Localization for indoor wireless networks using minimum intersection areas of iso-RSS lines

We present a new method for localization in wireless networks based on the measurement of the received signal strength (RSS) from multiple access points in an indoor setting. Our approach starts by learning a smoothed RSS surface for each of the access points from a set of training data. We then extract the isometric lines of the RSS surface (iso-RSS lines) for each access point. To perform the localization, the user measures the incoming signal strength of each access point and identifies the corresponding iso-RSS line. Ideally, the exact location would be the common intersection point of these lines. However, noise and measurement imperfections make the lines not intersect in a single point. We search for the smallest rectangular area which is intersected by all the selected iso-RSS lines. This rectangle is interpreted as the most likely location of the user; the area of the rectangle is an estimate of the localization error. We describe an efficient method for finding the minimal intersection area based on recursive grid partitioning. Through experiments over multiple indoor data sets we show that our approach provides a better localization accuracy than existing localization algorithms.

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