Fusion of RSS and inertial measurements for calibration-free indoor pedestrian tracking

RSS-based localization techniques are widely used in indoor environments to estimate the position of people or objects. Recent work proposes to combine this localization technique with a dead reckoning strategy (using inertial sensors on the person or object to be tracked) to improve the accuracy of the localization results and, at the same time, track the position of the user continuously. The RSS-based subsystem of most of the proposed hybrid systems rely on a time consuming calibration phase to build a radio map of the environment, which is used to find a correspondence between the RSS measurements and the position. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. In order to avoid these calibration and re-calibration needs, this paper proposes to use a nearly calibration-free RSS channel modeling localization approach and fuse it with the inertial measurements through a Kalman filter. The proposed strategy is tested through numerical simulation and experimental tests, showing accuracy gains with respect to other strategies. Furthermore, the proposed method has a low deployment cost, as very little calibration is needed prior to the operation.