A Novel Covariance Matrix Estimation via Cyclic Characteristic for STAP

Abstract

The accurate estimation of the clutter covariance matrix (CCM) is crucial for space-time adaptive processing (STAP). In this letter, a new intrinsic cyclic characteristic of CCM is found. Then, a novel STAP is proposed based on the cyclic characteristic. In the proposed method, the cyclic CCMs, i.e., the temporal cyclic CCM, the spatial cyclic CCM, and the spatial–temporal cyclic CCM, are first constructed based on the cyclic characteristic. Then, the cyclic CCMs are employed as the secondary data, and the more accurate CCM estimation is obtained by averaging the cyclic CCMs and the estimated CCM of the existing STAP methods. Compared with the existing methods, the proposed method has the following advantages: (1) the proposed method can be directly combined with the various existing STAP methods to improve their performance, (2) the output signal-to-clutter-plus-noise ratio (SCNR) of the proposed method is 2.055 dB higher than that of the traditional STAP methods reported in [7] – [9] , and (3) the output SCNR of the proposed method is 1.704 dB higher than that of the knowledge-aided STAP (KA-STAP) reported in [16] .