1. Improved Interacting Multiple Model Particle Filter Algorithm
- Author
-
Qiaoran Liu and Xun Yang
- Subjects
021103 operations research ,Computer science ,kalman particle filter ,imm ,0211 other engineering and technologies ,General Engineering ,extended kalman filter ,TL1-4050 ,010103 numerical & computational mathematics ,02 engineering and technology ,Kalman filter ,Paper based ,maneuvering target tracking ,Tracking (particle physics) ,tracking accuracy ,01 natural sciences ,Measure (mathematics) ,Extended Kalman filter ,Resampling ,State (computer science) ,0101 mathematics ,target tracking ,Algorithm ,Particle filtering algorithm ,Motor vehicles. Aeronautics. Astronautics - Abstract
For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.
- Published
- 2018