Abstract:

As tracks forecasting and associating accuracy of the traditional track segment association algorithms deteriorates seriously in maneuvering targets environment, a new algorithm based on multiple hypothesis motion models with priori information is proposed. The algorithm firstly builds multiple hypothesis motion models for tracks forecasting according to the priori information, for instance target property, target motion features, scenario condition, then describes the matching relations between forecasted old tracks and new tracks according to fuzzy correlation function on location and velocity information. Finally, the associated track segments on the basis of polynomial fitting connected. Simulation results showed that in the maneuvering targets scenario, the proposed algorithm remarkably outperformed the traditional track segment association algorithm. The proposed algorithm is suitable for complicated environment, after 50 times Monte Carlo simulation, when the break interval is less than 18, the average correct association rate of the maneuvering targets is more than 90%, and the global correct association rate is more than 85%.