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Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data
Joint Authors
Sun, Haixin
Miao, Yongchun
Qi, Jie
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-26
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data.
We propose the kernel sparse learning (KSL), developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies (IFs) ridges of the overlapped multicomponent from a time-frequency representation (TFR).
The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection.
The IFs first extraction is decided using the framework of the intrinsic mode polynomial chirp transform (IMPCT), which obtains a brief local orthogonal TFR of signals.
Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment.
After the IFs are extracted, component decomposition is performed through KSL.
Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications.
American Psychological Association (APA)
Sun, Haixin& Miao, Yongchun& Qi, Jie. 2018. Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data. Complexity،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136188
Modern Language Association (MLA)
Sun, Haixin…[et al.]. Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data. Complexity No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1136188
American Medical Association (AMA)
Sun, Haixin& Miao, Yongchun& Qi, Jie. Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data. Complexity. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136188
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136188