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Journal articles on the topic 'Structured Sparse Signal Estimation'
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1
Castro, Rui M., and Ervin Tanczos. "Adaptive Sensing for Estimation of Structured Sparse Signals." IEEE Transactions on Information Theory 61, no. 4 (April 2015): 2060–80. http://dx.doi.org/10.1109/tit.2015.2396917.
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Ahmed, Nisar. "Data-Free/Data-Sparse Softmax Parameter Estimation With Structured Class Geometries." IEEE Signal Processing Letters 25, no. 9 (September 2018): 1408–12. http://dx.doi.org/10.1109/lsp.2018.2860238.
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Sun, Xiaoyong, Shaojing Su, Junyu Wei, Xiaojun Guo, and Xiaopeng Tan. "Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems." Photonics 6, no. 4 (October 25, 2019): 111. http://dx.doi.org/10.3390/photonics6040111.
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A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.
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Chen, Tao, Jian Yang, Weitong Wang, and Muran Guo. "Generalized Sparse Polarization Array for DOA Estimation Using Compressive Measurements." Wireless Communications and Mobile Computing 2021 (March 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/5539709.
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The compressive array method, where a compression matrix is designed to reduce the dimension of the received signal vector, is an effective solution to obtain high estimation performance with low system complexity. While sparse arrays are often used to obtain higher degrees of freedom (DOFs), in this paper, an orthogonal dipole sparse array structure exploiting compressive measurements is proposed to estimate the direction of arrival (DOA) and polarization signal parameters jointly. Based on the proposed structure, we also propose an estimation algorithm using the compressed sensing (CS) method, where the DOAs are accurately estimated by the CS algorithm and the polarization parameters are obtained via the least-square method exploiting the previously estimated DOAs. Furthermore, the performance of the estimation of DOA and polarization parameters is explicitly discussed through the Cramér-Rao bound (CRB). The CRB expression for elevation angle and auxiliary polarization angle is derived to reveal the limit of estimation performance mathematically. The difference between the results given in this paper and the CRB results of other polarized reception structures is mainly due to the use of the compression matrix. Simulation results verify that, compared with the uncompressed structure, the proposed structure can achieve higher estimated performance with a given number of channels.
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Li, Yun, Lingxia Liao, Shanlin Sun, Zhicheng Tan, and Xing Yao. "Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing." International Journal of Distributed Sensor Networks 17, no. 6 (June 2021): 155014772110178. http://dx.doi.org/10.1177/15501477211017825.
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In multiple-input multiple-output–orthogonal frequency-division multiplexing underwater acoustic communication systems, the correlation of the sampling matrix is the key of the channel estimation algorithm based on compressed sensing. To reduce the cross-correlation of the sampling matrix and improve the channel estimation performance, a pilot design algorithm for co-sparse channel estimation based on compressed sensing is proposed in this article. Based on the time-domain correlation of the channel, the channel estimation is modeled as a common sparse signal reconstruction problem. When replacing each pilot indices position, the algorithm selects multiple pilot indices with the least cross-correlation from the alternative positions to replace the current pilot indices position, and it uses the inner and outer two-layer loops to realize the bit-by-bit optimal replacement of the pilot. The simulation results show that the channel estimation mean squared error of pilot design algorithm for co-sparse channel estimation based on compressed sensing can be reduced by approximately 18 dB compared with the least square algorithm. Compared with the genetic algorithm and search space size methods, the structural sequence search proposed by pilot design algorithm for co-sparse channel estimation based on compressed sensing is used to design the pilot to complete the channel estimation. Thus, the mean squared error of the channel estimation can be reduced by 2 dB. At the same bit error rate of 0.03, the signal-to-noise ratio can be decreased by approximately 7 dB.
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Zuo, Luo, Jun Wang, Te Zhao, and Zuhan Cheng. "A Joint Low-Rank and Sparse Method for Reference Signal Purification in DTMB-Based Passive Bistatic Radar." Sensors 21, no. 11 (May 22, 2021): 3607. http://dx.doi.org/10.3390/s21113607.
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In a digital terrestrial multimedia broadcasting (DTMB)-based passive bistatic radar (PBR) system, the received reference signal often suffers from serious multipath effect, which decreases the detection ability of low-observable targets in urban environments. In order to improve the target detection performance, a novel reference signal purification method based on the low-rank and sparse feature is proposed in this paper. Specifically, this method firstly performs synchronization operations to the received reference signal and thus obtains the corresponding pseudo-noise (PN) sequences. Then, by innovatively exploiting the inherent low-rank structure of DTMB signals, the noise component in PN sequences is reduced. After that, a temporal correlation (TC)-based adaptive orthogonal matching pursuit (OMP) method, i.e., TC-AOMP, is performed to acquire the reliable channel estimation, whereby the previous noise-reduced PN sequences and a new halting criterion are utilized to improve channel estimation accuracy. Finally, the purification reference signal is obtained via equalization operation. The advantage of the proposed method is that it can obtain superior channel estimation performance and is more efficient compared to existing methods. Numerical and experimental results collected from the DTMB-based PBR system are presented to demonstrate the effectiveness of the proposed method.
