Adaptive joint probabilistic data association algorithm for tracking multiple targets in cluttered environment

1997 ◽  
Vol 144 (6) ◽  
pp. 309 ◽  
Author(s):  
S.S. Ahmeda ◽  
M. Keche ◽  
I. Harrison ◽  
M.S. Woolfson
Keyword(s):  
Data Association ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association ◽  
Cluttered Environment ◽  
Association Algorithm

Modified Joint Probability Data Association Algorithm Avoiding Track Coalescence

2012 ◽  
Vol 433-440 ◽  
pp. 2298-2303 ◽  
Author(s):  
Song Lin Chen ◽  
Yi Bing Xu
Keyword(s):  
Small Angle ◽  
Data Association ◽  
Joint Probability ◽  
Tracking Performance ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association ◽  
Simulation Results ◽  
Exclusive Measurement ◽  
Association Algorithm

Joint Probabilistic Data Association has proven to be effective in tracking multiple targets from measurements amidst clutter and missed detections. But the traditional Joint Probabilistic Data Association algorithm will cause track coalescence when the targets are parallel neighboring or small-angle crossing. To avoid track coalescence, a modified Joint Probabilistic Data Association algorithm is proposed in this paper. An exclusive measurement is defined for every target in the new algorithm. The exclusive measurement of a target is one measurement which associates with the target and has the maximum associated probability. The associated events of the exclusive measurement with other targets will be pruned, which resists two or more targets share the same measurement as a key measurement and avoids track coalescence. The simulation results show that the new algorithm can effectively solve track coalescence problem in all kinds of scenarios and keep a high tracking performance.

scholarly journals A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2180 ◽  
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu ◽  
Xiaohua Li
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Cluttered Environment

Extended Probabilistic Data Association Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1600-1604
Author(s):  
Wan Li Xu ◽  
Zhun Liu ◽  
Jun Hui Liu
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Experimental Results ◽  
Effective Algorithm ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Event Based ◽  
Association Algorithm

[Purpos In order to improve the accuracy of target tracking and reduce losing rate of target in the multiple target tracking, a new algorithm called Extended Probabilistic Data Association (EPDA) is presented in this paper. [Metho This paper defines joint association event based on the number of target and puts forward the EPDA for target tracking. [Result Experimental results show that this algorithm has higher accuracy of target tracking than the Probabilistic Data Association algorithm and costs much less time relative to the Joint Probabilistic Data Association algorithm. [Conclusion Consequently, EPDA is an effective algorithm to balance the accuracy and the losing rate in target tracking.

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