horizontal 1.0 1.2 1.3 -

Average 1.3 1.2 1.4 2.0

Rtocky Area Vertical 1.1 2.2 1.8 1.6

Horizontal 1.2 1.7 1.7 1.7

Average 1.1 1.9 1.8 1.7

10" Curner Reflector - 1.0 1.0 1.2 1 .2

(Located in Grassy Field)

48

,•.'; r,; .;.m .,.__.___,_._•_.__,._-_____,__ -.. __•

"Deciduous Trees

. = Pine Trees

.6 =�Mixed Trees

C .5

.4 -

.3

.2

0 1 2 3 4 5 6 7 8 9 10 11 12

Standard Deviation (d3)

Figure 29. Probability density function for the measuredstandard deviations of the amplitude distributionsas a function of tree type; 9.j GHz.

I I I

SDeciduous Trees.7

Pine Trees

.6 -- Mixed Trees

7f .5

.4

.3

.2

.1

2 2 4 6 7 8 9 0 i1 12

Si;tandard Deviation (dB)

Figure 30. Probability density functions for the measuredstandard deviations of the amplitude distri-"butions as a function of tree type; 16.5 GHz.

49

Daciduous Trees

.7 ,

Pip~e Trees

.6- • Mixed Trees

),3 .4

.3-

0

0 18 9 10 11 1:

Stand'ird DeviaLion (dB)

Figure 31. Probability densJty functions for the measuredstandard deviations of the amplitude distri-butions as a function of tree type, 35 CH7.

Deciduous Tree, /

N* Pine Tiees

SMixed Trees

..

00

0 1 2 3 7 8 9 lO .1 12

SLandard Di.viation (dB)

"Figure 32. Probability densslIty functions for Che measuredstandard deviations of the amplitude diS•ri-butions as a function of tree type; 95 (.Hz.

50

Sm~

Trees - Vertical Polarization

"Trees Horizontal I'olarizaLion

lrees - Total

rI- .3

0

j 1 2 3 5 6 7 8 9 10 11 13 13 14

Standard Deviation (dB)

Figure 33. Probability density functions for the measuredstandard deviations of the amplitude distri-

butions as a function of polarization; 9.5 CHz.

• I I I I I I I I" '" I ]SIreus " Vertical I'olaritaLlonj

roees •horizontal PolariZaLItloI +Trees - Total

-_jI

S.,+, g 'l •, 1 :1 13 14

.;t.wiard NlVijt inI

Figurv 34. l1rob,,bility density functions for the measured"standard deviations of the amplitude distri-bLtiion, ;is a function of polarization; 16.5 GHz.

51

.8 j.7 - \Ix•\ Trees - VerLcal PolarizatLon

Trepesp " liorLzontal Polarization

Tre Total

- . 5

>' .4

.3

,I

.0

0 i 2 3 4 5 6 7 8 9 10 11 12 13 14

Standard l)eviation (dB)

Figure 35. Probability density functions for the measuredstandard deviations of the amplitude distri-butions as a functior. if polarization; 35 GHz.

X\l Vertical PolarizationTrees IloI'ZOc L21 Ioilarilzation

.6 - "tees lotai

0 1 2 3 4 5 b 7 8 9 I0 II 12 13 14

Standajrd D1vialE Ion (1II1)

Figure 36. Probability density functions for the measuredstandard deviations of the amplitude distri-butions as a function of polarization; 95 GHz.

5?

4. Spectral Distributions

All the radars employed in these tests were incoherent systems so that

it was not possible to obtain the coherent Doppler frequency spectrum of

the return signals from the land clutter. However, it was possible to deter-

mine the frequency compo,5.ntj without coherent phase relationship in the power

spectrum. The equipment and data-processing techniques for obtaining the

spectra are presented in a previous section of this report.

