Real time calibration for RSS indoor positioning systems

Due to the random characteristics of the indoor propagation channel, received signal strength-based localization systems usually need to be manually calibrated once and again to guarantee their best performance. Calibration processes are costly in terms of time and resources, so they should be eliminated or reduced to a minimum. In this direction, this paper presents an optimization algorithm to automatically calibrate a propagation channel model by using a Least Mean Squares technique: RSS samples gathered in a number of reference points (with known positions) are used by a LMS algorithm to calculate those values for the channel model´s constants that minimize the error computed by a hyperbolic triangulation positioning algorithm. Preliminary results on simulated and real data show that the localization error in distance is effectively reduced after a number of training samples. The LMS algorithm´s simplicity and its low computational and memory costs make it adequate to be used in real systems.