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A class of novel STAP algorithms based on sparse recovery technique werepresented. Intrinsic sparsity of distribution of clutter and target energy onspatial-frequency plane was exploited from the viewpoint of compressed sensing.The original sample data and distribution of target and clutter energy wasconnected by a ill-posed linear algebraic equation and popular $L_1$optimization method could be utilized to search for its solution with sparsecharacteristic. Several new filtering algorithm acting on this solution weredesigned to clean clutter component on spatial-frequency plane effectively fordetecting invisible targets buried in clutter. The method above is calledCS-STAP in general. CS-STAP showed their advantage compared with conventionalSTAP technique, such as SMI, in two ways: Firstly, the resolution of CS-STAP onestimation for distribution of clutter and target energy is ultra-high suchthat clutter energy might be annihilated almost completely by carefully tunedfilter. Output SCR of CS-STAP algorithms is far superior to the requirement ofdetection; Secondly, a much smaller size of training sample support comparedwith SMI method is requested for CS-STAP method. Even with only one snapshot(from target range cell) could CS-STAP method be able to reveal the existenceof target clearly. CS-STAP method display its great potential to be used inheterogeneous situation. Experimental result on dataset from mountaintopprogram has provided the evidence for our assertion on CS-STAP.

A Class of Novel STAP Algorithms Using Sparse Recovery Technique