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Abstract
In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.
Highlights
Localization systems have provided new services to users in compliance with their positions
The methodology is developed in five phases: data collection, channel state information (CSI) amplitude processing, features fusion, fingerprint dataset, and classification
In order to evaluate the robustness of our proposal, different experiments were implemented by using distinct sizes of training sets
Summary
Localization systems have provided new services to users in compliance with their positions. Two decades ago, authors in [1] anticipated a rapid increase of applications involving user location awareness. Nowadays, new localization approaches continue to develop due to advancement of new technologies, sensors and communication standards. The Global Navigation Satellite System (GNSS) is widely used for positioning in outdoor environments. It does not operate in indoor environments, when there is non line-of-sight (NLOS) with satellites, such as inside dwellings and constructions [2]. Indoor scenarios demand approaches that use other technologies capable of sensing location and achieving high accuracy
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