Dual Polarization Radar Signal Processing Dr Chandra Your

Dual Polarization Radar Signal Processing Dr. Chandra Your Name Joe Hoatam Your Title Josh Merritt Your Organization (Line #1) Aaron Nielsen Your Organization (Line #2)

Outline Radar Background Clutter Range Ambiguity Velocity Ambiguity Next Semester

Radar Background Transmitter & Receiver Signal Reflection Measurement Different Uses Point vs Distributed Targets Doppler Effect Pulsed Doppler Radar

Problem: Ground Clutter: There is always clutter in signals and it distorts the purposeful component of the signal. Getting rid of clutter, or compensating for the loss caused by clutter might be possible by applying appropriate filtering and enhancing techniques. Ground Clutter: Ground clutter is the return from the ground. The returns from ground scatters are usually very large with respect to other echoes, and so can be easily recognized Ground-based obstacles may be immediately in the line of site of the main radar beam, for instance hills, tall buildings, or towers.

Solution: IIR/Pulse-Pair approach Uses a fixed notch-width IIR clutter filter followed by timedomain autocorrelation processing (pulse-pair processing) Drawbacks to using this approach: Perturbations that are encountered will effect the filter output for many pulses, effecting the output for several beamwidths The filter width has to change accordingly with clutter strength Have to manually select a filter that is sufficiently wide to remove the clutter without being to wide so it doesn’t affect wanted data

Solution: FFT processing FFT: is essentially a finite impulse response block processing approach that does not have the transient behavior problems of the IIR filter. It minimizes the effects of filter bias. Drawbacks to this approach: Spectrum resolution is limited by the number of points in the FFT. If the number points is to low it will obscure weather targets When time-domain windows are applied such as Hamming or Blackman the number of samples that are processed are reduced

GMAP Algorithm Description First a hamming window is applied Replace clutter points Remove central clutter points

Plans For Next Semester Implement GMAP codes Test GMAP coding on received data from CHILL

Problem: Range Ambiguity: situation in radar signal processing where received signals from different ranges appear to have the same range

Problem: Range Ambiguity Pulse Repetition Frequency (PRF) low, range ambiguity decreases, but velocity ambiguity increases Trade off between maximum range and maximum velocity Techniques have been developed to allow higher PRF (thus higher velocity measurements) while not incurring more range ambiguity

Solution: Range Ambiguity One solution to reduce the effects of range ambiguity is a technique called phase coding Phase Coding has an encoding and a decoding stage In the encoding stage, transmitted signals from the radar are phase shifted by a code sequence, ak In the decoding stage, the received signal is phase shifted by ak* to restore the phase

Solution: Range Ambiguity Two important considerations are needed when choosing a phase code Spectrum of overlaid signal has the property that R(1) (autocorrelation at lag T) is equal to zero. This allows reconstruction of the stronger signal Capability to reconstruct signal spectrum from a small part of original spectrum SZ (Sachidananda-Zrnic) code is constructed as follows

Solution: Range Ambiguity Velocity is calculated from the autocorrelation function by arg[R(1)] Multiplying the received signal by ak* will make the first trip signal coherent When autocorrelation is calculated, the autocorrelation of the second trip signal will have lag 1 and be equal to zero, thus not affecting the velocity estimation of the first trip signal Velocity of the first trip signal (v 1) can now be recovered By use of a notch filter centered at v 1, the second trip signal velocity can also be recovered

Plans For Next Semester Test phase coding on received data from CHILL Simulate phase coding techniques Study phase coding techniques more in depth “Phase Coding for the Resolution of Range Ambiguities in Doppler Weather Radar” by M. Sachidananda and Dusan S. Zrnic

Problem: Velocity Ambiguity: problem in radar data processing where received signals from different velocities have a phase shift of greater than 2π If the wait between pulses is too long, the velocity of the object in question may exceed this maximum velocity, thereby overlapping our data and giving us a negative velocity

Solution: Velocity Ambiguity Use the Maximum Likelihood technique to help decrease both range and velocity ambiguities at medium- to high-PRF waveforms. ML technique Takes a data set and discriminates between real targets and ghost targets generated by range errors Uses the clustering algorithm to process data

Solution: Velocity Ambiguity Clustering Algorithm Given a velocity measurement vector Ri, all possible range values can be given by: After arranging the vector from smallest to largest, we can find the average squared error Cv(j) for m number of consecutive ranges as: The best cluster occurs with a data set where Cv(j) is at a minimum value By taking the ratio of the second lowest Cv(j) value to the minimum, we can find the probability that they're correct

Plans for Next Semester Possible test with CHILL data Implement the Maximum Likelihood algorithm

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