Abstract

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

Highlights

  • Indoor localization is critical for a variety of location-aware services, including health care monitoring, tracking, mobile resource management, and fraud detection

  • machine learning (ML) tools, the performance and computing cost of the hidden Markov model (HMM) are dependent on the feature dimension since a large quantity of received signal strength indicator (RSSI) measurements are required for the learning process

  • The results show that the HMM-dynamic mode decomposition (DMD) algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms

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Summary

Introduction

Indoor localization is critical for a variety of location-aware services, including health care monitoring, tracking, mobile resource management, and fraud detection. WiFi has received growing interest among other indoor localization systems, such as radio-frequency identification (RFID), Bluetooth, ultrasound, etc. This is due to the widespread implementation of wireless local area networks (WLANs) in indoor environments, and the ubiquity of mobile devices that are compatible with WiFi systems, providing a relatively low-cost method of user monitoring in the indoor environment. The WiFi fingerprinting localization technique is commonly implemented for location approximation since it does not require historical information of wireless access point (AP) distribution and does not necessitate computing a receiver’s angle. The technique utilizes received signal strength indicator (RSSI) measurements of accessible APs to predict the position of a user device in areas where the Global Positioning System (GPS) is insufficient, such as the indoor environment. Multidimensional vectors of RSSI values (fingerprints), are collected from various APs in a range and linked to identified locations

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