{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:44:55Z","timestamp":1760237095378,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious and time-consuming process. When the underground environments change, it may be necessary to update the signal received signal strength indication (RSSI) of all reference points, which will affect the normal working of a personnel positioning system. To solve this problem, an adaptive construction and update method based on a quantum-behaved particle swarm optimization\u2013user-location trajectory feedback (QPSO\u2013ULTF) for a radio fingerprint database is proposed. The principle of ULTF is that the mobile terminal records and uploads the related dataset in the process of user\u2019s walking, and it forms the user-location track with RSSI through the analysis and processing of the positioning system server. QPSO algorithm is used for the optimal radio fingerprint match between the RSSI of the access point (AP) contained in the dataset of user-location track and the calibration samples to achieve the adaptive generation and update of the radio fingerprint samples. The experimental results show that the radio fingerprint database generated by the QPSO\u2013ULTF is similar to the traditional radio fingerprint database in the statistical distribution characteristics of the signal received signal strength (RSS) at each reference point. Therefore, the adaptive radio fingerprint database can replace the traditional radio fingerprint database. The comparable results of well-known traditional positioning methods demonstrate that the radio fingerprint database generated or updated by the QPSO\u2013ULTF has a good positioning effect, which can ensure the normal operation of a personnel positioning system.<\/jats:p>","DOI":"10.3390\/s20041182","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T10:49:16Z","timestamp":1582282156000},"page":"1182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Research and Application of Underground WLAN Adaptive Radio Fingerprint Database"],"prefix":"10.3390","volume":"20","author":[{"given":"Jiansheng","family":"Qian","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,21]]},"reference":[{"key":"ref_1","first-page":"167","article-title":"Research on characteristics and key technology in coal mine internet of things","volume":"36","author":"Sun","year":"2011","journal-title":"J. China Coal Soc."},{"key":"ref_2","first-page":"313","article-title":"Research on coal-mine safe production conception","volume":"36","author":"Sun","year":"2011","journal-title":"J. China Coal Soc."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Y.Q., Li, L.L., and Zhang, Y.J. (2009, January 25\u201327). Research and design of location tracking system used in underground mine based on WiFi technology. Proceedings of the International Forum on Computer Science-Technology and Applications, Chongqing, China.","DOI":"10.1109\/IFCSTA.2009.341"},{"key":"ref_4","first-page":"72","article-title":"Research on mine underground positioning technology based on wireless local area network","volume":"36","author":"Tian","year":"2008","journal-title":"Coal Sci. Technol."},{"key":"ref_5","unstructured":"Wang, L.N. (2015). Study on Underground Colliery Personnel Locating Technology Based on Wi-Fi. [Master\u2019s Thesis, Henan Polytechnic University]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Feng, C., Au, W.S.A., Valaee, S., and Tan, Z.H. (2010, January 15\u201319). Compressive sensing based positioning using RSS of WLAN access points. Proceedings of the IEEE Infocom, San Deigo, CA, USA.","DOI":"10.1109\/INFCOM.2010.5461981"},{"key":"ref_7","first-page":"69","article-title":"Coal mine underground localization method based on wireless access point selection","volume":"45","author":"Ji","year":"2019","journal-title":"Ind. Mine Autom."},{"key":"ref_8","first-page":"854","article-title":"Research on underground fingerprint localization algorithm based on Wi-Fi","volume":"25","author":"Liu","year":"2012","journal-title":"Chin. J. Sens. Actuators"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cypriani, M., Delisle, G., and Hakem, N. (2013, January 28\u201331). Wi-Fi-based positioning in underground mine tunnels. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Montbeliard Belfort, France.","DOI":"10.1109\/IPIN.2013.6817894"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.pmcj.2015.02.001","article-title":"Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing","volume":"18","author":"Zhuang","year":"2015","journal-title":"Pervasive Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alshami, I.H., Ahmad, N.A., Sahibuddin, S., and Firdaus, F. (2017). Adaptive indoor positioning model based on WLAN-fingerprint for dynamic and multi-floor environments. Sensors, 17.","DOI":"10.3390\/s17081789"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TMC.2014.2320254","article-title":"Smartphones based crowdsourcing for Indoor Localization","volume":"14","author":"Wu","year":"2015","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TSMCC.2010.2093516","article-title":"Smartphone-based collaborative and autonomous radio fingerprinting","volume":"42","author":"Kim","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_14","unstructured":"Sun, J., Feng, B., and Xu, W. (2004, January 19\u201323). Particle swarm optimization with particles having quantum behavior. Proceedings of the IEEE Congress on Evolutionary Computation, Portland, OR, USA."},{"key":"ref_15","first-page":"1681","article-title":"Estimating walking distance based on single accelerometer","volume":"44","author":"Yang","year":"2010","journal-title":"J. Zhejiang Univ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.3390\/s17061339","article-title":"A smartphone indoor localization algorithm based on WLAN location fingerprinting with feature extraction and clustering","volume":"17","author":"Luo","year":"2017","journal-title":"Sensors"},{"key":"ref_17","first-page":"1628","article-title":"Coal mine underground personnel localization algorithm based on LQI filter and joint parameters estimation","volume":"42","author":"Xing","year":"2017","journal-title":"J. China Coal Soc."},{"key":"ref_18","first-page":"94","article-title":"Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi","volume":"49","author":"Tian","year":"2017","journal-title":"J. Harbin Inst. Technol."},{"key":"ref_19","unstructured":"Chen, H.T., Chang, H.W., and Liu, T. (2005, January 20\u201325). Local discriminant embedding and its variants. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4527","DOI":"10.1002\/dac.2633","article-title":"Green wireless local area network received signal strength dimensionality reduction and indoor localization based on fingerprint algorithm","volume":"27","author":"Ma","year":"2014","journal-title":"Int. J. Commun. Syst."},{"key":"ref_21","unstructured":"Mika, S., Scholkopf, B., Smola, A., Muller, K.R., Scholz, M., and Ratsch, G. (December, January 30). Kernel PCA and de-noising in feature spaces. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_22","first-page":"430","article-title":"Indoor positioning algorithm based on KPCA and improved GBRT","volume":"32","author":"Li","year":"2019","journal-title":"Chin. J. Sens. Actuators"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:59:38Z","timestamp":1760173178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,21]]},"references-count":22,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20041182"],"URL":"https:\/\/doi.org\/10.3390\/s20041182","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,2,21]]}}}