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A Two Stage Generalized Block Orthogonal matching pursuit (TSGBOMP) Algorithm

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5846-5858页

作者： Mukhopadhyay, Samrat Chakraborty, Mrityunjoy Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad 826004 Bihar India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Recovery of an unknown sparse signal from a few of its projections is the key objective of compressed sensing. Often one comes across signals that are not ordinarily sparse but are sparse blockwise. Existing block sparse recovery algorithms like BOMP make the assumption of uniform block size and known block boundaries, which are, however, not very practical in many applications. This paper addresses this problem and proposes a two step procedure, where the first stage is a coarse block location identification stage while the second stage carries out finer localization of a non-zero cluster within the window selected in the first stage. A detailed convergence analysis of the proposed algorithm is carried out by first defining a so-called pseudoblock-interleaved block RIP for the given generalized block sparse signal and then imposing upper bounds on the corresponding RIC. Simulation results confirm significantly improved performance of the proposed algorithm as compared to BOMP.

关键词： Clustering algorithms Signal processing algorithms Indexes Partitioning algorithms Correlation Sensors matching pursuit algorithms Compressed Sensing (CS) block sparse restricted isometry property (RIP)

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Reconstruction for Fluorescence Molecular Tomography via Adaptive Group Orthogonal matching pursuit

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第9期67卷 2518-2529页

作者： Kong, Lingxin An, Yu Liang, Qian Yin, Lin Du, Yang Tian, Jie Chinese Acad Sci Inst Automat Key Lab Mol Imaging Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci State Key Lab Management & Control Complex Syst Shanghai Peoples R China Beihang Univ Sch Med Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing 100190 Peoples R China Xidian Univ Sch Life Sci & Technol Minist Educ Engn Res Ctr Mol & Neuro Imaging Xian 710126 Shaanxi Peoples R China

Objective: Fluorescence molecular tomography (FMT) is a promising medical imaging technology aimed at the non-invasive, specific, and sensitive detection of the distribution of fluorophore. Conventional sparsity prior-based methods of FMT commonly face problems such as over-sparseness, spatial discontinuity, and poor robustness, due to the neglect of the interrelation within the local subspace. To address this, we propose an adaptive group orthogonal matching pursuit (AGOMP) method. Methods: AGOMP is based on a novel local spatial-structured sparse regularization, which leverages local spatial interrelations as group sparsity without the hard prior of the tumor region. The adaptive grouped subspace matching pursuit method was adopted to enhance the interrelatedness of elements within a group, which alleviates the over-sparsity problem to some extent and improves the accuracy, robustness, and morphological similarity of FMT reconstruction. A series of numerical simulation experiments, based on digital mouse with both one and several tumors, were conducted, as well as in vivo mouse experiments. Results: The results demonstrated that the proposed AGOMP method achieved better location accuracy, fluorescent yield reconstruction, relative sparsity, and morphology than state-of-the-art methods under complex conditions for levels of Gaussian noise ranging from 5-25%. Furthermore, the in vivo mouse experiments demonstrated the practical application of FMT with AGOMP. Conclusion: The proposed AGOMP can improve the accuracy and robustness for FMT reconstruction in biomedical application.

关键词： matching pursuit algorithms Image reconstruction Tumors Fluorescence Mathematical model Robustness Mice Adaptive group orthogonal matching pursuit fluorescence molecular tomography local spatial structured sparsity regularization inverse problem

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Channel Estimation in Uplink Multi-User Scenario Using OTFS Modulation

Channel Estimation in Uplink Multi-User Scenario Using OTFS ...

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International ITG Conference on Source and Channel Coding (SCC)

作者： Yatish Pachigolla Lorenzo Zaniboni Mahdi Mahvari Technical University of Munich Germany

ISBN: (数字)9798331522896

ISBN: (纸本)9798331522902

Channel estimation techniques for orthogonal time frequency space (OTFS) modulation scheme are investigated. The orthogonal matching pursuit algorithm is investigated with and without side channel information, and an efficient data placement is proposed alongside the pilot in the multi-user scenario based on impulse pilot-based estimation. Finally, the performance of the estimation techniques across different multi-user scenarios is evaluated and compared, highlighting the strengths and weaknesses of each method.

关键词： Time-frequency analysis matching pursuit algorithms Channel estimation Modulation Estimation Encoding Uplink

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Time-Varying XL-MIMO Channel Tracking by Image Keypoint Detection

Time-Varying XL-MIMO Channel Tracking by Image Keypoint Dete...

