TDOA/FDOA Mobile Target Localization and Tracking with Adaptive Extended Kalman Filter

The geolocation system using TDOA (time difference of arrival) measurement does not have enough accuracy to estimate the position of target. If there is a relative motion between the target and the receivers, frequency-difference of arrival (FDOA) measurements should be combined with TDOAs to estimate the target position and velocity accurately. This paper proposes a target tracking algorithm using TDOA and FDOA measurements for a mobile target in a distributed sensor network. As the conventional target tracking using TDOA measurement is not accurate enough to estimate the target location, we use the TDOA and FDOA measurement signals together to estimate the location and the velocity of a target at discrete times. Although, the Kalman filter shows remarkable performance in calculation and location estimation, the estimation error can be large when the priori noise covariances are assumed with improper values. We proposed an adaptive extended Kalman filter (AEKF) to update the noise covariance at each measurement and estimation process. The simulation results show that the algorithm efficiently reduces the position error and it also greatly improves the accuracy of target tracking. It is proven that the AEKF algorithm deals with the nonlinear nature of the mobile target tracking problem successfully.