Rfcm for Data Association and Multitarget Tracking Using 3D Radar

Chun-Nien Chan; Carrson C. Fung

ICASSP 2018accepted0 citations

Rfcm for Data Association and Multitarget Tracking Using 3D Radar

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.

BibTeX

@inproceedings{icassp2018_rfcmfordataassoc,
  title = {Rfcm for Data Association and Multitarget Tracking Using 3D Radar},
  author = {Chun-Nien Chan and Carrson C. Fung},
  booktitle = {ICASSP 2018},
  year = {2018}
}