Separation of multiple time delays using new spectral estimation schemes

Mohammed A. Hasan; Mahmood R. Azimi-Sadjadi

doi:10.1109/78.678471

Separation of multiple time delays using new spectral estimation schemes

Mohammed A. Hasan, Mahmood R. Azimi-Sadjadi

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

The problem of estimating multiple time delays in presence of colored noise is considered in this paper. This problem is first converted to a high-resolution frequency estimation problem. Then, the sample lagged covariance matrices of the resulting signal are computed and studied in terms of their eigenstructure. These matrices are shown to be as effective in extracting bases for the signal and noise subspaces as the standard autocorrelation matrix, which is normally used in MUSIC and the pencil-based methods. Frequency estimators are then derived using these subspaces. The effectiveness of the method is demonstrated on two examples: a standard frequency estimation problem in presence of colored noise and a real-world problem that involves separation of multiple specular components from the acoustic backscattered from an underwater target.

Original language	English (US)
Pages (from-to)	1580-1590
Number of pages	11
Journal	IEEE Transactions on Signal Processing
Volume	46
Issue number	6
DOIs	https://doi.org/10.1109/78.678471
State	Published - 1998

Bibliographical note

Funding Information:
Manuscript received August 6, 1996; revised December 5, 1997. This work was supported by the Office of Naval Research (ONR 321TS). The Technical Agent was Coastal Systems Station, Panama City, FL. The associate editor coordinating the review of this paper and approving it for publication was Dr. Eric Moulines.

Keywords

Data decimation
Spectral estimation
Underwater acoustics

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10.1109/78.678471

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abstract = "The problem of estimating multiple time delays in presence of colored noise is considered in this paper. This problem is first converted to a high-resolution frequency estimation problem. Then, the sample lagged covariance matrices of the resulting signal are computed and studied in terms of their eigenstructure. These matrices are shown to be as effective in extracting bases for the signal and noise subspaces as the standard autocorrelation matrix, which is normally used in MUSIC and the pencil-based methods. Frequency estimators are then derived using these subspaces. The effectiveness of the method is demonstrated on two examples: a standard frequency estimation problem in presence of colored noise and a real-world problem that involves separation of multiple specular components from the acoustic backscattered from an underwater target.",

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AU - Azimi-Sadjadi, Mahmood R.

N1 - Funding Information: Manuscript received August 6, 1996; revised December 5, 1997. This work was supported by the Office of Naval Research (ONR 321TS). The Technical Agent was Coastal Systems Station, Panama City, FL. The associate editor coordinating the review of this paper and approving it for publication was Dr. Eric Moulines.

PY - 1998

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N2 - The problem of estimating multiple time delays in presence of colored noise is considered in this paper. This problem is first converted to a high-resolution frequency estimation problem. Then, the sample lagged covariance matrices of the resulting signal are computed and studied in terms of their eigenstructure. These matrices are shown to be as effective in extracting bases for the signal and noise subspaces as the standard autocorrelation matrix, which is normally used in MUSIC and the pencil-based methods. Frequency estimators are then derived using these subspaces. The effectiveness of the method is demonstrated on two examples: a standard frequency estimation problem in presence of colored noise and a real-world problem that involves separation of multiple specular components from the acoustic backscattered from an underwater target.

AB - The problem of estimating multiple time delays in presence of colored noise is considered in this paper. This problem is first converted to a high-resolution frequency estimation problem. Then, the sample lagged covariance matrices of the resulting signal are computed and studied in terms of their eigenstructure. These matrices are shown to be as effective in extracting bases for the signal and noise subspaces as the standard autocorrelation matrix, which is normally used in MUSIC and the pencil-based methods. Frequency estimators are then derived using these subspaces. The effectiveness of the method is demonstrated on two examples: a standard frequency estimation problem in presence of colored noise and a real-world problem that involves separation of multiple specular components from the acoustic backscattered from an underwater target.

KW - Data decimation

KW - Spectral estimation

KW - Underwater acoustics

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Separation of multiple time delays using new spectral estimation schemes

Abstract

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