Abstract
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.
Highlights
W i-Fi RSSI based positioning [1]–[3] is one of standard approaches for indoor localization
The main contribution of this paper is to develop a new change detection algorithm, which can recognize a rapid variation of the RSSI pattern and can decide when to update based on the analysis of the variation impact on the positioning accuracy
100 number of survey points (SP) are used for the change detection evaluation
Summary
W i-Fi RSSI based positioning [1]–[3] is one of standard approaches for indoor localization. YOO: CHANGE DETECTION OF RSSI FINGERPRINT PATTERN FOR INDOOR POSITIONING SYSTEM the fusion with IMU (Inertial Measurement Unit) [23], and the probabilistic clustering [24] utilize the crowdsourcing for the same purpose. All of those methods focus on developing the positioning methods by leveraging some helpful models and crowdsourcing They cannot help avoiding the degradation of accuracy by sudden and radical environmental changes such as the relocation of APs. This paper proposes a different aspect to prevent a loss of accuracy by suggesting a change detection algorithm to alarm to update the old database. The change detection algorithm based on information theory is suggested to derive a similarity between the existing database and a test set.
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