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Electrical Resistance Tomography Image Reconstruction Based on Modified omp algorithm

IEEE SENSORS JOURNAL 2019年第14期19卷 5723-5731页

作者： Zhang, Wei Tan, Chao Xu, Yanbin Dong, Feng Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Proc Measurement & Control Tianjin 300072 Peoples R China

Electrical resistance tomography (ERT) is a sensing technique for process monitoring and measurement, the image reconstruction algorithms are required fast and accurate. The omp class algorithm has the property of reconstructing a higher quality signal by fewer iteration steps (shorter elapsed time) and faster convergence speed compared with other iterative algorithms. But the classical omp is not self-adapted and may fall into a local optimal solution. In this paper, a modified orthogonal matching pursuit (Momp) was presented by adding a continuous constraint to prevent local optimal solution and self-adaption process on classical omp algorithm which finds the optimal number of iterations based on the information entropy of measurement to make it suitable for ERT inverse problem. The algorithm was experimentally validated versus other algorithms through a 16-electrode ERT system. Comparing with other algorithms, Momp algorithm has a lower mean square error than non-iterative algorithms and a shorter time cost than iterative algorithms. Dynamic experiments showed that the Momp algorithm is promising for industrial process monitoring.

关键词： Electrical resistance tomography image reconstruction compressed sensing omp algorithm

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Multi-level Off grid DOA estimation of sparse arrays Using omp algorithm

Multi-level Off grid DOA estimation of sparse arrays Using O...

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IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)

作者： Neela, Pavani Vakkalagadda, Swathi Padidam, Pushyami Puli, Kishore Kumar NIT Andhra Pradesh Dept ECE Tadepalligudem India

ISBN: (纸本)9781538695494

Direction Of Arrival (DOA) estimation plays a major role in the operation of smart antennas. It has been a research area in array signal processing since decades. Although there are many algorithms to solve this problem, each algorithm has its own advantages and disadvantages. Recently, Compressive Sensing (CS) technique revolutionised the idea of sampling in signal processing. We can have sparse representation of DOA problem using angular grid model and hence CS techniques can be used to solve for DOA's. Using the idea of CS, even one snapshot is sufficient to estimate the angles. Orthogonal Matching Pursuit (omp), one of the algorithm in the class of greedy algorithms is used in this paper for DOA estimation. The simulations using this algorithm for uniform linear array and sparse array shows the performance of the algorithm. Also off grid model is employed to find DOA of sources that are impinging in grid spaces i.e., between grid points.

关键词： DOA estimation Compressive Sensing omp algorithm smart antennas Off grid model

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Micro-Doppler Signature Detection and Recognition of UAVs Based on omp algorithm

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SENSORS 2023年第18期23卷 7922-7922页

作者： Fan, Shiqi Wu, Ziyan Xu, Wenqiang Zhu, Jiabao Tu, Gangyi Nanjing Univ Informat Sci & Technol Dept Elect & Informat Engn Nanjing 210044 Peoples R China

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (omp) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time-frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on omp algorithm shows advantages in having a low signal-to-noise ratio (-10 dB). Field experiments indicate that this approach can effectively reduce clutter power (-15 dB) and successfully extract micro-Doppler signals for identifying different UAVs.

关键词： radar signal processing identification of UAV omp algorithm clutter suppression micro-Doppler

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Research of wind turbine clutter mitigation based on omp algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第19期2019卷 5689-5692页

作者： Cao, Yonggui Fang, Yu Wu, Daoqing Nanjing Res Inst Elect Technol Nanjing 210012 Jiangsu Peoples R China

With the rapid development of wind power generation, the number of installed wind turbine increases rapidly. Research shows that radar echoes from wind turbines may have the potential to distract and confuse the target signal by creating false detection. To solve this problem, this study proposes a method based on orthogonal matching pursuit (omp) algorithm for clutter mitigation. The algorithm first establishes an echo dictionary matrix and filters out clutter through loop operations. Experiments show that the method effectively filters the clutter of the wind turbine.

关键词： wind turbines radar clutter iterative methods time-frequency analysis radar detection wind power plants matrix algebra radar echoes orthogonal matching pursuit algorithm echo dictionary matrix wind turbine clutter mitigation omp algorithm wind power generation false detection loop operations

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Compressive Sensing Based Off-Grid DOA Estimation Using omp algorithm 6

Compressive Sensing Based Off-Grid DOA Estimation Using OMP ...

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6th International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Ganguly, Saurav Ghosh, Ishita Ranjan, Ratnesh Ghosh, Jayanta Kumar, Puli Kishore Mukhopadhyay, Mainak Natl Inst Technol Dept ECE Patna Bihar India Deloitte India Off US IoT Serv Bengaluru India Natl Inst Technol Dept ECE Tadepalligudem Andhra Pradesh India Birla Inst Technol Dept ECE Deoghar Campus Mesra India

ISBN: (纸本)9781728113807

In this paper we present a method of Direction of Arrival (DOA) estimation using a sparse array based on compressive sensing. The sources are assumed to be at off -grid location with respect to the receiver array. NV.: show that the off -grid DOA of the sources can be estimated almost accurately by using the Orthogonal Matching Pursuit (omp) algorithm for reconstruction from the sparse samples.