For the purpose of comparing data fT'Jm thi.s project to that obtained by

previous investigators, it is important to note differences in the measurement

radars employed as compared with those used by previous investigators. All of

the radars used in this investigation employed log-if amplifiers. Previous

investigators have used linear receiver systems. Thus, in order to obtain

the linear frequency spectrum from the logarithmic signals so as to be directly

comparable to previous results, the signals must be properly formated prior

to computation of the fast Fourier transform. However, since many radar

systems are currently designed with logarithmic receivers, it was decided

to also process the data in the same form as they were recorded, i.e. as

the output of a logarithmic receiver. This will allow system designers to

use thC final results directly. Thus, if a Doppler processor is used with

linear receivers, the spectral width expected from deciduous trees will be as

shown iv Figures 37 through 40 for vertical pclarization, and for a log

rcc•ivor th,! spectral widths will be as shown in Figures 41 through 44.

1iorizo.Ltal polarizatiun is not shown as the spectre are similar to the

vur•icai distributions.

Figures 37 through 40 indicate that the frequency si c.t ra slopes cen be fit

well by a cubic function at 9.5 GHz and 16.5 GHz and by a quadratic function

at 95 Gliz, with 35 Ghz being fit best by a power function with a 2.5 pcwer ex-

ponent. Many in'.estigators are currently using Gaussian shaped slopes for clutter

spectra rolloff which could lead to large errors between predicted and actual

M'1TL performance. The corner frequuncies can be seen to be both windspeed

;nld trI'aismitted frequency dependent increasing with increasing windspeed and

i•l ruasing frequency. A dependence of the corner frequencies on the wind direc-

t. ion, I.C ., parallel or perpendicular to the radar line-of-cight direction,

appears to exitL; although the data were scattered, it appears that the

53

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61

corner frequencies for individual data runs tended to be larger when the

wind direction was parallel to the radar line-of-sight.

It should be noted that the results of the 9.5 GHz data given here

compare favorably with results obtained by Fishbein [9] for X-band and

windspeeds of 10 to 15 mph. He determined that land clutter could be fitted

with a cubic function with a corner frequency of 6.7 Hz, whereas the data

obtained at 9.5 GHz shown in Figure 37 is best fitted by a cubic function with

a corner frequency of 9.0 Hz. The excellent agreement of the data presented

here at X-band with previous data obtained by Fishbein serves to reinforce

the validity of the results at the higher frequencies where no previous data

have been obtained.

Figures 41 through 44 give the frequency spectra from deciduous trees

for a system with a logarithmic receiver. By comparing these spectra for

log receivers with the previously discussed spectra for linear receivers, the

differences in the spectral characteristics can be determined. If the linear

and logarithmic spectra for each transmitted frequency are compared it can be

seen that the slopes are steeper for the log receiver case than for the

linear receiver. The log spectra have a fourth power roll-off characteristic

at 9.5 GHz and a cubic characteristic at 95 GHz as opposed to cubic and

quadratic roll-offs for the two respective frequencies for the linear case.

Furthermore the break frequencies are lower for the log receiver case. Thus, it

can be seen that use of logarithmic receiver will reduce the clutter spectral

widths appreciably. However, the target-to-clutter amplitude ratios will also

be reduced so that the beneficial effects of the narrowing of the clutter

spectrum by the use of a log receiver may be cancelled out.

5. Correlation Functions

The time required to obtain independent samples of radar returns from

clutter is of concern to the designer of radars used in a space scanning mode.

Since the auto-correlation function provides this basic information, the

62

I

radar backscatter data from trees were processed to obtain the linear

correlation functions. The results of this investigation ere shown in

Figure 45. Each point on this figure is the result of an average of a

number of data runs each consisting of 8 data samples lasting approximately

0.5 seconds. It is noted that in general as the windspeed increases and/or

the radar frequency is in,.reased, the decorrelation time decreases. This is

cornistent with the observation that the power spectrum distribution becomes

wider as the windspeed and/or the radar frequency is increased.

It should be noted that in some cases where the windspeed was very low

ie., u 2 mph, the decorrelation time was extremely long, i.e. several

thousand milliseconds, at 9.5 GHz. This probably occurred when the

major scatterers in the resolutiun cell were essentially at rest for a few

seconds, so that their respective phases remained relatively constant. An

c'. p- oF this phenomenon is given in Appendix B. The conclusions to be

.,- .. �the measured decorrelation time are that returns from trees

:'!.iays be correlated on a pulse-co.-pulse basis, and sometimes will

_,.- Ker several scan periods, particularly for a rapid scan radar.

