An Autonomous RSSI Filtering Method for Dealing with Human Movement Eff ects in an RSSI-Based Indoor Localization System

• Published in: Journal of electrical engineering & technology pp. 2299 - 2314
• Main Authors: Apidet Booranawong, Nattha Jindapetch, Hiroshi Saito
• Format: Journal Article
• Language: English
• Published: 대한전기학회 01.09.2020
• Subjects: 전기공학
• ISSN: 1975-0102; 2093-7423

In this paper, an experimental evaluation of received signal strength indicator (RSSI-based) localization methods in an indoor wireless network is studied. The major contributions of this work are twofold. First, the well-known and widely used min–max and trilateration methods are tested in the cases of without and with human movement eff ects. By this purpose, how RSSI data during human movements aff ect the accuracy of such methods and which method shows the best position estimation result, have been investigated. Second, we also design and develop a new RSSI fi lter to automatically reduce RSSI variation and the position estimation error caused by human movements. Experiments are carried out in a parking building. An LPC2103F microcontroller interfaced with a CC2500 RF transceiver as a low-cost, low power, 2.4 GHz radio module is used as a wireless node. Results demonstrate that without human movement eff ects, the performances by both localization methods are not much diff erent. However, with human movement eff ects, the min–max method shows better accuracy than the trilateration method in handling the RSSI variation problem. The results also indicate that by applying the proposed RSSI fi lter, it can directly cope with the RSSI variation problem caused by humans. The localization error decreases by 69.87% for the case of the min–max method, and it decreases by 72.74% for the case of the trilateration method (the best case). Compared with the case of employing the moving average fi lter as the commonly used fi lter, the localization error only decreases by 18.67% and 12.99%, respectively. KCI Citation Count: 0