Computationally efficient toeplitz approximation of structured covariance under a rank constraint


Disturbance covariance estimation is a centrally necessary problem in radar house-time adaptive processing (STAP). Because coaching is invariably scarce, estimators that exploit inherent structure and physical radar constraints are needed in follow. This paper develops a brand new computationally efficient estimator that obtains a Toeplitz approximation of the structured interference covariance under a rank constraint. Previous work has shown that exact most probability (ML) estimation of Toeplitz covariance matrix has no closed-form solution, and most versions of this drawback lead to iterative estimators that are computationally expensive. Our proposed solution focuses on a computationally efficient approximation and involves a cascade of 2 closed-type solutions. 1st, we acquire the rank-constrained ML estimator whose deserves have recently been established firmly for radar STAP. The central contribution of this paper is that the rank-preserving Toeplitz approximation, that we tend to demonstrate will be modeled as an equality-constrained quadratic program and additionally admits a closed kind. Intensive performance analysis on both simulated and information-aided sensor Signal Processing and knowledgeable reasoning data confirms that the proposed estimator yields unbeatable performance for radar STAP beneath the previously stated conditions of rank and Toeplitz constraints.

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