Discriminating Distributed Targets in Automotive Radar Using Fuzzy L-Shell Clustering Algorithm

被引：0

作者：

Ren, Zhouchang ^{[1
,2
]}

Tabrikian, Joseph ^{[1
]}

Bilik, Igal ^{[1
]}

Yi, Wei ^{[2
]}

机构：

[1] Ben Gurion Univ Negev, Sch Elect & Comp Engn, IL-8410501 Beer Sheva, Israel

[2] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2024年 / 60卷 / 06期

基金：

中国国家自然科学基金; 以色列科学基金会;

关键词：

Radar; Automotive engineering; Radar detection; Vectors; Shape; Reliability; Radar scattering; Automotive radar; distributed targets; fuzzy shell clustering; target discrimination; target enumeration;

D O I：

10.1109/TAES.2024.3435808

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Automotive radar is the commonly used sensor for autonomous driving and active safety. Modern automotive radars provide high spatial information on the host vehicle surroundings, and therefore, automotive radar targets appear as point clouds of radar detections. This article addresses the problem of discriminating between adjacent distributed targets using the distribution of radar detections in the range-azimuth domain. The proposed approach considers both the statistical information of the radar detections' distribution and the L-shape model of the target vehicles via the fuzzy L-shell clustering algorithm. The performance of the proposed approach is evaluated via simulations, and its superiority over the conventional methods is demonstrated in practical automotive scenarios.

引用

页码：8713 / 8725

页数：13

共 39 条

[1]

[Anonymous], 1997, Learning, Networks and Statistics

[2] A new possibilistic clustering algorithm for line detection in real world imagery [J].