Thinned knowledge‐aided STAP by exploiting structural covariance matrix

The authors propose a thinned knowledge‐aided space–time adaptive processing (STAP) scheme based on structural prior information of the clutter covariance matrix (CCM). Due to the low‐rank Toeplitz‐ block ‐Toeplitz structure of the CCM, the CCM can be expressed by a series of basis matrices on the clutter ridge. In contrast to the expression based on the Vandermonde decomposition of the CCM, this expression can avoid searching of the clutter subspace. This expression also allows reducing the dimension of STAP and estimating the CCM with compressed data. Based on this expression, the authors derive a closed‐form CCM estimate using a modified generalised least squares (GLS) method, and the proposed estimator is unbiased, consistent and more asymptotically efficient than the conventional GLS method. We derive the average signal‐to‐clutter‐noise ratio loss (SCNRL) of the STAP filter using the proposed CCM estimates. Exploiting the prior structural information of the CCM can enhance the STAP performance with a limited sample size, and a lower compression rate can achieve more improvement. Finally, a unified framework is proposed for covariance estimation and SCNRL analysis when structural information of the CCM is exploited. Simulations also validate these results.