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An adaptive Generalized S-transform for instantaneous frequency estimation by Wang Lin; Meng Xiaofeng is a Computer Science article available to read on EtoBox.

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The Generalized S-transform (GST) applied in Instantaneous Frequency (IF) estimation is addressed in this paper. The analytical model for IF estimation error using the GST is established, which reveals that the estimation bias is determined directly by two variables: the standard deviation of the Gaussian window and the Instantaneous Frequency Rate (IFR) of the test signal. Based on the mathematical model, performance of the GST as an IF estimator is analyzed by three steps. Firstly, the time-varying expression for IF estimation Relative Error (RE) using the GST is derived. Secondly, the relationships between the RE and the signal's frequency characters are found. Thirdly, the effects of the window parameters on the RE are discussed. Consequently, a novel Adaptive Generalized S-transform (AGST) is developed, in which the window parameters are automatically adjusted in order to process signals with fast changing frequency characters. The proposed method is evaluated through several kinds of test signals to show its efficiency in IF estimation.

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Author

Wang Lin; Meng Xiaofeng

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-1684)

Published

2011

Language

EN

Field

Computer Science (Physical Sciences)