Point‐track association method with unknown system model

Abstract The point‐track association methods are proposed on the premise that the system models are known, which obviously does not conform to the actual air target detection environment. Considering this situation, for the point‐track association problems in clutter environment, a point‐track association method with unknown system model (USMA) is proposed. The method integrates reinforcement learning (RL) theory and traditional point‐track association framework, utilises the association process migration of different models, simplifies the entire learning process, and improves the generalisation ability by designing an adaptive mechanism. The experimental results show that when the system model is unknown, the USMA method can more accurately correlate to the measurements, and can also solve the problems of point‐track association with a certain clutter density. Compared with other methods, the USMA method performs better.