Journal articles: 'Compressive sampling matching pursuit algorithm'

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Zhao, Yijiu, Xiaoyan Zhuang, Zhijian Dai, and Houjun Wang. "Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure." Mathematical Problems in Engineering 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/606974.

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This paper suggests an upside-down tree-based orthogonal matching pursuit (UDT-OMP) compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved.

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Zhao, Huihuang, Yaonan Wang, Zhijun Qiao, and Bin Fu. "Solder joint imagery compressing and recovery based on compressive sensing." Soldering & Surface Mount Technology 26, no. 3 (May 27, 2014): 129–38. http://dx.doi.org/10.1108/ssmt-09-2013-0024.

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Purpose – The purpose of this paper is to develop an improved compressive sensing algorithm for solder joint imagery compressing and recovery. The improved algorithm can improve the performance in terms of peak signal to noise ratio (PSNR) of solder joint imagery recovery. Design/methodology/approach – Unlike the traditional method, at first, the image was transformed into a sparse signal by discrete cosine transform; then the solder joint image was divided into blocks, and each image block was transformed into a one-dimensional data vector. At last, a block compressive sampling matching pursuit was proposed, and the proposed algorithm with different block sizes was used in recovering the solder joint imagery. Findings – The experiments showed that the proposed algorithm could achieve the best results on PSNR when compared to other methods such as the orthogonal matching pursuit algorithm, greedy basis pursuit algorithm, subspace pursuit algorithm and compressive sampling matching pursuit algorithm. When the block size was 16 × 16, the proposed algorithm could obtain better results than when the block size was 8 × 8 and 4 × 4. Practical implications – The paper provides a methodology for solder joint imagery compressing and recovery, and the proposed algorithm can also be used in other image compressing and recovery applications. Originality/value – According to the compressed sensing (CS) theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The findings provide fundamental guidelines to improve performance in image compressing and recovery based on compressive sensing.

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Rao, M. Sreenivasa, K. Krishna Naik, and K. Maheshwara Reddy. "Radar Signal Recovery using Compressive Sampling Matching Pursuit Algorithm." Defence Science Journal 67, no. 1 (December 23, 2016): 94. http://dx.doi.org/10.14429/dsj.67.9906.

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In this study, we propose compressive sampling matching pursuit (CoSaMP) algorithm for sub-Nyquist based electronic warfare (EW) receiver system. In compressed sensing (CS) theory time-frequency plane localisation and discretisation into a N×N grid in union of subspaces is established. The train of radar signals are sparse in time and frequency can be under sampled with almost no information loss. The CS theory may be applied to EW digital receivers to reduce sampling rate of analog to digital converter; to improve radar parameter resolution and increase input bandwidth. Simulated an efficient approach for radar signal recovery by CoSaMP algorithm by using a set of various sample and different sparsity level with various radar signals. This approach allows a scalable and flexible recovery process. The method has been satisfied with data in a wide frequency range up to 40 GHz. The simulation shows the feasibility of our method.

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Zhao, Huihuang, Jianzhen Chen, Shibiao Xu, Ying Wang, and Zhijun Qiao. "Compressive sensing for noisy solder joint imagery based on convex optimization." Soldering & Surface Mount Technology 28, no. 2 (April 4, 2016): 114–22. http://dx.doi.org/10.1108/ssmt-09-2014-0017.

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Purpose The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost. Design/methodology/approach Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters. Findings Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(k*k) instead of O(1/k). Practical implications This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery. Originality/value According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.

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Aziz, Ahmed, Karan Singh, Ahmed Elsawy, Walid Osamy, and Ahmed M. Khedr. "GWRA: grey wolf based reconstruction algorithm for compressive sensing signals." PeerJ Computer Science 5 (September 2, 2019): e217. http://dx.doi.org/10.7717/peerj-cs.217.

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The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.

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Jarrah, Amin, and Mohsin M. Jamali. "Reconfigurable FPGA/GPU-Based Architecture of Block Compressive Sampling Matching Pursuit Algorithm." Journal of Circuits, Systems and Computers 24, no. 04 (March 4, 2015): 1550055. http://dx.doi.org/10.1142/s0218126615500553.