关键词： Off-grid DOA estimation compressive sensing sparse array omp algorithm

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3D Compressed Spectrum Mapping With Sampling Locations Optimization in Spectrum-Heterogeneous Environment

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第1期21卷 326-338页

作者： Shen, Feng Wang, Zheng Ding, Guoru Li, Kezhi Wu, Qihui Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum Minist Ind & Informat Technol Nanjing 211106 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Nanjing Univ Aeronaut & Astronaut NUAA Coll Elect & Informat Engn Nanjing 211106 Peoples R China Army Engn Univ Coll Commun Engn Nanjing 210007 Peoples R China UCL Inst Hlth Informat London NW1 2DA England

Spectrum mapping has emerged as an important problem in wireless communications, which generates a spectrum map for the spectrum resource analysis and management. Given the constrained transceiver volume and the limited energy consumption, how to effectively reconstruct the spectrum situation by the limited sampling data is a pressing challenge for spectrum mapping. In this paper, by exploiting the sparse nature of spectrum situation, we firstly attempt to solve the three-dimensional (3D) compressed spectrum mapping problem in the way of compressed sensing. Then, we develop a quadrature and right-triangular (QR) pivoting based measurement matrix optimization algorithm. By iteratively selecting new dominant sampling locations, it promotes the recovery accuracy compared to random measurement. After that, we propose a 3D spatial subspace based orthogonal matching pursuit (omp) algorithm to recover spectrum situation for 3D compressed spectrum mapping. Finally, simulations are presented to show the comparisons in terms of localization, source signal strength recovery, recovery success rate and situation recovery. Results show our proposed 3D spectrum mapping scheme not only effectively reduces the sampling number, but also achieves a high level of spectrum mapping accuracy.

关键词： Three-dimensional displays Matching pursuit algorithms Compressed sensing Wireless communication Solid modeling Radio frequency Optimization Spectrum mapping compressed sensing spectrum-heterogeneity omp algorithm

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Dictionaries Construction Using Alternating Projection Method in Compressive Sensing

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IEEE SIGNAL PROCESSING LETTERS 2011年第11期18卷 663-666页

作者： Li, Bo Shen, Yi Li, Jia Harbin Inst Technol Harbin 150001 Peoples R China

This letter introduces a novel algorithm to construct sensing and measurement dictionaries in compressive sensing using alternating projection method. The cumulative and mutual cross coherence of the constructed dictionaries are lower than those of Gaussian random dictionary. The concept of General Restricted Isometry Constant (GRIC) is introduced. Low cumulative cross coherence puts bound on GRIC and small GRIC improves successful recovery rate of omp algorithm. Experiments demonstrate that omp algorithm performs better using dictionaries constructed by the proposed algorithm than Gaussian random dictionaries and those constructed by Schnass' algorithm.

关键词： Compressive sensing omp algorithm sensing dictionary

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Improved analysis of orthogonal matching pursuit in general perturbations

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ELECTRONICS LETTERS 2018年第5期54卷 295-U26页

作者： Zhang, Xiaobo Xu, Wenbo Tian, Yun Lin, Jiaru Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Peoples Publ Secur Univ China Beijing 100038 Peoples R China

Previous research on orthogonal matching pursuit ( omp) algorithm mainly focuses on the recovery performance of a sparse signal x given an acquired model y = Fx + n. A general perturbation model y = ( F + E) x + n in addition to the above acquired model exists, where E is the measurement perturbation. For this general perturbation model, the new restricted isometry constant of omp algorithm is shown.

关键词： iterative methods perturbation techniques compressed sensing orthogonal matching pursuit improved analysis omp algorithm sparse signal recovery performance general perturbation model measurement perturbation compressed sensing restricted isometry constant

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Cascaded clutter and jamming suppression method using sparse representation

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ELECTRONICS LETTERS 2015年第19期51卷 1524-1525页

作者： Feng, Weike Zhang, Yongshun He, Xingyu Guo, Yiduo Air Force Engn Univ Xian 710051 Shaanxi Peoples R China

To suppress ground clutter and barrage noise jamming for airborne radar effectively and efficiently, a cascaded method using the sparse representation technique is proposed. Based on the sparsity of jamming in the spatial domain, the jamming subspace is first estimated using the orthogonal matching pursuit algorithm with only one snapshot. Then, the jamming is rejected by projecting the received signal on the orthogonal complement subspace of the jamming. Lastly, reduced-dimension space-time adaptive processing algorithms are utilised directly to suppress clutter. Simulation results demonstrate that the novel method could achieve great performance as well as a significant saving of computation and training samples.

关键词： airborne radar approximation theory jamming signal representation space-time adaptive processing omp algorithm RD-STAP algorithms airborne radar cascaded clutter method jamming suppression method orthogonal matching pursuit algorithm reduced-dimension space-time adaptive processing algorithms sparse representation technique spatial domain

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MMSE-based version of omp for recovery of discrete-valued sparse signals

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ELECTRONICS LETTERS 2016年第1期52卷 75-76页

作者： Sparrer, S. Fischer, R. F. H. Univ Ulm Inst Commun Engn D-89069 Ulm Germany

An enhanced algorithm for the reconstruction of discrete-valued sparse vectors from an underdetermined system of linear equations under the presence of noise is presented. Knowledge from the field of compressed sensing and from digital communications is combined in optimising the estimation step in the orthogonal matching pursuit (omp) algorithm according to the minimum mean-squared error (MMSE) criterion. Thereby, a gain in performance of more than 1 dB is achieved compared with the conventional omp.

关键词： compressed sensing iterative methods least mean squares methods signal reconstruction vectors MMSE criterion omp algorithm compressed sensing digital communications discrete-valued sparse signals discrete-valued sparse vectors reconstruction estimation step linear equations minimum mean-squared error criterion orthogonal matching pursuit algorithm

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