Very Fast Algorithms and Detection Performance of Multi-channel and 2-d Parametric Adaptive Matched Filters for Airborne Radar

In a seminal paper [1] published in 2000, two algorithmic versions of the multichannel parametric adaptive matched filter (PAMF) applied to space-time adaptive processing (STAP) in an airborne radar application were shown to achieve superior test detection statistics over the conventional adaptive matched filter (AMF), which uses a non-parametric approach to estimate the detection weight vector. In fact, the performance of the PAMF approach is very close to the ideal matched filter (MF) detection statistics under exactly known covariance (the clairvoyant case). Improved versions of the two original multichannel PAMF algorithms [5], one new multichannel PAMF algorithm [6], and a new two-dimensional (2D) PAMF algorithm [all four with fast computational implementations] have been recently developed. In this paper, we provide the outline of the new 2D parametric algorithm and summarize the detection performance of 3 of the 4 new PAMF algorithms with actual Multi-Channel Airborne Radar Measurement (MCARM) data. In all cases, the performance is at least comparable to, and in some cases superior to, the original multichannel PAMF algorithms presented in [1], while also achieving computational savings over the originals.