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The signals in reality are sparse signal where a few numbers of samples are non-zero. So, a compression technique must be applied to reduce the overhead of processing, storing, and transmission. Blocking compressive sampling matching pursuit (BCoSaMP) algorithm is a recursive algorithm which provides an accurate reconstruction of sparse signal from a small number of noisy samples. It doesn't assume that the noise is Gaussian or bounded but it uses information about the noise magnitude for stopping criterion. However, BCoSaMP is a computationally intensive algorithm. So, BCoSaMP algorithm has been implemented on both field-programmable gate array (FPGA) and graphic processing units (GPU) by exploiting parallel and pipelining approaches. A new software tool called radar signal processing tool (RSPT) is also presented. It allows the designer to auto-generate fully optimized VHDL representation of BCoSaMP by specifying many user input parameters through graphical user interface (GUI). Moreover, it provides the designer a feedback on various performance parameters. This offer the designer the ability to make any adjustments to the BCoSaMP component until gets the desired performance of the overall system-on-chip (SoC). Our simulation results indicate that the achieved speed-up of FPGA and GPU over the sequential one is improved by up to 14 and 10.7, respectively.

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Ruiz, Milton, Manuel Jaramillo, Alexander Aguila, Leony Ortiz, and Silvana Varela. "A Novel Data Compression Methodology Focused on Power Quality Signals Using Compressive Sampling Matching Pursuit." Energies 15, no. 24 (December 9, 2022): 9345. http://dx.doi.org/10.3390/en15249345.

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In this research a new data compression technique for electrical signals was proposed. The methodology combined wavelets and compressed sensing techniques. Two algorithms were proposed; the first one was designed to find specific characteristics of any type of energy quality signal such as the number of samples per cycle, zero-crossing indices, and signal amplitude. With the data obtained, the second algorithm was designed to apply a biorthogonal wavelet transform resulting in a shifted signal, and its amplitude was modified with respect to the original. The errors were rectified with the attributes found in the early stage, and the application of filters was conducted to reduce the ripple attached. Then, the third algorithm was designed to apply Compressive Sampling Matching Pursuit, which is a greedy algorithm that creates a dictionary with orthogonal bases representing the original signal in a sparse vector. The results exhibited excellent features of quality and were accomplished by the suggested compression and reconstruction technique. These results were a compression ratio of 1020:1, that is, the signal was compressed by 99.90% with respect to the original one. The quality indicators achieved were RTE = 0.9938, NMSE = 0.0098, and COR = 0.99, exceeding the results of the most relevant research papers published in Q1 high-impact journals that were further discussed in the introduction section.

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Yunfeng, Hu, and Zhao Liquan. "A Fuzzy Selection Compressive Sampling Matching Pursuit Algorithm for its Practical Application." IEEE Access 7 (2019): 144101–24. http://dx.doi.org/10.1109/access.2019.2941725.

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Li, Guo Zhu, De Qiang Wang, Zi Kai Zhang, and Zhi Yong Li. "A Weighted OMP Algorithm for Compressive UWB Channel Estimation." Applied Mechanics and Materials 392 (September 2013): 852–56. http://dx.doi.org/10.4028/www.scientific.net/amm.392.852.

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We investigate ultra-wideband (UWB) channel estimation based on compressive sampling (CS), where the orthogonal matching pursuit (OMP) algorithm is employed to recover the channel waveform from noisy measurements. In order to boost the robustness of OMP in the presence of additive Gaussian noise (AWGN), we propose a weighted OMP (WOMP) algorithm. For a given sparse dictionary, weighting factors are assigned to the atoms and a weighted matching process is performed by WOMP. Simulation results show that the proposed WOMP is more robust than the original OMP and can be used to gain better channel estimation precision.

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Zhou, Huiyuan, and Ram M. Narayanan. "A DUAL-MESH MICROWAVE RECONSTRUCTION METHOD BASED ON COMPRESSIVE SAMPLING MATCHING PURSUIT ALGORITHM." Progress In Electromagnetics Research 166 (2019): 43–57. http://dx.doi.org/10.2528/pier19090203.

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Huang, Fang, Jian Tao, Yang Xiang, Peng Liu, Lei Dong, and Lizhe Wang. "Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL." Journal of Systems Architecture 72 (January 2017): 51–60. http://dx.doi.org/10.1016/j.sysarc.2016.07.002.

