A Coal Mine Underground Localization Algorithm Based on the Feature Vector
DOI:
https://doi.org/10.5614/j.eng.technol.sci.2019.51.2.3Keywords:
underground localization algorithm, RSSI, feature vector, database construction, FVMAAbstract
To enhance the position estimation accuracy of an underground localization system for coal mine roadways, an algorithm based on the feature vector of received signals is presented in this paper. The algorithm includes three steps: the construction process of a feature vector database and a distance database, the vector matching process and the localization process. When a signal vector is received, it only needs to calculate the distance from the received vector to the center vector of each subset and then compare it with the data in the distance database. After multiple filtering and comparing the source of the strongest signal, the coordinates closest to the received vector are found. The experiment showed that the maximum error of this algorithm was 4 m and the average error was 1.62 m. Furthermore, within a localization error of 1 m, the X-axis localization accuracy was 98% while the Y-axis localization accuracy was 86%. Also, the algorithm took much less time compared to the KNN and WKNN algorithms, so the algorithm meets the requirements of coal mine safety systems and underground personnel localization systems.
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References
Taniura, Y. & Oguchi, K., Indoor Location Recognition Method Using RSSI Values n System with Small Wireless Nodes, 2017 40th International Conference on Telecommunications and Signal Processing (TSP), pp. 52-55, 2017.
Weixing, X. & Weining, Q., Improved Wi-Fi RSSI Measurement for Indoor Localization, IEEE Sensors Journal, 17(7), pp. 2224-2230, 2017.
Simon, Y. & Marzieh, D., Wireless",RSSI",Fingerprinting Localization, Signal Processing, 131(1), pp. 235-244, 2017.
Jin, L. & Yinjing, G., Design of Underground Wireless Positioning System Based on Fingerprint Algorithm, Journal of Shandong University of Science and Technology (Natural Science Edition), 6(2), pp. 47-45, 2013.
Jiang, S., Pang G., Wu, M. & Kuang, L., An Improved K-Nearest-Neighbor Algorithm for Text Categorization, Expert Systems with Applications, 29(1), pp. 1503-1509, 2012.
Shaowu, M., Huanguo, Z. & Chongchao, H., Application of Improved K Shortest Path Algorithm in Communication Network, Journal of Wuhan University (Science Edition), 5(6), pp. 534-538, 2013.
Lakmali, B.D.S. & Dias, D., Database Correlation for GSM Location in Outdoor & Indoor Environments, International Conference on Information and Automation for Sustainability, pp. 42-47, 2008.
Chunyan, L. & Wangjian, Constrained KNN Indoor Positioning Model Based on Set Clustering Fingerprint Database, Journal of Wuhan University (Science Edition), 39(11), pp. 1287-1292, 2014.
Kong, C., Chunlei, S. & Jiabin, C., Positioning Fingerprint Indoor Positioning Algorithm Based on Improved WKNN, Navigation Positioning and Timing, 3(4), pp. 58-64, July.2016.
Lu, Y., The Design of Underground Personnel Positioning System Based on Improved Weighted Centroid Algorithm, China Mining Magazine. 26(2), pp. 169-173, 2017.
Zhong, Y., Wu, F., Zhang, J. & Dong, B., Wifi Indoor Location Based on K-means, Proceedings of International Conference on Audio, Language and Image Processing (ICALIP), pp. 663-667, 2016.
Abdullah, O. & Abdel-Qader, I., K-Means-Jensen-Shannon Divergence for a WLAN Indoor Positioning System, IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2016.
Zhou, F. & Lin, K., RSSI",Indoor Localization Through A Bayesian Strategy, Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1975-1979, 2017.
Cheng, Z. & Jiazheng, Y., Bluetooth Indoor Positioning Based on RSSI and Kalman Filter, Wireless Personal Communications, 96(3), pp. 4115-4130, 2017.
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