empirical evaluation of signal-strength fingerprint positioning in wireless lans
This paper proposes a novel localization technique based on a multivariate Gaussian modeling of the signal strength measurements collected from several access points (APs) at different locations. It considers a discretized grid-like form of the environment and computes a signature at each cell of the grid. At run time the system compares the signature at the unknown position with the signature of each cell using the Kullback-Leibler Divergence estimation (KLD) between their corresponding probability densities. The paper evaluates the performance of the proposed technique and compares it with other statistical fingerprint-based localization systems. The performance analysis studies were conducted at the premises of a research laboratory and an aquarium under various conditions. Furthermore, the paper evaluates the impact of the number of APs and the size of the measurement datasets.
multivariate gaussian model kullback-leibler divergence rssi measurements percentiles