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Dan, Hu. "Compressive Sampling Orthogonal Matching Pursuit Algorithm Based on Peak Signal to Noise Ratio." International Journal of Future Generation Communication and Networking 9, no. 8 (August 31, 2016): 23–32. http://dx.doi.org/10.14257/ijfgcn.2016.9.8.03.

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Sun, Guiling, Yangyang Li, Haojie Yuan, Jingfei He, and Tianyu Geng. "The Improvement of Compressive Sampling and Matching Pursuit Algorithm Based on Pre-estimation." International Journal of Wireless Information Networks 23, no. 2 (April 21, 2016): 129–34. http://dx.doi.org/10.1007/s10776-016-0310-7.

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Murali Krishna, P. V., and T. V. Ramana. "Millimeter Wave MIMO out Door Channel Estimation and Precoding." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012143. http://dx.doi.org/10.1088/1742-6596/2070/1/012143.

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Abstract Millimeter wave (mm Wave) communications is one of the technologies for 5G cellular systems. In the mm Wave communication, there is a lot of path loss can be reduced by Precoding. The channel state information (CSI) should be known at the transmitting station, in the design of precoding matrices and to get good accuracy in estimating sparse channels a Compressive sensing (CS) based recovery algorithms was used. Not only for good accuracy the algorithm is also used for mm Wave channel estimation for exploiting the mm Wave channel’s sparse in multi-path construction. Hence, in this paper, for mm Wave outdoor channel estimation, the CS recovery methods orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP) are used. The singular value decomposition (SVD) precoding is developed using the estimated channel. By (MSE) mean square error and spectral efficiency which were the performance metrics in channel estimation and precoding were done by using MATLAB simulations to get the efficacy of the OMP and CoSaMP algorithm.

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Chen, Jian, Jie Jia, Yansha Deng, Xingwei Wang, and Abdol-Hamid Aghvami. "Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks." Sensors 18, no. 10 (October 9, 2018): 3369. http://dx.doi.org/10.3390/s18103369.

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The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms.

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Yang, Liu, Ren Qinghua, Xu Bingzheng, and Li Xiazhao. "A Broadband Spectrum Sensing Algorithm in TDCS Based on ICoSaMP Reconstruction." MATEC Web of Conferences 173 (2018): 03073. http://dx.doi.org/10.1051/matecconf/201817303073.

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In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.

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Sun, Yajie, Feihong Gu, Sai Ji, and Lihua Wang. "Composite Plate Phased Array Structural Health Monitoring Signal Reconstruction Based on Orthogonal Matching Pursuit Algorithm." Journal of Sensors 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3157329.

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In order to ensure the safety of composite components, structural health monitoring is needed to detect structural performance in real-time at the early stage of damage occurred. This is difficult to detect complex components with single sensor detection technology, so that ultrasonic phased array technology using multisensor detection will be selected. Ultrasonic phased array technology can scan the structure in all directions and angles without moving or less moving the probe and becomes the first choice of structural health monitoring. However, a large amount of data will be generated when using ultrasonic phased array with Nyquist sampling theorem for structural health monitoring and is difficult to storage, transmission, and processing. Besides, traditional Nyquist sampling cannot satisfy the sampling of large amounts of data without distortion, so a more efficient acquisition technique must be chosen. Compressive sensing theory can ensure that if the signal is sparse, it can be sampled in low sampling rate which is much less than two times of the sampling rate as defined by Nyquist sampling theorem for a large number of data and reconstructed in high probability. Then, the experiment result indicated that the orthogonal matching pursuit algorithm can reconstruct the signal completely and accurately.

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Elaveini, Mathiyalakendran Aarthi, and Deepa Thangavel. "Performance analysis of compressive sensing recovery algorithms for image processing using block processing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5063. http://dx.doi.org/10.11591/ijece.v12i5.pp5063-5072.

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<p>The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based onmean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works.</p>

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Shang, Sai Qi, Min Gang Wang, Wei Li, and Yao Yang. "A Terahertz Compressive Imaging Method Based on Single Detector of Randomly Moving Template." Advanced Materials Research 756-759 (September 2013): 3785–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3785.

