Detection and estimation of generalized chirps using time-frequency representations4

Antonia Papandreou; G. Faye Boudreaux-Bartels; Steven M. Kay

doi:10.1109/ACSSC.1994.471415

Detection and estimation of generalized chirps using time-frequency representations4

Antonia Papandreou, G. Faye Boudreaux-Bartels, Steven M. Kay

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

17 Scopus citations

Abstract

We propose techniques for the detection and parameter estimation of generalized chirps in the presence of noise. Generalized chirps are nonstationary signals characterized by group delays with specific dispersion law characteristics. Special cases of generalized chirps include linear chirps, and hyperbolic chirps that are Doppler-invariant signals. We optimally detect generalized chirps using generalized timeshift covariant quadratic time-frequency representations (QTFRs) such as hyperbolic QTFRs used for detecting hyperbolic chirps. We also propose the parameter estimation of generalized chirps, and specialize our simulation results to hyperbolic chirps. We combine phase unwrapping with linear regression of the phase data at high signal-to-noise ratios (SNRs) to produce very simple and unbiased estimators that attain the Cramer-Rao lower bounds on variance. Maximum likelihood estimation performs well at low SNRs, but at the cost of high computational complexity.

Original language	English (US)
Title of host publication	Conference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994
Publisher	IEEE Computer Society
Pages	50-54
Number of pages	5
ISBN (Electronic)	0818664053
DOIs	https://doi.org/10.1109/ACSSC.1994.471415
State	Published - 1994
Externally published	Yes
Event	28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994 - Pacific Grove, United States Duration: Oct 31 1994 → Nov 2 1994

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	1
ISSN (Print)	1058-6393

Conference

Conference	28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994
Country/Territory	United States
City	Pacific Grove
Period	10/31/94 → 11/2/94

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.1994.471415

Cite this

Papandreou, A., Boudreaux-Bartels, G. F., & Kay, S. M. (1994). Detection and estimation of generalized chirps using time-frequency representations4. In Conference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994 (pp. 50-54). Article 471415 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ACSSC.1994.471415

Detection and estimation of generalized chirps using time-frequency representations4. / Papandreou, Antonia; Boudreaux-Bartels, G. Faye; Kay, Steven M.
Conference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994. IEEE Computer Society, 1994. p. 50-54 471415 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 1).

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

Papandreou, A, Boudreaux-Bartels, GF & Kay, SM 1994, Detection and estimation of generalized chirps using time-frequency representations4. in Conference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994., 471415, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 1, IEEE Computer Society, pp. 50-54, 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994, Pacific Grove, United States, 10/31/94. https://doi.org/10.1109/ACSSC.1994.471415

Papandreou A, Boudreaux-Bartels GF, Kay SM. Detection and estimation of generalized chirps using time-frequency representations4. In Conference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994. IEEE Computer Society. 1994. p. 50-54. 471415. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.1994.471415

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abstract = "We propose techniques for the detection and parameter estimation of generalized chirps in the presence of noise. Generalized chirps are nonstationary signals characterized by group delays with specific dispersion law characteristics. Special cases of generalized chirps include linear chirps, and hyperbolic chirps that are Doppler-invariant signals. We optimally detect generalized chirps using generalized timeshift covariant quadratic time-frequency representations (QTFRs) such as hyperbolic QTFRs used for detecting hyperbolic chirps. We also propose the parameter estimation of generalized chirps, and specialize our simulation results to hyperbolic chirps. We combine phase unwrapping with linear regression of the phase data at high signal-to-noise ratios (SNRs) to produce very simple and unbiased estimators that attain the Cramer-Rao lower bounds on variance. Maximum likelihood estimation performs well at low SNRs, but at the cost of high computational complexity.",

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note = "Funding Information: 'Funding by ONR grant N00014-92-5-1499,and by AFOSR 93-1-0006. Publisher Copyright: {\textcopyright} 1995 IEEE.; 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994 ; Conference date: 31-10-1994 Through 02-11-1994",

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N2 - We propose techniques for the detection and parameter estimation of generalized chirps in the presence of noise. Generalized chirps are nonstationary signals characterized by group delays with specific dispersion law characteristics. Special cases of generalized chirps include linear chirps, and hyperbolic chirps that are Doppler-invariant signals. We optimally detect generalized chirps using generalized timeshift covariant quadratic time-frequency representations (QTFRs) such as hyperbolic QTFRs used for detecting hyperbolic chirps. We also propose the parameter estimation of generalized chirps, and specialize our simulation results to hyperbolic chirps. We combine phase unwrapping with linear regression of the phase data at high signal-to-noise ratios (SNRs) to produce very simple and unbiased estimators that attain the Cramer-Rao lower bounds on variance. Maximum likelihood estimation performs well at low SNRs, but at the cost of high computational complexity.

AB - We propose techniques for the detection and parameter estimation of generalized chirps in the presence of noise. Generalized chirps are nonstationary signals characterized by group delays with specific dispersion law characteristics. Special cases of generalized chirps include linear chirps, and hyperbolic chirps that are Doppler-invariant signals. We optimally detect generalized chirps using generalized timeshift covariant quadratic time-frequency representations (QTFRs) such as hyperbolic QTFRs used for detecting hyperbolic chirps. We also propose the parameter estimation of generalized chirps, and specialize our simulation results to hyperbolic chirps. We combine phase unwrapping with linear regression of the phase data at high signal-to-noise ratios (SNRs) to produce very simple and unbiased estimators that attain the Cramer-Rao lower bounds on variance. Maximum likelihood estimation performs well at low SNRs, but at the cost of high computational complexity.

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Detection and estimation of generalized chirps using time-frequency representations4

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