New method for joint detection and tracking with variable maneuvering target number

Aiming at the problem of low target tracking performance with the variable number of maneuvering target, a new joint detection and tracking algorithm was put forward for the environment of low signal-to-noise ratio multi-sensor observation. The sampling of predict state particle set was completed according to the existence of the particle. And then, taking the association degree of the sets of particle state and the current observations into account, and using the theory of fuzzy auction algorithm and particle swarm optimization, the association problem between the state and observation sets was solved, and the criteria of target appear and disappear was given, and the updating of particle weight was realized. By the means of composite sampling, the sample particle sets with the model information and status information was obtained, and the local posteriori estimate and the mean square error of target state were given with the target model probability through particle state fusion. Finally, with the weighted fusion of each associated local sensor tracking information, the global state estimation of each target was obtained and compared with the simulation experiment with classical multiple model particle filter algorithm. The results show that the new algorithm is effective in motion model probability estimation, state estimation, and the target number estimation.