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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.

In this paper, the PD-type trajectory tracking controllevnr with the extended state observer (ESO) is proposed for the quadrotor unmanned aerial vehicle (UAV) to deal with the wind disturbance. A sixdegree-of-freedom quadrotor UAV model with the hyperbolic tangent saturation function is built. The trajectory tracking problem for the quadrotor can be divided into the position and attitude loop. Then, the PD-type trajectory tracking controller with the ESO is designed in the position and attitude loop to compensate the effect of the wind disturbance. And the stability of the system is proved by the circle criterion. Simulation results are given to demonstrate the effectiveness of the proposed method.