TY - GEN
T1 - A random matrix based method for tracking multiple extended targets
AU - Zhu, Hongyan
AU - Ma, Tingting
AU - Chen, Shuo
AU - Jiang, Wen
N1 - Publisher Copyright:
© 2014 International Society of Information Fusion.
PY - 2014/10/3
Y1 - 2014/10/3
N2 - The paper presented an extension of random matrix-based approach [1] to track multiple extended targets in the cluttered environment, which can provide the estimates for target kinematics and target extension, as well as the corresponding identity information. To do this, the DBSCAN clustering (Density Based Spatial Clustering of Applications with Noise) method is employed to divide the whole measurement set into several distinct clusters, with each cluster representing the measurement subset from the same target or false alarm. Moreover, when multiple extended targets are in close proximity, it is hard to partition the measurement set accurately. In this case, the FCM (Fuzzy C means) method is incorporated to produce a soft clustering result. After that, inspired by the idea of Joint Probabilistic Data Association (JPDA), a series of operations are implemented to produce state estimates for multiple extended targets, including generating the feasible events, evaluating the association probabilities and integrating the estimates from multiple association hypotheses, etc. Simulation results demonstrated the effectiveness of the proposed approach.
AB - The paper presented an extension of random matrix-based approach [1] to track multiple extended targets in the cluttered environment, which can provide the estimates for target kinematics and target extension, as well as the corresponding identity information. To do this, the DBSCAN clustering (Density Based Spatial Clustering of Applications with Noise) method is employed to divide the whole measurement set into several distinct clusters, with each cluster representing the measurement subset from the same target or false alarm. Moreover, when multiple extended targets are in close proximity, it is hard to partition the measurement set accurately. In this case, the FCM (Fuzzy C means) method is incorporated to produce a soft clustering result. After that, inspired by the idea of Joint Probabilistic Data Association (JPDA), a series of operations are implemented to produce state estimates for multiple extended targets, including generating the feasible events, evaluating the association probabilities and integrating the estimates from multiple association hypotheses, etc. Simulation results demonstrated the effectiveness of the proposed approach.
KW - DBSCAN
KW - Extended target
KW - FCM
KW - measurement set partitioning
KW - target extension
UR - http://www.scopus.com/inward/record.url?scp=84910604217&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:84910604217
T3 - FUSION 2014 - 17th International Conference on Information Fusion
BT - FUSION 2014 - 17th International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Information Fusion, FUSION 2014
Y2 - 7 July 2014 through 10 July 2014
ER -