Optimization of loading factor preventing target cancellation

TL;DR

This paper introduces an iterative algorithm to optimize the loading factor in adaptive radar detection algorithms, improving performance without relying on prior assumptions about covariance structure or signal penetration.

Contribution

An iterative, assumption-free method for optimizing the loading factor in sample matrix inversion algorithms for radar target detection.

Findings

The proposed algorithm enhances detection performance.

Simulation results confirm effectiveness of the method.

Abstract

Adaptive algorithms based on sample matrix inversion belong to an important class of algorithms used in radar target detection to overcome prior uncertainty of interference covariance. Sample matrix inversion problem is generally ill conditioned. Moreover, 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. 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. In this paper an iterative algorithm for loading factor optimization based on the maximization of empirical signal to interference plus noise ratio (SINR) is proposed. The proposed solution does not rely on any assumptions regarding the structure of empirical covariance matrix and signal penetration model. The paper also presents simulation examples showing the effectiveness of the proposed solution.