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Robust estimation of the magnitude squared coherence based on kernel signal processing

Author: De Cabrera, F.; Riba, J.; Vazquez, G.; Pujol-Vazquez, G.

Type of activity: Presentation of work at congresses
Name of edition: 51st Asilomar Conference on Signal, Systems and Computers
Date of publication: 2017
Presentation's date: 2017-10-31
Book of congress proceedings: Conference record of the Fifty-First Asilomar Conference on Signals, Systems & Computers: October 29-November 1, 2017 Pacific Grove, California
First page: 890
Last page: 894
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
DOI: https://doi.org/10.1109/ACSSC.2017.8335477
Project funding: Communications and Signal Processing in Adverse Scenarios - 1
Wrestling with Interference in Communications and Information Processing (TEC2016-76409-C2-1-R)

Repository: http://hdl.handle.net/2117/120304
URL: https://ieeexplore.ieee.org/document/8335477/
Abstract: A new outlier-robust approach to estimate the magnitude squared coherence of a random vector sequence, a common task required in a variety of estimation and detection problems, is proposed. The proposed estimator is based on Renyi's entropy, an information theoretic kernel-based measure that proves to be inversely proportional to the determinant of a regularized version of the covariance matrix in the proper Gaussian case. The trade-off between accuracy and robustness in terms of bias and varian...

A new outlier-robust approach to estimate the magnitude squared coherence of a random vector sequence, a common task required in a variety of estimation and detection problems, is proposed. The proposed estimator is based on Renyi's entropy, an information theoretic kernel-based measure that proves to be inversely proportional to the determinant of a regularized version of the covariance matrix in the proper Gaussian case. The trade-off between accuracy and robustness in terms of bias and variance is analytically and numerically characterized, showing a dependence on the relative kernel bandwidth and the available data size. A new outlier-robust approach to estimate the magnitude squared coherence of a random vector sequence, a common task required in a variety of estimation and detection problems, is proposed. The proposed estimator is based on Renyi's entropy, an information theoretic kernel-based measure that proves to be inversely proportional to the determinant of a regularized version of the covariance matrix in the proper Gaussian case. The trade-off between accuracy and robustness in terms of bias and variance is analytically and numerically characterized, showing a dependence on the relative kernel bandwidth and the available data size. © 2017 IEEE. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Citation: De Cabrera, F., Riba, J., Vazquez, G. Robust estimation of the magnitude squared coherence based on Kernel signal processing. A: Asilomar Conference on Signals, Systems, and Computers. "Conference record of the Fifty-First Asilomar Conference on Signals, Systems & Computers: October 29-November 1, 2017 Pacific Grove, California". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 890-894.
Keywords: Bias and variance, Covariance matrix, Economic and social effects, Estimation and detection, Magnitude squared coherences, Random vector sequence, Renyi's entropy, Robust approaches, Robust estimation, Signal processing, Trade off

Group of research: SPCOM - Signal Processing and Communications Group

Participants

De Cabrera Estanyol, Ferran (author and speaker )
Riba Sagarra, Jaume (author and speaker )
Vazquez Grau, Gregorio (author and speaker )
Riba Ruiz, Jordi Roger (author and speaker )
Pujol Vazquez, Gisela (author and speaker )

Attachments

C2_compass_RobustEstimation.pdf (2700717 bytes)