Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties

Youngchul Sung; Lang Tong; H. Vincent Poor

doi:10.1109/ISIT.2005.1523608

Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties

Youngchul Sung, Lang Tong, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

The performance of Neyman-Pearson detection of correlated stochastic signals using noisy observations is investigated via the error exponent for the miss probability with a fixed level. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) > 1 the error exponent decreases monotonically as the correlation becomes stronger, whereas for SNR < 1 there is an optimal correlation that maximizes the error exponent for a given SNR.

Original language	English (US)
Title of host publication	Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05
Pages	1568-1572
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2005.1523608
State	Published - 2005
Externally published	Yes
Event	2005 IEEE International Symposium on Information Theory, ISIT 05 - Adelaide, Australia Duration: Sep 4 2005 → Sep 9 2005

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2005
ISSN (Print)	2157-8099

Other

Other	2005 IEEE International Symposium on Information Theory, ISIT 05
Country/Territory	Australia
City	Adelaide
Period	9/4/05 → 9/9/05

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2005.1523608

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Sung, Y., Tong, L., & Poor, H. V. (2005). Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties. In Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05 (pp. 1568-1572). Article 1523608 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2005). https://doi.org/10.1109/ISIT.2005.1523608

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title = "Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties",

abstract = "The performance of Neyman-Pearson detection of correlated stochastic signals using noisy observations is investigated via the error exponent for the miss probability with a fixed level. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) > 1 the error exponent decreases monotonically as the correlation becomes stronger, whereas for SNR < 1 there is an optimal correlation that maximizes the error exponent for a given SNR.",

author = "Youngchul Sung and Lang Tong and Poor, {H. Vincent}",

year = "2005",

doi = "10.1109/ISIT.2005.1523608",

language = "English (US)",

isbn = "0780391519",

series = "IEEE International Symposium on Information Theory - Proceedings",

pages = "1568--1572",

booktitle = "Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05",

note = "2005 IEEE International Symposium on Information Theory, ISIT 05 ; Conference date: 04-09-2005 Through 09-09-2005",

}

Sung, Y, Tong, L & Poor, HV 2005, Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties. in Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05., 1523608, IEEE International Symposium on Information Theory - Proceedings, vol. 2005, pp. 1568-1572, 2005 IEEE International Symposium on Information Theory, ISIT 05, Adelaide, Australia, 9/4/05. https://doi.org/10.1109/ISIT.2005.1523608

Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties. / Sung, Youngchul; Tong, Lang; Poor, H. Vincent.
Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05. 2005. p. 1568-1572 1523608 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - The performance of Neyman-Pearson detection of correlated stochastic signals using noisy observations is investigated via the error exponent for the miss probability with a fixed level. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) > 1 the error exponent decreases monotonically as the correlation becomes stronger, whereas for SNR < 1 there is an optimal correlation that maximizes the error exponent for a given SNR.

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Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties

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