Time-varying space-time autoregressive filtering algorithm for space-time adaptive processing

This study introduces a new type of space-time autoregressive (STAR) filtering algorithm for space-time adaptive processing (STAP) operating in a clutter environment that is not strictly stationary in slow time. The original STAR approach based on stationary autoregressive (AR) model, despite enjoying a fast convergence rate, suffers significant performance degradation when dealing with non-stationary clutter processes. To remedy this, the new proposed algorithm invokes a -relaxed- AR model, that is, the time-varying autoregressive (TVAR) model, and is called time-varying space-time autoregressive (TV-STAR) filtering. The authors demonstrate that, for stationary case, the two filters have identical output signal-to-interference plus noise ratio (SINR) with known interference covariance, but the convergence rate of TV-STAR is somewhat inferior to STAR with finite sample support. However, in the non-stationary case, the STAR filter totally fails because of -model-mismatch- whereas TV-STAR exhibits a commensurate performance with respect to the stationary case. Meanwhile, TV-STAR is shown to offer a favourable convergence rate over reduced-rank STAP techniques such as eigencanceler method in both cases. Simulated data as well as two sets of measured airborne radar data are used to demonstrate the performance of TV-STAR algorithm.