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Locally Optimized Adaptive Directional Time–Frequency Distributions

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Abstract

This paper addresses the problem of estimating the parameters of adaptive directional time–frequency distributions (ADTFDs). ADTFDs locally optimize the direction of the smoothing kernel on the basis of directional Gaussian or double derivative directional Gaussian filter. Conventionally, the parameters of these techniques have to be tuned manually for each particular signal. Global optimization of the parameters fails to provide the desired results when the signal encompasses close or short-duration components. We propose a two-stage algorithm to resolve this issue. As part of the first stage, the length of the smoothing kernel is optimized globally. In the second stage, the parameters which control the shape of the selected smoothing window are optimized, locally. It is shown that the multistage algorithm can result in a time–frequency distribution that has both high resolution for close components and good concentration of signal energy for short-duration signal components. Experimental findings reveal the superiority of the proposed technique over the existing methods in the case of complete signals and its benefits in the case of signals with missing samples.

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Authors and Affiliations

  1. Laboratory of Advanced Industrial Signal Processing and AI, Shahrood University of Technology, Shahrood, Iran

    Mokhtar Mohammadi, Ali Akbar Pouyan & Vahid Abolghasemi

  2. Department of Computer Science, University of Human Development, Slemani, Iraq

    Mokhtar Mohammadi

  3. Electrical Engineering, Foundation University, Islamabad, Pakistan

    Nabeel Ali Khan

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  1. Mokhtar Mohammadi
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  2. Ali Akbar Pouyan
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  3. Nabeel Ali Khan
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  4. Vahid Abolghasemi
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Correspondence to Ali Akbar Pouyan.

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Mohammadi, M., Pouyan, A.A., Khan, N.A. et al. Locally Optimized Adaptive Directional Time–Frequency Distributions. Circuits Syst Signal Process 37, 3154–3174 (2018). https://doi.org/10.1007/s00034-018-0802-z

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