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IEEE Conference on Wireless Communications and Networking

作者： Mengyuan Li Yu Han Xiao Li Shi Jin Chao-Kai Wen National Mobile Communications Research Laboratory Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the near-field region of extremely large-scale multi-input multi-output (XL-MIMO) systems, efficient channel estimation is a critical challenge, often demanding substantial computational resources. This issue becomes even more pressing in dynamic, time-varying environments where both users and scatterers are in motion, necessitating lower computational complexity for real-time channel estimation. In this paper, we propose an innovative solution for near-field XL-MIMO systems with time-varying channels, introducing a fast and accurate channel estimation and tracking scheme inspired by keypoint detection techniques from computer vision. First, we design and train a high-precision, anchor-free channel keypoint detector (CKDet) using a fine-grained orthogonal matching pursuit (OMP) framework as an effective channel estimator. Building on this, we present a novel conditional cascaded OMP-based channel tracking scheme that exploits spatial correlations between consecutive time slots to significantly reduce computational complexity. After obtaining the keypoint locations at all time slots, we apply the Hungarian algorithm to match users and scatterers across all time slots, enabling the construction of motion trajectories for use in environmental sensing applications. Experimental results validate the proposed channel tracking algorithm, showcasing its superior performance, speed, and resilience across a range of signal-to-noise ratios (SNR).

关键词： Accuracy Tracking Channel estimation matching pursuit algorithms Time-varying channels Real-time systems Trajectory Sensors Computational complexity Signal to noise ratio

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Sparse Delay Doppler Estimation for Zak-OTFS Enabled ISAC System

Sparse Delay Doppler Estimation for Zak-OTFS Enabled ISAC Sy...

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Joint Communications & Sensing (JC&S), IEEE International Online Symposium on

作者： Guddu Kumar Vimal Bhatia Markku Juntti Centre for Wireless Communications University of Oulu P.O.Box 4500 Finland

ISBN: (数字)9798331531652

ISBN: (纸本)9798331531669

This article introduces a compressive sensing technique for parameter estimation in integrated sensing and communication (ISAC) systems using Zak-orthogonal time frequency space (OTFS) modulation, which is suitable for high-Doppler environments over traditional multicarrierbased systems. While the research has mainly focused on multicarrier(MC)-OTFS due to its compatibility with the existing schemes. Zak space-OTFS has recently been acknowledged for its superior performance compared to the MC-OTFS approach. However, using Zak-OTFS for radar parameter estimation in the ISAC system remains largely unexplored. This study introduces efficient estimation algorithms for Zak-OTFS that handle large frame sizes and improve performance by exploiting radar channel sparsity in the delay-Doppler domain. Numerical simulations confirm the effectiveness of the proposed techniques.

关键词： Doppler shift Parameter estimation Estimation matching pursuit algorithms Transforms Integrated sensing and communication Doppler radar Delays Compressed sensing Signal to noise ratio

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An Efficient matching pursuit Based Compressive Sensing Detector For Uplink Grant-Free NOMA

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第2期70卷 2012-2017页

作者： Li, Shuo Xiao, Lixia Jiang, Tao Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci Wuhan 430074 Peoples R China

In this paper, an efficient compressive sensing detector is proposed for uplink grant-free non-orthogonal multiple access (NOMA), where the indices and symbols of the active users are detected separately by using efficient matching pursuit (MP) based algorithms. Specifically, a low-complexity subspace MP (SMP) algorithm is designed for detecting the active user's indices, which is helpful for obtaining an initial estimated symbol vector. Then an efficient MP based symbol correcting mechanism (SCM) is designed for finding and correcting the erroneous results encountered in the initial result. Simulation results indicate that our proposed SMP based SCM (SMP-SCM) algorithm is capable of exhibiting better bit error rate (BER) performance over the oracle least squares (OLS) counterpart in the high SNR regime, which is always taken as a benchmark in conventional multi-user detection of grant-free NOMA system.

关键词： matching pursuit algorithms Detectors NOMA Indexes Uplink Switched mode power supplies Approximation algorithms matching pursuit (MP) grant-free NOMA oracle least squares (OLS)

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A Potential Orthogonal matching pursuit Algorithm for Device Free Moving Target Localization

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IEEE COMMUNICATIONS LETTERS 2020年第8期24卷 1691-1694页

作者： Yang, Sixing Guo, Yan Li, Ning Jiang, Xinhua Army Engn Univ Coll Commun Engn Nanjing 210007 Peoples R China

Existing Compressive Sensing (CS) based Device-Free Localization (DFL) schemes focus on stationary targets. However, targets to be localized are always movable, whose positions are not the simple combination of the stationary ones. We investigate the moving target localization scheme in this letter and propose a novel approach named as Potential Orthogonal matching pursuit (POMP), which can recover the target positions in multiple time intervals simultaneously. Firstly, we transfer the moving characteristic to the relationship of the location vectors under the CS framework, which significantly reduces the possible location domain of the target. Secondly, we seek all the possible target traces using the tree searching operation and design a novel measure to select the most valuable columns of the sensing matrixes through iteration. Finally, the location vectors can be obtained by the Least-Square algorithm. Simulations show that the proposed POMP can significantly improve the recovery performance compared with the existing OMP algorithm.

