Frequency diverse statistic filtering for clutter suppression

Abstract

By combining the conventional order-statistic filtering concept with the split-spectrum processing (SSP) technique, a method called frequency-diverse statistic filtering is obtained. Three types of frequency-diverse statistic filters, namely, weighted mean, median, and absolute-minimization, are examined. It is shown that if the target and the clutter spectra are known individually, the Wiener filter can be realized by frequency-diverse statistic filtering using a linear operation (i.e., weighted mean). However, if only the input signal is known, the frequency-diverse statistic filter with a nonlinear order-statistic operation (i.e., median or absolute-minimization) can be used, resulting in SNR (signal/noise ratio) enhancement. Both computer simulation and experimental data have been used to evaluate the performance of the filters and verify the theoretical analyses.