Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

TL;DR

This paper introduces a novel interference mitigation method for FMCW radar using sparse and low-rank Hankel matrix decomposition, improving signal separation without interference detection and applicable to various target scenarios.

Contribution

The paper proposes a new optimization-based approach leveraging Hankel matrix decomposition for interference mitigation in FMCW radar, enhancing accuracy and robustness over existing methods.

Findings

Effective interference suppression demonstrated in simulations and experiments.

Improved target signal estimation accuracy compared to traditional methods.

Applicable to both stationary and moving targets.

Abstract

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.