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Adaptive polymorphic mode decomposition
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DOI：10.1016/j.dsp.2024.104913.png)
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Abstract
En 中文
Signal mode decomposition methods have been widely studied and applied for long. Most of them aim at handling specific non-linear signals, like AM-FM signal, close-spaced frequency chirplet signal, dispersive signal, crossed modes signal, periodic impactive signal, etc. For signal modes of multiple types, classical methods may yield undesirable results sometimes. To extract modes from multi-component multiform complex signal, a framework-like Adaptive Polymorphic Mode Decomposition (APMD) method is put forward in this article. First, Short-Time Fourier Transform (STFT) with optimal window length is applied to obtain the Time-Frequency Representation (TFR) of signal. Then, ridges and bandwidths of each mode are consecutively detected and optimized by iteration. Finally, the signal modes are restored by integration and squeezed in TFR. The idea is simple but novel with combination of Variational Mode Decomposition (VMD)-like methods and SynchroSqueezing Transform (SST)-like methods, which is non-parameterized and fully adaptive. Results of decomposing some typical signal verify the effectiveness and robustness in analyzing complex polymorphic signals, being more suitable than traditional methods for decomposing signals mixed with both time-dominant and frequency-dominant components.
Keywords:
Mode decomposition
Signal processing
Time-frequency representation (TFR)
polymorphic
optimization
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