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Semester 2 UpdateJoe HoatamJosh Merritt

Aaron Nielsen

Outline

Range AmbiguityVelocity AmbiguityClutter Filtering

More on Sachidananda/Zrnic coding

Previously, random phase coding (Zrnic 1979, etc) was implemented to combat range ambiguity

Systematic π/4 and π/2 phase coding was introduced by Sachidananda and Zrnic in 1985

Recently, Sachidananda and Zrnic presented a new phase coding system called SZ coding

More on Sachidananda/Zrnic coding

SZ (Sachidananda-Zrnic) code is constructed as follows:

Mk /n 2(k)1)(k(k)

SZ has autocorrelation of one at lags of M/n and zero autocorrelation at any other lag

SZ(n/M) code, M=number of samples, is specified by the following:

)]/(exp[ 2 Mmnjak

Simulation Results

To simulate the performance of the SZ code versus other coding schemes, a time series is simulated for a first trip and second trip signal

Coding schemes are then implemented and tested

A random phase error is introduced by adding a uniformly distributed random phase sequence to the time series

Performance of coding schemes are determined by viewing the standard errors of v2 as a function of the power ratio (p1/p2) and spectral width

Simulation Results

As spectral width increases, the implemented notch filter removes less of v1 causing a more noise v2 estimate

The upper limit of p1/p2 is around 40 dB (due to the random phase error)

Simulation Results

Simulation Conclusions

SZ coding outperformed previous coding schemes in simulation results

Weaker signals are able to be recovered and the standard errors in mean velocity are smaller for SZ coding than other coding schemes

Velocity Ambiguity

Common solution to ambiguities in weather radar measurements is also multiple PRF processing.Clustering Algorithm:

E n= j=n

n N

∣ V n−V oj∣2

Clustering Algorithm Results

Previous experiments showed the clustering algorithm to perform better than a comparable algorithm that is used for processing ambiguous velocities (Chinese Remainder Theorem).For very low Signal to Noise Ratios, the CRT performed better than the clustering algorithm, however above 2 times, SNR wasn't as much as a factor.

Maximum Likelihood Algorithm

Figures the probability that a measurement correctly represents a measurement.Calculates the probability of obtaining a specified number of false alarms, the probability that a component of a given radar measurement is a false alarm, and the correct measurement likelihood for all targets.Shows good results for accurate measurement calculations when used in conjunction with a clustering algorithm

ML Algorithm

Large Ns number of targets gives a higher probability that the set contains true targets, small Ns number of targets makes the algorithm run faster.Selection Process, approximately:

Select smallest 3 measurements from unique PRFsCalculate Squared ErrorSave if it's in the smallest set of numbersAdd the next smallest velocityRemove existing measurement if it's the same PRFIf N>3

Calculate Squared ErrorCompare to a threshold velocity

If smaller, save as a “super target” and add the next smallest rangeIf greater, remove smallest velocity, and repeat squared error

If N=3, begin again from squared Error

What I went over this break

More articles on GMAP

Went over gmap.m

GMAP

“Radar Operations Center Evaluation of New signal processing Techniques”

GMAP reduces ground clutter while adequately reconstructing weak signalsGMAP adequately filters AP (Anomalous Propagation) clutterGMAP recovers velocity estimates in the presence of clutterGMAP out performs other filtering processes

GMAP

GMAP

“Multi-PRI Signal Processing for the Terminal Doppler Weather Radar.Part I: Clutter Filtering”

For CNR 0 dB, no clutter filter is applied. For 0 dB CNR 20 dB, the 20-dB filter is applied. For 20 dB CNR 40 dB, the 40-dB filter is applied. For CNR 40 dB, the 60-dB filter is applied.

Spring Timeline (Approximately for the Group)

Turn in final paper, completed website15

Final presentation, last revisions of final report, upload all necessary files to website

14

Complete rough draft of final report, brainstorm on alternative algorithms, prepare for E-Days (April 13th)

12-13

Implement algorithms on data from CHILL, test different algorithms, devise conclusions on results

6-11

Begin work with CHILL data, learn the data format, conduct research on data format as needed (review syntax of C)

4-5

Simulate techniques using Matlab, edit simulations as needed, make conclusions on results

1-3

Study technical papers more in depth and gain a complete understanding of techniques to be used

0 (Over Winter Break)

ActivitiesWeek Number(s)