#### Tamás Pető

###### Topics in Passive Radar

## Clutter cancellation

This page shows several range-Doppler map animations made with different time domain filtering algorithms. Each of the animation displays the results obtained with different parameter setups. The effect of the parameters on the range-Doppler map can be observed and utilized for the selection of the most adequate algorithm for each specific application.

The first animation shows the filtering performance of the standard Wiener filter implemented with Sample Matrix inversion technique. In the animation below the tap size of the filter takes the following values: 0,16,32,64,128,256 and 512.

The next range- Doppler matrix animation shows the operation of ECA-B (Extensive Cancellation Algorithm- Batch) filter. The tap size of the filter is constantly 128, but the batch duration used for the coefficient estimation and filtering is varied.

The following animation shows the benefits of the ECA-S (Sliding window Extensive Cancellation) algorithm. You can observe how the unwanted Doppler side peaks can be mitigated by changing the window size of the coefficient estimation. In the animation the filter tap size is 128. Tthe batch duration used for the filtering is fixed but the window size used for the coefficient estimation is changed

In the next animation the iterative NLMS (Normalized Least Mean Square) algorithm can be seen. The temporal dimension is set to 128, while the step size parameter is changed. The extension of the clutter into Doppler domain can be attributed to the slow convergence speed. To overcome on this unwanted effect the convergence speed must be increased when filtering the samples in a coherent processing interval. A possible solution could be to avoid the time gaps between the coherent processing intervals either in a way to implement the filter in a continuous (streaming mode) or to inherit the coefficient vector from the previous coherent processing.

Recursive Least Squares algorithm. The animation shows the range-Doppler map for different forgetting factor parameters. In this particular scenario the forgetting factor takes the following values: 0.95, 0.99, 0.995, 0999, 0.9995,0.9997,0.9999