Robust and Adaptive Radar Elliptical Density-Based Spatial Clustering and labeling for mmWave Radar Point Cloud Data

Renyuan Zhang, Siyang Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper, a robust and adaptive radar point cloud clustering algorithm, named radar elliptical density-based spatial clustering of applications with noise (REDBSCAN), is presented. The proposed algorithm shows better clustering results for adapting to the arbitrary shape of targets as well as any number of targets comparing with traditional clustering methods. The algorithm is presented and is implemented in experiments using the state-of-art mmWave radar sensor with multiple-input multiple-output (MIMO) antennas. The related signal processing chain and the clustering outcomes are also discussed.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages919-924
Number of pages6
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period11/3/1911/6/19

Keywords

  • Clustering
  • DBSCAN
  • Point Cloud
  • REDBSCAN
  • Radar Clustering
  • Radar Signal Processing

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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