Abstract: Unscented particle filter (UPF) algorithm in urban vehicle navigation system is prone to have the phenomenon of particle dilution and loss of particle diversity. In order to overcome these defects and to improve the navigation accuracy and reliability of the combination of navigation and positioning system , chaos particle swarm optimization algorithm (CPSO) is integrated into the re-sampling process of UPF algorithm , and a new particle filter algorithm : Chaos Particle Swann Optimize Unscented Particle Filter (CPSO-UPF) algorithm is proposed , which speeds up the convergence rate of the filter particles , in order to ease the problem of particle dilution. Finally , PF algorithm , UPF algorithm and CPSO-UPF algorithm are used in the simulation experiment of vehicle integrated navigation system. Through the comparative analysis of experimental results , the filtering perfonnance of CPSO-UPF algorithm , which has higher accuracy of state estimation , is significantly better than that of UPF and PF algorithm.

Key words: ITS, unscented particle filter, chaos particle swann optimization, re-sampling, GPS/DR integrated navigation

摘要： 针对目前无迹粒子泼、波算法(UPF)在城市车载组合导航系统滤波巾容易出现粒子贫化及粒子多样性损失等 现象，为了克服以上缺陷来提高组合导航定位系统的导航精度和可靠'性;将混沌粒子群优化(CPSO)融入UPF算法 的重采样过程中，提出了一种新型粒子滤波算法一-1昆沌粒子群优化无迹粒子滤波(CPSO-UPF)算法，其加快了滤 波粒子的收敛速度，以缓解粒子的贫化问题。最后，将PF算法、UPF算法以及CPSO-UPF算法应用于车载组合导航 系统中进行仿真实验，通过实验结果比较分析，CPSO-UPF算法的滤波性能明显优于UPF和PF算法，具有更高的定 位系统状态估计精度。

关键词: 智能交通系统, 无迹粒子滤波, 混沌粒子群优化, 重采样, GPS/DR组合导航