Change Detection of RSSI Fingerprint Pattern for Indoor Positioning System

• Published in: IEEE sensors journal Vol. 20; no. 5; pp. 2608 - 2615
• Main Author: Yoo, Jaehyun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Change detection; Detection algorithms; Eigenvalues and eigenfunctions; Feature extraction; Fingerprints; Gaussian process; indoor localization; KL divergence; Localization; Machine learning; Multistory buildings; Principal component analysis; Principal components analysis; Probabilistic logic; Relocation; Signal strength; Test procedures; Test sets; Wi-Fi RSSI fingerprint; Wireless fidelity
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2951712

A set of Wi-Fi RSSI (Received Signal Strength Indicator) measurements is one of basic sensory observation available for indoor localization. One major drawback of the RSSI based localization is maintenance of the RSSI fingerprint database, which should be periodically updated against measurement pattern changes caused by relocation, removal and malfunction of Wi-Fi APs (access points). To address this problem, a new change detection method is proposed in this paper. First, by machine learning techniques, the RSSI database is reconstructed to a probabilistic feature database by the implementations of PCA (Principal Component Analysis) and GP (Gaussian Process). Then, KL (Kullback-Leibler) divergence is used as a metric to measure the similarity of the existing database and a newly arrived test sets. The proposed method is evaluated by a real experiment at a multi-storey building. For experimental study, different cases that provoke changes of RSSI patterns are considered, and the positioning accuracy is examined by the k-NN (Nearest Neighbor) method. From the experimental results, it is found that the bigger the RSSI pattern changes, the large the KL divergences become. Also, when a modified change detection algorithm as the benchmark, which does not implement the PCA feature extraction, is compared, the proposed algorithm yields accurate and fast computing performances. In addition, the required number of survey points is empirically found associated with the threshold value to trigger the detection alarm.