Abstract
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.
Highlights
Measurement data association in a cluttered environment is considered to be a high potential and challenging technique in the field of multiple target tracking [1,2]
The reinforcement learning (RL) is embedded into the traditional joint probabilistic data association (JPDA) method to obtain the relationship between the measurement distribution and its associated probability at the presence of dense measurement clutters; The motion characteristics of the targets is considered to improve the accuracy of data association
The proposed method reconstructs the compute mode of joint association probabilities in JPDA by the state-action map of RL to acquire the available information of measurements
Summary
Measurement data association in a cluttered environment is considered to be a high potential and challenging technique in the field of multiple target tracking [1,2]. To solve the multiple targets tracking problem, reference [23] proposed an intuitionistic fuzzy based JPDA method. Reference [25] proposed a modified JPDA method based on a soft and evolutionary computation method for solving the multiple targets tracking problem. Reference [26] proposed a cheap joint probabilistic data association (CJPDA) to solve multiple targets tracking problem. To overcome the aforementioned drawbacks of the classical JPDA method, this paper leverages the emerging reinforcement learning technique to handle measurement clutters, yielding a novel RL-JPDA method for the multiple targets tracking data association problem. The RL is embedded into the traditional JPDA method to obtain the relationship between the measurement distribution and its associated probability at the presence of dense measurement clutters; The motion characteristics of the targets is considered to improve the accuracy of data association.
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