Rfcm for Data Association and Multitarget Tracking Using 3D Radar

Chun Nien Chan, Carrson C. Fung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Performance of object classification using 3D automotive radar relies on accurate data association and multitarget tracking' which are greatly affected by data bias and proximity of objects to each other. A regularized fuzzy c-means (RFCM) algorithm is proposed herein to resolve the data association uncertainty problem that has shown to outperform the conventional FCM algorithm. The proposed method exploits results from the companion tracker to increase performance robustness. Simulation results using simulated and field data have proven the efficacy of the proposed method.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2621-2625
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18

Keywords

  • ADAS
  • Autonomous driving
  • Data association
  • Multitarget tracking
  • Regularized fuzzy c-means

Fingerprint Dive into the research topics of 'Rfcm for Data Association and Multitarget Tracking Using 3D Radar'. Together they form a unique fingerprint.

Cite this