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)

计量
- 文章访问数: 1431
- HTML全文浏览量: 175
- PDF下载量: 149
- 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

HTML全文

参考文献(22)

El ASSAF A, ZAIDI S, AFFES S, et al. Accurate sensors localization in underground mines or tunnels[C]. IEEE International Conference on Ubiquitous Wireless Broadband, Montreal, Canada, 2015: 1-6. doi: 10.1109/ICUWB.2015. 7324418.

刘文远, 吕倩, 王林, 等. 基于动态地标的在线室内平面图生成方法[J]. 电子与信息学报, 2016, 38(6): 1519-1527. doi: 10.11999/JEIT150926.

LIU Wenyuan, L Qian, WANG Lin, et al. Multidimensional fingerprints method for indoor mobile trajectory mapping with geomagnetic information[J]. Journal of Electronics Information Technology, 2016, 38(6): 1519-1527. doi: 10.11999/JEIT150926.

YE Y, ZHANG L, SONG X, et al. A novel coal mine security monitoring system based on ZigBee[C]. International Conference on Intelligent Transportation, Big Data and Smart City, Halong Bay, Vietnam, 2015: 39-42. doi: 10.1109/ ICITBS.2015.16.

MI J and TAKAHASHI Y. Low cost design of HF-band RFID system for mobile robot self-localization based on multiple readers and tags[C]. IEEE International Conference on Robotics and Biomimetics, Zhuhai, China, 2015: 194-199. doi: 10.1109/ROBIO.2015.7418766.

LI J and LIU H P. A new weighted centroid localization algorithm in coal Mine wireless sensor networks[C]. International Conference on Computer Research and Development, Shanghai, China, 2011: 106-109. doi: 10.1109 /ICCRD.2011.5764256.

WANG Jie, GAO Qinghua, YU Yan, et al. Toward robust indoor localization based on bayesian filter using chirp- spread-spectrum ranging[J]. IEEE Transactions on Industrial Electronics, 2012, 59(3): 1622-1629. doi: 10.1109/TIE.2011. 2165462.

WANG Jie, GAO Qinghua, Pan Miao, et al. Toward accurate device-free wireless localization with a saddle surface model[J]. IEEE Transactions on Vehicular Technology, 2016, 65(8): 6665-6677. doi: 10.1109/TVT.2015.2476495.

FAN Qigao, SUN Biwen, SUN Yan, et al. Performance enhancement of MEMS-based INS/UWB integration for indoor navigation applications[J]. IEEE Sensors Journal, 2017, 17(10): 3116-3130. doi: 10.1109/JSEN.2017.2689802.

谷阳, 宋千, 李杨寰, 等. 基于惯性鞋载传感器的人员自主定位粒子滤波方法[J]. 电子与信息学报, 2015, 37(2): 484-488. doi: 10.11999/JEIT140362.

GU Yang, SONG Qian, LI Yanghuan, et al. A particle filter method for pedestrian navigation using foot-mounted inertial sensors[J]. Journal of Electronics Information Technology, 2015, 37(2): 484-488. doi: 10.11999/JEIT140362.

何坚, 万志江, 刘金伟. 基于电源线和位置指纹的室内定位技术[J]. 电子与信息学报, 2014, 36(12): 2902-2908. doi: 10.3724 /SP.J.1146.2013.02022.

HE Jian, WAN Zhijiang, and LIU Jinwei. Indoor positioning technology based on powerline and location fingerprint[J]. Journal of Electronics Information Technology, 2014, 36(12): 2902-2908. doi: 10.3724/SP.J.1146.2013.02022.

YUAN Yazhou, CHEN Cailian, GUAN Xinping, et al. An energy-efficient underground localization system based on heterogeneous wireless networks[J]. Sensors, 2015, 15(6): 12358-12376. doi: 10.3390/s150612358.

INDERST F, PASCUCCI F, and RENAUDIN V. PDR and GPS trajectory parts matching for an improved self- contained personal navigation solution with handheld device[C]. Navigation Conference, Lausanne, Switzerland, 2017: 100-107. doi: 10.1109/EURONAV.2017.7954198.

ROHRIG C and MULLER M. Localization of sensor nodes in a wireless sensor network using the nanoLOC TRX transceiver[C]. Vehicular Technology Conference, Barcelona, Spain, 2009: 1-5.

HOF A L. Scaling gait data to body size[J]. Gait Posture, 1996, 4(3): 222-223. doi: 10.1016/0966-6362(95)01057-2

LI Yili and WONG Konmax. Riemannian distances for signal classification by power spectral density[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(4): 655-669. doi: 10.1109/JSTSP.2013.2260320.

YANG Jian, ZHANG D, FRANGI A F, et al. Two- dimensional PCA: A new approach to appearance-based face representation and recognition.[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2004, 26(1): 131-137. doi: 10.1109/TPAMI.2004.1261097.

MARTINEZ A M and KAK A C. PCA versus LDA[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2002, 23(2): 228-233.

TORRES G A and BENITEZ V H. Finger movements classification from grasping spherical objects with surface electromyography using time domain based features[C]. Mechatronics, Adaptive and Intelligent Systems, Hermosillo, Mexico, 2016. doi: 10.1109/MAIS.2016.7761904.

SVETNIK V, LIAW A, TONG C, et al. Random forest: A classification and regression tool for compound classification and QSAR modeling[J]. Journal of Chemical Information Computer Sciences, 2003, 43(6): 1947-1958. doi: 10.1021/ ci034160g.

施引文献

资源附件(0)

访问统计