Orthogonal Matching Pursuit Algorithm Research Articles

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas.

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

A novel gearbox local fault feature extraction method based on quality coefficient and dictionary learning

The performance of sparse decomposition is directly determined by the similarity between impact atoms and the actual fault impact waveform. The shift-invariant K-singular value decomposition (K-SVD) dictionary learning algorithm is capable of training impact patterns from vibration signals collected by sensors to construct impact atoms, thereby extracting fault impact components from the vibration signals. However, the impact pattern training using the shift-invariant K-SVD algorithm is influenced by the presence of harmonics and white noise in the gear transmission system vibration signals. To solve the above problems, a novel gearbox local fault feature extraction method based on the quality coefficient and dictionary learning is proposed in this paper. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by empirical mode decomposition. Then, a new quality coefficient is proposed by comprehensively considering the intensity of the impact, harmonics and noise components in each IMF, as well as the degree of correlation with the original signal. The IMF with the largest quality coefficient is used to train the impact pattern and solve the sparse coefficients. Finally, the orthogonal matching pursuit algorithm is adopted to solve the sparse coefficients, which are used to reconstruct the fault impact response signal from the dictionary. Simulation and experimental analysis demonstrate the superior performance of the proposed method compared to other state-of-the-art methods.

Robust multidimensional reconstruction via group sparsity with Radon operators

Seismic data processing, specifically tasks like denoising and interpolation, often hinges on sparse solutions of linear systems. Group sparsity plays an essential role in this context by enhancing sparse inversion. It introduces more refined constraints, which preserve the inherent relationships within seismic data. To this end, we propose a robust orthogonal matching pursuit algorithm, combined with Radon operators in the frequency-slowness [Formula: see text] domain, to tackle the strong group sparsity problem. This approach is vital for interpolating seismic data and attenuating erratic noise simultaneously. Our algorithm takes advantage of group sparsity by selecting the dominant slowness group in each iteration and fitting Radon coefficients with a robust [Formula: see text] norm using the alternating direction method of multipliers (ADMM) solver. Its ability to resist erratic noise, along with its superior performance in applications such as simultaneous source deblending and reconstruction of noisy onshore data sets, underscores the importance of group sparsity. Synthetic and real comparative analyses further demonstrate that strong group sparsity inversion consistently outperforms corresponding traditional methods without the group sparsity constraint. These comparisons emphasize the necessity of integrating group sparsity in these applications, thereby indicating its indispensable role in optimizing seismic data processing.

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

SummaryIn the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods.

DOA Estimation of Broadband Sources Using Dimension-Reduced Matrix Filter with Deep Nulling in a Strong Interference Environment

To enhance the performance of the adaptive matrix filter with nulling, a dimension-reduced matrix filter with deep nulling (DR-MFDN) is proposed in this paper and used for direction of arrival (DOA) estimation of broadband sources in a strong interference environment. Consider the change of noise after filtering, the gaussian matrix is used for prewhitening to prevent the noise from converting into colored noise without affecting the response characteristics of the beam-space. Finally, the multiple measurement vector orthogonal matching pursuit (MOMP) algorithm is used for DOA estimation of weak targets after filtering, and the simulation results show a better DOA estimation performance compared to the OMP and MUSIC algorithms with short snapshots.

FPGA Implementation of Threshold Projection Orthogonal Matching Pursuit Algorithm for Compressed Sensing Reconstruction

FPGA Implementation of Threshold Projection Orthogonal Matching Pursuit Algorithm for Compressed Sensing Reconstruction

Equalizer Parameters’ Adjustment Based on an Oversampled Channel Model for OFDM Modulation Systems

A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a greedy OFDM (Orthogonal Frequency Division Multiplexing) equalizer. The equalizer works flawlessly if delays between propagation paths are in the sampling grid. Otherwise, the channel impulse response loses its compressible characteristic and the number of coefficients to find increases. It is possible to get back to the simple channel model by data oversampling. The paper describes how the above idea helps the OMP (Orthogonal Matching Pursuit) algorithm estimate channel coefficients. The authors analyze the oversampling algorithm on the one hand to assess the influence of filtering function and signal resolution on the quality of the channel impulse response reconstruction. On the other hand, the abilities of the OMP algorithm are analyzed to distinguish components of the oversampled signal. Based on these analyses, we proposed modifications to the compressible channel’s impulse response reconstruction algorithm to minimize the number of transmission errors. A distinction was made between the filters used in the OMP search and channel reconstruction stages before calculating equalizer coefficients. Additionally, the results of the search stage were considered as elements within the groups.

Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.

I-OMP: an improved OMP algorithm for channel state identification in an underwater wireless optical communication scenario.

