https://scholars.lib.ntu.edu.tw/handle/123456789/581088
標題: | Multi-Detector Deep Neural Network for High Accuracy Wi-Fi Fingerprint Positioning | 作者: | Chen C.-Y Lai A.I.-C RUEY-BEEI WU |
關鍵字: | deep neural network; fingerprinting; indoor navigation; indoor positioning; Internet of Things; machine learning | 公開日期: | 2021 | 起(迄)頁: | 37-39 | 來源出版物: | 2021 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2021 | 摘要: | A 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105498784&doi=10.1109%2fWiSNeT51848.2021.9413791&partnerID=40&md5=d836fad664fcf7b82bc7bc128db001e2 https://scholars.lib.ntu.edu.tw/handle/123456789/581088 |
DOI: | 10.1109/WiSNeT51848.2021.9413791 | SDG/關鍵字: | Deep 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 networks |
顯示於: | 電機工程學系 |
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