Toeplitz structured subspace for multi-channel blind identification methods

Lawal, Abdulmajid; Mayyala, Qadri; Abed-Meraim, Karim; Iqbal, Naveed; Zerguine, Azzedine

doi:10.1016/j.sigpro.2021.108152

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Hence, the SSS can effectively deal with such situations by estimating the actual input in a blind context. It is worth mentioning that the SSS method has been applied to both linear [11] and nonlinear [12] systems. Therefore, the SSS is capable of handling nonlinear dynamics present in the two-phase multichannel signal.…”

Section: B Proposed Pca-sss Methodsmentioning

confidence: 99%

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A Novel PCA-Based Slug Flow Characterization Using Acoustic Sensing

Lawal,

Iqbal,

Zerguine

et al. 2024

IEEE Access

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In a modern industrial process, the accurate measurement of two-phase flow signals is of vital importance for various purposes including condition-based monitoring of pipelines, the avoidance of costly material and environmental damages, and danger to the operational personnel. In this work, a novel blind approach for denoising two-phase liquid and gas flow in a horizontal liquid-gas pipe is proposed. This approach employs the structured signal subspace (SSS) method on the multichannel signal acquired from the transducers connected to the wall of the pipe. The proposed approach utilizes only the output observations from the sensor, i.e, the recorded signal, and does not require any knowledge of the input signal or the channel. The multichannel signals recorded are first pre-processed using the principal component analysis (PCA) and then the blind SSS method is used to denoise the input signal before estimating it. The numerical results showed that the proposed algorithm outperforms the state-of-the-art algorithms (SOTA) which includes the eigen value decoposition (EGD)-based method and the independent component analysis (ICA)-based method, while the proposed PCA-SSS method achieved a performance of −22.7dB in the presence of Gaussian noise, the EGD and ICA acheived −16.39dB and −18.09dB, respectively, showing the superiority of the proposed method. Similar analysis were performed in the presence of a non-Gaussian noise and colored noise and the proposed algorithm also outperformed the other methods. Hence, the PCA-SSS method can be used for a better characterization of a slug flow regime by exploiting the Toeplitz structure embedded in the signal vector acquired from the array of sensors via the communication model for denoising the two-phase flow, and does not rely on the knowledge of the input signal vector.

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Section: B Proposed Pca-sss Methodsmentioning

confidence: 99%

“…In this section, based on the recently developed algorithms [11], [22], [23], the step-by-step method involved in performing the structured subspace method is presented here again. Also, the procedure of applying it in this context is explained.…”

Section: Structured Signal Subspace (Sss) Methodsmentioning

confidence: 99%

A Novel PCA-Based Slug Flow Characterization Using Acoustic Sensing

Lawal,

Iqbal,

Zerguine

et al. 2024

IEEE Access

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“…the first p columns of the matrix X K which corresponds to the transmitted pilots. In a similar way, the Kronecker product can be used to vectorize the cost function in (15) as follows:…”

Section: Semi-blind Mre Methodsmentioning

confidence: 99%

“…Other methods are fully blind, which means they only use observed (output) data to predict the input signal. Many of these techniques, including the standard subspace (SS) techniques [13], the structured subspace methods [14], [15], [16], the mutually referenced equalizers (MRE) techniques [17], the truncated transfer matrix (TTM) techniques [18], the minimum mean square error (MMSE) techniques [19], the maximum likelihood (ML) like techniques [20], have been employed for linear system identification.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Signal Estimation Using Semi-Blind Mutually Referenced Equalizers in Convolutive Mixture

2023

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Nonlinearity is a common source of signal and channel distortion in digital communication. In many circumstances, such distortions must be compensated by equalization algorithms or devices. This paper presents a semi-blind equalization method for nonlinear multichannel signal recovery in convolutive mixtures. The proposed work takes into account quadratic nonlinearities and applies the mutually referenced equalization (MRE) approach to estimate the equalizer as well as estimate the transmitted signal from the nonlinear convolutive mixture. The proposed semi-blind model combines both data and pilots in addition to the MRE technique to develop a cost function that provides an equalization solution. The proposed technique has various advantages, including robustness to channel order misspecification, implementational simplicity, and provides a single solution for several equalization delays. The simulation results show that the proposed technique has intriguing performance features with robustness to low SNR.

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