Line Spectral Estimation via Unlimited Sampling

TL;DR

This paper introduces a novel line spectral estimation method using unlimited sampling for FMCW radar, improving target detection accuracy in high dynamic range scenarios through a combined DP and OMP approach.

Contribution

It develops a new US-based LSE framework with a two-stage algorithm, enhancing spectral estimation performance over existing methods.

Findings

USLSE outperforms existing algorithms in simulations

The proposed method effectively handles high dynamic range in radar signals

Numerical and real experiments validate the approach's effectiveness

Abstract

Frequency Modulated Continuous Wave (FMCW) radar has been widely applied in automotive anti-collision systems, automatic cruise control, and indoor monitoring. However, conventional analog-to-digital converters (ADCs) can suffer from significant information loss when strong and weak targets coexist in ranging applications. To address this issue, the Unlimited Sampling (US) strategy was proposed, which applies a modulo operator prior to sampling. In this paper, we investigate the range estimation problem using FMCW radar in the context of US, which can be formulated as a one-dimensional line spectral estimation (LSE) via US. By exploiting the oversampling property and proving that the leakage onto a certain frequency band can be controlled, we establish an integer optimization problem in the Fourier and first-order difference domain. We then propose a dynamic programming (DP) based algorithm followed by the orthogonal matching pursuit (OMP) method to solve it. In addition, a two-stage US LSE (USLSE) is proposed, where the line spectral signal is first recovered by iteratively executing DP and OMP, and then the parameters are estimated by applying a state-of-the-art LSE algorithm. Substantial numerical simulations and real experiments demonstrate that the proposed algorithm, USLSE, outperforms existing algorithms.