"-' Lation functions were obLained b,.twoen the signals from the

.,,hen operating at exactly the same time. The 9.5 G~lz

•....... . .. .... '.:.,........he reiference system and correlation functions between

" .aw .:,"tems, 9.5 and 35 GIL systems, and 9.5 and 95 (liz

* - ,. .. . ,.

. , ,rC)osi.u 51 give examples of the cross-correlation functions

for L;o wi•q: .,Q , 2 nph and 8 mph. It should be noted that the dc levels

of the signals hay.'~ •ccn suppressed so that these figures give only the

correlation of the vaiying components of the respective returns.

As can be seen from the figures, the cross-correlations are quite low,

typically less than 0.3. However, a periodic component does appear evident

in the correlation functions; the frequency and amplitude of this periodic

component increase with increasing wind speed and transmitted frequency.

Since a 0.3 correlation coefficient or less between two signals is gen-

erally considered to indicate linear independence, the radar returns are

63

...............................................

300 3

200 50 ~ 2 mph Windspeed

| 3-5 mph Windspecd* 6-10 mph Windspeed£11-14 mph Windspeed

10"25

.LUU.

100.i3I50 20 E

4J30-"-

20 50 o20

4J4

1. 4-%L 10 100 rL-

U

3 333A

2 500

10 20 30 50 100 120

Trdnsmitted Frequency (02~)

Figure 45. Decorrelation time versus frequency and wind-speed for return from deciduous treesi.

64

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70

essentially independent when averaged over the 15 second interval required

to obtain the calculated results shown in the figures. (See Section Il-A

for a discussion of how the cross-correlation functions were computed.)

71

III. CONCLUSIONS AND RECOMMENDATIONS

The design of the experiments described here was based on the recogni-

tion of a number of deficiencies in the knowledge of the radar backscatter

from foliage and other natural objects at millimeter wavelengths [Il.

The experiments were structured to obtain detailed data on the nature of the

backscatter at a variety of grazing angles between 2 degrees and 20 degrees,

for approximately octave steps in frequency between 9.5 Giiz and 95 G11z.

Due to funding and time limitations, as much as for technical reasons,

the results reported here are primarily for scattering from foliage. The

emphasis on foliage is not necessarily an undue restriction at this time,

since the properties of the scattering from foliage are critically needed

in the design of signal-processing schemes and may actually represent the

Most important single case in this respect, especially for the development

Of MTI and related techniques. Further investigation of discrete scatterers

should, of course, be undertaken to complement the data reported here.

The major results of the summaries of the average backscatter character-

isLics (c°) were as follows.

(1) The scattering was found to be weakly dependent upon frequency.

(2) The average polarization effects were less at the higher frequencies

and generally less than the total variability due to other causes.

(3) T*he scattering was approximately independent of angle of arrival,

for angles above 5 degrees for all frequencies.

(4) The variance of the data was approximately independent of frequency

polarization and angle.

(5) Tht. spread of the observed values of u is almost certainly due

to real variations of the scattering objects.

(b) The median value and spread of the resulting data are remarkably

(: Ins is tent with those data previously available.

Preceding page blank

73[I

y in• e+ + - -,

The major results of the investigations of the amplitude statisticz of

the backscatter are as follows.

(1) The observed amplitude distributions were generally well represented

by the log normal function.

(2) The general behavior of the observed amplitude distributions was

essentially independent of frequency above 9.5 GHz.

(3) The observed amplitude distributions were more regular at the

higher windspeeds and appeared to be weakly dependent upon

windspeed at the higher frequencies.

(4) The amplitude statistics appear to be weakly dependent upon the

type of tree for fall or summer conditions.

The investigations of the temporal and spectral properties of the back-

scatter from foliage were limited to observations of the incoherent properties,

since only incoherent pulse radars were available. The principal results

were as follows.

(1) The normalized frequency spectra were dependent on windspeed and

frequency, with the corner frequency observed to be higher for the

higher windspeeds and/or higher frequencies.

(2) The slopes of the linear frequency spectra were observed to vary

inversely with frequency and could be fitted with power laws ranging

from cubic at 9.5 GHz to quadratic at 95 GHz.

