期刊名称：International Journal of Computers and Communications
印刷版ISSN：2074-1294
出版年度：2017
卷号：11
页码：11-16
出版社：University Press
摘要：This paper considers recursive tracking of one mobile target using a sequence of time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement pairs obtained by distributed sensor network in a three dimension situation. 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 TDOA/FDOA measurement and estimation process. Although many methods derive the estimates of position and velocity with iterative numerical techniques, the proposed AEKF method can be a good alternative to update the noise covariance guess under conditions of measurement error. 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.
