Model adaptive maneuvering target tracking algorithm based on particle filtering

Aiming at the existing problems of current multi-model algorithm for maneuvering target tracking, the paper proposes a model adaptive maneuvering target tracking algorithm based on particle filtering. Firstly, the algorithm achieves the sampling for current moment model according to the previous moment particle model information and the model transfer probability. Then, the prediction for current particle is accomplished by combining the model sampling result with state transfer equation, and the weight of prediction particle is measured based on current moment measurement. Finally, the re-sampling step and the principle of probability maximization are utilized to realize reasonable selection for model and effective estimation for state. Simulation results show the effectiveness of the algorithm.