摘要
针对密集杂波环境下的多目标点迹-航迹关联问题,以强化学习(Reinforcement Learning,RL)方法为基础,提出了一种基于Q学习的多目标点迹-航迹关联方法。首先,根据整个过程中目标的运动状态,建立马尔可夫决策过程(Markov Decision Process,MDP)模型。其次,利用各状态间的相关程度构成策略函数,选择准确的动作,并设定相应的奖励函数。最后,考虑杂波密集时虚假量测难以分辨,结合目标先验信息,增加了Q表再学习环节,进一步优化关联精度。仿真结果表明,在非机动和强机动两种环境下,该方法都能准确地关联到目标的量测,具有较好的点迹-航迹关联性能。
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,the paper uses the degree of correlation between the states to form a strategy function,selects the correct action,and sets 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.
作者
丁国胜
蔡民杰
DING Guo-sheng;CAI Min-jie(Nanjing Research Institute of Electronics Technology,Nanjing 210039,China)
出处
《指挥控制与仿真》
2022年第2期43-48,共6页
Command Control & Simulation
关键词
多目标点迹-航迹关联
强化学习
MDP模型
策略函数
Q表再学习
Multi-target point-track association
reinforcement learning
MDP model
strategy function
Q table relearning