Multi-target Point-Track Association Method Based on Q-learning.

Aiming at the problem of multi-target point-track association in dense clutter environment, based on the reinforcement learning(RL) method, a multi-target point-track association method based on Q-learning is proposed. First, according to the movement state of the target in the whole process, a Markov decision process(MDP) model is established. Secondly, use the degree of correlation between the states to form a strategy function, select the correct action, and set the corresponding reward function. Finally, considering that false measurements are difficult to distinguish when the clutter is dense, combined with the prior information of the target, the Q-meter re-learning link is added to further optimize the correlation accuracy. The simulation results show that in both non-maneuvering and strong maneuvering environments, the method in this paper can accurately correlate to the measurement of the target, and has a better point-track-track correlation performance.