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Expensiveness and lack of N-pixels sensor affect the application of terahertz imaging. New compressed sensing theory recently achieved a major breakthrough in the field of signal codec, making it possible to recover the original image by using the measured values, which have much smaller number than the pixels in the image. In this paper, by comparing the measurement matrices based on different reconstruction algorithms, such as Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit and Minimum L_1 Norm algorithms, we proposed a terahertz imaging method based on single detector of randomly moving measurement matrices, designed the mobile random templates and an automatically template changing mechanism, constructed a single detector imaging system, and completed the single terahertz detector imaging experiments.

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Zha, Changjun, Yao Li, Jinyao Gui, Huimin Duan, and Tailong Xu. "Compressive Imaging of Moving Object Based on Linear Array Sensor." Journal of Electrical and Computer Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/4560642.

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Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

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Xiao, Yuzhu, Guoli Dong, and Xueli Song. "Data-Based Reconstruction of Chaotic Systems by Stochastic Iterative Greedy Algorithm." Mathematical Problems in Engineering 2020 (October 31, 2020): 1–9. http://dx.doi.org/10.1155/2020/6718304.

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It is challenging to reconstruct a nonlinear dynamical system when sufficient observations are not available. Recent study shows this problem can be solved by paradigm of compressive sensing. In this paper, we study the reconstruction of chaotic systems based on the stochastic gradient matching pursuit (StoGradMP) method. Comparing with the previous method based on convex optimization, the study results show that the StoGradMP method performs much better when the numerical sampling period is small. So the present study enables potential application of the reconstruction method using limited observations in some special situations where limited observations can be acquired in limited time.

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Zhang, Lin. "Image Reconstruction Based on Compressive Sensing via CoSaMP." Applied Mechanics and Materials 631-632 (September 2014): 436–40. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.436.

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Compressive Sampling Matching Pursuit (CoSaMP) is a new iterative recovery algorithm which has splendid theoretical guarantees for convergence and delivers the same guarantees as the best optimization-based approaches. In this paper, we propose a new signal recovery framework which combines CoSaMP and Curvelet transform for better performance. In classic CoSaMP, the number of iterations is fixed. We discuss a new stopping rule to halting the algorithm in this paper. In addition, the choice of several adjustable parameters in algorithm such as the number of measurements and the sparse level of the signal also will impact the performance. So we gain above parameters via a large number of experiments. According to experiments, we determine an optimum value for the parameters to use in this application. The experiments show that the new method not only has better recovery quality and higher PSNRs, but also can achieve optimization steadily and effectively.

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Xu, Ping, Bingqiang Chen, Lingyun Xue, Jingcheng Zhang, and Lei Zhu. "A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm." Sensors 18, no. 10 (September 30, 2018): 3289. http://dx.doi.org/10.3390/s18103289.

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In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.

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M. Devendrappa, Deepak, Karthik P., Deepak N. Ananth, and Arun Kumar P. "Comparison of different sparse dictionaries for compressive sampling." Bulletin of Electrical Engineering and Informatics 11, no. 5 (October 1, 2022): 2611–20. http://dx.doi.org/10.11591/eei.v11i5.4014.

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Compressive sampling/compressed sensing (CS) is building on the observation that most of the signals in nature are sparse or compressible concerning some transform domain. And by converse, the same can be reconstructed with high accuracy by making use of far fewer samples than what is required by violating Shannon-Nyquist theorem. Some of the transform techniques like discrete cosine transform, fast fourier transforms discrete wavelet transform, discrete fourier transforms. In this paper, novel CS techniques like FFTCoSAMP, DCTCoSaMP, and DWTCoSaMP are introduced and compared on different sparse transforms for CS in magnetic resonance (MR) images based on sparse signal sequences/dictionaries by means of transform techniques with respect to objective quality assessment algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for compressive sampling matching pursuit. DWTCoSaMP is giving the PSNR values of 37.16 (DB4), 38.12 (Coif3), 38.5 (Sym8), for DCTCoSaMP and FFTCoSaMP, it’s 36.33 and 36.01 respectively. For DWTCoSaMP, SSIM value is 0.81, and for DCTCoSaMP and FFTCoSaMP, it’s 0.73 and 0.7 respectively. And finally, for DWTCoSaMP, RMSE value is 0.66, and for DCTCoSaMP and FFTCoSaMP, it’s 0.53 and 0.41 respectively. DWTCoSaMP reveals the best than rest of the methods and traditional CS techniques by the detailed comparison and analysis.

