Estimation of locally stationary covariance matrices from data

Local stationarity of an L²(R) bandpass random process reflects in specific regions of either the frequency plane of its 2 dimensional power spectrum or the time-frequency plane of its Wigner distribution. The paper addresses the problem of estimating from data a covariance matrix that satisfies the constraint of being locally stationary. We also show, with a real-data case study, the improvement in performance achieved by using locally stationary covariance matrices in the development of low cost quadratic detectors.