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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Foy, W.H.: Position Location Solutions by Taylor Series Estimation. IEEE Trans. Aerospace Electronic Systems 12(2), 187–194 (1976)
Chan, Y.T.: A Simple and Efficient Estimator for Hyperbolic Location. IEEE Trans. Signal Processing 42(8), 1905–1915 (1994)
Ho, K.C., Chan, Y.T.: Geolocation of a Known Altitude Object from TDOA and FDOA Measurements. IEEE Trans. Aerospace Electronic Systems 33(3) (1997)
Li, W.C., Xiao, X.C.: Robust TDOA-based Location Method and Its Performance Analysis. Sci. China. Ser. F-Inf. Sci. 52(5), 876–882 (2009)
Takabayashi, Y., Matsuzaki, T., Kameda, H., Ito, M.: Target Tracking using TDOA/FDOA Measurements in the Distributed Sensor Network. The University Electro-Communications, Japan (2008)
Han, J.H., Kim, D.C., Sunwoo, M.H.: State-of-charge Estimation of Lead-acid Batteries Using an Adaptive Extended Kalman Filter. Journal of Power Sources 188, 606–612 (2009)
Kluge, S., Reif, K., Brokate, M.: Stochastic Stability of the Extended Kalman Filter With Intermittent Observations. IEEE Trans. Automatic Control 55(2), 514–518 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shao, H., Kim, D., You, K. (2010). TDOA/FDOA Geolocation with Adaptive Extended Kalman Filter. In: Kim, Th., Yau, S.S., Gervasi, O., Kang, BH., Stoica, A., Ślęzak, D. (eds) Grid and Distributed Computing, Control and Automation. GDC CA 2010 2010. Communications in Computer and Information Science, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17625-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-17625-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17624-1
Online ISBN: 978-3-642-17625-8
eBook Packages: Computer ScienceComputer Science (R0)