Low-Rank Structured Clutter Covariance Matrix Estimation for Airborne STAP Radar

TL;DR

This paper introduces a novel low-rank covariance matrix estimation method for airborne STAP radar that improves clutter suppression with limited samples by leveraging inverse Wishart prior and structural properties.

Contribution

It proposes a joint statistics and structural priority approach for small-sample clutter covariance estimation in airborne radar, utilizing inverse Wishart prior and low-rank symmetry.

Findings

Enhanced clutter suppression performance compared to traditional methods

Effective in small-sample scenarios

Efficient computational approach

Abstract

In space-time adaptive processing (STAP) of the airborne radar system, it is very important to realize sparse restoration of the clutter covariance matrix with a small number of samples. In this paper, a clutter suppression method for airborne forward-looking array radar based on joint statistics and structural priority is proposed, which can estimate the clutter covariance matrix in the case of small samples. Assuming that the clutter covariance matrix obeys the inverse Wishart prior distribution, the maximum posterior estimate is obtained by using the low-rank symmetry of the matrix itself. The simulation results based on the radar forward-looking array model show that compared with the traditional covariance matrix estimation method, the proposed method can effectively improve the clutter suppression performance of airborne radar while efficiently calculating.