| 杨承志,张晓明,张鸽.基于WLS-KF的UWB室内定位滤波算法研究[J].电子测量与仪器学报,2024,38(1):25-33 |
| 基于WLS-KF的UWB室内定位滤波算法研究 |
| Research on UWB indoor localization filtering algorithm based on WLS-KF |
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| DOI： |
| 中文关键词: 抗差估计 卡尔曼滤波 UWB NLOS误差 加权最小二乘估计 |
| 英文关键词:robust estimation Kalman filtering UWB NLOS errors weighted least squares |
| 基金项目:山西重点研发计划项目（201903D121169）资助 |
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| Author | Institution |
| Yang Chengzhi | National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China |
| Zhang Xiaoming | 1.National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China;
2.Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education,
North University of China, Taiyuan 030051,China |
| Zhang Ge | National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China |
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| 中文摘要: |
| 针对室内超宽带（UWB）定位过程中受到非视距误差（NLOS）干扰而导致定位精度下降的问题，提出了基于抗差估计原理的自适应卡尔曼滤波方法，结合加权最小二乘法对测距信息解算得到定位坐标。在通视环境下进行测距，利用测得的数据计算新息向量和协方差，并基于此构建阈值信息，对NLOS环境产生的量测异常值进行判别，在此基础上利用Sage-Husa滤波对系统噪声协方差进行估计。采用加权最小二乘法对测距信息进行处理，得到标签解算坐标的最优估计。通过MATLAB仿真验证算法的可行性和有效性并在室内环境下进行测距、定位试验验证。仿真和实验结果表明，基于抗差估计原理的自适应卡尔曼滤波方法，结合加权最小二乘法能有效识别NLOS误差，且对定位过程中发生的状态突变能有效进行跟踪，解算得到的标签坐标x方向误差1 cm左右，y方向误差2 cm左右，提高了UWB室内定位的精度。 |
| 英文摘要: |
| In response to the issue of decreased positioning accuracy in indoor Ultra-Wideband (UWB) localization due to non-line-of-sight (NLOS) interference, an adaptive Kalman filtering method based on robust estimation principles is proposed. This method combines weighted least squares estimation for distance measurements to derive positioning coordinates. In a line-of-sight scenario, distance measurements are conducted. The acquired data is utilized to compute the innovation vector and covariance. Based on this information, threshold criteria are established to identify measurement outliers resulting from non-line-of-sight (NLOS) conditions. Subsequently, the Sage-Husa filter is employed to estimate the system noise covariance. Weighted least squares estimation is applied to process distance measurements, resulting in the optimal estimation of tag coordinates. Verify the feasibility and effectiveness of the algorithm through MATLAB simulation and carry out distance measurement and positioning tests in indoor environments. Simulation and experimental results demonstrate that the adaptive Kalman filtering method based on robust estimation principles, combined with weighted least squares, effectively identifies NLOS errors and tracks sudden state changes during the localization process, the error in the x-direction is about 1 cm and in the y-direction is about 2 cm, thereby enhancing the accuracy of indoor UWB positioning. |
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