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De Canditiis, Daniela, and Italia De Feis. "Anomaly Detection in Multichannel Data Using Sparse Representation in RADWT Frames." Mathematics 9, no. 11 (June 3, 2021): 1288. http://dx.doi.org/10.3390/math9111288.
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We introduce a new methodology for anomaly detection (AD) in multichannel fast oscillating signals based on nonparametric penalized regression. Assuming the signals share similar shapes and characteristics, the estimation procedures are based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. Under the standard hypothesis of Gaussian additive noise, we model the signals by the RADWT and the anomalies as additive in each signal. Then we perform AD imposing a double penalty on the multiple regression model we obtained, promoting group sparsity both on the regression coefficients and on the anomalies. The first constraint preserves a common structure on the underlying signal components; the second one aims to identify the presence/absence of anomalies. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case.
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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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Zhang, Wenjie, Hui Li, Rong Jin, Shanlin Wei, Wei Cheng, Weisi Kong, and Penglu Liu. "Distributed Structured Compressive Sensing-Based Time-Frequency Joint Channel Estimation for Massive MIMO-OFDM Systems." Mobile Information Systems 2019 (May 2, 2019): 1–16. http://dx.doi.org/10.1155/2019/2634361.
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In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. However, high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency. In this paper, we propose a more efficient time-frequency joint channel estimation scheme for massive MIMO-OFDM systems to resolve those problems. First, partial channel common support (PCCS) is obtained by using time-domain training. Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain. Third, through performance analysis of the proposed algorithm, two signal power reference thresholds are given, which can ensure that the signal can be recovered accurately under power-limited noise and accurately recovered according to probability under Gaussian noise. Finally, pilot design, computational complexity, spectrum efficiency, and energy efficiency are discussed as well. Simulation results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods.
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Qin, Guodong, and Moeness G. Amin. "Structured sparse array design exploiting two uniform subarrays for DOA estimation on moving platform." Signal Processing 180 (March 2021): 107872. http://dx.doi.org/10.1016/j.sigpro.2020.107872.
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Hameed, Khurram, Shanshan Tu, Nauman Ahmed, Wasim Khan, Ammar Armghan, Fayadh Alenezi, Norah Alnaim, Muhammad Salman Qamar, Abdul Basit, and Farman Ali. "DOA Estimation in Low SNR Environment through Coprime Antenna Arrays: An Innovative Approach by Applying Flower Pollination Algorithm." Applied Sciences 11, no. 17 (August 29, 2021): 7985. http://dx.doi.org/10.3390/app11177985.
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The design of the modern computing paradigm of heuristics is an innovative development for parameter estimation of direction of arrival (DOA) using sparse antenna arrays. In this study, the optimization strength of the flower pollination algorithm (FPA) is exploited for the DOA estimation in a low signal to noise ratio (SNR) regime by applying coprime sensor arrays (CSA). The enhanced degree of freedom (DOF) is achieved with FPA by investigating the global minima of highly nonlinear cost function with multiple local minimas. The sparse structure of CSA demonstrates that the DOF up to O(MN) is achieved by employing M+N CSA elements, where M and N are the numbers of antenna elements used to construct the CSA. Performance analysis is conducted for estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of root mean square error (RMSE), variability analysis of RMSE, cumulative distribution function (CDF) of RMSE over Monte Carlo runs and the comparative studies of particle swarm optimization (PSO). These reveal the worth of the proposed methodology for estimating DOA.
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Zu, Shaohuan, Hui Zhou, Rushan Wu, Maocai Jiang, and Yangkang Chen. "Dictionary learning based on dip patch selection training for random noise attenuation." GEOPHYSICS 84, no. 3 (May 1, 2019): V169—V183. http://dx.doi.org/10.1190/geo2018-0596.1.
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In recent years, sparse representation is seeing increasing application to fundamental signal and image-processing tasks. In sparse representation, a signal can be expressed as a linear combination of a dictionary (atom signals) and sparse coefficients. Dictionary learning has a critical role in obtaining a state-of-the-art sparse representation. A good dictionary should capture the representative features of the data. The whole signal can be used as training patches to learn a dictionary. However, this approach suffers from high computational costs, especially for a 3D cube. A common method is to randomly select some patches from given data as training patches to accelerate the learning process. However, the random selection method without any prior information will damage the signal if the selected patches for training are inappropriately chosen from a simple structure (e.g., training patches are chosen from a simple structure to recover the complex structure). We have developed a dip-oriented dictionary learning method, which incorporates an estimation of the dip field into the selection procedure of training patches. In the proposed approach, patches with a large dip value are selected for the training. However, it is not easy to estimate an accurate dip field from the noisy data directly. Hence, we first apply a curvelet-transform noise reduction method to remove some fine-scale components that presumably contain mostly random noise, and we then calculate a more reliable dip field from the preprocessed data to guide the patch selection. Numerical tests on synthetic shot records and field seismic image examples demonstrate that the proposed method can obtain a similar result compared with the method trained on the entire data set and obtain a better denoised result compared with the random selection method. We also compare the performance using of the proposed method and those methods based on curvelet thresholding and rank reduction on a synthetic shot record.
