Deconvolution-based indoor localization with WLAN signals and unknown access point locations

In this paper, the problem of Received Signal Strength (RSS)-based WLAN positioning is newly formulated as a deconvolution problem and three deconvolution methods (namely Least Squares, Weighted Least Squares and Minimum Mean Square Error) are investigated with several RSS path loss models. The deconvolution approaches are compared with the fingerprinting approach in terms of performance and complexity. The main advantage of the deconvolution-based approaches versus the fingerprinting methods is the significant reduction in the size of the training database that need to be stored at the server side (and transferred to the mobile device) for the WLAN-based positioning. We will show that the deconvolution based estimation can decrease of the order of ten times the size of the training database, while still being able to achieve comparable root mean square errors in the distance estimation.