Adaptive filters for the moving target indicator system

TL;DR

This paper proposes adaptive filtering techniques for radar moving target indication systems that enhance convergence and robustness by maximizing empirical SINR, addressing issues of covariance contamination and signal interference.

Contribution

It introduces a novel approach based on maximizing empirical SINR to improve adaptive filter convergence and mitigate target signal contamination effects.

Findings

Effective in simulated radar scenarios

Improves convergence of adaptive algorithms

Reduces impact of target signal contamination

Abstract

Adaptive algorithms belong to an important class of algorithms used in radar target detection to overcome prior uncertainty of interference covariance. The contamination of the empirical covariance matrix by the useful signal leads to significant degradation of performance of this class of adaptive algorithms. Regularization, also known in radar literature as sample covariance loading, can be used to combat both ill conditioning of the original problem and contamination of the empirical covariance by the desired signal for the adaptive algorithms based on sample covariance matrix inversion. However, the optimum value of loading factor cannot be derived unless strong assumptions are made regarding the structure of covariance matrix and useful signal penetration model. Similarly, least mean square algorithm with linear constraint or without constraint, is also sensitive to the contamination of the learning sample with the target signal. We synthesize two approaches to improve the convergence of adaptive algorithms and protect them from the contamination of the learning sample with the signal from the target. The proposed approach is based on the maximization of empirical signal to interference plus noise ratio (SINR). Its effectiveness is demonstrated using simulated data.