RFCM FOR DATA ASSOCIATION AND MULTITARGET TRACKING USING 3D RADAR

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dc.citation.epage2625en_US
dc.citation.spage2621en_US
dc.citation.woscount0en_US
dc.contributor.authorChan, Chun-Nienen_US
dc.contributor.authorFung, Carrson C.en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.date.accessioned2019-04-02T06:04:13Z
dc.date.available2019-04-02T06:04:13Z
dc.date.issued2018-01-01en_US
dc.description.abstractPerformance 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.en_US
dc.identifier.journal2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/150761
dc.identifier.wosnumberWOS:000446384602157en_US
dc.language.isoen_USen_US
dc.subjectAutonomous drivingen_US
dc.subjectADASen_US
dc.subjectdata associationen_US
dc.subjectmultitarget trackingen_US
dc.subjectregularized fuzzy c-meansen_US
dc.titleRFCM FOR DATA ASSOCIATION AND MULTITARGET TRACKING USING 3D RADARen_US
dc.typeProceedings Paperen_US

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