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Estimation of time-varying autocorrelation and its application to time-frequency analysis of nonstationary signals

By Z Fu, Z Zhang and SC Chan

Abstract

This paper introduces a new method for adaptively estimating the time-varying autocorrelation (TV-AC) of nonstationary signals and studies its application to time-frequency analysis. The proposed method employs local estimation with a sliding window having a certain bandwidth to estimate the TV-AC locally. The window bandwidths are selected adaptively by a local plug-in rule to address the bias and variance tradeoff problem. Further, based on the proposed adaptive TV-AC estimation, a new time-frequency analysis method called adaptive windowed minimum variance spectral estimation (AWMVSE) is developed. Simulation results show that the proposed adaptive TV-AC estimation method and AWMVSE method have improved performances over conventional estimators with a fixed window. © 2013 IEEE.published_or_final_versio

Topics: Adaptive window selection, Minimum variance spectral estimation, Time varying autocorrelation, Nonstationary signal, Time-frequency analysis
Publisher: United States
Year: 2013
DOI identifier: 10.1109/ISCAS.2013.6572148
OAI identifier: oai:hub.hku.hk:10722/189876
Provided by: HKU Scholars Hub
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