粒子群优化噪声参数的行人导航零速修正算法  

Zero Velocity Update Algorithm for Pedestrian Navigation Based on Particle Swarm Optimization

作　　者：李磊 苏中[1] 吴学佳 雷明 王一静 LI Lei;SU Zhong;WU Xuejia;LEI Ming;WANG Yijing(Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学高动态导航技术北京市重点实验室,北京100192

出　　处：《传感技术学报》2024年第1期42-49,共8页Chinese Journal of Sensors and Actuators

基　　金：国家重点研发计划项目(2020YFC1511702);国家自然科学基金项目(61771059,61801032);北京市自然科学基金项目(4212003)。

摘　　要：针对使用零速修正算法进行行人惯性导航时,固定噪声参数不适用所造成的导航精度较低的问题,提出了一种基于粒子群算法优化噪声参数的零速修正算法。通过最小化零速阶段的滤波器新息序列可以在线调整滤波参数,以适应当前行人运动状态,最后通过对多组惯性数据进行解算来验证算法有效性。分别使用了标准零速修正算法和粒子群优化的零速修正算法对惯性数据进行解算,并计算两种算法解算结果的位置误差,结果表明,提出的算法能够得到更小的定位误差和更平滑的解算轨迹。To deal with the problem of low navigation accuracy caused by inappropriate fixed noise parameters when using zero velocity update algorithm for pedestrian navigation,a zero velocity update algorithm based on particle swarm optimization is presented.Filter pa-rameters can be adjusted online by minimizing the filter innovation sequence in the zero velocity phase to adapt to the current pedestrian motion state.Finally,the algorithm effectiveness is verified by solving the multiple inertial data.The standard zero velocity update algo-rithm and the proposed algorithm are used to solve the inertial data,and the position errors of the two algorithms are calculated.The re-sults show that the proposed algorithm can obtain smaller position error and smoother solution trajectory.

关 键 词：行人导航 零速修正 粒子群优化 扩展卡尔曼滤波 新息序列 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]