RFI Mitigation for One-bit UWB Radar Systems

TL;DR

This paper introduces a novel RFI mitigation method for one-bit UWB radar systems using CTBV sampling, modeling RFI as sparse sinusoids, and extending the 1bRELAX algorithm with ADMM for improved efficiency and performance.

Contribution

It proposes a new RFI mitigation approach tailored for one-bit UWB radar systems employing CTBV sampling, extending existing algorithms with ADMM-based techniques.

Findings

Outperforms existing digital integration methods in severe RFI conditions

Effective RFI source parameter estimation from signed measurements

Significant computational efficiency improvements with ADMM-based initialization

Abstract

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultra-wideband (UWB) radar systems since RFI can severely degrade the radar imaging capability and target detection performance. In this paper, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology. This sampling strategy compares the signal to a known threshold varying with slow-time and therefore can be used to achieve a rather high sampling rate and quantization resolution with rather simple and affordable hardware. This paper establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we first model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We extend a majorization-minimization based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method based on the Alternating Direction Method of Multipliers (ADMM) methodology for the extended 1bMMRELAX algorithm to significantly improve its computational efficiency. Moreover, an ADMM-based sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.