A Relaxed Energy Function Based Analog Neural Network Approach to Target Localization in Distributed MIMO Radar

TL;DR

This paper introduces a relaxed energy function neural network for target localization in distributed MIMO radar, offering improved convexity, faster convergence, and robustness to location errors, outperforming existing methods.

Contribution

The paper proposes a novel relaxed energy function neural network that simplifies the optimization process and enhances localization accuracy in distributed MIMO radar.

Findings

Achieves CRLB performance across various SNRs

Faster convergence compared to existing neural network methods

Demonstrates robustness to transmitter and receiver location errors

Abstract

Analog neural networks are highly effective to solve some optimization problems, and they have been used for target localization in distributed multiple-input multiple-output (MIMO) radar. In this work, we design a new relaxed energy function based neural network (RNFNN) for target localization in distributed MIMO radar. We start with the maximum likelihood (ML) target localization with a complicated objective function, which can be transformed to a tractable one with equality constraints by introducing some auxiliary variables. Different from the existing Lagrangian programming neural network (LPNN) methods, we further relax the optimization problem formulated for target localization, so that the Lagrangian multiplier terms are no longer needed, leading to a relaxed energy function with better convexity. Based on the relaxed energy function, a RNFNN is implemented with much simpler structure and faster convergence speed. Furthermore, the RNFNN method is extended to localization in the presence of transmitter and receiver location errors. It is shown that the performance of the proposed localization approach achieves the Cram\'er-Rao lower bound (CRLB) within a wider range of signal-to-noise ratios (SNRs). Extensive comparisons with the state-of-the-art approaches are provided, which demonstrate the advantages of the proposed approach in terms of performance improvement and computational complexity (or convergence speed).