Persymmetric Structured Covariance Matrix Estimation Based on Whitening for Airborne STAP

摘要

The estimation problem of structured clutter covariancematrix (CCM) in space-time adaptive processing (STAP) for airborne radarsystems is studied in this letter. By employing the prior knowledge and thepersymmetric covariance structure, a new estimation algorithm is proposedbased on the whitening ability of the covariance matrix. The proposedalgorithm is robust to prior knowledge of different accuracy, and can whitenthe observed interference data to obtain the optimal solution. In addition,the extended factored approach (EFA) is used in the optimization for dimensionalityreduction, which reduces the computational burden. Simulationresults show that the proposed algorithm can effectively improve STAPperformance even under the condition of some errors in prior knowledge.