| 马杰,王健,李智.基于改进PDR算法的室内定位方法研究[J].电子测量与仪器学报,2024,38(12):211-217 |
| 基于改进PDR算法的室内定位方法研究 |
| Research on indoor localization method based on improved PDR algorithm |
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| DOI： |
| 中文关键词: 室内行人定位；PDR算法；零速检测；步长估计 卡尔曼滤波；FIR滤波；EKF |
| 英文关键词:indoor pedestrian localization PDR algorithm zero speed detection step size estimation Kalman filter FIR filter EKF |
| 基金项目: |
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| Author | Institution |
| Ma Jie | School of Information and Communication, Guilin University of Electronic Science and Technology, Guilin 541004, China |
| Wang Jian | 1.School of Information and Communication, Guilin University of Electronic Science and Technology, Guilin 541004, China;
2.Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing, Guilin 541004, China |
| Li Zhi | Key Laboratory of Unmanned Aerial Vehicle Telemetry, Guilin University
of Aerospace Technology, Guilin 541004,China |
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| 摘要点击次数: 343 |
| 全文下载次数: 211 |
| 中文摘要: |
| 针对室内封闭场所卫星导航系统定位穿透力不够导致的定位精度太低而传统的惯导在室内行人定位时航迹偏移较大的问题，通过对行人航迹推算算法(PDR)的深入分析，提出了改进的PDR算法，旨在提高室内定位中的定位精度。该算法首先设计了卡尔曼滤波器和FIR滤波器对传感器数据进行预处理，提升数据的平滑性和抗噪性能；其次对传统的Weinberg步长计算模型进行改进，增加了新的变量作为步频检测和步长计算的联合参考，有助于减少步长估计的累积误差；然后取合适的阈值作为行人零速判断，以修正步数以及行人位置；最后设计一个扩展卡尔曼滤波器(EKF)对行人的位置进行优化,实现对行人实际轨迹的动态优化。仿真实验结果表明，改进后的PDR算法显著提高了定位精度，行人轨迹的平均误差由5.5 m降低到1.2 m。总体而言，该改进PDR算法能够有效减少航迹偏差和累积误差，提高了行人定位精度，具有广泛的应用前景。 |
| 英文摘要: |
| Aiming at the problem that the positioning accuracy of satellite navigation system in indoor closed places is too low due to insufficient positioning penetration and the traditional inertial guidance has a large trajectory offset in indoor pedestrian positioning, through the in-depth analysis of pedestrian trajectory projection algorithm (PDR), an improved PDR algorithm is proposed, which aims to improve the positioning accuracy in indoor positioning. The algorithm firstly designs Kalman filter and FIR filter to preprocess the sensor data to improve the data smoothing and anti-noise performance; secondly, it improves the traditional Weinberg step calculation model by adding new variables as the joint reference of step frequency detection and step calculation, which helps to reduce the cumulative error of the step estimation; and then it takes the appropriate threshold value as the pedestrian zero-speed judgment to correct the step number as well as the pedestrian’s position; finally, an extended Kalman filter (EKF) is designed to optimize the pedestrian’s position to achieve the dynamic optimization of the actual pedestrian trajectory. The simulation results show that the improved PDR algorithm significantly improves the positioning accuracy, and the average error of the pedestrian trajectory is reduced from 5.5 m to 1.2 m. Overall, the improved PDR algorithm can effectively reduce the trajectory deviation and the cumulative error, and improve the accuracy of the pedestrian positioning, which is of wide application prospect. |
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