Chen C.-YLai A.I.-CRUEY-BEEI WU2021-09-022021-09-022021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105498784&doi=10.1109%2fWiSNeT51848.2021.9413791&partnerID=40&md5=d836fad664fcf7b82bc7bc128db001e2https://scholars.lib.ntu.edu.tw/handle/123456789/581088A Deep Neural Network (DNN)-based positioning algorithm with multi-detector architecture is proposed for high accuracy Wi-Fi fingerprint positioning. Our DNN-based approach fuses the scalability of classifiers and the precision of regressors. Moreover, a pre-processing pipeline of signal readings is added for characteristic grouping and intra-sample normalization to improve the robustness. The algorithm was trained and tested on a robotically surveyed indoor fingerprint dataset including 349 reference points and 191 effective Wi-Fi access points in a 30 m × 12m area. As a result, our algorithm is capable of positioning with 1.08 m mean distance error in a leave-10%-out test, performing nearly three times as good as the referenced WKNN baseline. ? 2021 IEEE.deep neural network; fingerprinting; indoor navigation; indoor positioning; Internet of Things; machine learningDeep neural networks; Palmprint recognition; Wi-Fi; Wireless local area networks (WLAN); Wireless sensor networks; Fingerprint dataset; High-accuracy; Mean distances; Multi-detectors; Positioning algorithms; Pre-processing; Reference points; Wi-fi access points; Neural networksconference paper10.1109/WiSNeT51848.2021.94137912-s2.0-85105498784