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Information 2018, 9(12), 322; https://doi.org/10.3390/info9120322

Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

1
ATR National Key Laboratory of Defense Technology, Shenzhen University, Shenzhen 518060, China
2
Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China
3
College of Software Engineering, Lanzhou Institute of Technology, Lanzhou 730050, China
4
Jožef Stefan International Postgraduate School, Jamova cesta 29, 1000 Ljubljana, Slovenia
5
Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
6
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Received: 22 November 2018 / Revised: 8 December 2018 / Accepted: 8 December 2018 / Published: 13 December 2018
(This article belongs to the Section Information Processes)
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Abstract

To track multiple maneuvering targets in cluttered environments with uncertain measurement noises and uncertain target dynamic models, an improved joint probabilistic data association-fuzzy recursive least squares filter (IJPDA-FRLSF) is proposed. In the proposed filter, two uncertain models of measurements and observed angles are first established. Next, these two models are further employed to construct an additive fusion strategy, which is then utilized to calculate generalized joint association probabilities of measurements belonging to different targets. Moreover, the obtained probabilities are applied to replace the joint association probabilities calculated by the standard joint probabilistic data association (JPDA) method. Considering the advantage of the fuzzy recursive least squares filter (FRLSF) on tracking a single maneuvering target, which can relax the restrictive assumption of measurement noise covariances and target dynamic models, FRLSF is still used to update the state of each target track. Thus, the proposed filter can not only provide the advantage of FRLSF but can also adjust the weights of measurements and observed angles in the generalized joint association probabilities adaptively according to their uncertainty. The performance of the proposed filter is evaluated in two experiments with simulation data and real data. It is found to be better than the performance of other three filters in terms of the tracking accuracy and the average run time. View Full-Text
Keywords: multiple maneuvering target tracking; joint probabilistic data association; fuzzy recursive least square filter; information fusion multiple maneuvering target tracking; joint probabilistic data association; fuzzy recursive least square filter; information fusion
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Fan, E.; Xie, W.; Pei, J.; Hu, K.; Li, X.; Podpečan, V. Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations. Information 2018, 9, 322.

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