Target Localization with Jammer Removal Using Frequency Diverse Array

TL;DR

This paper introduces a novel decomposition method for FDA-MIMO radar to effectively suppress both barrage and burst jamming signals, including non-Gaussian impulsive noise, improving target localization accuracy.

Contribution

It proposes a two-step GoDec-based approach that leverages prior rank information to suppress mixed jamming signals in radar systems, handling non-Gaussian noise.

Findings

Robust suppression of barrage and burst jamming signals demonstrated.

Enhanced target signal extraction in non-Gaussian interference environments.

Simulation results confirm improved estimation performance.

Abstract

A foremost task in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is to efficiently obtain the target signal in the presence of interferences. In this paper, we employ a novel "low-rank + low-rank + sparse" decomposition model to extract the low-rank desired signal and suppress the jamming signals from both barrage and burst jammers. In the literature, the barrage jamming signals, which are intentionally interfered by enemy jammer radar, are usually assumed Gaussian distributed. However, such assumption is oversimplified to hold in practice as the interferences often exhibit non-Gaussian properties. Those non-Gaussian jamming signals, known as impulsive noise or burst jamming, are involuntarily deviated from friendly radar or other working radio equipment including amplifier saturation and sensor failures, thunderstorms and man-made noise. The estimation performance of the existing estimators, relied crucially on the Gaussian noise assumption, may degrade substantially since the probability density function (PDF) of burst jamming has heavier tails that exceed a few standard deviations than the Gaussian distribution. To capture a more general signal model with burst jamming in practice, both barrage jamming and burst jamming are included and a two-step "Go Decomposition" (GoDec) method via alternating minimization is devised for such mixed jamming signal model, where the $a$ $priori$ rank information is exploited to suppress two kinds of jammers and estimate the desired target. Simulation results verify the robust performance of the devised scheme.