Adaptive Radar Detection and Bearing Estimation in the Presence of Unknown Mutual Coupling

TL;DR

This paper introduces adaptive radar detection and bearing estimation methods that account for mutual coupling effects in array elements, improving detection performance in complex environments.

Contribution

It develops a novel GLRT detector using a linearized array model and a MM framework, also addressing unknown coupling coefficients with a MFLRT approach.

Findings

The proposed detectors outperform existing methods ignoring mutual coupling.

Considering mutual coupling is essential for near-benchmark radar detection performance.

The MM-based optimization ensures convergence and reliable detection results.

Abstract

This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the Uniform Linear Array (ULA) manifold around the nominal array looking direction together with the use of symmetric Toeplitz structured matrices to represent the mutual coupling effects. Hence, the Generalized Likelihood Ratio Test (GLRT) detector is evaluated under the assumption of homogeneous radar environment. Its computation leverages a specific Minorization-Maximization (MM) framework, with proven convergence properties, to optimize the concentrated likelihood function under the target presence hypothesis. Besides, when the number of active mutual coupling coefficients is unknown, a Multifamily Likelihood Ratio Test (MFLRT) approach is invoked. During the analysis phase, the performance of the new detectors is compared with benchmarks as well as with counterparts available in the open literature which neglect the mutual coupling phenomenon. The results indicate that it is necessary to consider judiciously the coupling effect since the design phase, to guarantee performance levels close to the benchmark.