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Hase, Nils, Scot M. Miller, Peter Maaß, Justus Notholt, Mathias Palm, and Thorsten Warneke. "Atmospheric inverse modeling via sparse reconstruction." Geoscientific Model Development 10, no. 10 (October 10, 2017): 3695–713. http://dx.doi.org/10.5194/gmd-10-3695-2017.
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Abstract. Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
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Xie, Zhonghua, Lingjun Liu, and Cui Yang. "An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery." Entropy 21, no. 9 (September 17, 2019): 900. http://dx.doi.org/10.3390/e21090900.
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Image recovery from compressive sensing (CS) measurement data, especially noisy data has always been challenging due to its implicit ill-posed nature, thus, to seek a domain where a signal can exhibit a high degree of sparsity and to design an effective algorithm have drawn increasingly more attention. Among various sparsity-based models, structured or group sparsity often leads to more powerful signal reconstruction techniques. In this paper, we propose a novel entropy-based algorithm for CS recovery to enhance image sparsity through learning the group sparsity of residual. To reduce the residual of similar packed patches, the group sparsity of residual is described by a Laplacian scale mixture (LSM) model, therefore, each singular value of the residual of similar packed patches is modeled as a Laplacian distribution with a variable scale parameter, to exploit the benefits of high-order dependency among sparse coefficients. Due to the latent variables, the maximum a posteriori (MAP) estimation of the sparse coefficients cannot be obtained, thus, we design a loss function for expectation–maximization (EM) method based on relative entropy. In the frame of EM iteration, the sparse coefficients can be estimated with the denoising-based approximate message passing (D-AMP) algorithm. Experimental results have shown that the proposed algorithm can significantly outperform existing CS techniques for image recovery.
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Guo, Qiang, Lian-gang Qi, and Jianhong Xiang. "Space-Time-Frequency Adaptive Processor for Multiple Interference Suppression in GNSS Applications." International Journal of Antennas and Propagation 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2301052.
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To enhance the multiple interference suppression performance of global navigation satellite system (GNSS) receivers without extra antenna elements, a space-time-frequency adaptive processor (STFAP) is investigated. Firstly, based on the analysis of the autocorrelation function of the multicomponent signal, we propose a common period estimation and data block technique to segment the received signal data into blocks. Secondly, the signal data in each block are short-time Fourier transformed into time-frequency (TF) domain, and the corresponding TF points with similar frequency characteristics are regrouped to structure space-time-frequency (STF) data matrixes. Finally, a space-time-frequency minimum output power- (STF-MOP) based weight calculation method is introduced to suppress multiple interfering signals according to their sparse characteristics in TF and space domains. Simulation results show that the proposed STFAP can effectively combat more wideband periodic frequency-modulated (WBPFM) interferences even some of them arriving from the same direction as GNSS signals without increasing the number of antenna elements.
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Dziendzikowski, Michal, Krzysztof Dragan, Artur Kurnyta, Sylwester Klysz, and Andrzej Leski. "Damage Size Estimation of the Aircraft Structure with Use of Embedded Sensor Network Generating Elastic Waves." Key Engineering Materials 598 (January 2014): 57–62. http://dx.doi.org/10.4028/www.scientific.net/kem.598.57.
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One of the approach to develop a system of continues, automated monitoring of the health of the structures is to use elastic waves excited in a given medium by piezoelectric transducers network. Elastic waves depending on their source and the geometry of the structure under consideration can propagate over significant distance. They are also sensitive to local structure discontinuities and deformations providing a tool to detect local damage of large aerospace structures. In the paper the issue of fatigue crack growth monitoring by means of elastic guided waves actuated by a sparse array of sensors will be presented. In particular we propose signal characteristics, robust enough to detect different kinds of damages: Barely Visible Impact Damages (BVIDs) in composite materials and fatigue cracks of metallic structures. The model description and the results of specimen tests verifying damage detection capabilities of the proposed signal characteristics are delivered in the paper. Some issues concerning the proposed damage indices and its application to damage detection and its monitoring are also discussed.
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Zhou, Lin, Qianxiang Yu, Daozhi Liu, Ming Li, Shukai Chi, and Lanjun Liu. "Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit." Advances in Mechanical Engineering 10, no. 8 (August 2018): 168781401879087. http://dx.doi.org/10.1177/1687814018790877.
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Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.
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Gui, Guan, Li Xu, Lin Shan, and Fumiyuki Adachi. "Compressive Sensing Based Bayesian Sparse Channel Estimation for OFDM Communication Systems: High Performance and Low Complexity." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/927894.
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In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods.
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Wang, Andong, Guoxu Zhou, and Qibin Zhao. "Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data." Remote Sensing 13, no. 18 (September 14, 2021): 3671. http://dx.doi.org/10.3390/rs13183671.
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This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, communication loss, electromagnetic interferences, cloud shadows, etc. To estimate the underlying tensor, a new penalized least squares estimator is first formulated by exploiting the low rankness of the signal tensor within the framework of tensor ∗L-Singular Value Decomposition (∗L-SVD) and leveraging the sparse structure of the outlier tensor. Then, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) is designed to compute the estimator in an efficient way. Statistically, the non-asymptotic upper bound on the estimation error is established and further proved to be optimal (up to a log factor) in a minimax sense. Simulation studies on synthetic data demonstrate that the proposed error bound can predict the scaling behavior of the estimation error with problem parameters (i.e., tubal rank of the underlying tensor, sparsity of the outliers, and the number of uncorrupted observations). Both the effectiveness and efficiency of the proposed algorithm are evaluated through experiments for robust completion on seven different types of remote sensing data.
