{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:32:33Z","timestamp":1762252353433,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,19]],"date-time":"2016-03-19T00:00:00Z","timestamp":1458345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["71031007","61273198"],"award-info":[{"award-number":["71031007","61273198"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Indoor positioning based on existing Wi-Fi fingerprints is becoming more and more common. Unfortunately, the Wi-Fi fingerprint is susceptible to multiple path interferences, signal attenuation, and environmental changes, which leads to low accuracy. Meanwhile, with the recent advances in charge-coupled device (CCD) technologies and the processing speed of smartphones, indoor positioning using the optical camera on a smartphone has become an attractive research topic; however, the major challenge is its high computational complexity; as a result, real-time positioning cannot be achieved. In this paper we introduce a crowd-sourcing indoor localization algorithm via an optical camera and orientation sensor on a smartphone to address these issues. First, we use Wi-Fi fingerprint based on the K Weighted Nearest Neighbor (KWNN) algorithm to make a coarse estimation. Second, we adopt a mean-weighted exponent algorithm to fuse optical image features and orientation sensor data as well as KWNN in the smartphone to refine the result. Furthermore, a crowd-sourcing approach is utilized to update and supplement the positioning database. We perform several experiments comparing our approach with other positioning algorithms on a common smartphone to evaluate the performance of the proposed sensor-calibrated algorithm, and the results demonstrate that the proposed algorithm could significantly improve accuracy, stability, and applicability of positioning.<\/jats:p>","DOI":"10.3390\/s16030410","type":"journal-article","created":{"date-parts":[[2016,3,21]],"date-time":"2016-03-21T11:14:24Z","timestamp":1458558864000},"page":"410","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Crowd-Sourcing Indoor Localization Algorithm via Optical Camera on a Smartphone Assisted by Wi-Fi Fingerprint RSSI"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2193-1344","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Qun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Qiang","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Hongtao","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,19]]},"reference":[{"key":"ref_1","unstructured":"Monnerat, M., Couty, R., Vincent, N., Huez, O., and Chatre, E. 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