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

Knowledge-aided space-time adaptive processing (KA-STAP) algorithms, which incorporate a priori knowledge into radar signal processing methods, have the potential to substantially enhance detection performance while combating heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate the inverse interference covariance matrix rather than the covariance matrix itself, by combining the inverse of the covariance known a priori, R₀^-1, and the inverse sample covariance matrix estimate R̂^-1. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. We also develop a cost-effective algorithm based on the minimum variance (MV) criterion for computing the mixing parameter that performs a convex combination of R₀^-1 and R̂^-1. Simulations show the potential of our proposed algorithm, which obtain substantial performance improvements over prior art.