Volume 36, Number 1, February 2018
|Page(s)||169 - 175|
|Published online||18 May 2018|
Improved Interacting Multiple Model Particle Filter Algorithm
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.
Key words: maneuvering target tracking / IMM / Kalman particle filter / tracking accuracy / extended kalman filter / target tracking
关键字 : 机动目标跟踪 / 交互多模型 / 卡尔曼粒子滤波 / 跟踪精度 / 扩展卡尔曼滤波 / 目标跟踪
© 2018 Journal of Northwestern Polytechnical University. All rights reserved.
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