Robust, Adaptive Radar Detection and Estimation.

摘要

This work introduces estimation of disturbance covariance matrices for radar STAP. In particular, we first exploit physically inspired constraints, both the structure of the disturbance covariance and importantly the knowledge of the clutter rank to yield a new rank constrained maximum likelihood (RCML) estimator of clutter/disturbance covariance. We demonstrate that the rank-constrained estimation problem can in fact be cast in the framework of a tractable convex optimization problem, and derive closed form expressions for the estimated covariance matrix. On top of that, this work also introduces a new computationally efficient covariance estimator which jointly enforces a Toeplitz structure and a rank constraint. Our proposed solution focuses on a computationally efficient approximation and involves a cascade of two closed form solutions, the RCML estimator and the rank preserving Toeplitz approximation. Finally, we develop robust estimators that can adapt to imperfect knowledge of physical constraints using an expected likelihood (EL) approach. We analyze covariance estimation algorithms under three cases of imperfect constraints: only rank constraint, both rank and noise power constraints, and condition number constraint.