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Simultaneous Seismic Sources Separation Based on Matrioshka Orthogonal matching pursuit, Application in Oil and Gas Exploration

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第7期58卷 4529-4546页

作者： Shipilova, Ekaterina Barret, Michel Bloch, Matthieu Boelle, Jean-Luc Collette, Jean-Luc TOTAL CSTJF F-64018 Pau France CentraleSupelec F-57070 Metz France UMI 2958 Georgia Tech CNRS F-57070 Metz France Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

We present Matrioshka orthogonal matching pursuit (OMP), a method consisting of two nested OMPs for separating seismic sources at an early stage of the signal processing chain. Matrioshka OMP is based on models of sensor signals that place nonrestrictive assumptions on the seismic survey using simultaneous sources. Our seismic event model is based on the spatial coherence of signals, which results in a straight or slightly curved feature in the trace representation of the data with a specific wavelet, whose magnitude can linearly vary according to the offset. We demonstrate the effectiveness of the approach on synthetic and real data.

关键词： matching pursuit algorithms Earth Receivers Signal processing Data models Dictionaries Crosstalk Acquisition matching pursuit optimization methods seismic signal processing sources separation

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Cluster Adaptive matching pursuit for Multipolarization Through-Wall Radar Imaging

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IEEE SENSORS JOURNAL 2023年第1期23卷 414-424页

作者： Wang, Anran Chen, Chang Chen, Weidong Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

Among various types of sensors, through-wall radar is a promising sensor that can penetrate obstacles and sense the targets behind them. In this article, by exploiting the common sparsity property of the multipolarization images, we propose a cluster adaptive matching pursuit (CAMP) algorithm for multipolarization through-wall radar imaging (TWRI). Our proposed CAMP algorithm is developed from compressive sensing (CS) theory. In the proposed CAMP algorithm, the common sparsity among the multipolarization images is utilized through a multiple-measurement vector (MMV) model. The target area is obtained through a support set. We model the support set as a combination of several clusters, thus the number of clusters and the pattern of each cluster are two important factors that can describe the support set. To obtain the pattern of a cluster that corresponds to a certain target in the imaging scene, we propose an adaptive clustering strategy based on the backtracking scheme. On this basis, the number of clusters is adaptively selected through a two-layer matching pursuit method. Different from conventional matching pursuit algorithms that often use atom methods, the support set is acquired cluster by cluster in the two-layer matching pursuit method, which accelerates the speed of acquiring the support set. Experimental results show that our proposed algorithm can locate targets accurately in TWRI with a high target-to-clutter ratio (TCR) performance.

关键词： matching pursuit algorithms Sensors Clustering algorithms Radar imaging Imaging Indexes Radar Adaptive matching pursuit backtracking compressive sensing (CS) multipolarization through-wall radar imaging (TWRI)

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Estimating Instrument Spectral Response Functions Using Sparse Representations and Quadratic Envelopes

Estimating Instrument Spectral Response Functions Using Spar...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jihanne El Haouari Marcus Carlsson Jean-Yves Tourneret Herwig Wendt Jean-Michel Gaucel Christelle Pittet TéSA Laboratory Toulouse France IRIT-ENSEEIHT CNRS University of Toulouse Toulouse France Department of Mathematics Lund University Sweden Thales Alenia Space Cannes France Centre National d’Etudes Spatiales Centre Spatial de Toulouse France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The estimation of high resolution spectrometer Instrument Spectral Response Functions (ISRFs) is crucial because an imperfect knowledge of these functions can induce errors in the measurements. The state-of-the-art for this problem currently relies on the use of parametric models, which frequently lack flexibility to accurately model real-world ISRFs. To address this limitation, this paper proposes and investigates the use of sparse representations for modeling and estimating ISRFs, where the ISRFs are decomposed in a fixed dictionary of atoms. To estimate the sparse coefficient vector, a novel sparsity inducing regularization of the problem based on quadratic envelopes is studied and compared to the classical LASSO estimator and to a greedy method based on the Orthogonal matching pursuit (OMP) algorithm. Results for simulated ISRFs from the MicroCarb mission indicate that the proposed spectral representations yield excellent ISRF estimates, and that the use of quadratic envelopes can yield significantly better precision than competing methods.

关键词： Dictionaries Sparse approximation Instruments Simulation matching pursuit algorithms Signal processing algorithms Estimation Stray light Vectors Speech processing

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Sparse Channel Estimation for Hybrid MIMO Millimeter Wave Communication Impaired by Impulsive Noise

Sparse Channel Estimation for Hybrid MIMO Millimeter Wave Co...

