TY - GEN
T1 - Generalized probability data association algorithm
AU - Pan, Quan
AU - Ye, Xining
AU - Yang, Feng
AU - Zhang, Hongcai
PY - 2006
Y1 - 2006
N2 - With the development of modern multi-target tracking system, it is very difficult to deal with data association problems by simply using the feasible rule based on the hypothesis in which the association of measurements with targets is one-to-one correlated to each other, as is commonly used in JPDA. A new feasible rule is firstly put forward which is more suitable for practical environment of multi-target tracking system. Based on the new feasible rule, generalized joint event is defined. The generalized joint event set is divided into two generalized event sets and then a combination method with the two sub-sets is put forwarded. Further a Generalized Probability Data Association (GPDA) algorithm is deduced by using Bayesian rule. Additionally, the performance of GPDA algorithm is analyzed in various given tracking environments by using Monte Carlo simulation. All simulation results show that the performance of GPDA is superior to that of JPDA, and the algorithm has much smaller computational burden than JPDA.
AB - With the development of modern multi-target tracking system, it is very difficult to deal with data association problems by simply using the feasible rule based on the hypothesis in which the association of measurements with targets is one-to-one correlated to each other, as is commonly used in JPDA. A new feasible rule is firstly put forward which is more suitable for practical environment of multi-target tracking system. Based on the new feasible rule, generalized joint event is defined. The generalized joint event set is divided into two generalized event sets and then a combination method with the two sub-sets is put forwarded. Further a Generalized Probability Data Association (GPDA) algorithm is deduced by using Bayesian rule. Additionally, the performance of GPDA algorithm is analyzed in various given tracking environments by using Monte Carlo simulation. All simulation results show that the performance of GPDA is superior to that of JPDA, and the algorithm has much smaller computational burden than JPDA.
KW - Data association
KW - Generalized joint event
KW - Generalized probability data association
KW - Multi-target tracking
UR - https://www.scopus.com/pages/publications/34047186268
M3 - 会议稿件
AN - SCOPUS:34047186268
SN - 0889865531
SN - 9780889865532
T3 - Proceedings of the Eight IASTED International Conference on Control and Applications
SP - 150
EP - 155
BT - Proceedings of the Eight IASTED International Conference on Control and Applications
T2 - Eight IASTED International Conference on Control and Applications
Y2 - 24 May 2006 through 26 May 2006
ER -