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- W4283771330 abstract "Time-frequency (TF) analysis (TFA) is an effective tool to characterize non-stationary features of time-varying signals, which has been developing towards the goal of higher energy concentration and better reconstruction performance in recent years. However, it is still a challenging task to analyze the multi-component signal with strong and distinct modulated frequencies. We put forward a novel TFA method based on chirplet transform to deal with this problem. This paper first optimizes the matching chirplet transform by measuring Rényi entropy to generate a more accurate TF result. Given the sharpness based on accuracy, this paper further studies an unbiased IF estimation that plays a crucial role in the reassignment of inevitable diffusion energy. In this work, we provide the theoretical analysis of the novel TFA method to show that this is an upgrade and extension of classical chirplet transform and synchrosqueezing transform. Moreover, we prove in theory that the proposed method retains perfect signal reconstruction ability. Numerical and experimental signals are analyzed, and the results validate that this method has a better performance in representing a wide variety of multi-component signals with complex modulation features than some advanced TFA methods." @default.
- W4283771330 created "2022-07-03" @default.
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- W4283771330 date "2022-12-01" @default.
- W4283771330 modified "2023-09-26" @default.
- W4283771330 title "Local maximum synchrosqueezes from entropy matching chirplet transform" @default.
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- W4283771330 doi "https://doi.org/10.1016/j.ymssp.2022.109476" @default.
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