FCLoc: A Novel Indoor Wi-Fi Fingerprints Localization Approach to Enhance Robustness and Positioning Accuracy

For wireless local area network (WLAN) fingerprint-based indoor localization approach, wireless fidelity (Wi-Fi) received signal strength (RSS) is widely focused because it is easy to obtain and inexpensive. However, RSS measurements are vulnerable to environmental fluctuations and there is a lot of noise in the measured signal, which seriously affects the stability of RSS-based positioning systems. Meanwhile, the unreasonable value of
${K}$
will also cause large positioning errors in the fingerprint-based
${K}$
-nearest neighbors (KNN) and weighted KNN algorithms. Therefore, a novel flexible reference points and confidence access points localization (FCLoc) method is proposed in this article, to enhance the robustness of the system while improving positioning accuracy. First, considering that there is outlier noise in the sampling data, this article proposes to filter out noise by the robust principal. Then, the more reliable access points (APs) are selected according to the stability of the online data, as a more stable AP should have more weight to reduce the impact of the weak APs. When at least three APs are available, the system can work normally. Finally, the Jenks natural breaks algorithm (JNBA) in statistics is introduced to realize a flexible reference point (RP) selection, that is, a more reasonable value of
${K}$
is adaptively selected for position estimation to improve the positioning accuracy. The proposed method only applies to scenarios where at least three APs are available. Simulation and the real-world experimental results show that the proposed algorithm is superior to those existing algorithms in terms of robustness and positioning accuracy.