Deconvolution of sensor array signals using time-scaling

Tayfun Akgül; Amro El-Jaroudi; Marwan Simaan

doi:10.1109/ICASSP.1994.389645

Deconvolution of sensor array signals using time-scaling

Tayfun Akgül, Amro El-Jaroudi, Marwan Simaan

Univ. of Pittsburgh

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

We present a novel solution to a deconvolution problem in which the data to be deconvolved consist of sensor array measurements. Our model assumes that the data are generated as a convolution of an unknown wavelet with various time-scaled versions of an unknown reflectivity sequence. This type of data, occurs in many array signal processing applications, including radar, sonar and seismic processing. Our approach relies on exploiting the redundancy in the measurements due to time-scaling, and does not require knowledge of the wavelet or reflectivity sequence. Furthermore, we make no assumptions on the statistical properties of these signals. We formulate and solve the deconvolution problem as a quadratic minimization subject to a quadratic constraint. We also illustrate the performance of the technique using simulation examples.

Original language	English
Article number	389645
Pages (from-to)	II365-II368
Journal	Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume	2
DOIs	https://doi.org/10.1109/ICASSP.1994.389645
Publication status	Published - 1994
Externally published	Yes
Event	Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust Duration: 19 Apr 1994 → 22 Apr 1994

Bibliographical note

Publisher Copyright:
© 1994 IEEE.

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10.1109/ICASSP.1994.389645

Cite this

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title = "Deconvolution of sensor array signals using time-scaling",

abstract = "We present a novel solution to a deconvolution problem in which the data to be deconvolved consist of sensor array measurements. Our model assumes that the data are generated as a convolution of an unknown wavelet with various time-scaled versions of an unknown reflectivity sequence. This type of data, occurs in many array signal processing applications, including radar, sonar and seismic processing. Our approach relies on exploiting the redundancy in the measurements due to time-scaling, and does not require knowledge of the wavelet or reflectivity sequence. Furthermore, we make no assumptions on the statistical properties of these signals. We formulate and solve the deconvolution problem as a quadratic minimization subject to a quadratic constraint. We also illustrate the performance of the technique using simulation examples.",

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AU - El-Jaroudi, Amro

AU - Simaan, Marwan

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AB - We present a novel solution to a deconvolution problem in which the data to be deconvolved consist of sensor array measurements. Our model assumes that the data are generated as a convolution of an unknown wavelet with various time-scaled versions of an unknown reflectivity sequence. This type of data, occurs in many array signal processing applications, including radar, sonar and seismic processing. Our approach relies on exploiting the redundancy in the measurements due to time-scaling, and does not require knowledge of the wavelet or reflectivity sequence. Furthermore, we make no assumptions on the statistical properties of these signals. We formulate and solve the deconvolution problem as a quadratic minimization subject to a quadratic constraint. We also illustrate the performance of the technique using simulation examples.

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M3 - Conference article

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SP - II365-II368

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

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T2 - Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6)

Y2 - 19 April 1994 through 22 April 1994

ER -

Deconvolution of sensor array signals using time-scaling

Abstract

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