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Introduction Page 1

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

AESA Airborne Radar Theory and

Operations Course Sampler

Robert A Phillips AnnapolisStar@gmail.com

Introduction: Page 2

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Objective Number 1 1) Learn how to interleave modes, intercept targets using advanced LPI techniques, and develop requirements for an AESA radar from the pilots point of view.

AESA Radar engaging and launching missiles on three targets

Introduction: Page 3

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Objective Number 2

Square law

Detector

Pulse Compress CFAR M of N

Correlate FFT

Clutter Template

Block Diagram for Search mode

Antenna Receive Pattern using a diamond layout with true symmetric Dolph Chebyschev sidelobe weighting (from supplied Radar Theory eBook)

2) Present the theory of an AESA Radar and learn how to design the air-air and air-ground modes from the requirements up

Introduction: Page 4

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Objective Number 3 3) Provide the simulations, tools, and references for “putting the theory into practice”

Win 7 Professional Radar mode design spread sheet with software

"You cannot mandate productivity, you must provide the tools to let people become their best.“ Steve Jobs

500 page interactive electronic book on class material including antennas, Space Time Adaptive Processing, Kalman filters, and automatic target recognition, with simulations & examples

AESA Radar Theory eBook

Introduction: Page 5

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Some of the Questions to be Answered

1) How do you design and compute the performance for the AESA search modes? 2) How do you design an AESA mode to track 50 targets ? 3) What is Space Time Adaptive Processing (STAP) and how do you design for it? 4) How can you use an AESA antenna to detect slow moving ground targets which are much smaller than the background clutter. 5) How do you design an automatic target detection and recognition mode? This sampler presents the top level charts from the course on

how to answer these tough questions

Introduction: Page 6

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Sampler

1) Design of an AESA Medium PRF Search Mode

AESA Radar in Medium PRF search

Introduction: Page 7

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

MED PRF Search Block Diagram

Square law

detect CFAR Unfold

Detects

M of N Range

Correlator

M of N Doppler

Correlator

FFT Compress

Target Reports Range, Doppler, Cross Section

Sum Channel

The Block Diagram for a MED PRF Search radar [Skolnick,fig 17.6]

Smallest allowable Target size m2

Skolnick Fig 17.12

Altitude Speed

Clutter Template

[12]

We will use an AESA antenna and receiver with parameters like size, noise figure, power and cooling appropriate for a fighter type aircraft (from Stimson) to design the modes and compute the performance

Introduction: Page 8

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Clutter Template from Supplied Simulation

Tail aspect Head

The template [12] guides us in choosing a CFAR design

• In this region the template tells us we should use a backend STC or guard channel

• In this region we are competing with altitude line – Use special processing to blank returns

• In this region the template tells us we are competing with noise only and we can use the noise PFA threshold.

• In this region we are competing with Main Beam Clutter. Due to its magnitude we will use a notch filter

Introduction: Page 9

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Baseline MED PRF Search Parameters Parameter Value Comments FFT Size 512 Controls S/N and scan rate PRF 70KHz For good tail aspect visibility CHIP 0.5mics For reduced clutter PCR 4 Higher average power M of N 3 of 7 Range correlation TFA 30sec Specification time between FA’s Freq Agile Look-Look Good LPI design Xmit Pulse 2mics Duty 14% Avg Power 471watts Pfa 5.8E-6 CFAR probability of false alarm

The course will show the student how to select the parameters and enter them into the Mode Design spreadsheet

Derived parameters

Introduction: Page 10

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

MED PRF Single Scan Performance

Cross Section 5m2

The Mode Design Spreadsheet 1) Guides the student in the designing a mode, 2) Captures the designs and 3) Compares

the performance for different configurations

Single Scan PD - Low PRF .VS. Medium PRF

Chart from the mode design spreadsheet using VBA software from the eBook on Detection Theory (supplied with course)

Introduction Page 11

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Sampler

2) How to Track 50 Targets with an AESA Radar

AESA Radar engaging and launching missiles on six targets

Introduction: Page 12

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Vector Tracking Loop

General radar tracking loop

Compute Monopulse Error ∆kANT

Transform To NAV Coords

Transform to ANT Coords

Steering vector k(α,β)

k(α,β) antenna steering

kT(θ,φ) target

Kalman Filter in

NAV

Target Relative Position

Error ∼∆kANT

Ownship position vector in NAV reference

The Σ and ∆ channels are used to compute the error vector ∆k in ANT coordinates

For monopulse vector processing see [9] Haupt and eBook on antennas

Introduction: Page 13

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Three Channel (Az,El,Range) Kalman [1]