(3) The decorrelation times of the returns (as obtained from the auto-

correlation function) varied with transmitted frequency and wind-

speed in the expected manner and had values consistent with generallyaccepted theories; however, the effects of motions of individual

leaves and other small scattering elements were observed to be quite

important at 95 GHz, especially at low wind epeeds.

(4) Returns at different transmitted frequencies appeared to be only

weakly correlated (as obtained from the cross-correlation functions)

for most observations; however, at very low windspeeds the degree of

correlation was significant.

74

The temporal and spectral behavior of the observations made of rocky

terrain, corner reflectors, etc., was also as anticipated, with the chaiact-

eristics somewhat a function of signal-to-background ratio (i.e., fixed

target-to-foliage and/or -noise) and exhibited similar values at all freq-

uencies for high signal-to-background ratios.

It is to be concluded that, while much new information has been obtained

as a result of this investigation, a number of important questions remain to

be answered and shoulu be the subject of future investigations. Specifically,

the main emphasis of the work reported herein was on investigation of returns

from foliage; however, this work represents only one step towards the

identification of the characteristics of returns from targets immersed in

cI'tter. Since the crucial questions concerning application of millimeter

frequencies to radars for the detecting and identifying targets in clutter

involve specification of target-to-background ratios, target/clutter

signatures, and the general reliability of the specification of "standard"

values of target and clutter properties for design purposes, a much more

0xtensive program of investigation is needed. Specific data are needed on the

characteristics of target returns in clutter, including especially polarization

1h),i~ivior, spectral characteristics, and amplitude statistics. In addition, the

ioiagu penetration capability needs to be determined as a function of

jruquvncy to assist in scaling the measured target-to-background ratios. A

ronmitment should be made to the development of the necessary tools and techniques

ior obtaining both coherent data and bistatic data for frequencies at least as

high as 95 Ghlz. Ln addition, future planning should include the development of

the nucessary supporting instrumentation (both meteorological and signal

S,] ibration) and measurement standards to facilitate comparison of data from

:uccessivC experiments, especially from differei t g2ographic and climatic

75

IV. REFERENCES

1. R. D. Hayes and F. B. Dyer, "Land Clutter Characteristics For ComputerModeling of Fire Control Radar Systems," Technical Report No. 1 onContract DAAA 25-73-C-0256, Engineering Experiment Station, GeorgiaInstitute of Technology, 15 May 1973.

2. F. B. Dyer, "Engineering Services in Support of Millimeter Rain ClutterExperiment," Final Letter Report on Contract DAAA 25-73-C-0256, Mod.1100002, Engineering Experiment Station, Georgia Institute of Technology,25 October 1973.

3. N. C. Currie, F. B. Dyer, and R. D. Hayes, "Analysis of Radar RainReturn at Frequencies of 9.375, 35, 70, and 95 Glz," Technical ReportNo. 2 on Contract DAAA 25-73-C-0256, Engineering Experiment Stations

Georgia Institute of Technology, 1 February 1975.

4. Louis N. Ridenour, Radar System Engineering, Vol. 1 of the M.I.1.Radiation Laboratory Series, Dover Publications, Inc., New York, 1947,p. 21.

5. D. E. Wrege and D. S. Harmer, "FOCAL/F An Extended Version of 8-KFOCAL 6 9TM," School of Nuclear Engineering, Georgia Institute ofTechnology, 1 June 1972.

6. W. Rivers, "Low-Angle Radar Sea Return at 3 nita Wavelength," Final Tech-niical Report on Contract N62269-70-C-0489, Engineering ExperimentStation, Georgia Institute of Technology, 15 November 1970.

7. C. M. Close, The Analysis of Linear Circuits, Harcourt, Brace, and World,Inc., 1966.

8. David K. Barton, Radar System Analysis, Prentice-Hall, Inc., EnglewoodCliff, N.J., 1964, p. 96-97.

'i. W. Fishbein et al., "Clutter Attenuation Analysis," Technical ReportNo. ECOM-2808, U.S. Army Electronics Command March 1967.

