scholarly journals Joint probabilistic data association-feedback particle filter for multiple target tracking applications

2012 ◽  
Author(s):  
Tao Yang ◽  
Geng Huang ◽  
P. G. Mehta
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association

scholarly journals Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Tao Yang ◽  
Prashant G. Mehta
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Particle Filter Algorithm ◽  
Gaussian Case ◽  
Non Gaussian

This paper is concerned with the problem of tracking single or multiple targets with multiple nontarget-specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization—to the nonlinear non-Gaussian case—of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking (MTT) applications.

scholarly journals Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6595
Author(s):  
Chengzhi Qu ◽  
Yan Zhang ◽  
Xin Zhang ◽  
Yang Yang
Keyword(s):  
Reinforcement Learning ◽  
Target Tracking ◽  
Nearest Neighbor ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Association Method ◽  
Motion Characteristics

Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.

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.

Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association

2001 ◽  
Author(s):  
Derek Caveney ◽  
J. Karl Hedrick
Keyword(s):  
Target Tracking ◽  
Adaptive Cruise Control ◽  
Data Association ◽  
Multiple Model ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Cruise Control ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Position Errors

Abstract The multiple target tracking (MTT) performance of a new combination of the fuzzy interacting multiple model (FIMM) algorithm and the probabilistic data association filter (PDAF) is investigated. The ability of a set of these FIMMPDAFs to maintain the tracks of multiple targets in a cluttered adaptive cruise control (ACC) environment is compared to that of the likelihood approach, the IMMPDAF. The differences between the two methods are highlighted and simulation results for a typical highway driving scenario demonstrate the performance of each approach. These results show that both the IMMPDAF and FIMMPDAF strategies are capable of tracking multiple vehicles with low RMS position errors, while the FIMMPDAF appears to detect the initiation of a target maneuver more rapidly by adjusting model probabilities more quickly.

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