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Bao, Yuequan, Yibing Guo, and Hui Li. "A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring." Structural Health Monitoring 19, no. 6 (April 14, 2020): 1963–75. http://dx.doi.org/10.1177/1475921720909440.
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Time–frequency analysis is an essential subject in nonlinear and non-stationary signal processing in structural health monitoring, which can give a clear illustration of the variation trend of time-varying parameters. Thus, it plays a significant role in structural health monitoring, such as data analysis, and nonlinear damage detection. Adaptive sparse time–frequency analysis is a recently developed method used to estimate an instantaneous frequency, which can achieve high-resolution adaptivity by looking for the sparsest time–frequency representation of the signal within the largest possible time–frequency dictionary. However, in adaptive sparse time–frequency analysis, non-convex least-square optimization is the most important and difficult part of the algorithm; therefore, in this research the powerful optimization capabilities of machine learning were employed to solve the non-convex least-square optimization and achieve the accurate estimation of the instantaneous frequency. First, the adaptive sparse time–frequency analysis was formalized into a machine-learning task. Then, a four-layer neural network was designed, the first layer of which was used for training the coefficients of the envelope of each basic functions in a linear space. The next two merge layers were used to solve the complex calculation in a neural network. Finally, the real and imaginary parts of the reconstructed signal were the outputs of the output layer. The optimal weights in this designed neural network were trained and optimized by comparing the output reconstructed signal with the target signal, and a stochastic gradient descent optimizer was used to update the weights of the network. Finally, the numerical examples and experimental examples of a cable model were employed to illustrate the ability of the proposed method. The results show that the proposed method which is called neural network–adaptive sparse time–frequency analysis can give accurate identification of the instantaneous frequency, and it has a better robustness to initial values when compared with adaptive sparse time–frequency analysis.
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Xu, Tengxian, Xianpeng Wang, Mengxing Huang, Xiang Lan, and Lu Sun. "Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar." Remote Sensing 13, no. 18 (September 20, 2021): 3772. http://dx.doi.org/10.3390/rs13183772.
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Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method.
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Martín-Vega, Francisco J., and Gerardo Gómez. "A Low-Complexity Pilot-Based Frequency-Domain Channel Estimation for ICI Mitigation in OFDM Systems." Electronics 10, no. 12 (June 10, 2021): 1404. http://dx.doi.org/10.3390/electronics10121404.
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A low-complexity pilot pattern and a frequency-domain channel estimation method for Inter-Carrier Interference (ICI) mitigation is proposed for Orthogonal Frequency Division Multiple Access (OFDM) systems. The proposed method exploits the band structure of the coupling matrix to perform an ICI-free channel estimation in the frequency domain. This ICI-free estimation relies on some conditions imposed over the pilot pattern that simplify the complexity of channel estimation significantly, since its complexity is the same as classical least squares (LS) channel estimation used in low mobility scenarios. Then, the ICI is removed by using a modified version of Minimum Mean Square Error (MMSE) equalization, which reduces the computational complexity considerably. This modified MMSE equalization relies on the sparse and banded structure of the coupling matrix and on a low complexity variant of the Cholesky decomposition, which is named LDLH factorization. It is shown that the proposed method greatly improves the Bit Error Rate (BER) in the high Signal-to-Noise Ratio (SNR) regime.
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Ma, Fangchang, Luca Carlone, Ulas Ayaz, and Sertac Karaman. "Sparse depth sensing for resource-constrained robots." International Journal of Robotics Research 38, no. 8 (June 24, 2019): 935–80. http://dx.doi.org/10.1177/0278364919850296.
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We consider the case in which a robot has to navigate in an unknown environment, but does not have enough on-board power or payload to carry a traditional depth sensor (e.g., a 3D lidar) and thus can only acquire a few (point-wise) depth measurements. We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements? Reconstruction from incomplete data is not possible in general, but when the robot operates in man-made environments, the depth exhibits some regularity (e.g., many planar surfaces with only a few edges); we leverage this regularity to infer depth from a small number of measurements. Our first contribution is a formulation of the depth reconstruction problem that bridges robot perception with the compressive sensing literature in signal processing. The second contribution includes a set of formal results that ascertain the exactness and stability of the depth reconstruction in 2D and 3D problems, and completely characterize the geometry of the profiles that we can reconstruct. Our third contribution is a set of practical algorithms for depth reconstruction: our formulation directly translates into algorithms for depth estimation based on convex programming. In real-world problems, these convex programs are very large and general-purpose solvers are relatively slow. For this reason, we discuss ad-hoc solvers that enable fast depth reconstruction in real problems. The last contribution is an extensive experimental evaluation in 2D and 3D problems, including Monte Carlo runs on simulated instances and testing on multiple real datasets. Empirical results confirm that the proposed approach ensures accurate depth reconstruction, outperforms interpolation-based strategies, and performs well even when the assumption of a structured environment is violated.
