An Autonomous RSSI Filtering Method for Dealing with Human Movement Effects in an RSSI-Based Indoor Localization System
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ORIGINAL ARTICLE
An Autonomous RSSI Filtering Method for Dealing with Human Movement Effects in an RSSI‑Based Indoor Localization System Apidet Booranawong1 · Nattha Jindapetch1 · Hiroshi Saito2 Received: 23 April 2020 / Revised: 23 June 2020 / Accepted: 6 July 2020 © The Korean Institute of Electrical Engineers 2020
Abstract 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 effects. By this purpose, how RSSI data during human movements affect 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 filter 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 effects, the performances by both localization methods are not much different. However, with human movement effects, 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 filter, 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 filter as the commonly used filter, the localization error only decreases by 18.67% and 12.99%, respectively. Keywords RSSI · Indoor localization · Human movements · Min–max · Trilateration · Filter
1 Introduction Indoor target localization is an essential subject in the context of wireless networks [1, 2] because position information can be used in several applications, including human monitoring in buildings [1, 2], patient and equipment tracking in hospitals [3], rescue robot tracking [4], industrial robot guidance [5], position detection of products stored in * Apidet Booranawong [email protected] Nattha Jindapetch [email protected] Hiroshi Saito hiroshis@u‑aizu.ac.jp 1
Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90112, Thailand
Division of Computer Engineering, The University of Aizu, Aizu‑Wakamatsu 965‑8580, Japan
2
warehouses [2, 5], worker monitoring in construction sites [6–8], location-based services in smart spaces [40] (i.e., airports, shopping centers etc.), intrusion detection [41], automated control of devices [9–11], and so on. Hence, one of the challenges in wireless networks is the indoor target localization. To determine an un
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