Shape-Based Online Multitarget Tracking and Detection for Targets Causing Multiple Measurements: Variational Bayesian Clustering and Lossless Data Association

2011 ◽  
Vol 33 (12) ◽  
pp. 2477-2491 ◽  
Author(s):  
T. De Laet ◽  
H. Bruyninckx ◽  
J. De Schutter
Keyword(s):  
Data Association ◽  
Bayesian Clustering ◽  
Multitarget Tracking ◽  
Variational Bayesian ◽  
Multiple Measurements

scholarly journals The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context

2015 ◽  
Vol 15 (7) ◽  
pp. 88-98
Author(s):  
J. Dezert ◽  
A. Tchamova ◽  
P. Konstantinova
Keyword(s):  
Quality Assessment ◽  
Data Association ◽  
New Method ◽  
Multitarget Tracking ◽  
Belief Functions ◽  
Tracking Systems ◽  
Optimal Assignment ◽  
The Individual ◽  
The Impact

Abstract The main purpose of this paper is to apply and to test the performance of a new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the optimal data association solution for improving the performances of multisensor-multitarget tracking systems. The advantages of its implementation in an illustrative realistic surveillance context, when some of the association decisions are unreliable and doubtful and lead to potentially critical mistake, are discussed. A comparison with the results obtained on the base of Generalized Data Association is made.

scholarly journals Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1512 ◽  
Author(s):  
Jing Hou ◽  
Yan Yang ◽  
Tian Gao
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Tracking Performance ◽  
Computation Complexity ◽  
Variational Bayesian ◽  
Target State ◽  
Scenarios Simulation ◽  
Simulation Results

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.

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