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Ye, Changbo, Luo Chen, and Beizuo Zhu. "Sparse Array Design for DOA Estimation of Non-Gaussian Signals: From Global Postage-Stamp Problem Perspective." Wireless Communications and Mobile Computing 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6616112.
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In this paper, a sparse array design problem for non-Gaussian signal direction of arrival (DOA) estimation is investigated. Compared with conventional second-order cumulant- (SOC-) based methods, fourth-order cumulant- (FOC-) based methods achieve improved DOA estimation performance by utilizing all information from received non-Gaussian sources. Considering the virtual sensor location of vectorized FOC-based methods can be calculated from the second order difference coarray of sum coarray (2-DCSC) of physical sensors, it is important to devise a sparse array design principle to obtain extended degree of freedom (DOF). Based on the properties of unfolded coprime linear array (UCLA), we formulate the sparse array design problem as a global postage-stamp problem (GPSP) and then present an array design method from GPSP perspective. Specifically, for vectorized FOC-based methods, we divide the process of obtaining physical sensor location into two steps; the first step is to obtain the two consecutive second order sum coarrays (2-SC), which can be modeled as GPSP, and the solutions to GPSP can also be utilized to determine the physical sensor location sets without interelement spacing coefficients. The second step is to adjust the physical sensor sets by multiplying the appropriate coprime coefficients, which is determined by the structure of UCLA. In addition, the 2-DCSC can be calculated from physical sensors directly, and the properties of UCLA are given to confirm the degree of freedom (DOF) of the proposed geometry. Simulation results validate the effectiveness and superiority of the proposed array geometry.
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Liu, Lina, Jianwei Ma, and Gerlind Plonka. "Sparse graph-regularized dictionary learning for suppressing random seismic noise." GEOPHYSICS 83, no. 3 (May 1, 2018): V215—V231. http://dx.doi.org/10.1190/geo2017-0310.1.
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We have developed a new regularization method for the sparse representation and denoising of seismic data. Our approach is based on two components: a sparse data representation in a learned dictionary and a similarity measure for image patches that is evaluated using the Laplacian matrix of a graph. Dictionary-learning (DL) methods aim to find a data-dependent basis or a frame that admits a sparse data representation while capturing the characteristics of the given data. We have developed two algorithms for DL based on clustering and singular-value decomposition, called the first and second dictionary constructions. Besides using an adapted dictionary, we also consider a similarity measure for the local geometric structures of the seismic data using the Laplacian matrix of a graph. Our method achieves better denoising performance than existing denoising methods, in terms of peak signal-to-noise ratio values and visual estimation of weak-event preservation. Comparisons of experimental results on field data using traditional [Formula: see text]-[Formula: see text] deconvolution (FX-Decon) and curvelet thresholding methods are also provided.
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Weng, Haolei, and Arian Maleki. "Low noise sensitivity analysis of -minimization in oversampled systems." Information and Inference: A Journal of the IMA 9, no. 1 (January 27, 2019): 113–55. http://dx.doi.org/10.1093/imaiai/iay024.
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Abstract The class of $\ell _q$-regularized least squares (LQLS) are considered for estimating $\beta \in \mathbb{R}^p$ from its $n$ noisy linear observations $y=X\beta + w$. The performance of these schemes are studied under the high-dimensional asymptotic setting in which the dimension of the signal grows linearly with the number of measurements. In this asymptotic setting, phase transition (PT) diagrams are often used for comparing the performance of different estimators. PT specifies the minimum number of observations required by a certain estimator to recover a structured signal, e.g. a sparse one, from its noiseless linear observations. Although PT analysis is shown to provide useful information for compressed sensing, the fact that it ignores the measurement noise not only limits its applicability in many application areas, but also may lead to misunderstandings. For instance, consider a linear regression problem in which $n>p$ and the signal is not exactly sparse. If the measurement noise is ignored in such systems, regularization techniques, such as LQLS, seem to be irrelevant since even the ordinary least squares (OLS) returns the exact solution. However, it is well known that if $n$ is not much larger than $p$, then the regularization techniques improve the performance of OLS. In response to this limitation of PT analysis, we consider the low-noise sensitivity analysis. We show that this analysis framework (i) reveals the advantage of LQLS over OLS, (ii) captures the difference between different LQLS estimators even when $n>p$, and (iii) provides a fair comparison among different estimators in high signal-to-noise ratios. As an application of this framework, we will show that under mild conditions LASSO outperforms other LQLS even when the signal is dense. Finally, by a simple transformation, we connect our low-noise sensitivity framework to the classical asymptotic regime in which $n/p \rightarrow \infty$, and characterize how and when regularization techniques offer improvements over ordinary least squares, and which regularizer gives the most improvement when the sample size is large.
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Kiayani, Adnan, Lauri Anttila, Yaning Zou, and Mikko Valkama. "Advanced Receiver Design for Mitigating Multiple RF Impairments in OFDM Systems: Algorithms and RF Measurements." Journal of Electrical and Computer Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/730537.
