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...
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.