Abstract:In order to overcome the limitation of one-dimensional model in accuracy of mine workers’ fingerprint location, a two-dimensional fingerprint location database algorithm for mine workers is proposed. The problem of the large data acquisition workload brought by the two-dimensional model is also solved by SVR-Kriging interpolation. Firstly, Gaussian filtering is used to preprocess the fingerprint information of the collected sampling point and the variation function is fitted by the Support Vector Regression (SVR). Then, the Kriging interpolation is used to complete the position fingerprint information of the un-sampled area in the two-dimensional meshing. Finally, the fingerprint location database of the mine workers is established by integrating the location fingerprint information of the sampling points and the interpolation points, laying the foundation for the follow-up mine workers’ fingerprint location. The simulation results show that the proposed algorithm can reduce the workload of data acquisition while ensuring the feasibility and the effectiveness of the algorithm and can guarantee high accuracy when positioning is performed through the location fingerprint.
王红军,周宇,王伦文. 基于SVR-Kriging插值的矿井工人二维指纹定位数据库构建算法[J]. 电子与信息学报, 2017, 39(11): 2571-2578.
WANG Hongjun, ZHOU Yu, WANG Lunwen. Establishment Algorithm of Two Dimensional Fingerprint Database for Mine Workers Based on SVR-Kriging Interpolation. JEIT, 2017, 39(11): 2571-2578.
HU Qingsong, ZHANG Shen, WU Lixin, et al. Localization techniques of mobile objects in coal mines: Challenges, solutions and trends[J]. Journal of China Coal Society, 2016, 41(5): 1059-1068. doi: 10.13225/j.cnki.jccs.2015.1267.
[2]
WANG Jie, GAO Qinghua, YU Yan, et al. Toward robust indoor localization based on Bayesian filter using chrip-spread-spectrum ranging[J]. IEEE Transactions on Industrial Electronics, 2012, 59(3): 1622-1629. doi: 10.1109/TIE.2011.2165462.
[3]
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.
[4]
ERRINGTON A F C, DAKU B L F, and PRUGGER A F. Initial position estimation using RFID tags: A least-squares approach[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(11): 2863-2869. doi: 10.1109/TIM. 2010.2046366.
[5]
YU Gu and REN Fuji. Energy-efficient indoor localization of smart hand-held devices using Bluetooth[J]. IEEE Access, 2015, 3: 1450-1461. doi: 10.1109/ACCESS.2015.2441694.
[6]
WEI Jiaxi, CHEN Yan, and SUN Shuo. An improved TDOA algorithm applied person localization system in coal mine[C]. 2011 Third International Conference on Measuring Technology and Mechatronics Automation, Shanghai, 2011, 1: 428-431. doi: 10.1109/ICMTMA.2011.108.
HAO Lina, ZHANG Xiujun, YU Wanli, et al. Underground coal mine WLAN localization algorithm based on RSS fingerprinting[J]. Transducer and Microsystem Technologies, 2012, 31(9): 46-49. doi: 10.13873/j.1000-97872012.09.020.
[8]
GUO Jiateng, JIANG Jizhou, WU Lixin, et al. 3D modeling for mine roadway from laser scanning point cloud[C]. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016: 4452-4455. doi: 10.1109/IGARSS.2016.7730160.
WANG Tao. Research of positioning algorithm in coal mine based on location fingerprint[D]. [Master dissertation], China University of Mining & Technology, 2015: 29-39.
[10]
JIANG Qideng, MA Yongtao, LIU Kaihua, et al. A probabilistic radio map construction scheme for crowdsourcing-based fingerprinting localization[J]. IEEE Sensors Journal, 2016, 16(10): 3764-3774. doi: 10.1109/JSEN. 2016.2535250.
PENG Yuxu and YANG Yanhong. Bayesian indoor location algorithm based on RSSI[J]. Computer Engineering, 2012, 38(10): 237-240. doi: 10.3969/j.issn.1000-3428.2012.10.073.
[12]
XIAO Song, ROTARU M, and SYKULSKI J K. Adaptive weighted expected improvement with rewards approach in kriging assisted electromagnetic design[J]. IEEE Transactions on Magnetics, 2013, 49(5): 2057-2060. doi: 10.1109/TMGA.2013.2240662.
[13]
ZIMOS E, TOUMPAKARIS D, MUNTEANU A, et al. Multiterminal source coding with copula regression for wireless sensor networks gathering diverse data[J]. IEEE Sensors Journal, 2017, 17(1): 139-150. doi: 10.1109/JSEN. 2016.2585042.
[14]
WU Qiang and ZHOU Dingxuan. SVM soft margin classifiers: Linear programming versus quadratic programming[J]. Neural Computation, 2005, 17(5): 1160-1187. doi: 10.1162/ 0899766053491896.
[15]
TAKAHASHI N, GUO J, and NISHI T. Global convergence of SMO algorithm for support vector regression[J]. IEEE Transactions on Neural Networks, 2008, 19(6): 971-982. doi: 10.1109/TNN.2007.915116.
[16]
SHAMSHIRBAND S, PETKOVIC D, JAVIDNIA H, et al. Sensor data fusion by support vector regression methodologyA comparative study[J]. IEEE Sensors Journal, 2015, 15(2): 850-854. doi: 10.1109/JSEN.2014. 2356501.
LI Mingshan, WANG Zhengming, and ZHANG Yi. New method for selecting parameters of support vector machine regression based on uniform design[J]. Journal of System Simulation, 2008, 20(8): 2195-2199. doi: 10.16182/j.cnki. joss.2008.08.067.
HE Fei and FANG Jinyun. Algorithm for spatial interpolation based on self-adaptive parallel programming[J]. Journal of System Simulation, 2014, 26(4): 761-768. doi: 10.16182/j.cnki.joss.2014.04.030.
CHEN Li, CHEN Jing, GAO Qingtao, et al. Classification algorithm research based on support vector machine and reverse K-nearest neighbor[J]. Computer Engineering and Applications, 2010, 46(24): 135-137.
[20]
NI L M, LIU Y, LAN Y C, et al. LANDMARC: Indoor location sensing using active RFID[J]. Wireless Networks, 2004, 10(6): 701-710. doi: 10.1023/B:WINE.0000044029. 06344.DD.