Neural network data association with application to multiple‐target tracking

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

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Decoupling joint probabilistic data association algorithm for multiple target tracking

IEE Proceedings - Radar Sonar and Navigation ◽

10.1049/ip-rsn:19990709 ◽

1999 ◽

Vol 146 (5) ◽

pp. 251 ◽

Cited By ~ 8

Author(s):

Z. Ding ◽

H. Leung ◽

L. Hong

Keyword(s):

Target Tracking ◽

Data Association ◽

Multiple Target Tracking ◽

Multiple Target ◽

Probabilistic Data ◽

Probabilistic Data Association ◽

Association Algorithm

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

Sensors ◽

10.3390/s20226595 ◽

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.

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Extended Probabilistic Data Association Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1600 ◽

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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