基于先验特征的矿下人员定位校准方法

袁亚洲; 孙小芹; 李岳峰; 关新平

doi:10.11999/JEIT170749

基于先验特征的矿下人员定位校准方法

doi: 10.11999/JEIT170749

2.
(燕山大学电气工程学院秦皇岛 066004) ②(河北省工业计算机控制工程重点实验室秦皇岛 066004) ③(上海交通大学电子信息与电气工程学院上海 200240)

基金项目:

河北省自然科学基金(F2017203084)，河北省博士后优先资助项目(B2017003009)

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- 文章访问数: 1426
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- 被引次数: 0
出版历程
- 收稿日期: 2017-07-25
- 修回日期: 2018-03-02
- 刊出日期: 2018-06-19

Positioning and Calibration Method of Underground Personnel Based on Priori Features

2.
(School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Funds:

The Natural Science Foundation of Hebei Province (F2017203084), The Postdoctoral Priority Funding of Hebei Province (B2017003009)

摘要

摘要: 针对目前人员定位方法普遍存在易受环境影响，累计误差大等问题，该文提出一种利用地图先验知识与井下人员行进方向识别相结合的位置校正方法。该方法首先通过线性判别分析(LDA)降低传感器特征集的维度，之后利用随机森林(RF)与设置阈值的方法对井下人员的行进方向分类并标记特殊点，将特殊点与巷道结构的先验知识进行匹配，修正并更新通过步行者航位推算算法(PDR)得到的井下人员的初步运动轨迹。实验结果表明：LDA的预处理方法能够有效提高后续分类器的精度高达6%以上。该文提出的位置估算方法能够有效减小累积误差，具有较高的准确性和鲁棒性，活动识别精度能够达到98%，可以实现可靠的实时定位。
- 室内定位 /
- 多传感器 /
- 线性判别分析 /
- 随机森林 /
- 先验特征
Abstract: Focusing on the problem that the personnel positioning methods are seriously influenced by the indoor environment, big cumulative error and other issues, a method is proposed to correct the position, which combines the prior knowledge of the map and the heading recognition. Firstly, the dimension of the feature set is reduced by Linear Discriminant Analysis (LDA). Then, the heading of the underground personnel is classified and the special points are marked through combining Random Forest (RF) and the method of setting a threshold value. Finally, the movement trajectory of the underground personnel, which is obtained by the Pedestrian Dead Reckoning (PDR) algorithm, is corrected and updated by matching the special point with the prior knowledge of the roadway structure. The experimental results show that the pre-processing method of LDA can effectively improve the precision of the classifier by more than 6%. The proposed method can effectively reduce the cumulative error, with high accuracy and robustness. The activity recognition accuracy can reach 98%, which can achieve reliable real- time location.
- Indoor localization /
- Multi-sensors /
- Linear Discriminant Analysis (LDA) /
- Random Forest (RF) /
- Priori features

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参考文献(22)

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