Mutual Interference Mitigation of Millimeter-Wave Radar Based on Variational Mode Decomposition and Signal Reconstruction

Author:

Li Yanbing1ORCID,Feng Bo2ORCID,Zhang Weichuan3ORCID

Affiliation:

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

2. Beijing Institute of Radio Measurement, Beijing 100854, China

3. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia

Abstract

As an important remote sensing technology, millimeter-wave radar is used for environmental sensing in many fields due to its advantages of all-day, all-weather operation. With the increasing use of radars, inter-radar interference becomes increasingly critical. Severe mutual interference degrades radar signal quality and affects the performance of post-processing, e.g., synthetic aperture radar (SAR) imaging and target tracking. Aiming to deal with mutual interference, we propose an interference mitigation method based on variational mode decomposition (VMD). With the characteristics that the target is a single-frequency sine wave and the interference is a broadband signal, VMD is used for decomposing the radar received signal and separating the target from the interference. Interference mitigation is then implemented in each decomposed mode, and an interference-free signal is obtained through the reconstruction process. Simulation results of multi-target scenarios demonstrate that the proposed method outperforms existing decomposition-based methods. This conclusion is also confirmed by the experimental results on real data.

Funder

Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference42 articles.

1. MRPT: Millimeter-Wave Radar-based Pedestrian Trajectory-Tracking for Autonomous Urban Driving;Zhang;IEEE Trans. Instrum. Meas.,2021

2. Millimeter-wave technology for automotive radar sensors in the 77 GHz frequency band;Hasch;IEEE Trans. Microw. Theory Tech.,2012

3. Radar-on-chip/in-package in autonomous driving vehicles and intelligent transport systems: Opportunities and challenges;Saponara;IEEE Signal Process. Mag.,2019

4. Advances in technologies, architectures, and applications of highly-integrated low-power radars;Neri;IEEE Aerosp. Electron. Syst. Mag.,2012

5. The rise of radar for autonomous vehicles: Signal processing solutions and future research directions;Bilik;IEEE Signal Process. Mag.,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3