A Point Cloud Improvement Method for High-Resolution 4D mmWave Radar Imagery

Qingmian Wan; Hongli Peng; Xing Liao; Weihao Li; Kuayue Liu; Junfa Mao

doi:10.3390/rs16152856

Remote Sensing (Aug 2024)

A Point Cloud Improvement Method for High-Resolution 4D mmWave Radar Imagery

Qingmian Wan,
Hongli Peng,
Xing Liao,
Weihao Li,
Kuayue Liu,
Junfa Mao

Affiliations

Qingmian Wan: State Key Laboratory of Radio Frequency Heterogeneous Integration, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Hongli Peng: State Key Laboratory of Radio Frequency Heterogeneous Integration, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xing Liao: State Key Laboratory of Radio Frequency Heterogeneous Integration, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Weihao Li: State Key Laboratory of Radio Frequency Heterogeneous Integration, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Kuayue Liu: State Key Laboratory of Radio Frequency Heterogeneous Integration, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Junfa Mao: State Key Laboratory of Radio Frequency Heterogeneous Integration, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

DOI: https://doi.org/10.3390/rs16152856
Journal volume & issue: Vol. 16, no. 15
p. 2856

Abstract

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To meet the requirement of autonomous driving development, high-quality point cloud generation of the environment has become the focus of 4D mmWave radar development. On the basis of mass producibility and physical verifiability, a design method for improving the quality and density of point cloud imagery is proposed in this paper, including antenna design, array design, and the dynamic detection method. The utilization of apertures is promoted through antenna design and sparse MIMO array optimization using the genetic algorithm (GA). The hybrid strategy for complex point clouds is adopted using the proposed dynamic CFAR algorithm, which enables dynamic adjustment of the threshold by discriminating and calculating different scanning regions. The effectiveness of the proposed method is verified by simulations and practical experiments. Aiming at system manufacture, analysis methods for the ambiguity function (AF) and shooting and bouncing rays (SBR) tracing are introduced, and an mmWave radar system is realized based on the proposed method, with its performance proven by practical experiments.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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