Distance-based tuning of the EKF for indoor positioning in WSNs

This work proposes a filtering method for indoor positioning and tracking applications which combines position, speed and heading measurements with the aim of achieving more accurate position estimates both in the short and the long term. We combine all this data using the well-known Extended Kalman Filter (EKF). The particularity in our proposal is that the EKF is configured using the designed statistical covariance matrix tuning method (SCMT), which is based on the the statistical characteristics of the position measurements. Thanks to the SCMT, the EKF is able to efficiently cope with measurements that have different degrees of uncertainty and, therefore, it achieves high accuracy also in the long-term. The system has been validated in a real environment and the results show a reduction in the positioning error of more than 48% when compared to a regular EKF in the tested scenarios.