Enhanced Knowledge Aided Space-Time Adaptive Processing (KA-STAP) Applying the Slepian Transform

Knowledge Aided High Dynamic Range Space-Time Adaptive Processing (KA-HDR-STAP) approach enhances the High Dynamic Range STAP approach that uses the Fast Slepian Transform (FST) to filter out more clutter energy than traditional STAP, Reduced Rank Eigen decomposition (RR) STAP, and KA-STAP approaches using clutter typing prior to STAP. STAP relies on samples collected across sensor elements, space, and time to estimate interference with limitations due to dynamic clutter characteristics, spectrum spreading, and limited separation between clutter and signals. KA-HDR-STAP uses a particle swarm optimizer (PSO) that optimally combines the current data source covariance with a prior data source covariance based on maximizing the signal to clutter enhancement (SCE). This paper is an extended review from Osadciw, et. all [1] to assess the performance of KA-HDR-STAP to the same clutter scenarios using the signal to clutter enhancement metric. The novelty of KA-HDR-STAP includes the process of combining the two or more covariance estimates and projecting the covariance onto Slepian basis functions. The signals sample rate and selected bandwidth define the number of orthogonal Slepian basis functions. The FST from Karnik, et. all [2] is applied in KA-HDR-STAP for a more practical implementation. This article demonstrates that KA-HDR-STAP removes more clutter with a large improvement in SCE in comparison to traditional KA-STAP approaches which include sample matrix inversion (SMI) and Eigen-decomposition while achieving signal losses below those predicted by the Reed, Mallet, and Brennon (RMB) rule.