Divided Difference Kalman Filter for indoor mobile localization

Knowledge of the positions of sensor nodes is crucial for numerous applications in wireless sensors network. In this paper, we propose to use the Divided Difference Kalman Filter (DDKF) as a solution for locating and tracking a mobile node. This approach is an alternative variant of the nonlinear Kalman filtering, already used in this type of applications. The advantage of this approach is that it does not require calculation of the Jacobian as for the Extended Kalman Filter (EKF) and it does not need to use several parameters, as for the Unscented Kalman Filter (UKF) whose accuracy is closely dependent on the good choice of such parameters. In this work, a comparative performance study of four localization methods is conducted, namely the DDKF, the EKF, the UKF and the Least Squares Kalman Filter (LS-KF), which is a method based on multilateration in the least squares sense, followed by a smoothing step, using Kalman filtering. This study reveals many advantages in favor of the DDKF which, when applied for indoor localization, provides up to 40% gain in terms of Root Mean Squares Errors (RMSE) in position estimation, as compared to the other considered methods and which has a location error that is less than 2 meters in 95% of the considered cases.