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National Conference on Communications (NCC)

作者： Dhruv Sanghvi Sandesh Jain Manisha Pattanaik Vimal Bhatia Department of Information Technology ABV-Indian Institute of Information Technology and Management Gwalior India Department of Electrical and Electronics Engineering ABV-Indian Institute of Information Technology and Management Gwalior India Center for Autonomous Systems ABV-IIITM Gwalior India Department of Electrical Engineering Indian Institute of Technology Indore India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

Multiple-input multiple-output (MIMO) millimeter wave (mmWave) technology is a key enabler for beyond 5G communication systems, offering high data rates, ultra-low latency, massive connectivity, and enhanced spectral efficiency. However, the performance of a MIMO-mmWave communication link is degraded by path loss and non-Gaussian/impulsive distortions. Furthermore, blockage and scattering effects make the overall MIMO-mmWave channel sparse in nature. The existing orthog-onal matching pursuit (OMP), zero-attracting least mean square (ZA-LMS), and their variants are inadequate to achieve near-optimal performance for mmWave systems impaired by impulsive noise. To circumvent this limitation, we propose a novel sparsity-aware maximum Versoria criterion (SA-MVC) based channel estimator for MIMO-mmWave communication, which is robust in the presence of impulsive/ non-Gaussian distortions. Simulations conducted on a realistic MIMO-mmWave channel demonstrate that the proposed SA-MV C algorithm delivers better convergence performance over the existing state-of-art methods.

关键词： Communication systems Spectral efficiency Noise Millimeter wave technology Channel estimation matching pursuit algorithms Scattering Distortion MIMO Millimeter wave communication

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Sparse parity random linear codes for URLLC in channels with memory

Sparse parity random linear codes for URLLC in channels with...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Steven Jones A. Brinton Cooper Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

The use of short error-correcting codes is vital for achieving an Ultra-Reliable Low Latency Communications capability for supporting the Internet of Things with minimal decoding complexity. Here we examine the performance and decoding complexity of two code-agnostic decoders for a new class of full-rank sparse random linear codes derived from the MacKay-Neal LDPC construction. We consider decoding by a noise guessing decoder such as GRAND-MO and by a binary matching pursuit decoder to which list decoding is added. Comparisons are made in the context of error correction performance and computational complexity.

关键词： Noise Employment matching pursuit algorithms Ultra reliable low latency communication Linear codes Parity check codes Decoding Error correction codes Error correction Internet of Things

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ISAR 2-D Imaging of Uniformly Rotating Targets via matching pursuit

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2012年第2期48卷 1838-1846页

作者： Li, Gang Zhang, Hao Wang, Xiqin Xia, Xiang-Gen Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Delaware Dept Elect Comp Engn Newark DE 19715 USA Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China

An algorithm based on matching pursuit (MP) is proposed for inverse synthetic aperture radar (ISAR) two-dimensional (2-D) imaging of uniformly rotating targets. The ISAR echo is decomposed into many subsignals that are generated by discretizing spatial domain and synthesizing the ISAR data for every discretized spatial position. The subsignals that indeed contribute to the ISAR echo are selected by the MP, and their coefficients represent the superresolution image. The target rotation rate is estimated by combining MP with maximum contrast search.

关键词： Fourier transforms Imaging matching pursuit algorithms Signal resolution Spatial resolution Time frequency analysis

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Convolutional Sparse Coding Using Pathfinder Algorithm-Optimized Orthogonal matching pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection

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IEEE SENSORS JOURNAL 2021年第16期21卷 18132-18145页

作者： Zhou, Qiuyang Zhang, Yuhui Yi, Cai Lin, Jianhui He, Liu Hu, Qiwei Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 260061 Peoples R China China Railway Elect Survey Design & Res Inst Co L Tianjin 300250 Peoples R China

Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.

关键词： Dictionaries Convolution matching pursuit algorithms Fault diagnosis Vibrations Convolutional codes Encoding Bearing fault diagnosis convolutional sparse coding asymmetric Gaussian chirplet model pathfinder algorithm orthogonal matching pursuit

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Fractional Delay and Doppler Estimation for OTFS Systems with Doppler Squint Effect

Fractional Delay and Doppler Estimation for OTFS Systems wit...

