DocumentCode
497681
Title
Multitarget tracking via joint PHD filtering and multiscan association
Author
Papi, F. ; Battistelli, G. ; Chisci, L. ; Morrocchi, S. ; Farina, A. ; Graziano, A.
Author_Institution
Dip. Sist. e Inf., Univ. di Firenze, Firenze, Italy
fYear
2009
fDate
6-9 July 2009
Firstpage
1163
Lastpage
1170
Abstract
A PHD (probability hypothesis density) filter and multiscan association are combined in a feedback fashion in order to provide robust and efficient multitarget tracking. The resulting hybrid tracker, thanks to the feedback connection, provides remarkable performance improvements with respect to both an open-loop PHD filter with estimate extraction via clustering and a traditional tracker equipped with a track formation logic.
Keywords
filtering theory; probability; sensor fusion; target tracking; estimate extraction; joint PHD filtering; multiscan association; multitarget tracking; probability hypothesis density filter; track formation logic; Density functional theory; Feedback; Filtering; Filters; Logic; Radar tracking; Recursive estimation; Robustness; State estimation; Target tracking; PHD filtering; Random set tracking; multiscan association; multitarget tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Fusion, 2009. FUSION '09. 12th International Conference on
Conference_Location
Seattle, WA
Print_ISBN
978-0-9824-4380-4
Type
conf
Filename
5203775
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