A Modified Parametric Adaptive Matched Filter without Dimensionality Loss

The parametric adaptive matched filter (PAMF) for space-time adaptive processing (STAP) has been an interesting research area in the airborne radar data processing community over the last decade. The existing PAMF however has a problem of dimensionality loss which in turn causes a loss in coherent processing gain and a loss in Doppler resolution. Proposed is a modified PAMF which uses both forward and backward predictions to eliminate the dimensionality loss. The performance of PAMF using simulated data generated by the high fidelity airborne radar simulation software, RLSTAP, as well as real airborne data collected by the multi-channel airborne radar measurements (MCARM) system is assessed with the result of the conventional STAP as a benchmark. The PAMF outperforms the STAP in the sample reduced space