Multiple model based generalized labeled multi-Bernoulli filter

In standard generalized labeled multi-Bernoulli (GLMB), the target state transfer density is not discussed in detail. In maneuvering target tracking scenario, GLMB will not work properly when bringing into the case of determining the motion model. To solve this problem, the theory of merging multiple jump Markov chains in multi-model (MM) maneuvering target tracking algorithm is introduced. Three MM-GLMB filters, the interacting multiple model based GLMB (IMM-GLMB), the generalized pseudo Bayesl based GLMB (GPB1-GLMB) and the generalized pseudo Bayes2 based GLMB (GPB2-GLMB) are presented in this paper. Three filters are compared with the jump Markov system based GLMB (JMS-GLMB) which is also designed to solve the multi-maneuvering targets tracking problem. Simulation results show that the proposed three filters have lower computational cost and higher tracking accuracy compared with JMS-GLMB. Among them, GPB1-GLMB has the lowest computational cost, GPB2-GLMB has the highest tracking accuracy, while IMM-GLMB has the best overall performance. © 2022 Chinese Institute of Electronics. All rights reserved.