1. Problem: Ground Clutter 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.

2. Solution: IIR/Pulse-Pair approach Uses a fixed notch-width IIR clutter filter followed by time- domain 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

3. 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

4. Solution: GMAP GMAP: GMAP is a frequency domain approach that uses a Gaussian clutter model to remove ground clutter over a variable number of spectral components that is dependent on the assumed clutter width, signal power, nyquist interval and number of samples. Then a Gaussian weather model is used to iteratively interpolate over the components that were removed, restoring any of the overlapped weather spectrum with minimal bias

5. Solution: GMAP GMAP assumptions: Spectrum width of the weather signal is greater then clutter. Doppler spectrum consists of ground clutter, a single weather target and noise. The width of the clutter is approximately known. The shape of the clutter is a Gaussian. The shape of the weather is a Gaussian

6. GMAP Algorithm Description First a Hamming window weighting function is applied to the In phase and quadrature phase (IQ) values and a discrete Fourier transform (DFT) is then performed. The Hamming window is used as the first guess after analysis is complete a decision is made to either accept results or use a more aggressive window based on the clutter to signal ratio (CSR).

7. GMAP Algorithm Description Remove Clutter points The power in the three central spectrum components is summed and compared to the power that would be in the three central components of a normalized Gaussian spectrum. Normalizes the power of the Gaussian to the observed power the Gaussian is extended down to the noise level and all spectral components that fall within the gaussian curve are removed. The removed components are the “clutter power”

8. GMAP Algorithm Description Replace Clutter points Dynamic noise case Fit a Gaussian and fill-in the clutter points that were removed earlier keep doing this until the computed power does not change more then.2dB and the velocity does not change by more than.5% of the Nyquist velocity. Fixed noise case Similar to dynamic noise case except the spectrum points that are larger than the noise level are used

9. GMAP Algorithm Description Recompute GMAP with optimal window Determin if the optimal window was used based on the CSR IF CSR > 40 dB repeat GMAP using a Blackman window and dynamic noise calculation. IF CSR > 20 dB repeat GMAP using a Blackman window. Then if CSR>25dB use Blackman results. IF CSR < 2.5 dB repeat GMAP using a rectangular window. Then if CSR < 1 dB use rectangular results. ELSE accept the Hamming window result.

10. Gmap With Data From CASA Power from PRF1

11. Gmap With Data From CASA Power with gmap from PRF1

12. Gmap With Data From CASA Velocity from Prf1

13. Gmap With Data From CASA Velocity of Prf1

14. Gmap With Data From CASA Velocity from PRF1with GMAP

15. Gmap With Data From CASA Power from Prf1 using Hamming

16. Gmap With Data From CASA Power from Prf1 using Hamming [0 60] dbs

17. Gmap With Data From CASA Power from Prf1 using Hamming [-20 20] dbs