Radar Adaptive Detection Architectures for Heterogeneous Environments

TL;DR

This paper introduces four adaptive radar detection architectures designed for heterogeneous Gaussian environments, emphasizing robustness and false alarm rate stability through innovative data transformation and estimation techniques.

Contribution

It proposes new radar detection architectures combining ML and EM methods, with one architecture ensuring constant false alarm rate in variable interference conditions.

Findings

Transformed domain architecture maintains constant false alarm rate.

Performance close to optimal with limited detection loss.

Detection thresholds sensitive to interference power in some architectures.

Abstract

In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.