Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition

Chen, Shiqian; Dong, Xingjian; Xing, Guanpei; Peng, Zhike; Zhang, Wenming; Meng, Guang

doi:10.1109/jsen.2017.2737467

Cited by 162 publications

(49 citation statements)

References 45 publications

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“…For signal frequency extraction, we used VSME and RPRG (Ridge Path Regrouping) [42] algorithm to compare the extraction effect. RPRG method is a novel non-parametric algorithm to extract IF from signal with overlapping components in time-frequency representation.…”

Section: Methods Validationmentioning

confidence: 99%

“…signal frequency extraction, we used VSME and RPRG (Ridge Path Regrouping)[42] algorithm to compare the extraction effect. RPRG method is a novel non-parametric algorithm to extract IF from signal with overlapping components in time-frequency representation.Figure 2a is an image of a mixed signal ( ) f t , and Figure 2b shows the IF of original component signal ( ) IF of component signal extracted by RPRG is shown in Figure 2e.…”

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confidence: 99%

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Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Wang

Zhao

2020

Algorithms

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The non-contact detection of buried ferromagnetic pipeline is a long-standing problem in the field of inspection of outside pipelines, and the extraction of magnetic anomaly signal is a prerequisite for accurate detection. Pipeline defects can cause the fluctuation of magnetic signals, which are easily submerged in wide-band background noise without external excitation sources. Previously, Variational Mode Decomposition (VMD) was used to separate modal components; however, VMD is based on narrow-band signal processing algorithm and the calculation is complex. In this article, a method of pipeline defect signal based on Variational Specific Mode Extraction (VSME) is employed to extract the signal of a specific central frequency by signal modal decomposition, i.e., the specific mode is weak magnetic anomaly signal of pipeline defects. VSME is based on the fact that a wide-band signal can be converted into a narrow-band signal by demodulation method. Furthermore, the problem of wide-band signal decomposition is expressed as an optimal demodulation problem, which can be solved by alternating direction method of multipliers. The proposed algorithm is verified by artificially synthesized signals, and its performance is better than that of VMD. The results showed that the VSME method can extract the magnetic anomaly signal of pipeline damage using experimental data, while obtaining a better accuracy.

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Section: Methods Validationmentioning

confidence: 99%

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confidence: 99%

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Wang

Zhao

2020

Algorithms

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“…Recently, the intrinsic chirp component decomposition (ICCD) method proposed in [13] and used in [14] revealed the incapable of separating crossed or overlapped chirp signals by using a joint-estimation scheme. Essentially, the ICCD based on the short-time Fourier transform (STFT) can accurately reconstruct overlapped components.…”

Section: Related Workmentioning

confidence: 99%

“…Especially assuming that the chirp phase search space is [ , ], the set of the discrete polynomial chirp phase can adjust the grid resolution. Considering the training set in the kernel sparse learning, our method searches out proper path from a dictionary which is reconstructed by possible partial optimal paths in the set (13). Each subset is generated from a different starting point of the sample, implemented independently to link the each other and used to update a different pool of optimal paths.…”

Section: Ifs Estimation By the T-f Reassignmentmentioning

confidence: 99%

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Sun

Miao

2018

Complexity

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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.

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“…Source localization has been an important research topic for array signal processing [1][2][3]. This research topic is widely applied in many fields such as sonar and electronic surveillance [4], where the signals are often non-stationary [5]. The wavefront of a far-field source signal can be assumed to be a plane when it impinges on the receiver array.…”

Section: Introductionmentioning

confidence: 99%

Mixed-Field Source Localization Based on the Non-Hermitian Matrix

Sun

2020

Information

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In this paper, an efficient high-order multiple signal classification (MUSIC)-like method is proposed for mixed-field source localization. Firstly, a non-Hermitian matrix is designed based on a high-order cumulant. One of the steering matrices, that is related only with the directions of arrival (DOA), is proved to be orthogonal with the eigenvectors corresponding to the zero eigenvalues. The other steering matrix that contains the information of both the DOA and range is proved to span the same column subspace with the eigenvectors corresponding to the non-zero eigenvalues. By applying the Gram–Schmidt orthogonalization, the range estimation can be achieved one by one after substituting each estimated DOA. The analysis shows that the computational complexity of the proposed method is lower than other methods, and the effectiveness of the proposed method is shown with some simulation results.

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Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition

Cited by 162 publications

References 45 publications

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Mixed-Field Source Localization Based on the Non-Hermitian Matrix

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