@inproceedings{40aa8924620142a3bf4957b2fa45727a,
title = "WiFi Location Fingerprint Indoor Positioning Method Based on WKNN",
abstract = "Wireless Fidelity (WiFi) based fingerprint indoor positioning can directly utilize existing commercial WiFi devices, the deployment cost is low, easy to expand, and has good non-invasiveness, which has gradually become a hot spot of indoor positioning technology researchers. The positioning method of this paper combines the Received Signal Strength (RSS) ranging method and the location fingerprint method. On this basis, the Weighted K-Nearest Neighbor (WKNN) matching algorithm is used to match the fingerprint data in the location fingerprint database. In view of the strong problem of indoor wireless signal oscillation, this paper uses Kalman filtering method to process the signal strength value. The simulation is carried out under the MATLAB platform. The results show that the proposed method is superior to the existing K-Nearest Neighbors (KNN) and Nearest Neighbors (NN) algorithms in the same simulation environment, which significantly improves the indoor positioning accuracy.",
keywords = "Kalman filter, RSS, WKNN, WiFi fingerprint location",
author = "Xinxin Wang and Danyang Qin and Lin Ma",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 ; Conference date: 20-07-2019 Through 22-07-2019",
year = "2020",
doi = "10.1007/978-981-13-9409-6\_191",
language = "英语",
isbn = "9789811394089",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "1589--1596",
editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Min Jia and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019",
address = "德国",
}