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He, Xiaowei, Hongbo Guo, Jingjing Yu, Xu Zhang, and Yuqing Hou. "Effective and robust approach for fluorescence molecular tomography based on CoSaMP and SP3 model." Journal of Innovative Optical Health Sciences 09, no. 06 (August 2016): 1650024. http://dx.doi.org/10.1142/s1793545816500243.

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Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically [Formula: see text], is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP(StOMP), and regularized OMP (ROMP). The CoSaMP combined with [Formula: see text] shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

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Feng, Wang, Chen Feng-wei, and Wang Jia. "Reconstruction Technique Based on the Theory of Compressed Sensing Satellite Images." Open Electrical & Electronic Engineering Journal 9, no. 1 (March 16, 2015): 74–81. http://dx.doi.org/10.2174/1874129001509010074.

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Owing to the characteristics such as high resolution, large capacity, and great quantity, thus far, how to efficient store and transmit satellite images is still an unsolved technical problem. Satellite image Compressed sensing (CS) theory breaks through the limitations of traditional Nyquist sampling theory, it is based on signal sparsity, randomness of measurement matrix and nonlinear optimization algorithms to complete the sampling compression and restoring reconstruction of signal. This article firstly discusses the study of satellite image compression based on compression sensing theory. It then optimizes the widely used orthogonal matching pursuit algorithm in order to make it fits for satellite image processing. Finally, a simulation experiment for the optimized algorithm is carried out to prove this approach is able to provide high compression ratio and low signal to noise ratio, and it is worthy of further study.

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Gargouri, Yosra, Hervé Petit, Patrick Loumeau, Baptiste Cecconi, and Patricia Desgreys. "Compressive Sampling for Efficient Astrophysical Signals Digitizing: From Compressibility Study to Data Recovery." Journal of Astronomical Instrumentation 05, no. 04 (December 2016): 1641020. http://dx.doi.org/10.1142/s2251171716410208.

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The design of a new digital radio receiver for radio astronomical observations in outer space is challenged with energy and bandwidth constraints. This paper proposes a new solution to reduce the number of samples acquired under the Shannon–Nyquist limit while retaining the relevant information of the signal. For this, it proposes to exploit the sparsity of the signal by using a compressive sampling process (also called Compressed Sensing (CS)) at the Analog-to-Digital Converter (ADC) to reduce the amount of data acquired and the energy consumption. As an example of an astrophysical signal, we have analyzed a real Jovian signal within a bandwidth of 40[Formula: see text]MHz. We have demonstrated that its best sparsity is in the frequency domain with a sparsity level of at least 10% and we have chosen, through a literature review, the Non-Uniform Sampler (NUS) as the receiver architecture. A method for evaluating the reconstruction of the Jovian signal is implemented to assess the impact of CS compression on the relevant information and to calibrate the detection threshold. Through extensive numerical simulations, and by using Orthogonal Matching Pursuit (OMP) as the reconstruction algorithm, we have shown that the Jovian signal could be sensed by taking only 20% of samples at random, while still recovering the relevant information.

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Liu, Haoqiang, Hongbo Zhao, and Wenquan Feng. "Filtering-Based Regularized Sparsity Variable Step-Size Matching Pursuit and Its Applications in Vehicle Health Monitoring." Applied Sciences 11, no. 11 (May 24, 2021): 4816. http://dx.doi.org/10.3390/app11114816.

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Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods.

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Chatterjee, Ayan, and Peter W. T. Yuen. "Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions." Journal of Imaging 5, no. 11 (November 15, 2019): 85. http://dx.doi.org/10.3390/jimaging5110085.

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This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

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Liu, Gang, Honggui Deng, Kai Yang, Zaoxing Zhu, Jitai Liu, and Hu Dong. "A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems." Symmetry 13, no. 5 (April 23, 2021): 743. http://dx.doi.org/10.3390/sym13050743.

