Paper Info
Title
TDOA/FDOA Geolocation with Adaptive Extended Kalman Filter.
Abstract
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.
Year
DOI
Venue
2010
10.1007/978-3-642-17625-8_23
Communications in Computer and Information Science
Keywords
Field
DocType
Adaptive extended Kalman filter,time difference of arrival,frequency difference of arrival,target tracking,geolocation
Extended Kalman filter,Control theory,Geolocation,Position error,Kalman filter,FDOA,Multilateration,Invariant extended Kalman filter,Geography,Covariance
Conference
Volume
ISSN
Citations 
121
1865-0929
2
PageRank 
References 
Authors
0.49
3
3
Name
Order
Citations
PageRank
Hongshuo Shao120.49
Dongkyun Kim2942110.31
Kwan-Ho You3114.20