Preceding page blank

77

V. APPENDICES

Preceding page blank79

APPENDIX A

Selected Spectral Distributions

This appendix contains examples of computer generated x-y plots of the

noncoherent spectra of returns from deciduous trees for windspeeds of 5 mph

and 10 mph a, 9.5, 16.5, 35, and 95 GHz. Each plot represents about 5 seconds

of data, F d the data for earh frequency were processed for the same time

interval so that direct comparisons can be made. The odd numbered figures

give the normalized linear frequency spectra for the returns. That is, the

recorded logarithmic data were exponentiated prior to computation of the

Fourier transform so as to obtain the linear frequency spectra as discussed

in Section Il-A-i-b. The even numbered figures represent the spectra

obtained by processing the same data as for odd numbered figures without

exponentiating the data prior to calculation of the Fourier transform, and

thus represent the normalized frequency spectrum of a logarithmic receiver.

Looking at the figures, it can be seen that the logarithmic frequency

spectra always have lower cutoff frequencies and a steeper roll-off character-

istics than the corresponding linear frequency spectra runs. In general the

corner frequencies of the spectra increase with increasing transmitted fre-

quency and windspeed. It should be noted that the linear spectra appear noisier

than the logarithmic spectra because the exponentiation process greatly increased

the dynamic range of the data, and to prevent overflow in the computer, the

data had to be "clipped" on the low end. Thus the "spikes" which appear on

some of the spectra in the 100 Hz to 1000 Hz region are due to noise result-

ing from this problem.

Preceding page blank

81

4 _ ----

_4 -

IT 11+4

Figure A-1. Example of the frequency spectr~un of the returnfrom deciduous trees; 9.5 GHz, vertical polarization,and 5 mph wind speed (linear dete~ctor).

4 -T....

A T-

Fig~~tr4 A-2 Exml fIifeunc ptu rmelogarithic rc ive ftereunfo

deiuu tes .4-izvria oai

zaton an 5 t1p...nd.speed

1W I 82

-T -

..... 1, - t. . -r-T - 1

1~ t4...

-2 . ,

-. . . . . . . .... . :......-1--r

-4 +4-4.

_I

-4--- T 1

-i• " .- . [ . . ....

...

-.

t 50) UO

Figure A-3. Example of the frequency spectrum of the return fromdeciduous trees; 16.5 GHz, verticd l polarization, and, 5 m p h w in d s p e e d ( lin e ar r e c e iv e r ).

........................ . . ...* |! . , i • .2! ! I I

,-I I ii ...............................................................

I I" :• -• • t",-t •• "..

, • i l ! I k ! I t: ; ,

. I', ,5','• , I ,! .

*Figure A-4. Lxainple of the frequency spectrum from alogarithmic receiver of the return fromdeciduous trees; 16.5 GHz, verticalpolarization, and 5 mph wind speed.

4 A[

-, - ----- -.--- --

-25

•" : :1 I7""

'2 _ - .I Fi -

_ .....1..1 2 5 +0 j 05t 1i 2iFre'4uemy OWii

Figure A-5. Example of the frequency spectrum of the returnfrom deciduous trees; 35 GHz, vertical polarization,and 5 mph wind speed (linear receiver).

-. . .... ....-

-- ( -......-- +--

2-1J0i'' -. .................. . .......-..

.... -..=.. . ---fr-- - 1- ........... ..jj. .. j..j~ i~ '* - _ _ _1

Fr~quenu (i.11,

Figure A-6. Example of the frequency spectrum from alogarithmic receiver of the return fromdeciduous trees; 3 3 GHz, verticalpolarization, and 5 mph wind speed.

84

t I <I .... I .. - --.---- -

7 i2

Itt

ao. 2 0 0 11111

Figure A-7. Example oi the frequency speC~trum of the returnfrom deciduous trees; 95 GHz, vertical polarization,and 5 mph wind speed (linear receiver).

.4~ >.......UL14T4iti

1 44-

t t I I

Figure A-8. E'xample of the frequency spect rum from a

logarithmic receiver of the return from

deciduIous trees; 95 GiIz, vertical polarizationI,

and 5 mp~h wind speed.