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The precoding scheme based on codebooks is used to save the same set of codebook in advance at the transmitter and the receiver, then, the receiver selects the most appropriate precoding matrix from codebooks according to different channel state information (CSI). Therefore, the design of codebook plays an important role in the performance of the whole scheme. The symmetry-based hybrid precoder and combiner is a highly energy efficient structure in the millimeter-wave massive multiple-input multiple-output (MIMO) system, but at the same time, it also has the problems of high bit error rate and low spectral efficiency. In order to improve the spectral efficiency, we formulate the codebook design as a joint optimization problem and propose an iteration algorithm to obtain the enhanced codebook by combining the compressive sampling matching pursuit (CoSaMP) algorithm with the dictionary learning algorithm. In order to prove the validity of the proposed algorithm, we simulate and analyze the change of the spectral efficiency of the algorithm with the signal-to-noise ratio (SNR) and the number of radio frequency (RF) chains of different precoding schemes. The simulation results demonstrate that the spectral efficiency of the algorithm is obviously outstanding compared with that of the OMP-based joint codebook algorithm and the hybrid precoding algorithm with quantization algorithm under low SNR and different numbers of RF chains. Particularly, when SNR is lower than 0 dB, the proposed algorithm performs very close to the optimal unconstrained precoding algorithm.

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Zhang, Yong, Jie Jiang, and Guangjun Zhang. "Compression of Remotely Sensed Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space Exploration." Remote Sensing 13, no. 2 (January 15, 2021): 288. http://dx.doi.org/10.3390/rs13020288.

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Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate.

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Xiao, Hu, Shao, and Li. "A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition." Sensors 19, no. 23 (December 3, 2019): 5330. http://dx.doi.org/10.3390/s19235330.

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Biometric systems allow recognition and veriﬁcation of an individual through his or her physiological or behavioral characteristics. It is a growing ﬁeld of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

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33

Wang, Rui, Jinglei Zhang, Suli Ren, and Qingjuan Li. "A reducing iteration orthogonal matching pursuit algorithm for compressive sensing." Tsinghua Science and Technology 21, no. 1 (February 2016): 71–79. http://dx.doi.org/10.1109/tst.2016.7399284.

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Geng, Liping, Jinchuan Zhou, Zhongfeng Sun, and Jingyong Tang. "Compressive hard thresholding pursuit algorithm for sparse signal recovery." AIMS Mathematics 7, no. 9 (2022): 16811–31. http://dx.doi.org/10.3934/math.2022923.

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<abstract><p>Hard Thresholding Pursuit (HTP) is one of the important and efficient algorithms for reconstructing sparse signals. Unfortunately, the hard thresholding operator is independent of the objective function and hence leads to numerical oscillation in the course of iterations. To alleviate this drawback, the hard thresholding operator should be applied to a compressible vector. Motivated by this idea, we propose a new algorithm called Compressive Hard Thresholding Pursuit (CHTP) by introducing a compressive step first to the standard HTP. Convergence analysis and stability of CHTP are established in terms of the restricted isometry property of a sensing matrix. Numerical experiments show that CHTP is competitive with other mainstream algorithms such as the HTP, Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP) algorithms both in the sparse signal reconstruction ability and average recovery runtime.</p></abstract>

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35

Liquan, Zhao, Ma Ke, and Jia Yanfei. "Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing." Journal of Electrical and Computer Engineering 2020 (April 10, 2020): 1–11. http://dx.doi.org/10.1155/2020/2782149.

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The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The number of selected atoms is the integral multiple of initial step size. In respect of sparsity estimation, the proposed method introduces the exponential function to sparsity estimation. It uses a larger step size to estimate sparsity at the beginning of iteration to accelerate the algorithm convergence speed and a smaller step size to improve the reconstruction accuracy. Simulations show that the proposed method has better performance in terms of convergence speed and reconstruction accuracy for one-dimension signal and two-dimension signal.

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Xia, C. Y., Z. L. Zhou, Chun-Bo Guo, Y. S. Hao, and C. B. Hou. "A New Analysis for Support Performance with Block Generalized Orthogonal Matching Pursuit." Mathematical Problems in Engineering 2021 (January 29, 2021): 1–7. http://dx.doi.org/10.1155/2021/9438793.

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For recovering block-sparse signals with unknown block structures using compressive sensing, a block orthogonal matching pursuit- (BOMP-) like block generalized orthogonal matching pursuit (BgOMP) algorithm has been proposed recently. This paper focuses on support conditions of recovery of any K -sparse block signals incorporating BgOMP under the framework of restricted isometry property (RIP). The proposed support conditions guarantee that BgOMP can achieve accurate recovery block-sparse signals within k iterations.