Underwater wireless optical communication (UWOC) has attracted considerable interest owing to its capability of high data rates and low latency. As a crucial component of UWOC, the transmission characteristics of an underwater channel directly impact the system's performance metrics. However, the existing channel models cannot exactly capture the underwater channel states, thus degrading the observability of channel states. This paper proposes a hybrid-field channel model containing both far-field and near-field path components, in which the signal-dependent shot noise (SDSN) is incorporated as well to accurately describe the underwater channel behavior. Then an improved orthogonal matching pursuit (I-OMP) algorithm that estimates the far-field and near-field path components independently with different transform matrices is developed to obtain the underwater channel state. The performance analyses show that I-OMP can improve the estimation accuracy of underwater channels by iteratively minimizing the mean square error (MSE) and utilizing two different transform matrices, demonstrating the advantage of the proposed I-OMP over the existing methods.

High resolution imaging of acoustic orbital angular momentum wave based on orthogonal matching tracking

The orbital angular momentum (OAM) wave has shown great potential for improving radar imaging and underwater communication performance due to its helical wavefront phase and infinite orthogonal modes. However, there are currently no known applications of this technology in underwater imaging. In this paper, we employed acoustic OAM wave for underwater imaging and established transceiver signal models using the uniform circular array. We concurrently achieved two-dimensional imaging of azimuth and elevation angles, which differs from radar imaging. We proposed a matching process for the echo signal in the modal domain, the OAM wave beam image's sidelobe decreased by 7.9 dB in the elevation direction and 6.1 dB in the azimuth direction compared to the plane wave, with the mainlobe decreased by 0.2° in the elevation direction and 0.4° in the azimuth direction. Furthermore, this paper introduced OAM wave high-resolution image reconstruction based on the orthogonal matching pursuit (OMP) algorithm. Finally, we implemented broadband acoustic OAM wave for underwater imaging and introduced an image reconstruction method based on the modal domain OMP algorithm. Simulation results demonstrate that the use of OAM wave in underwater imaging is feasible, and the proposed scheme can achieve high-resolution imaging.

Self-Supervised Medical Image Denoising Based on WISTA-Net for Human Healthcare in Metaverse.

Medical image processing plays an important role in the interaction of real world and metaverse for healthcare. Self-supervised denoising based on sparse coding methods, without any prerequisite on large-scale training samples, has been attracting extensive attention for medical image processing. Whereas, existing self-supervised methods suffer from poor performance and low efficiency. In this paper, to achieve state-of-the-art denoising performance on the one hand, we present a self-supervised sparse coding method, named the weighted iterative shrinkage thresholding algorithm (WISTA). It does not rely on noisy-clean ground-truth image pairs to learn from only a single noisy image. On the other hand, to further improve denoising efficiency, we unfold the WISTA to construct a deep neural network (DNN) structured WISTA, named WISTA-Net. Specifically, in WISTA, motivated by the merit of the lp-norm, WISTA-Net has better denoising performance than the classical orthogonal matching pursuit (OMP) algorithm and the ISTA. Moreover, leveraging the high-efficiency of DNN structure in parameter updating, WISTA-Net outperforms the compared methods in denoising efficiency. In detail, for a 256 by 256 noisy image, the running time of WISTA-Net is 4.72 s on the CPU, which is much faster than WISTA, OMP, and ISTA by 32.88 s, 13.06 s, and 6.17 s, respectively.

Adaptive refined random orthogonal matching pursuit algorithm for FBMC/OQAM MIMO framework

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.

DIFFERENTIAL SAR TOMOGRAPHY OF LARGE-SCALE WATER CONSERVANCY PROJECTS UNDER STEEP TERRAIN – THE CASE STUDY OF LAXIWA HYDROPOWER STATION

Abstract. Differential SAR Tomography (D-TomoSAR), as an extension of InSAR technology, combines D-InSAR and TomoSAR technology to achieve imaging in the height-deformation rate (s-v) plane. It not only solves the layover problem of SAR imaging, but also obtains the height and deformation rate of each scatterer within the image element. The technique is currently mainly applied to complex scenes in urban areas where the layover problem is serious, and the layover effect also exists for hydropower plants with extremely steep slopes. In this paper, the Differential SAR Tomography technique is applied to the four-dimensional imaging of hydraulic engineering for the first time, taking the La Siwa hydropower station as an example. This experiment establishes the signal model based on permanent scatterer points, so interferometric processing and PS point selection should be performed to obtain the differential interferogram sequence and PS points in the region; the orthogonal matching pursuit (OMP) algorithm is selected for differential SAR tomography imaging processing to reconstruct the elevation-deformation rate backward scattering profile. The experiments use the 23-view TerraSAR-X satellite one-meter resolution time series image dataset for deformation monitoring of the Laxiwa hydropower dam in GuiDe County, Qinghai Province, China, and finally the resolution and reconstruction estimation performance are evaluated by theoretical analysis and application case study. A comparison with the traditional InSAR technique shows that the differential SAR tomography technique not only maintains the advantages of high resolution, but also significantly improves the probability of accurate reconstruction of scattered points, and achieves higher accuracy in estimating the deformation of hydropower dams. This paper mainly discusses the application of differential SAR tomography technology in water conservancy projects, hoping to provide reference and help for the future large-scale application of differential SAR tomography technology in hydroelectric dam deformation monitoring.

