| 胡悦,范建华,胡永扬,魏祥麟,李旭.面向非视距环境的智能车多传感鲁棒融合定位[J].电子测量与仪器学报,2025,39(5):1-10 |
| 面向非视距环境的智能车多传感鲁棒融合定位 |
| Robust multi-sensor fusion positioning for intelligent vehiclesin non-line-of-sight environments |
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| DOI： |
| 中文关键词: 智能车 融合定位 非视距判别 两级模型 因子图 |
| 英文关键词:intelligent vehicles fusion positioning non-line-of-sight identification two-stage model factor graph |
| 基金项目:国家自然科学基金（62473099）项目资助 |
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| Author | Institution |
| Hu Yue | The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China |
| Fan Jianhua | The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China |
| Hu Yongyang | The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China |
| Wei Xianglin | The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China |
| Li Xu | School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China |
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| 中文摘要: |
| 针对智能车无线定位易受非视距信号影响从而导致定位误差增大的问题，提出了一种基于非视距信号可靠判别的超宽带/惯性测量单元（UWB/IMU）鲁棒融合定位方法。首先，分别基于支持向量机（SVM）学习模型和多传感器一致性数学模型对非视距信号进行粗判别；接着，设计了基于D-S证据理论的非视距信号精判别模型，在决策级对上述模型的结果进行有效融合；最后，提出了一种基于因子图的多传感自适应融合定位方法，根据非视距判别结果动态调节融合模型，以实现非视距环境下的智能车鲁棒定位。实车试验结果表明，在非视距判别效果方面，相较于常规的SVM模型，所提方法非视距判别的精度、召回率和准确率分别提高了6.97%、5.37%和6.36%；在定位性能方面，与现有常规的最小二乘定位方法相比，所提出方法的均方根误差、最大误差和标准差分别减少了12.55%、63.40%以及13.23%，有效提升了非视距环境下智能车的定位精度和鲁棒性，克服了传统方法在非视距环境下定位精度低、可靠性差的缺陷。 |
| 英文摘要: |
| To address the problem that the increased positioning errors in wireless positioning for intelligent vehicles caused by non-line-of-sight (NLOS) signals, a robust UWB/IMU fusion positioning methodology based on reliable identification of NLOS signals is proposed. Firstly, the coarse NLOS identification is conducted based on a support vector machine （SVM） learning model and a multi-sensor consistency mathematical model respectively. Subsequently, the fine NLOS identification model based on D-S evidence theory is designed to effectively integrate the results of the aforementioned models at the decision level. Finally, a multi-sensor adaptive fusion positioning method based on factor graph is proposed to dynamically adjust the fusion model according to the results of NLOS identification, in order to achieve robust positioning for intelligent vehicles in NLOS environments. The results of real vehicle experiments indicate that, in terms of NLOS identification performance, compared with the conventional SVM model, the proposed method improves the precision, recall and accuracy by 6.97%, 5.37% and 6.36% respectively. In terms of positioning performance, compared with the existing conventional least squares positioning method, the proposed method reduces the root mean square error, the maximum error, and the standard deviation by 12.55%, 63.40%, and 13.23%, respectively, effectively improving the positioning accuracy and robustness of intelligent vehicles in NLOS environments, and overcoming the shortcomings of traditional methods in low positioning accuracy and poor reliability in NLOS environments. |
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