Your Name Your Title Your Organization (Line #1) Your Organization (Line #2) Semester 2 Update Joe Hoatam Josh Merritt Aaron Nielsen

Outline Range Ambiguity Velocity Ambiguity Clutter 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: 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:

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:

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 PRFs Calculate Squared Error Save if it's in the smallest set of numbers Add the next smallest velocity Remove existing measurement if it's the same PRF If N>3 Calculate Squared Error Compare to a threshold velocity If smaller, save as a “super target” and add the next smallest range If 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 signals GMAP adequately filters AP (Anomalous Propagation) clutter GMAP recovers velocity estimates in the presence of clutter GMAP out performs other filtering processes

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) 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)