Self-organizing Competitive Neural Network Based Adaptive Sparse Representation for Magnetotelluric Data Denoising

The existing sparse decomposition denoising methods for magnetotelluric (MT) data need to set the iterative stop condition manually, which not only has a large workload and high difficulty, but also easily causes subjective bias. To this end, we propose a new adaptive sparse representation method for MT data denoising. First, the data to be processed is divided into high-quality segments and noisy segments by machine learning algorithm. Then, the characteristic parameters of high-quality segments are calculated, and the boundary value of the characteristic parameters is taken as the threshold. The threshold has two functions, one is as a criterion for signal-to-noise identification, and the other is as an iterative stop condition for subsequent sparse decomposition. Finally, the optimized orthogonal matching pursuit algorithm is used to separate the signal and noise of the noisy segments, and the denoised segments and high-quality segments are combined to obtain the complete denoised MT data. The field data processing results show that this method is a fully automatic and intelligent MT data denoising method. It greatly improves the signal-to-noise ratio and the apparent resistivity-phase curves.

An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation

Wireless broadband transmission channels usually have time-domain-sparse properties, and the reconstruction of these channels using a greedy search-based orthogonal matching pursuit (OMP) algorithm can effectively improve channel estimation performance while decreasing the length of the reference signal. In this research, the improved OMP and SOMP algorithms for compressed-sensing (CS)-based channel estimation are proposed for single-carrier frequency domain equalization (SC-FDE) systems, which, in comparison with conventional algorithms, calculate the path gain after obtaining the path delay and updating the observation matrices. The reliability of the communication system is further enhanced because the channel path gain is calculated using longer observation vectors, which lowers the Cramér–Rao lower bound (CRLB) and results in better channel estimation performance. The developed method can also be applied to time-domain-synchronous OFDM (TDS-OFDM) systems, and it is applicable to the improvement of other matching pursuit algorithms.

Damage identification of hinge joint in hollow slab bridge based on model updating and orthogonal matching pursuit algorithm

Hinge joints are crucial for transverse force transfer in prestressed hollow slab bridges. Damages to hinge joints cause overall instability, load distribution changes, and reduced bearing capacity of the bridge. A new method using model updating and the orthogonal matching pursuit (OMP) algorithm is proposed to accurately assess the damage of hinge joints in such bridges using a single measurement point. The hinge joint damage index obtained by the OMP algorithm is assigned to the corresponding hinge joint in the FE model to complete the model update. Results show that this method can accurately identify hinge joint damage locations and degrees with a single measurement point. The damage identification of 12 hinge joints of an in-service hollow slab bridge is carried out, and the findings reveal that the damage occurred on the fourth, fifth, and seventh hinge joints.

Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks.

In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing-Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.

Common structured sparsity based multi-user channel estimation for double-RIS assisted millimeter wave systems

Compared to a single reconfigurable intelligent surface (RIS) system, a double RIS system can achieve better passive beamforming gain, while requiring more accurate channel state information (CSI). However, due to the presence of double-reflection links, channel estimation with low pilot overhead is quite challenging. In this paper, we investigate channel estimation of double-RIS assisted multi-user millimeter wave (mmWave) communication systems. Firstly, the channel estimation problem for double-reflection links is formulated as a sparse signal recovery problem. Then, we analyze and exploit the common structured sparsity of the cascaded channel among multiple users, i.e., the common row and column sparsity in the angular channel matrix, and the corresponding common sparsity after being transformed into the channel vector. Inspired by this, we further propose a novel common structured sparsity based orthogonal matching pursuit (CSS-OMP) algorithm for multi-user joint channel estimation. Simulation results show that CSS-OMP algorithm can provide more accurate channel estimation with reduced pilot overhead compared to the conventional OMP algorithm.

Channel Prediction With Time-Varying Doppler Spectrum in High-Mobility Scenarios: A Polynomial Fourier Transform Based Approach and Field Measurements

Beamforming for multi-antenna wireless communication systems has been widely studied and applied in practice. However, its performance in high mobility scenarios deteriorates dramatically due to severe channel aging. This issue stimulates the research of channel prediction in high mobility scenarios. This paper studies the general high-mobility Doppler domain wireless channel characterization and reveals that: 1) the Doppler frequency of the cluster corresponding to the near scatterers varies approximately linearly over short periods; 2) the linear-fitting characteristics of autoregressive model leads to poor channel prediction performance; 3) the parameter estimation algorithms of the chirp model which encompasses the characteristics are particularly complex. Given the shortcomings of existing works, this paper adopts the short-time Fourier transform to pre-estimate the parameter range and leverages the polynomial Fourier transform to obtain the initial values of the parameters. The downhill simplex algorithm is used to search for the fine-grained values of the parameters. In addition, the orthogonal matching pursuit algorithm is utilized to reconstruct the sparse signal. Evaluation results under the COST2100 channel model and a channel measurement campaign indicate that the proposed scheme can enhance the channel prediction accuracy or reduce the computing overhead compared to the existing matrix completion and polynomial iteration method.

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products.