Knowledge-aided STAP algorithm using affine combination of inverse covariance matrices for heterogenous clutter

By incorporating a priori knowledge into radar signal processing architectures, knowledge-aided space-time adaptive processing (KA-STAP) algorithms can offer the potential to substantially enhance detection performance and to combat heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate directly the interference covariance matrix inverse rather than the covariance matrix itself, by using a linear combination of inverse covariance matrices (LCICM), which leads to an equivalent expression of the combination of two filters. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. The performance of the LCICM scheme can be further improved by applying a modification. Moreover, adaptive algorithms for the mixing parameters are developed using affine combinations (AC). Numerical examples show the potential of our proposed algorithms for substantial performance improvement.