Change Detection of RSSI Fingerprint Pattern for Indoor Positioning System

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 pat...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2020-03, Vol.20 (5), p.2608-2615
Main Author: Yoo, Jaehyun
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2951712