( ) 1

For n in 1..3

Gain Computatio

n

T Tn n n nR

−= +K P H HP H

For n in 1..3

State Update

n n= + NavX X K E

( )For n in 1..3

P Update

1n n n= −P K H P

For n in 1..3

Extrapolat

e

n n n n

=

= +

XΦX

P PΦP Q

Where n is one of the 3 orthogonal channels

Rng, Az , El

The lectures will define each matrix in the design

Introduction: Page 14

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Typical Track Performance

Tracking a Steady 3G S Turn at 20nm. RMS velocity errors typically approach 200+ft/sec and are entirely adequate to guide

missiles to intercept

Angle Error

RMS Velocity Error

Introduction: Page 15

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

AESA Time Line

15 Target track interleaved with search while displaying a SAR image. Room for lots more!!

Introduction: Page 16

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Sampler

3) Space Time Adaptive Cancellers

Introduction: Page 17

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Space Time Adaptive Filters (Stimson, Haupt)

Standoff sidelobe jammer

• The STAP canceller can remove multiple sidelobe jammer(s) without prior knowledge of the jammer(s) location or antenna gains.

• STAP uses an Interferometric (space based) canceller. • For each expected jammer we need one receiver and Auxilliary

antenna with a gain larger than the sidelobes of the main antenna.

STAP computes jammer phase angles and antenna gains and applies a spaced based adaptive notch filter. By combining this with an FFT to separate moving targets we have a two dimensional Space – Time adaptive filter

Target Gain of AUX

Adaptive Cancellers Stimson [3,Ch 40], Skolnick[4,Ch 9]

Introduction: Page 18

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

The Adaptive Canceller [7] Elbert See also Stimson [3,Pg509]

Sum the weighted outputs of the multiple antennas to cancel the jammer.

Note the order of the matrix inverse is equal to the number of channels i.e. two channels means we have to invert a 2x2 matrix

The space filter is a direct application of linear estimation theory [7]

Main

V

Store samples from each channel in the rows of the

H matrix H=[m a2 a3…an]

The optimal weights X are the 1st column of

the inverse of the covariance matrix

(HTH)-1

AUX2

V

AUXn

V

∑

X1 X2 Xn

Introduction: Page 19

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Example of STAP With Multiple Jammers Example of STAP with 4 Jammers. 4 Aux horns

1 1 2 2 3 3 4 4

The cancelled jammer output equation is:Output=Main+x +x +x +xAux Aux Aux Aux

One 5th order Matrix Inversion and 25 dot products of length 10

10deg 20deg

30deg 40deg

Weighted Sum

Target

1

The optimal weights are:=1st Column of CovarianceMatrix−x

See eBook on Antennas for detailed simulation of multiple jammers

Introduction: Page 20

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

FFT Before and After Cancellation The target cannot be seen in the FFT with 4 Sidelobe jammers. Notice the magnitude of the noise at 100 Q or more!

After cancellation the target is easily seen in the FFT and the noise is down to 5 quanta

Example from eBook on Antennas

Uncancelled Jammer + Target Cancelled Jammer + Target

Introduction: Page 21

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Sampler

4) Slow Ground moving target indicator Main Beam Clutter Canceller

Introduction: Page 22

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Slow Moving Target Detection

Combining the Interferometer technique (used in STAP) with multiple antenna beams we can implement a high performance mode to cancel main beam clutter and detect small slow moving targets in a situation which otherwise would be completely hopeless

SAR display with outputs from the slow moving target detector

One of the most impressive applications of an AESA canceller..

Introduction: Page 23

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Spatial vs Frequency Filtering Frequency Filtering: With an FFT we can separate targets with different Doppler frequencies. This fast moving target is separated by frequency from main beam clutter and is easily detected with an FFT

Spatial Filtering This slow moving target, overwhelmed in an FFT by main beam clutter at the same frequency, can only be detected by spatial filtering with an interferometer

FFT range/Doppler map

Tail aspect Head

The course will describe this essential diagram in detail

Introduction: Page 24

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

θt

Slow Moving Targets and Clutter

θc Angle Space Map

Stationary target at angle θt

Large MBC Clutter at angle θc

Doppler Frequency Space Map

In a space diagram the target and clutter are separable

A Spatial Notch with multiple antennas can remove the clutter

Slow moving target at angle θt

Whereas in a normal FFT frequency diagram the target and clutter overlay each other and the smaller target cannot be detected

Introduction: Page 25

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Slow Mover - Canceller [Stimson Pg321]

Rg x Filter matrix

Rg x Filter matrix

Left Right

A little complicated but very powerful

Get α,β for each FFT

Cell

Get Gain for each FFT Cell

Cancel Clutter

Recompute FFT

rel

The phase for clutter at angle , :

