Algorithms and Methods for Received Signal Strength Based Wireless Localization

In the era of wireless communications, the demand for localization and localizationbased services has been continuously growing, as increasingly smarter wireless devices have emerged to the market. Besides the already available satellite-based localization systems, such as the GPS and GLONASS, also other localization approaches are needed to complement the existing solutions. Finding different types of low-cost localization methods, especially for indoors, has become one of the most important research topics in recent years. One of the most used approaches in localization is based on Received Signal Strength (RSS) information. Specific fingerprints about RSS are collected and stored and positioning can be done through pattern or feature matching algorithms or through statistical inference. A great and immediate advantage of the RSS-based localization is its ability to exploit the already existing infrastructure of different communications networks without the need to install additional system hardware. Furthermore, due to the evident connection between the RSS level and the quality of a communications signal, the RSS is usually inherently included in the network measurements. This favors the availability of the RSS measurements in the current and future wireless communications systems. In this thesis, we study the suitability of RSS for localization in various communications systems including cellular networks, wireless local area networks, personal area networks, such as WiFi, Bluetooth and Radio Frequency Identification (RFID) tags. Based on substantial real-life measurement campaigns, we study different characteristics of RSS measurements and propose several Path Loss (PL) models to capture the essential behavior of the RSS levels in 2D outdoor and 3D indoor environments. By using the PL models, we show that it is possible to attain similar performance to fingerprinting with a database size of only 1-2% of the database size needed in fingerprinting. In addition, we study the effect of different error sources, such as database calibration errors, on the localization accuracy. Moreover, we propose a novel method for studying how coverage gaps in the fingerprint database affect the localization performance. Here, by using various interpolation and extrapolation methods, we improve the localization accuracy with imperfect fingerprint databases, such as those including substantial coverage gaps due to inaccessible parts of the buildings.

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