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使用全球定系统/北斗卫星导航(Global positioning system/BeiDou navigation satellite system,GPS/BDS)双模导航系统定位列车时,根据双模观测量的特点,若卡尔曼滤波方法对列车位置解算,需对算法进行相应的调整;并且受多径效应影响,传统卡尔曼滤波算法的定位精度较低.针对这些问题,提出了混浊免疫粒子群优化算法以提高卡尔曼滤波输出精度.首先,利用混沌映射及免疫接种方法,对粒子群算法进行优化,再用优化后的粒子群算法对观测误差协方差矩阵寻优.然后,将最优参数提供给扩展卡尔曼滤波器,可有效减小因多径效应引起的观测值震荡对定位精度的影响.最后,通过实验验证并比较分析了采用EKF和CIPSO_EKF两种算法的列车定位结果.从北向、东向的位置、速度定位误差结果可以看出,CIPSO_EKF算法收敛速度更快,实时性更高,能有效抑制干扰,提高定位精度.“,”When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.