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Direct-conversion architecture-based orthogonal frequency division multiplexing (OFDM) systems are troubled by impairments such as in-phase and quadrature-phase (I/Q) imbalance and carrier frequency offset (CFO). These impairments are unavoidable in any practical implementation and severely degrade the obtainable link performance. In this contribution, we study the joint impact of frequency-selective I/Q imbalance at both transmitter and receiver together with channel distortions and CFO error. Two estimation and compensation structures based on different pilot patterns are proposed for coping with such impairments. The first structure is based on preamble pilot pattern while the second one assumes a sparse pilot pattern. The proposed estimation/compensation structures are able to separate the individual impairments, which are then compensated in the reverse order of their appearance at the receiver. We present time-domain estimation and compensation algorithms for receiver I/Q imbalance and CFO and propose low-complexity algorithms for the compensation of channel distortions and transmitter IQ imbalance. The performance of the compensation algorithms is investigated with computer simulations as well as with practical radio frequency (RF) measurements. The performance results indicate that the proposed techniques provide close to the ideal performance both in simulations and measurements.
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Zang, Tingpeng, Guangrui Wen, and Zhifen Zhang. "Robust Estimation of the Unbalance of Rotor Systems Based on Sparsity Control of the Residual Model." Shock and Vibration 2018 (August 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/6508695.
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The vibration signals of rotating machinery are frequently disturbed by background noise and external disturbances because of the equipment’s particular working environment. Thus, robustness has become one of the most important problems in identifying the unbalance of rotor systems. Based on the observation that external disturbance of the unbalance response often displays sparsity compared with measured vibration data, we present a new robust method for identifying the unbalance of rotor systems based on model residual sparsity control. The residual model is composed of two parts: one part takes regular measurements of noise, while the other part evaluates the impact of external disturbances. With the help of the sparsity of external disturbances, the unbalance identification is converted into a convex optimization problem and solved by a sparse signal reconstruction algorithm. Experiment results have shown that the proposed method is robust and effective in identifying the unbalance of rotor systems in a complex environment, improving the precision of unbalance estimation and simplifying the balancing process.
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He, Liangtian, and Yilun Wang. "A Greedy Multistage Convex Relaxation Algorithm Applied to Structured Group Sparse Reconstruction Problems Based on Iterative Support Detection." Mathematical Problems in Engineering 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/358742.
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We propose a new effective algorithm for recovering a group sparse signal from very limited observations or measured data. As we know that a better reconstruction quality can be achieved when encoding more structural information besides sparsity, the commonly employedl2,1-regularization incorporating the prior grouping information has a better performance than the plainl1-regularized models as expected. In this paper we make a further use of the prior grouping information as well as possibly other prior information by considering a weightedl2,1model. Specifically, we propose a multistage convex relaxation procedure to alternatively estimate weights and solve the resulted weighted problem. The procedure of estimating weights makes better use of the prior grouping information and is implemented based on the iterative support detection (Wang and Yin, 2010). Comprehensive numerical experiments show that our approach brings significant recovery enhancements compared with the plainl2,1model, solved via the alternating direction method (ADM) (Deng et al., 2013), either in noiseless or in noisy environments.
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Saraereh, Omar A., Imran Khan, Qais Alsafasfeh, Salem Alemaishat, and Sunghwan Kim. "Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems." Symmetry 11, no. 5 (May 25, 2019): 713. http://dx.doi.org/10.3390/sym11050713.
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Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.
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Gomes, Paulo R. B., André L. F. de Almeida, João Paulo C. L. da Costa, and Rafael T. de Sousa. "Joint DL and UL Channel Estimation for Millimeter Wave MIMO Systems Using Tensor Modeling." Wireless Communications and Mobile Computing 2019 (September 15, 2019): 1–13. http://dx.doi.org/10.1155/2019/4858137.
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In this paper, we address the problem of joint downlink (DL) and uplink (UL) channel estimation for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Assuming a closed-loop and multifrequency-based channel training framework in which pilot signals received by multiple antenna mobile stations (MSs) are coded and spread in the frequency domain via multiple adjacent subcarriers, we propose two tensor-based semiblind receivers by capitalizing on the multilinear structure and sparse feature of the received signal at the BS equipped with a hybrid analog-digital beamforming (HB) architecture. As a first processing stage, the joint estimation of the compressed DL and UL channel matrices can be obtained in an iterative way by means of an alternating least squares (ALS) algorithm that capitalizes on a parallel factors model for the received signals. Alternatively, for more restricted scenarios, a closed-form solution is also proposed. From the estimated effective channel matrices, the users’ channel parameters such as angles of departure (AoD), angles of arrival (AoA), and path gains are then estimated in a second processing stage by solving independent compressed sensing (CS) problems (one for each MS). In contrast to the classical approach in the literature, in which the DL and UL channel estimation problems are usually considered as two separate problems, our idea is to jointly estimate both the DL and UL channels as a single problem by concentrating most of the processing burden for channel estimation at the BS side. Simulation results demonstrate that the proposed receivers achieve a performance close to the classical approach that is applied on DL and UL communication links separately, with the advantage of avoiding complex computations for channel estimation at the MS side as well as dedicated feedback channels for each MS, which are attractive features for massive MIMO systems.
