摘要: To remedy the performance degradation of the original space-time autoregressive (STAR) filtering algorithm when operating in nonstationary clutter environments, this paper proposes a new type of STAR algorithm that invokes the tim... 展开 To remedy the performance degradation of the original space-time autoregressive (STAR) filtering algorithm when operating in nonstationary clutter environments, this paper proposes a new type of STAR algorithm that invokes the time-varying autoregressive (TVAR) model and is called time-varying space-time autoregressive (TV-STAR) filtering. We demonstrate that, in the nonstationary clutter environment, the TV-STAR algorithm exhibits a commensurate performance with respect to the stationary case while the STAR filter totally fails due to “model-mismatch”. Meanwhile, TV-STAR is shown to offer a favourable convergence rate over reduced-rank STAP techniques such as loaded sample matrix inversion (LSMI) method. Simulated data as well as two sets of measured airborne radar data are used to demonstrate the performance of TV-STAR algorithm. 收起