关键词： matching pursuit algorithms Indexes Sensors Wireless sensor networks Dictionaries Sparse matrices Wireless communication Device free localization compressive sensing moving target localization matching pursuit

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Synchrosqueezing matching pursuit Time-Frequency Analysis

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第3期18卷 411-415页

作者： Xu, Lu Yin, Xingyao Zong, Zhaoyun Li, Kun China Univ Petr East China Coll Geosci & Technol Qingdao 266580 Peoples R China

Time-frequency analysis of the nonstationary signals is one of the hot researches in geophysics, environmental science, geology, and other fields. matching pursuit (MP) has been widely used to reveal the characteristics in time-frequency domain, which helps to study the changes and differences of subtle geological structures. Compared with traditional time-frequency analysis methods, MP has higher time-frequency resolution. The synchrosqueezing transforms (SSTs) have been proven to be an effective methodology to improve the time-frequency resolution by squeezing and redistributing the time-frequency coefficients in the frequency direction. Considering the superiority of the SSTs, this letter proposes a novel time-frequency analysis method called synchrosqueezing MP to enhance the energy aggregation of the time-frequency plane. The proposed method can squeeze the time-frequency distribution into near the center time and frequency of the selected wavelet by a 2-D Gaussian function. We used synthetic and field seismic data to demonstrate the feasibility of our method. The results prove the superiority of our method in describing reservoir boundaries.

关键词： matching pursuit algorithms Time-frequency analysis Signal resolution Continuous wavelet transforms Geology matching pursuit (MP) Ricker wavelet synchrosqueezing transforms (SSTs) time– frequency analysis time– frequency resolution

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Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal matching pursuit Algorithm

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IEEE ACCESS 2022年 10卷 37382-37395页

作者： Wei, Xu Yang, Jun Lv, Mingjiu Chen, Wenfeng Ma, Xiaoyan Air Force Early Warning Acad Wuhan 430019 Peoples R China

Accurate translational motion compensation (TMC) is the key procedure of inverse synthetic aperture radar (ISAR) imaging. Under the condition of sparse aperture, the correlation of adjacent pulses is severely destroyed, which brings a few challenges to TMC and imaging. Thus, this paper proposes a novel TMC method for ISAR imaging. In this technique, the signal model of joint phase adjustment and ISAR imaging is established first. Then, the process of TMC is designed. That is, after range alignment with improved global minimum entropy (IGME), the range joint fast orthogonal matching pursuit (RJFOMP) algorithm is applied to ISAR imaging, and the minimum entropy method (MEM) is adopted to estimate the phase error. Meanwhile, the joint optimization of phase adjustment and ISAR imaging is realized through the cyclic iteration. Extensive experiments based on both simulated and measured data demonstrate that the proposed TMC method is effective for two modes of motion errors, which are named as coherent mode (CM) and non-coherent mode (NCM), respectively. When RJFOMP is used for joint phase adjustment and ISAR imaging, it has excellent imaging performance under noisy and sparse conditions.

关键词： Imaging Radar imaging matching pursuit algorithms Entropy Apertures Signal processing algorithms Scattering Inverse synthetic aperture radar (ISAR) translational motion compensation (TMC) improved global minimum entropy (IGME) range joint fast orthogonal matching pursuit (RJFOMP) algorithm minimum entropy method (MEM)

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Federated Gradient matching pursuit

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4512-4537页

作者： Jeong, Halyun Needell, Deanna Qin, Jing Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Univ Kentucky Dept Math Lexington KY 40506 USA

Traditional machine learning techniques require centralizing all training data on one server or data hub. However, with the development of communication technologies and a huge amount of decentralized data on many clients, collaborative machine learning has become the main interest while providing privacy-preserving frameworks. Federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. On the other hand, in a wide range of machine learning and signal processing applications, the desired solution naturally has a certain structure that can be framed as sparsity with respect to a certain dictionary. This problem can be formulated as an optimization problem with sparsity constraints and solving it efficiently has been one of the primary research topics in the traditional centralized setting. In this paper, we propose a novel algorithmic framework, federated gradient matching pursuit (FedGradMP), to solve the sparsity constrained minimization problem in the FL setting. We also generalize our algorithms to accommodate various practical FL scenarios when only a subset of clients participate per round, when the local model estimation at clients could be inexact, or when the model parameters are sparse with respect to general dictionaries. Our theoretical analysis shows the linear convergence of the proposed algorithms. A variety of numerical experiments are conducted to demonstrate the great potential of the proposed framework - fast convergence both in communication rounds and computation time for many important scenarios without intricate parameter tuning.

关键词： Convergence Servers Dictionaries matching pursuit algorithms Federated learning Tuning Distributed databases sparse recovery gradient matching pursuit random algorithm

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