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Hu, Zhuhua, Yong Bai, Mengxing Huang, Mingshan Xie, and Yaochi Zhao. "A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing." Sensors 18, no. 9 (September 8, 2018): 3011. http://dx.doi.org/10.3390/s18093011.
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The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.
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El Mountassir, Mahjoub, Slah Yaacoubi, Gilles Mourot, and Didier Maquin. "Sparse estimation based monitoring method for damage detection and localization: A case of study." Mechanical Systems and Signal Processing 112 (November 2018): 61–76. http://dx.doi.org/10.1016/j.ymssp.2018.04.024.
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Yu, Kaiping, Kai Yang, and Yunhe Bai. "Estimation of modal parameters using the sparse component analysis based underdetermined blind source separation." Mechanical Systems and Signal Processing 45, no. 2 (April 2014): 302–16. http://dx.doi.org/10.1016/j.ymssp.2013.11.018.
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Nakano, Yuki, Essam A. Rashed, Tatsuhito Nakane, Ilkka Laakso, and Akimasa Hirata. "ECG Localization Method Based on Volume Conductor Model and Kalman Filtering." Sensors 21, no. 13 (June 22, 2021): 4275. http://dx.doi.org/10.3390/s21134275.
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The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.
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Zhu, Yongjun, Wenbo Liu, and Qian Shen. "Adaptive Algorithm on Block-Compressive Sensing and Noisy Data Estimation." Electronics 8, no. 7 (July 3, 2019): 753. http://dx.doi.org/10.3390/electronics8070753.
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In this paper, an altered adaptive algorithm on block-compressive sensing (BCS) is developed by using saliency and error analysis. A phenomenon has been observed that the performance of BCS can be improved by means of rational block and uneven sampling ratio as well as adopting error analysis in the process of reconstruction. The weighted mean information entropy is adopted as the basis for partitioning of BCS which results in a flexible block group. Furthermore, the synthetic feature (SF) based on local saliency and variance is introduced to step-less adaptive sampling that works well in distinguishing and sampling between smooth blocks and detail blocks. The error analysis method is used to estimate the optimal number of iterations in sparse reconstruction. Based on the above points, an altered adaptive block-compressive sensing algorithm with flexible partitioning and error analysis is proposed in the article. On the one hand, it provides a feasible solution for the partitioning and sampling of an image, on the other hand, it also changes the iteration stop condition of reconstruction, and then improves the quality of the reconstructed image. The experimental results verify the effectiveness of the proposed algorithm and illustrate a good improvement in the indexes of the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Gradient Magnitude Similarity Deviation (GMSD), and Block Effect Index (BEI).
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Pham, M. Nhat, and Arne F. Jacob. "High-performance MIMO imaging radar with hybrid transmit and receive arrays." International Journal of Microwave and Wireless Technologies 8, no. 4-5 (March 28, 2016): 807–13. http://dx.doi.org/10.1017/s1759078716000386.
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In this study, a sparse multiple-input multiple-output radar system with difference co-array processing is presented. It consists of two non-uniform transmitter arrays in split configuration and a symmetrical hybrid receiver array. A combination of a periodically thinned array and two non-uniform sub-arrays is used to construct the receiver structure. A low-cost three-step optimization procedure is proposed to synthesize the array arrangement with system constraints. In addition, a combination of fast Fourier transform (FFT) and MUltiple SIgnal Classification (MUSIC) techniques is applied for a range/direction-of-arrival estimation with low computational effort. High angular resolution is achieved in the MUSIC spectrum by implementing a difference co-array concept together with spatial smoothing. A thinning rate in excess of 95% is demonstrated. Lastly, the performance of the proposed system is validated by measurements of point scatterers and extended objects.
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Durant, Szonya, and Johannes M. Zanker. "Variation in the Local Motion Statistics of Real-Life Optic Flow Scenes." Neural Computation 24, no. 7 (July 2012): 1781–805. http://dx.doi.org/10.1162/neco_a_00294.
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Optic flow motion patterns can be a rich source of information about our own movement and about the structure of the environment we are moving in. We investigate the information available to the brain under real operating conditions by analyzing video sequences generated by physically moving a camera through various typical human environments. We consider to what extent the motion signal maps generated by a biologically plausible, two-dimensional array of correlation-based motion detectors (2DMD) not only depend on egomotion, but also reflect the spatial setup of such environments. We analyzed the local motion outputs by extracting the relative amounts of detected directions and comparing the spatial distribution of the motion signals to that of idealized optic flow. Using a simple template matching estimation technique, we are able to extract the focus of expansion and find relatively small errors that are distributed in characteristic patterns in different scenes. This shows that all types of scenes provide suitable motion information for extracting ego motion despite the substantial levels of noise affecting the motion signal distributions, attributed to the sparse nature of optic flow and the presence of camera jitter. However, there are large differences in the shape of the direction distributions between different types of scenes; in particular, man-made office scenes are heavily dominated by directions in the cardinal axes, which is much less apparent in outdoor forest scenes. Further examination of motion magnitudes at different scales and the location of motion information in a scene revealed different patterns across different scene categories. This suggests that self-motion patterns are not only relevant for deducing heading direction and speed but also provide a rich information source for scene structure and could be important for the rapid formation of the gist of a scene under normal human locomotion.
