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RFCM for Data Association and Multitarget Tracking Using 3D Radar

Citation Author(s):
Chun-Nien Chan, Carrson C. Fung
Submitted by:
Carrson Fung
Last updated:
13 April 2018 - 11:39am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Chun-Nien Chan, Carrson C. Fung
Paper Code:
2401

Abstract 

Abstract: 

erformance 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.

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ICASSP 2018 poster

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