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Zhao, Hongtu, Chong Chen, and Chenxu Shi. "Image reconstruction algorithm based on variable atomic number matching pursuit." Journal of Algorithms & Computational Technology 11, no. 2 (October 12, 2016): 103–9. http://dx.doi.org/10.1177/1748301816673074.

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As the most critical part of compressive sensing theory, reconstruction algorithm has an impact on the quality and speed of image reconstruction. After studying some existing convex optimization algorithms and greedy algorithms, we find that convex optimization algorithms should possess higher complexity to achieve higher reconstruction quality. Also, fixed atomic numbers used in most greedy algorithms increase the complexity of reconstruction. In this context, we propose a novel algorithm, called variable atomic number matching pursuit, which can improve the accuracy and speed of reconstruction. Simulation results show that variable atomic number matching pursuit is a fast and stable reconstruction algorithm and better than the other reconstruction algorithms under the same conditions.

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38

Fang, Shuheng, Zhengmin Kong, Ping Hu, and Li Ding. "A novel topology identification method based on compressive sensing for multidimensional networks." International Journal of Modern Physics B 34, no. 30 (October 28, 2020): 2050294. http://dx.doi.org/10.1142/s021797922050294x.

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In real-world scenarios, it is difficult to know about the complete topology of a huge network with different types of links. In this brief, we propose a method to identify the topology of multidimensional networks from information transmission data. We consider information propagating over edges of a two-dimensional (2D) network, where one type of links is known and the other type is unknown. Given the state of all nodes at each unit time, we can transform the topology identification problem into a compressive sensing framework. A modified reconstruction algorithm, called Sparsity Adaptive Matching Pursuit with Mixed Threshold Mechanism (SAMPMTM), is proposed to tackle the problem. Compared with the classical Sparsity Adaptive Matching Pursuit (SAMP) algorithm, the proposed SAMPMTM algorithm can reduce the conflict rate and improve the accuracy of network recovery. We further demonstrate the performance of this improved algorithm through Monte-Carlo simulations under different network models.

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39

Yu, Yong Fang, Fei Yi, Huan Xin Cheng, and Li Cheng. "Ultrasonic Testing Signal De-Noising Processing Based on Orthogonal Matching Pursuit Algorithm." Applied Mechanics and Materials 423-426 (September 2013): 2468–71. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2468.

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Ultrasonic NDT signal de-noising effect is the key indicators to determine whether the pipe is defective.Orthogonal matching pursuit algorithm optimization process, the sampling signal and reconstructed signal by the method of least squares.Using Orthogonal Matching Pursuit Algorithm for the sampling signal to optimize and reconstruct. It is successfully to verify the OMP algorithm can recover the signal based on the MATLAB platform.Designed a signal de-noising processing module to help staff to determine pipeline defects conveniently,it achieved de-noising successfully.

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40

Zhang, Xiaobo, Wenbo Xu, Yupeng Cui, Liyang Lu, and Jiaru Lin. "On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit." IEEE Access 7 (2019): 175554–63. http://dx.doi.org/10.1109/access.2019.2955759.

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Liu, Ya-xin, Rui-zhen Zhao, Shao-hai Hu, and Chun-hui Jiang. "Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Based on Compressive Sensing." Journal of Electronics & Information Technology 32, no. 11 (December 10, 2010): 2713–17. http://dx.doi.org/10.3724/sp.j.1146.2009.01623.

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42

Zhao, Rongqiang, Qiang Wang, Yi Shen, and Jia Li. "Multiatom tensor orthogonal matching pursuit algorithm for compressive-sensing–based hyperspectral image reconstruction." Journal of Applied Remote Sensing 10, no. 4 (October 6, 2016): 045002. http://dx.doi.org/10.1117/1.jrs.10.045002.

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43

Bi, Xue, Lu Leng, Cheonshik Kim, Xinwen Liu, Yajun Du, and Feng Liu. "Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing." Applied Sciences 11, no. 4 (February 5, 2021): 1435. http://dx.doi.org/10.3390/app11041435.

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Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

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44

Chen, Wengu, and Huanmin Ge. "A Sharp Bound on RIC in Generalized Orthogonal Matching Pursuit." Canadian Mathematical Bulletin 61, no. 1 (March 1, 2018): 40–54. http://dx.doi.org/10.4153/cmb-2017-009-6.

