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Subspace-based estimation of time of arrival and Doppler shift for a signal of known waveform

Published online by Cambridge University Press:  15 May 2009

V.V. Latyshev
V.V. Latyshev*
Affiliation:
Moscow Aviation Institute (State Technical University), Moscow, Russia
*
Corresponding author: V.V. Latyshev Email: [email protected]
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Abstract

The subspace-based technique is used for the estimation of the time of arrival and Doppler shift of a signal of known waveform. The tool to find required subspaces is a special orthogonal decomposition of received data. It allows one to concentrate Fisher information on the desired parameter in just a few of the first terms of the decomposition. This approach offers a low-dimensional vector of sufficient statistics. It leads to computationally efficient Bayesian estimation. Besides, it results in expansion of the signal-to-noise ratio (SNR) range for effective maximum likelihood (ML) estimation. Finally, we can obtain independent time arrival and Doppler shift estimations based on generalized eigenvectors.

Keywords

Time of arrivalDoppler shift estimatessubspace-based technique
Type
Original Article
Information
International Journal of Microwave and Wireless Technologies , Volume 1 , Special Issue 3: Surveillance systems for air and airport traffic control , June 2009 , pp. 209 - 214
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2009

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References

REFERENCES

[1]Jolliffe, I.T.: Principal Component Analysis, Springer-Verlag, 1986.CrossRefGoogle Scholar
[2]Van Trees, H.L.: Detection, Estimation, and Modulation Theory, Part 1, Wiley, New York, 2001, ch. 2.CrossRefGoogle Scholar
[3]Latyshev, V.: The optimum decrease of the dimensionality of data for estimating signal parameters. Sov. J. Commun. Technol. Electron., 33 (1988), 172–175.Google Scholar
[4]Fukunaga, K.: Introduction to Statistical Pattern Recognition, San Diego, Academic Press, 1990, ch. 2.Google Scholar
[5]Latyshev, V.: Informational analysis of statistics in time delay estimation problem, in Proc. IRS2007, Cologne, Germany, pp. 169–173.Google Scholar
[6]Latyshev, V.: Subspace-based estimation of time of arrival and Doppler shift for a signal of known waveform, in Proc.ESAV'08, Capri, Italy, pp. 94–99.Google Scholar