Adaptive Extended Kalman Filter Based Geolocation Using TDOA/FDOA

We propose a moving target tracking algorithm using the measurement signals of time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) in this paper. The geolocation system using TDOA measurement does not have enough accuracy to estimate the position of target. We use both TDOA and FDOA measurement signals to estimate the target location and target’s velocity at discrete times. The Kalman filter performs remarkably in calculation and location estimation. However, the estimation error can be large when the priori noise covariances are assumed with improper values. Therefore, we offer an adaptive extended Kalman filter (AEKF) to update the noise covariance at every measurement and estimation process to find proper noise covariance at each steps. The simulation results show our proposed algorithm reduces the position error effectively and improves the accuracy of target tracking greatly.