TDOA/FDOA Geolocation with Adaptive Extended Kalman Filter

• Published in: Grid and Distributed Computing, Control and Automation Vol. 121; pp. 226 - 235
• Main Authors: Shao, Hongshuo, Kim, Dongkyun, You, Kwanho
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2010; Springer Berlin Heidelberg
• Series: Communications in Computer and Information Science
• Subjects: Adaptive extended Kalman filter; frequency difference of arrival; geolocation; target tracking; time difference of arrival
• ISBN: 3642176240; 9783642176241
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-642-17625-8_23

In this paper, we propose a moving target tracking algorithm using the measurement signals of time difference of arrival (TDOA) and the frequency difference of arrival (FDOA). 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 suggest 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.