Abstract
Phased-array multiple-input multiple-output air-borne Radar faces a severe sample shortage problem in space-time adaptive processing (STAP) applications. Fortunately, the sparse recovery STAP (SR-STAP) offers a new perspective to drastically reduce the requirement of training samples, however, its application is greatly limited by the high complexity of SR algorithms. This paper attempts to introduce the tensor representation to reformulate the SR-STAP problem. We give a basic tensor-based SR-STAP problem formulation and further develop a novel fast OMP SR-STAP method with the tensor representation. The numerical experiments show that the proposed tensor-based method has a great advantage in complexity over the conventional SR-STAP methods.
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