Enhanced multiple model particle filter for maneuvering target tracking
改进的多模型粒子滤波机动目标跟踪算法

Classical multiple model particle filters(MMPF) can not effectively control particles of each mode in maneuvering target tracking. An enhanced multiple model particle filter(EMMPF) algorithm is proposed, which estimates the mode and state independently. The posterior probability of each mode is updated with the associate likelihood function. The number of particles of each mode is given in advance according to the mode property and is kept constant in the recursion. Simulation shows that the EMMPF achieves better tracking accuracy with fewer particles.