针对空时自适应处理(STAP)中时域非平稳杂波过程造成的空时自回归(STAR)算法失效问题,文中将时变自回归(TVAR)模型引入STAP处理中,提出了一种新的时变空时自回归(TV-STAR)算法。TV-STAR-算法能够有效弥补平稳自回归(AR)模型与实际非平稳杂波环境失配造成的STAR算法性能损耗,在非平稳杂波环境中具有良好的检测性能。同时,TV-STAR 算法由于引入了低阶数的TVAR模型,其收敛速度显著优于降秩STAP算法。文中分别通过仿真实验以及机

According to the failure of space-time autoregressive (STAR) algorithm when dealing with nonstationary (in slow-time) clutter processes, a new type of parametric space-time adaptive processing (STAP) algorithm is proposed in this paper that introduces the time-varying autoregressive (TVAR) model and is referred as time-varying space-time autoregressive (TV-STAR) algorithm. The new proposed algorithm, that effectively remedies the performance loss of STAR caused by the “model-mismatch”between stationary autoregressive (AR) model and nonstationary clutter process, exhibits a favorable performance in nonstationary clutter environment. Meanwhile, TV-STAR is shown to offer a superior convergence rate over most of the reduced-rank STAP techniques. Simulation results as well as the processing of measured airborne radar data are employed to demonstrate the performance of TV-STAR.

国防基础科研计划资助;国家自然科学基金; 中国博士后科学基金;江苏省自然科学基金