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AbstractThe generalized orthogonal matching pursuit (gOMP) algorithm has received much attention in recent years as a natural extension of the orthogonal matching pursuit (OMP). It is used to recover sparse signals in compressive sensing. In this paper, a new bound is obtained for the exact reconstruction of every K-sparse signal via the gOMP algorithm in the noiseless case. That is, if the restricted isometry constant (RIC) δNK+1 of the sensing matrix A satisfiesthen the gOMP can perfectly recover every K-sparse signal x from y = Ax. Furthermore, the bound is proved to be sharp. In the noisy case, the above bound on RIC combining with an extra condition on the minimum magnitude of the nonzero components of K-sparse signals can guarantee that the gOMP selects all of the support indices of the K-sparse signals.

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45

Zhang, Shuo, Dongqing Wang, and Yaru Yan. "Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems." Complexity 2018 (July 22, 2018): 1–10. http://dx.doi.org/10.1155/2018/8420426.

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Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system. The idea is, by the filtering technique and the instrumental variable method, to transform the Hammerstein system into a simple form with a separated nonlinear expression and to parameterize the system into an autoregressive model, then to perform an instrumental variable-based orthogonal matching pursuit (IV-OMP) identification method for the Hammerstein system. Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency.

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46

Zhang, Lin, Xia Ling Zeng, and Sun Li. "Image Adaptive Denoising Method Based on Compressive Sensing." Applied Mechanics and Materials 635-637 (September 2014): 993–96. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.993.

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We present a new adaptive denosing method using compressive sensing (CS) and genetic algorithm (GA). We use Regularized Orthogonal Matching Pursuit (ROMP) to remove the noise of image. ROMP algorithm has the advantage of correct performance, stability and fast speed. In order to obtain the optimal denoising effect, we determine the values of the parameters of ROMP by GA. Experimental results show that the proposed method can remove the noise of image effectively. Compared with other traditional methods, the new method retains the most abundant edge information and important details of the image. Therefore, our method has optimal image quality and a good performance on PSNR.

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Zhu, Wen Jie, Guang Long Wang, Zhong Tao Qiao, and Feng Qi Gao. "A Novel Noise Reduction Algorithm of MEMS Gyroscope Based on Compressive Sensing and Lifting Wavelet Transform." Key Engineering Materials 609-610 (April 2014): 1138–43. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.1138.

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A novel noise reduction algorithm combined with compressive sensing (CS) and lifting wavelet transform (LWT) is proposed in this paper. This algorithm can overcome the limitations of traditional noise reduction methods based on Kalman filtering and wavelet threshold filtering. The characteristics of wavelet time-frequency distribution of the microelectromechanical system (MEMS) gyroscope are discussed to illustrate the demerit of the classical filtering methods. Noise reduction algorithm of MEMS gyroscope signal is studied in detail by combining CS theory with lifting wavelet transform. De-noising effect, time-consumption of computation as well as traditional CS reconstruction algorithms are analyzed. The results show that the signal reconstruction algorithm of conventional matching pursuit (MP) greedy algorithms contains more glitches and computation time-consumption, the basis pursuit de-noising (BPDN) algorithm is better and it has advantages of high computational efficiency and ease of implementation.

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48

Gao Rui, 高睿, 赵瑞珍 Zhao Ruizhen, and 胡绍海 Hu Shaohai. "Variable Step Size Adaptive Matching Pursuit Algorithm for Image Reconstruction Based on Compressive Sensing." Acta Optica Sinica 30, no. 6 (2010): 1639–44. http://dx.doi.org/10.3788/aos20103006.1639.

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49

Nam, Yunseo, and Namyoon Lee. "Bayesian Matching Pursuit: A Finite-Alphabet Sparse Signal Recovery Algorithm for Quantized Compressive Sensing." IEEE Signal Processing Letters 26, no. 9 (September 2019): 1285–89. http://dx.doi.org/10.1109/lsp.2019.2927848.

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Zhao, Ruizhen, Xiaoxin Ren, Xuelian Han, and Shaohai Hu. "An improved sparsity adaptive matching pursuit algorithm for compressive sensing based on regularized backtracking." Journal of Electronics (China) 29, no. 6 (October 30, 2012): 580–84. http://dx.doi.org/10.1007/s11767-012-0880-1.

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Journal articles on the topic 'Compressive sampling matching pursuit algorithm'

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