Robust Space-Time Adaptive Processing (STAP) Algorithms for

We propose robust space-time adaptive processing (STAP) algorithms for efficient clutter suppression and target detection in RPAA systems. We first identify two main causes of the performance degradation in non-Gaussian clutter environments, then design the robust STAP algorithm to suppress the impulsive and often correlated clutters in the active mode. With the “clean” target signals obtained from the STAP processor, we propose a low-cost, efficient target detection algorithm for reliable target detection also in the active mode without requiring passive training data. The proposed STAP uses an innovative generalized sidelobe canceller structure with two adaptive filter blocks and a new iterative adaptation algorithm based on fractional lower order statistics. The proposed low-complexity target detection algorithm leverages the target tracking system commonly available in radar system to feedback the correct target decisions to the Constant False Alarm Rate detector so that the target signals are removed from the test statistic and correct detection threshold can be set when the target is present in the active mode. The new STAP and target detection algorithms are combined in a synergetic manner with the Automatic Target Recognition system to yield superior performance in active operation mode.