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Xu, Caibin, Zhibo Yang, Baijie Qiao, and Xuefeng Chen. "A parameter estimation based sparse representation approach for mode separation and dispersion compensation of Lamb waves in isotropic plate." Smart Materials and Structures 29, no. 3 (February 7, 2020): 035020. http://dx.doi.org/10.1088/1361-665x/ab6ce7.
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Selesnick, Ivan W., and Ilker Bayram. "Sparse Signal Estimation by Maximally Sparse Convex Optimization." IEEE Transactions on Signal Processing 62, no. 5 (March 2014): 1078–92. http://dx.doi.org/10.1109/tsp.2014.2298839.
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Hu, Nan, Zhongfu Ye, Xu Xu, and Ming Bao. "DOA Estimation for Sparse Array via Sparse Signal Reconstruction." IEEE Transactions on Aerospace and Electronic Systems 49, no. 2 (April 2013): 760–73. http://dx.doi.org/10.1109/taes.2013.6494379.
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Kennett, B. L. N. "Signal Parameter Estimation for Sparse Arrays." Bulletin of the Seismological Society of America 93, no. 4 (August 1, 2003): 1765–72. http://dx.doi.org/10.1785/0120020221.
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Grams, Heather M., Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley. "Naïve Bayesian Precipitation Type Retrieval from Satellite Using a Cloud-Top and Ground-Radar Matched Climatology." Journal of Hydrometeorology 17, no. 10 (October 1, 2016): 2649–65. http://dx.doi.org/10.1175/jhm-d-16-0058.1.
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Abstract Satellite-based precipitation estimates are a vital resource for hydrologic applications in data-sparse regions of the world, particularly at daily or longer time scales. With the launch of a new generation of high-resolution imagers on geostationary platforms such as the Geostationary Operational Environmental Satellite series R (GOES-R), an opportunity exists to advance the detection and estimation of flash-flood-scale precipitation events from space beyond what is currently available. Because visible and infrared sensors can only observe cloud-top properties, many visible- and infrared-band-based rainfall algorithms attempt to first classify clouds before deriving a rain rate. This study uses a 2-yr database of cloud-top properties from proxy Advanced Baseline Imager radiances from GOES-R matched to surface precipitation types from the Multi-Radar Multi-Sensor (MRMS) system to develop a naïve Bayesian precipitation type classifier for the four major types of precipitation in MRMS: stratiform, convective, tropical, and hail. Evaluation of the naïve Bayesian precipitation type product showed a bias toward classifying convective and stratiform at the expense of tropical and hail. The tropical and hail classes in MRMS are derived based on the vertical structure and magnitude of radar reflectivity, which may not translate to an obvious signal at cloud top for a satellite-based algorithm. However, the satellite-based product correctly classified the hail areas as being convective in nature for the vast majority of missed hail events.
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Ye, Chen, Guan Gui, Shin-ya Matsushita, and Li Xu. "Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1119–26. http://dx.doi.org/10.20965/jaciii.2016.p1119.
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Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “blockℓ0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.
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Garcia Guzman, Yuneisy E., and Michael Lunglmayr. "Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation." Signals 2, no. 2 (April 15, 2021): 189–200. http://dx.doi.org/10.3390/signals2020015.
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The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this letter, such a formulation is derived and an algorithm based on sparse cyclic coordinate descent (SCCD) for estimating the frequency parameters is proposed. The algorithm adaptively reduces the size of the used frequency grid, which eases the computational burden. Simulation results revealed that the proposed algorithm achieves similar performance to the original formulation and the Root-multiple signal classification (MUSIC) algorithm in terms of the mean square error (MSE), with significantly less complexity.
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Lőrincz, András, Zoltán Á. Milacski, Balázs Pintér, and Anita L. Verő. "Columnar Machine: Fast estimation of structured sparse codes." Biologically Inspired Cognitive Architectures 15 (January 2016): 19–33. http://dx.doi.org/10.1016/j.bica.2015.10.003.
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Basha, D. Khalandar, and T. Venkateswarlu. "Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration." Journal of Intelligent Systems 29, no. 1 (July 13, 2019): 1480–95. http://dx.doi.org/10.1515/jisys-2018-0492.
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Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.
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He, Guodong, Maozhong Song, Shanshan Zhang, Huiping Qin, and Xiaojuan Xie. "GPS Sparse Multipath Signal Estimation Based on Compressive Sensing." Wireless Communications and Mobile Computing 2021 (May 11, 2021): 1–9. http://dx.doi.org/10.1155/2021/5583429.
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A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.
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Wiesel, Ami, and Teng Zhang. "Structured Robust Covariance Estimation." Foundations and Trends® in Signal Processing 8, no. 3 (2015): 127–216. http://dx.doi.org/10.1561/2000000053.
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Lazzaro, Damiana, Laura B. Montefusco, and Serena Papi. "Blind cluster structured sparse signal recovery: A nonconvex approach." Signal Processing 109 (April 2015): 212–25. http://dx.doi.org/10.1016/j.sigpro.2014.11.002.
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