基于粒子群算法和平方根平淡卡尔曼滤波的北斗导航系统定位估计算法

发布时间：2018-04-11 11:08

本文选题：平淡卡尔曼滤波算法 + 平方根平淡卡尔曼滤波算法　；参考：《上海交通大学学报》2017年05期

【摘要】：为提高北斗定位系统(BDS)的估计精度,克服传统平淡卡尔曼滤波(UKF)算法中可能因状态量协方差矩阵失去正定性而导致滤波器发散的问题,将平方根平淡卡尔曼滤波(SRUKF)算法应用于BDS定位估计.在此基础上,为进一步提高SRUKF算法的性能,引入粒子群优化(PSO)算法,提出基于PSO和SRUKF算法的BDS定位估计(PSO-SRUKF)算法.结果表明,PSO-SRUKF算法可以降低系统噪声和测量噪声特性估计不准确带来的误差,有效提高了BDS定位精度和稳定性.
[Abstract]:In order to improve the estimation accuracy of Beidou Positioning system (BDS), the filter divergence may be caused by the loss of positive definiteness in the state covariance matrix in the traditional plain Kalman filter UKF algorithm.The square root plain Kalman filter (SRUKF) algorithm is applied to BDS location estimation.On this basis, in order to further improve the performance of SRUKF algorithm, the particle swarm optimization (PSO) algorithm is introduced, and the PSO-SRUKF algorithm based on PSO and SRUKF algorithm is proposed.The results show that the PSO-SRUKF algorithm can reduce the errors caused by the inaccurate estimation of system noise and measurement noise characteristics, and effectively improve the positioning accuracy and stability of BDS.
【作者单位】：上海交通大学电子信息与电气工程学院;
【分类号】：TN713;TN967.1

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