TY - GEN
T1 - Label Matching
T2 - 27th International Conference on Information Fusion, FUSION 2024
AU - Di, Kuangyu
AU - Li, Tiancheng
AU - Li, Guchong
AU - Song, Yan
AU - Dang, Xudong
N1 - Publisher Copyright:
© 2024 ISIF.
PY - 2024
Y1 - 2024
N2 - This paper addresses the intractable track matching problem involved in multi-sensor multi-target tracking using the labeled multi-Bernoulli filters. Unlike the unlabeled density defined in the common state space, the labeled multi-target density is defined in the joint state and label space, where the label contains time-series/history information of the underlying track. To measure the similarity between labeled densities (individual tracks) that is required for inter-sensor track matching and fusion, one has to account for the divergences in both state and label spaces. The challenge, however, arises from the lack of a proper metric to measure the label difference. It requires considering the entire trajectory of the track, encompassing the whole-life information from the birth of the track to the present. In this paper, we provide a solution of comparing and matching labels based on the whole-life time-series state distributions of the labels/tracks, by extending the common divergences like the Cauchy-Schwarz and Kullback-Leibler from distributions at a single time-instant to those over time-series. Representative scenarios are considered for illustration.
AB - This paper addresses the intractable track matching problem involved in multi-sensor multi-target tracking using the labeled multi-Bernoulli filters. Unlike the unlabeled density defined in the common state space, the labeled multi-target density is defined in the joint state and label space, where the label contains time-series/history information of the underlying track. To measure the similarity between labeled densities (individual tracks) that is required for inter-sensor track matching and fusion, one has to account for the divergences in both state and label spaces. The challenge, however, arises from the lack of a proper metric to measure the label difference. It requires considering the entire trajectory of the track, encompassing the whole-life information from the birth of the track to the present. In this paper, we provide a solution of comparing and matching labels based on the whole-life time-series state distributions of the labels/tracks, by extending the common divergences like the Cauchy-Schwarz and Kullback-Leibler from distributions at a single time-instant to those over time-series. Representative scenarios are considered for illustration.
KW - Random finite set
KW - label matching
KW - labeled multi-Bernoulli filter
KW - multiple target tracking
KW - track association
UR - http://www.scopus.com/inward/record.url?scp=85207697206&partnerID=8YFLogxK
U2 - 10.23919/FUSION59988.2024.10706505
DO - 10.23919/FUSION59988.2024.10706505
M3 - 会议稿件
AN - SCOPUS:85207697206
T3 - FUSION 2024 - 27th International Conference on Information Fusion
BT - FUSION 2024 - 27th International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 July 2024 through 11 July 2024
ER -