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Adaptive multivariate chirp mode decomposition
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AI
DOI：10.1016/j.ymssp.2022.109897.png)
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Abstract
En 中文
Multivariate signal processing methods are becoming more prevalent as sensors and modern science and technology improve. However, most existing multivariate signal decomposition methods suffer from the following challenges: (i) requiring prior knowledge of multivariate signal modes; (ii) limiting to narrowband signal analysis; (iii) presenting mode mixing. To overcome the challenges mentioned above, this study proposes a novel method, named adaptive multivariate chirp mode decomposition (AMCMD). The method captures time-varying joint modes one-by-one in a recursive framework without knowing the precise number of modes. Specifically, a multivariate chirp mode (MCM) is modeled first based on AM-FM signals, with the constraint that there is a joint frequency component between all signal channels. Furthermore, demodulation techniques are used to entail the wideband multivariate signal mode exhibit narrowband characteristics. Finally, the objective function is established and the modes of each channel signal are estimated one by one. The efficacy and superiority of the method are verified by a series of numerical examples. In addition, the analysis of real-world time-varying vibration signals also confirms the practicality of the method. The findings demonstrate that the proposed method can converge faster to the same satisfactory results as existing state-of-the-art methods at a faster rate, even without knowing the precise number of modes.
Keywords:
Adaptive chirp mode pursuit
Multivariate variational mode decomposition
Multivariate signal processing
Recursive extraction
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W
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