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IEEE Conference on Wireless Communications and Networking

作者： Meiying Zhang Ruoxiao Cao Jun Qian Hengtao He Shenghui Song Jun Zhang Khaled B. Letaief Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Orthogonal time frequency space (OTFS) modulation is a promising technology for mitigating severe Doppler effects in high-mobility scenarios. However, existing OTFS channel estimation methods neglect the Doppler Squint Effect (DSE), which incurs serious performance loss. In this paper, we propose a channel estimation algorithm based on Newton's method to accurately estimate fractional delay and Doppler in OTFS systems with DSE. In particular, we obtain the maximum delay and Doppler grid spacing for codebook design to guarantee the convergence of the algorithm. Additionally, we derive the Cramér-Rao lower bound (CRLB) for testing channel parameter estimation performance of our proposed algorithm. Simulation results demonstrate that our proposed algorithm outperforms the orthogonal matching pursuit (OMP) algorithm in terms of normalized mean square error (NMSE), surpassing the Newtonized OMP algorithm with traditional dictionary matrix and approaching the CRLB performance.

关键词： Time-frequency analysis Simulation matching pursuit algorithms Signal processing algorithms Channel estimation Integrated sensing and communication MIMO Delays Doppler effect Convergence

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Performance Comparison of K-SVD, Discriminative K-SVD, and Approximate K-SVD Dictionary Learning algorithms for Public Road Type Recognition

Performance Comparison of K-SVD, Discriminative K-SVD, and A...

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International Conference on Signal Processing and Communication (ICSC)

作者： Ashwin Rai Pubali De Saibal Ghosh Pritam Paral Amitava Chatterjee Business Analyst Deloitte Touche Tohmatsu India LLP Institute of Engineering and Management University of Engineering and Management Kolkata India Dept. of Electrical Engineering. Jadavpur University Kolkata India Dept. of Electrical Engineering. IIEST Shibpur Howrah India Electrical Engineering Department Jadavpur University Kolkata India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In today's world, having the ability to drive has become of significant importance, especially while traveling. As a consequence, the importance of studying road safety is rising. Hence, the importance of research in designing intelligent systems to aid drivers in providing comfort, security, and safety is also gaining much momentum in recent times. On the other hand, designing Dictionary learning (DL) algorithms in conjunction with sparse representation/coding has become a forerunner in machine learning based research problems in the current decade. Many such DL based sparse solutions have been effectively proposed for a variety of signal processing and image processing algorithms. In some of our earlier works, we have shown how various dictionary learning based algorithms and system solutions can be successfully developed for home automation and ambient assisted leaving problems. Taking our research efforts in DL algorithms further, in this work we show how DL algorithms can be effectively used to categorize four common road types: urban roads, highways, residential areas, and rural areas. The algorithms have been developed based on sensory signals acquired by a driver wearing smart glasses, while driving in those road conditions. The DL based system developed can help providing guidance to a driver during driving. Three prominent varieties of DL algorithms, called K-singular value decomposition (K-SVD) algorithm, approximate K-SVD algorithm, and discriminative K-SVD algorithm, have been successfully implemented here for a benchmark, open dataset available for this problem and their suitable effectiveness has been investigated in detail. Although all the three varieties of K-SVD algorithms showed encouraging performances under different recognition problems, the discriminative K-SVD algorithm, employing orthogonal matching pursuit (OMP) algorithm at its core for sparse coding purposes, emerged as the best overall performer.

关键词： Machine learning algorithms Roads Signal processing algorithms matching pursuit algorithms Machine learning Signal processing Approximation algorithms Safety Vehicles Smart glasses

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Transient Feature Extraction by the Improved Orthogonal matching pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第1期16卷 215-227页

作者： Qin, Yi Zou, Jingqiang Tang, Baoping Wang, Yi Chen, Haizhou Chongqing Univ State Key Lab Mech Transmiss Coll Mech Engn Chongqing 400044 Peoples R China Qingdao Univ Sci & Technol Coll Electromech Engn Qingdao 266000 Peoples R China

To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper. First, the stopping criterion of adaptive spark is developed, and then the corresponding OMP algorithm is used to remove the modulated and harmonic signals adaptively. Second, the residual signal is reformulated as a signal matrix by period segmentation and circulating shift, and the initial transient dictionary is constructed via the time-domain average technique. Subsequently, a novel K-SVD algorithm is proposed to get the optimized transient dictionary for the one-dimensional signal. Finally, the repetitive transient signal is recovered by the optimized dictionary. The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional K-SVD method.

关键词： Transient analysis Dictionaries matching pursuit algorithms Harmonic analysis Gears Vibrations Feature extraction Bearing fault diagnosis circulating shift dictionary learning impulsive feature gear fault diagnosis

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