85

I -

-7-

soq~( a02

Figure A-9. Example of the frequency spectrum of the return

from deciduous trees; 9.5 GHz, vertical polarization.

and 10 mph wind speed (linear receiver).

4----4

4+4

...... .. .L.... FZ711'2 10 50 1

Figure 1 A -10 Example ~ oftefeuny- pcrmfo

FigureA-b. oEartmpleeeie of the frqeny returun froma

deciduous trees; 9.5 GHz, verticalpo-ar..za..ion, and JLJ mph wind speed.

86

T -7,b~ ~ ~ ~ ~ ~~~~~L ji9? I I••*•+. I o -••

Figurc A-I!. Exjmp)1v (it the frequencl; spectrum of tile returnfrom deciduous trees; Ib.5 GHz. vertical polarizationt.ind 1I) mill wind spee.d (I iticar reck i ver).

. ... 4.... . . . 4. ...

Figure.-12 .. x.....pl of ..... frequency- spetru fro a..

• o ... .... .i, ...

* 4 . . ,

S.. . . . . . . . ... . . . . .. . . . . . . . . . . .I . . •. 2

F'igure' A-I?. Fxrrtple: of the frequenacy spectrum fom ah e~r

logarithmic receiver of the return fromdeciduous trees; 16.5 GHz, verticalpolarization, and 10 mph wind speed.

871

* -tt6 . .-. . .. i _Jj . . . . . .

4---. >

S- ,- 4 . .... + -.. . . - - .t---- - -S ...

~4

a.-•~ -- 2---. *- -*-*-• •- --- • .--- ~--- >, --t - ... --

-4 +-- -*

Figure A-1 3. Ex;ample. of the trequencv ,•pctrrunm of the ro.turnfrom d,.ciduo.s trees; 35 Gii-. vertical p,,la-iz;iti(,n,and 10 mph %.ind .peed (lineair rs,-oivier).

• - i l "V ..'' . 6.. .. i " -- --- *-t-*-+ 6- . -- + '- - --

*. . . .. - " . .-+ 6 . . -

I t -- f-l•<+ .1.

..... . ...4 6 jI.. ... +6.. - -+.. f- 1 6 .i+ .+ii 66- . 6 .6 . 4 6 '--.. . I. -. + .. . . .. . J . . + + + -. -

IW

-4 S .. ,'- , 6.. .. . . 6 4 4f- ,t i + -4-- 4 4 .4- 1...

S. . .+ "t"- - i-- L- .-"-. . . . . . ... . " . .. 6-

' • 4 ... .6 , ,.6 , . .. j... .6.-......

.6 i , '6 . +

.t, ~ --. -I51

Figure A-14. Example of the frequency spectrum from alogarithmic receiver of the return fromdeciduous trees; 75 GHz, verticalpolarization, and 10 mph wýnd speed.

88

L!

-' 1-, - -- -

ILLIFigure. A-15. Ehawaple of the frequency spectrum of the return

fruui deciduous trees; 95 (;Hz, vertical polariza~ion,and 10 mph wind speed (linear receiver)'.

-4

* ~- t 4- 4

Figure A-16. Example of the frequency spectrum from alogarithmic receiver of the return fromdeciduous trees; 95 GHz, verticalpolarization, and 10 mph wind speed.

89

APPENDIX B

Auto-Correlation Functions for Selected Data Runs

This appendix contains selecteQ examples of the computer generated auto-

correlation functions for the returns from deciduous trees for wind speeds

of 2, 5, 8, and 10 mph. The horizontal axis represents q maximum delay time

of 230 milliseconds determined by the satple rate.

ligures B-1 through B-4 represent a data run for which the indicated

windspeed was 2 mph, however it i3 obvious that, at the instant that these

data were processed, the wind was essenti.lly calm. This arocounts for the

extrem•ly long decorrelation Limes for 9.5 and 16.5 GHz. Note thac the

35 GHz and 95 GHz runs still decorrelate fairly rapidly, probably due to

leaf flutter which is nut resolved at the lower frequencies.

In general these examples show that the decorrelation time decreases

with increasing transmitted frequency and increasing windspeed.

Preceding pag blank

91

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