Robust estimation of the magnitude squared coherence based on Kernel signal processing
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Cita com:
hdl:2117/120304
Tipus de documentComunicació de congrés
Data publicació2018
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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ProjecteCOMUNICACIONES Y PROCESADO DE SEÑAL EN ESCENARIOS ADVERSOS - 1 (MINECO-TEC2013-47020-C2-2-R)
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 variance is analytically and numerically characterized, showing a dependence on the relative kernel bandwidth and the available data size.
Descripció
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CitacióDe 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.
ISBN978-1-5386-1823-3
Versió de l'editorhttps://ieeexplore.ieee.org/document/8335477/
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C2_compass_RobustEstimation.pdf | 2,575Mb | Visualitza/Obre |