{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T12:03:33Z","timestamp":1722686613366},"reference-count":15,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2013,9,3]],"date-time":"2013-09-03T00:00:00Z","timestamp":1378166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"Ubiquitous positioning provides continuous positional information in both indoor and outdoor environments for a wide spectrum of location based service (LBS) applications. With the rapid development of the low-cost and high speed data communication, Wi-Fi networks in many metropolitan cities, strength of signals propagated from the Wi-Fi access points (APs) namely received signal strength (RSS) have been cleverly adopted for indoor positioning. In this paper, a Wi-Fi positioning algorithm based on neural network modeling of Wi-Fi signal patterns is proposed. This algorithm is based on the correlation between the initial parameter setting for neural network training and output of the mean square error to obtain better modeling of the nonlinear highly complex Wi-Fi signal power propagation surface. The test results show that this neural network based data processing algorithm can significantly improve the neural network training surface to achieve the highest possible accuracy of the Wi-Fi fingerprinting positioning method.<\/jats:p>","DOI":"10.3390\/ijgi2030854","type":"journal-article","created":{"date-parts":[[2013,9,3]],"date-time":"2013-09-03T16:43:48Z","timestamp":1378226628000},"page":"854-868","source":"Crossref","is-referenced-by-count":23,"title":["An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning"],"prefix":"10.3390","volume":"2","author":[{"given":"Esmond","family":"Mok","sequence":"first","affiliation":[{"name":"Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"}]},{"given":"Bernard","family":"Cheung","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"},{"name":"CIRRELT & Department of Mathematics and Industrial Engineering, Ecole Polytechnique de Montreal, P.O. Box 6079, Station Centre-Ville, Montr\u00e9al, PQ H3C 3A7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2013,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/10225706.2013.789971","article-title":"A study on the use of Wi-Fi positioning technology for wayfinding in large shopping centers","volume":"30","author":"Mok","year":"2013","journal-title":"Asian Geogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1080\/17489720701781905","article-title":"Location determination using WiFi fingerprinting versus WiFi trilateration","volume":"1","author":"Mok","year":"2007","journal-title":"J. Location Based Serv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1109\/TWC.2011.081611.102209","article-title":"Weighted centroid algorithm for estimating primary user location: Theoretical analysis and distributed implementation","volume":"10","author":"Wang","year":"2011","journal-title":"Trans. Wirel. Commun."},{"key":"ref_4","unstructured":"Theodore, S.R. (2002). Wireless Communications, Principles and Practice, Prentice-Hall, Inc.. [2nd ed.]."},{"key":"ref_5","unstructured":"Cho, Y., Ji, M., Lee, Y., Kim, J., and Park, S. (2012, January 13\u201315). Improved Wi-Fi AP Positioning Estimation Using Regression Based Approach. Proceedings of the 3rd Proceedings of International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, Australia."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nurminen, H., Talvitie, J., Ali-Loytty, S., Muller, P., Lohan, E., Piche, R., and Renfors, M. (2012, January 13\u201315). 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Proceedings of 5th IEEE International Symposium on Wireless Pervasive Computing, Modena, Italy.","DOI":"10.1109\/ISWPC.2010.5483731"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Koski, L., PER\u00c4L\u00c4, T., and PICH\u00c9, R. (2010, January 15\u201317). Indoor Positioning Using WLAN Coverage Area Estimates. Proceedings of the 1st International Conference on Indoor Positioning and Indoor Navigation (IPIN), ETH Zurich, Zurich, Switzerland.","DOI":"10.1109\/IPIN.2010.5648284"},{"key":"ref_10","unstructured":"Liu, H.H., and Yang, Y.N. (2011, January 21\u201324). WiFi-Based Indoor Positioning for Multi-Floor Environment. Proceedings of the IEEE TENCON 2011, Bali, Indonesia."},{"key":"ref_11","unstructured":"Shi, J., and Shin, Y. (2013, January 23\u201328). A Low-Complexity Floor Determination Method Based on WiFi for Multi-Floor Buildings. 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