Interacting multiple model Poisson multi-Bernoulli mixture filter for maneuvering targets tracking
Poisson multi-Bernoulli mixture(PMBM)filter,which satisfies the conjugate prior property,partitions the target state into Poisson and multi-Bernoulli mixture.The filter performs the prediction and update steps for those two parts separately and has fast operation speed while keeping high tracking accuracy.In the case of multi-target maneuvering,the single model is not enough to describe the target motion which will lead to a decline in tracking performance.To solve this problem,an interacting multiple model(IMM)PMBM filter is proposed,which can effectively track multiple maneuvering targets by making full use of interactive information between models.The proposed algorithm employs the sequential Monte Carlo(SMC)method to realize the PMBM filter,which can be applied in nonlinear scenes.The simulation results show that the proposed IMM-SMC-PMBM algorithm can effectively track multiple maneuvering targets with varying number in nonlinear environments.In comparison to the IMM-SMC-PHD filter,the proposed filter has high tracking accuracy and stability.
maneuvering target trackinginteracting multiple model(IMM)sequential Monte Carlo(SMC)Poisson multi-Bernoulli mixture(PMBM)
万方数据
维普
