A Fast and Robust Maneuvering Target Tracking Method without Markov Assumption

In the traditional methods of maneuvering target tracking, it is necessary to adjust the state transition model in time to match the maneuvering target, which will cause the problems of model decision delay and competition. Besides, the commonly adopted first-order Markov assumption can lead to the loss of information when motion modes are relevant to time. In order to solve these problems, a data-driven algorithm based on LightGBM is proposed in this paper. Maneuvering target tracking is modeled as a non-probabilistic method of direct mapping from sensor measurement to target state, track samples of different motion modes are used for training, and fast online tracking is realized. Comparing it with interacting multiple model (IMM) algorithm in a variety of different scenarios, simulation results show that the proposed method has advantages in accuracy and speed. Finally, the robustness of the algorithm is verified under the compound noise of Cauchy and Gaussian distributions.