Improved Interacting Multiple Model Particle Filter Algorithm

基于改进的交互式多模型粒子滤波算法

Received: 2 March 2017

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.

摘要

针对交互式多模型粒子滤波算法中因采样粒子缺乏最新量测信息而造成的滤波精度受限问题，在混合卡尔曼粒子滤波算法的基础上，对交互式多模型粒子滤波算法进行了改进，提出了交互式多模型混合卡尔曼粒子滤波算法，并研究了不同组合方式对跟踪精度的影响。首先用无迹卡尔曼滤波产生系统的状态估计，然后用扩展卡尔曼滤波得到粒子的重要性建议分布，充分利用量测信息，对粒子状态进行更新。仿真结果表明，所提出的改进交互式多模型粒子滤波算法目标跟踪精度优于交互多模型无迹卡尔曼粒子滤波算法以及交互多模型扩展卡尔曼粒子滤波算法，从而证明了该算法的有效性。该方法对于进一步提高非线性、非高斯环境下机动目标的跟踪精度具有重要意义。