Signal Selection for Robust Matched Filtering

Sergio Verdú; H. Vincent Poor

doi:10.1109/TCOM.1983.1095861

Signal Selection for Robust Matched Filtering

Sergio Verdú, H. Vincent Poor

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

11 Scopus citations

Abstract

The optimum signal selection problem under a power constraint for minimax robust discrete-time finite-length matched filtering is studied. The classical solution, which is to choose the signal as a minimum-eigenvalue eigenvector of the noise covariance matrix, is generalized to cases in which the transmitted signal is subject to channel distortion modeled by mean-square, maximum-absolute, and mean-absolute distortion uncertainty classes.

Original language	English (US)
Pages (from-to)	667-670
Number of pages	4
Journal	IEEE Transactions on Communications
Volume	31
Issue number	5
DOIs	https://doi.org/10.1109/TCOM.1983.1095861
State	Published - May 1983

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/TCOM.1983.1095861

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title = "Signal Selection for Robust Matched Filtering",

abstract = "The optimum signal selection problem under a power constraint for minimax robust discrete-time finite-length matched filtering is studied. The classical solution, which is to choose the signal as a minimum-eigenvalue eigenvector of the noise covariance matrix, is generalized to cases in which the transmitted signal is subject to channel distortion modeled by mean-square, maximum-absolute, and mean-absolute distortion uncertainty classes.",

author = "Sergio Verd{\'u} and Poor, \{H. Vincent\}",

year = "1983",

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AU - Verdú, Sergio

AU - Poor, H. Vincent

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AB - The optimum signal selection problem under a power constraint for minimax robust discrete-time finite-length matched filtering is studied. The classical solution, which is to choose the signal as a minimum-eigenvalue eigenvector of the noise covariance matrix, is generalized to cases in which the transmitted signal is subject to channel distortion modeled by mean-square, maximum-absolute, and mean-absolute distortion uncertainty classes.

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DO - 10.1109/TCOM.1983.1095861

M3 - Article

AN - SCOPUS:0020751337

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SP - 667

EP - 670

JO - IEEE Transactions on Communications

JF - IEEE Transactions on Communications

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ER -

Signal Selection for Robust Matched Filtering

Abstract

All Science Journal Classification (ASJC) codes

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