OMP-type Algorithm with Structured Sparsity Patterns for Multipath Radar Signals

TL;DR

This paper introduces a new OMP-type algorithm tailored for structured sparsity patterns to improve radar signal recovery and DOA estimation in multipath environments, demonstrating significant performance gains in noisy conditions.

Contribution

The paper presents a novel structured sparsity-based OMP algorithm specifically designed for multipath radar signals, enhancing scalability and estimation accuracy.

Findings

Achieves a 20 dB gain in DOA estimation accuracy.

Performs well even at low signal-to-noise ratios.

Demonstrates effectiveness through Monte-Carlo simulations.

Abstract

A transmitted, unknown radar signal is observed at the receiver through more than one path in additive noise. The aim is to recover the waveform of the intercepted signal and to simultaneously estimate the direction of arrival (DOA). We propose an approach exploiting the parsimonious time-frequency representation of the signal by applying a new OMP-type algorithm for structured sparsity patterns. An important issue is the scalability of the proposed algorithm since high-dimensional models shall be used for radar signals. Monte-Carlo simulations for modulated signals illustrate the good performance of the method even for low signal-to-noise ratios and a gain of 20 dB for the DOA estimation compared to some elementary method.