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Chirp Parameter Estimation Using Rank Reduction
KTH, Superseded Departments, Signals, Sensors and Systems.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
1998 (English)In: Proceedings of the 32nd Asilomar Conference on Signals, Systems and Computers, IEEE , 1998, 1443-1446 p.Conference paper (Refereed)
Abstract [en]

This paper considers the problem of estimating the bandwidth and the center frequency of a linear chirp signal from discrete-time noisy observations. The non-stationarity property of chirp signals implies that the signal has high rank and reduces the applicability of subspace based algorithms significantly. However, the special structure of the sample covariance matrix invites to use regular frequency estimation algorithms. We show how subspace type algorithms may be modified to provide accurate signal parameter estimates for linear chirp signals. The root-MUSIC algorithm is used as an example. Simulations compare the algorithm with a rank reduction method proposed by DiMonte and Arun (1990).

Place, publisher, year, edition, pages
IEEE , 1998. 1443-1446 p.
Keyword [en]
Bandwidth, Biomedical signal processing, Chirp, Covariance matrix, Frequency estimation, Matrix decomposition, Maximum likelihood estimation, Parameter estimation, Sensor systems, Signal processing algorithms
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-92547DOI: 10.1109/ACSSC.1998.751565OAI: diva2:513567
Thirty-Second Asilomar Conference on Signals, Systems & Computers, 1-4 Nov. 1998. Pacific Grove, CA, USA
NR 20140805Available from: 2012-04-02 Created: 2012-04-02 Last updated: 2012-04-02Bibliographically approved

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