= = sin( )cos( ), G =

Using the canceller equation:

- exp( 2 )

The cancelled clutter for each filter is:e

Leftc

Ri

n

ght

n n

M

A

GdG

GOutput

Cancelled Left Rig

Main Aux jG

ht

α β

πϕ α βλ

ϕ

•

=

−

−

=

R k

xp( 2 )cj ϕ−

Slow moving ground targets

-d/2 d/2

( , )T θ φk ( , )MBC α βk MBC comes from a known angle α,β

The target at the same frequency as clutter

CFAR

Introduction: Page 26

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

S

S

S

ATR Finds 3 S-300 Surface – Air Missile

Launchers with Pd>0.95 in 2 sec Sampler

5) Automatic Target Recognition Target Detection

Bushehr nuclear power plant from Google Maps

Introduction: Page 27

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Automatic Target Detection Outline [13]

Detector uses general target signatures to find “military like” targets

CFAR Detector

Binarize Image

Open/Close Shapes

Compute Moments Statistics

Edit Clutter False Tgts

Get Enhanced Tgt Chips

SAR Targets + Clutter

Detected targets sans clutter

Clumped Detects

Target List

Target Recognition

Clutter Shadow Removal

Data from MSTARS public website, algorithms from Lincoln labs and Mathcad image processing library

Library

Introduction: Page 28

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Theory of Moments from [11] HU Characterization of an image by statistical moments

like variance, and kurtosis, and invariant moments like the eigenvalues is a common approach in ATR.

The Uniqueness theorem states that you can completely reconstruct an image with knowledge of the moments of the image.

If you use amplitude, translation, scale and rotation invariant moments you increase the power of this approach

E All three E’s in this example are uniquely identified by the same simple moments

which are independent of where they are on the paper, their amplitude, scale or

rotation

We can also characterize tanks, trucks and guns by moments

Introduction: Page 29

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

Example Automatic Target Recognition[13] Enhanced M113 Chip from ATD with feature vector consisting of moments, stats and

pose

Feature Vec BTR60 M113 BMP2 BTR70 T72 M109 M2 HMMW M1

Correlation 0.81 1 0.92 0.88 0.87 0.86 .91 .93 .85 Eigenvalues 0.62 1 0.71 0.61 0.73 0.54 .72 .70 .41 Area 0.89 1 1 1 0.81 0.69 .91 .91 .67 Combined 0.45 1 0.66 0.54 0.51 0.32 .59 .59 .23

Comparison of feature vectors for each target in library

3) The highest score is the ID

1) Use the pose to index the library

2) Compute Score for each target in the library using feature vectors

Pose=-30deg

Goo

d M

atch

Library Chips with same pose as detected target

Introduction: Page 30

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

References 1) Decoupled Kalman filters for phased array radar tracking: Automatic Control, IEEE transactions on: Date of Publication: Mar 1983 Author(s):Daum F. Raytheon Company, Wayland, MA, USA 2) Blinchikoff and Zverev, “Filtering in the Time and Frequency Domain” 1975 3) Rabiner and Gold, Theory and Application of Digital Signal Processing 1975 4) Stimson, “Introduction to Airborne radar” 1998 5) Skolnick “Introduction to Radar” 1995 6) William Skillman “Radar Calculations” Artech House ,1983 7) “Estimation and Control of Systems” Elbert 1984 – Contains all aspects of linear estimation from least squares to the Kalman filter 9) Antenna Arrays - Randy Haupt IEEE Press 10) SDMS MSTARS Public Data Website https://www.sdms.afrl.af.mil/ Contains 1ft SAR images of military targets 11) M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Information Theory, vol. 8, no. 2, pp. 179–187, 1962. 12) Radar CFAR Thresholding in Clutter and MultipleTarget Situations Hermann Rohling AEG-Telefunken, IEEE Transactions On Aerospace and Electronic Systems VOL. AES-19, NO. 4 JULY 1983 Discusses clutter maps for describing clutter regions of differing clutter type. Excellent analysis of CA, GO CFAR and ordered statistic CFAR

Introduction: Page 31

Copyright 2013 R.A. Phillips

AESA Airborne Radar Theory and Operations

References

Provides overview of the Automatic Target Recognition and Detection including Super resolution SAR , CFAR’s and effects of polarization and resolution on recognition

13) MIT Lincoln Lab Journal Archives http://www.ll.mit.edu/publications/journal/journalarchives.html

Vol 10, Number 2 - 1997 Vol 6, Number 1 - 1993 Vol 8, Number 1 - 1995