Improved multiple targets angle tracking algorithm using the joint probabilistic data association filter

1998 ◽  
Vol 145 (6) ◽  
pp. 331 ◽  
Author(s):  
M. Keche ◽  
M.S. Woolfson ◽  
I. Harrison ◽  
A. Ouamri
Keyword(s):  
Data Association ◽  
Tracking Algorithm ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association ◽  
Angle Tracking

Linear Multi-Target Integrated Probabilistic Data Association Filter With Automatic Track Management for Autonomous Vehicles

2018 ◽  
Author(s):  
Andinet Hunde ◽  
Beshah Ayalew
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Tracking Algorithm ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Address Data ◽  
Multi Target Tracking ◽  
Track Management ◽  
Track Initiation

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

Improved adaptive joint probabilistic data association filter for multiple targets angles tracking

1997 ◽  
Vol 33 (16) ◽  
pp. 1361 ◽  
Author(s):  
M. Keche ◽  
M.S. Woolfson ◽  
I. Harrison ◽  
A. Ouamri ◽  
S.S. Ahmeda
Keyword(s):  
Data Association ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association

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.

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