Traditional Orthogonal Matching Pursuit Research Articles

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

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.

Energy leakage in OFDM sparse channel estimation: the drawback of OMP and the application of image deblurring

In this paper, in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems, we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting theory. Then, we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method, Orthogonal Matching Pursuit (OMP). The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval, which prevents OMP-based methods from achieving better performance. To overcome this drawback, the image deblurring-based channel estimation method, in which the channel estimation problem is analogized to one-dimensional image deblurring, was proposed to improve the large compensation distance of traditional OMP. The advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

Underwater Acoustic Channel Estimation via an Orthogonal Matching Pursuit Algorithm Based on the Modified Phase-Transform-Weighted Function

In the context of torpedo guidance systems, the performance of active sonar in channel parameter estimation and target detection and recognition is significantly degraded by the multipath effect and the time-varying characteristics of the underwater acoustic (UWA) channel. Therefore, it is urgent to propose an algorithm that can accurately estimate the channel parameters in multipath time-varying UWA channels. To solve these problems, this study developed a modified phase transform (PHAT)-weighted function and applied it to the orthogonal matching pursuit (OMP) algorithm, named M-PHAT-OMP. The proposed algorithm is more robust, improves the resolution of the time delay and further improves the estimation accuracy of the parameters in the case of motion. Furthermore, with the aim of solving the problem of the difficulty that the traditional OMP algorithm has in determining sparsity, this study proposes a joint-threshold method, where the threshold value serves as the condition for terminating the algorithm’s iteration. The simulation results demonstrate that the M-PHAT-OMP algorithm proposed in this study exhibits a superior performance compared to other algorithms, as evidenced by its lower root mean square error (RMSE) for delay. Moreover, the experimental results also validate that the proposed algorithm has superior robustness and resolution of the time delay in practical applications.

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE’s advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy.

Enhanced Bayesian sparse representation of mechanical fault signals by structural feature-oriented matching composite dictionary construction

The traditional orthogonal matching pursuit algorithm exploits only the overall sparsity of signals without considering the effects caused by structural characteristics. To this end, this paper proposes an enhanced Bayesian sparse representation (EBSR) of mechanical fault signals by structural feature-oriented matching redundant dictionary construction. First, an EBSR model which can improve sparse reconstruction results is proposed. The proposed model improves the recovery accuracy and robustness of the sparse representation by using the structural information as a priori information. Subsequently, a composite dictionary is designed combining a Sin-Chirplet dictionary with an Impulse dictionary, and a multi-group and multi-strategy grey wolf optimizer algorithm is employed to enable the composite dictionary match the structural features of the fault signal and reduce its redundancy degree. Finally, the optimized matching composite dictionary is introduced into the EBSR algorithm, endowing it with an efficient atom selection strategy and reducing the complexity of the sparse representation. The simulation and experimental results demonstrated that the proposed method can effectively reduce the interference from background noise and impurity frequencies, verifying the effectiveness and applicability of the proposed method for the sparse representation of mechanical faults.

Low-Resolution Face Recognition and Sports Training Action Analysis Based on Wireless Sensors

This paper constructs a low-resolution model for face recognition and sports training actions based on wireless sensors. The model obtains the distribution of the information size in the face image by calculating the image entropy value, and assigns different weights according to the size of the information to perform face recognition calculation, so that the original module-based algorithm is simply based on image segmentation into one based on entropy. The size of the value is divided into blocks, which solves the problem of computational quantification of category information. In the test stage, the traditional orthogonal matching pursuit algorithm is used to solve the coding coefficients, and the excellent classification and recognition results are obtained by calculating the intra-class matrix of the face image and the inter-class matrix of the sports training action image. Methods that perform well on classification problems further improve face recognition rates. The specific processing process is to add Gaussian noise, salt and pepper noise to the input face image and reduce the size of the face image in the input image, so that the improved algorithms are improved. The experimental results show that the high-efficiency resolution sensing technology is used to learn the sports training actions corresponding to the two modalities, and the matrix coefficient between the obtained high-resolution modal and low-resolution modal images reaches 0.971, and the iteration rate is improved by 71.5%, effectively promoting the high recognition rate of faces and actions.

An improved compressed sensing method for dynamic impact signals based on cubic spline interpolation.

During the dynamic acquisition of impact signals, a high sampling frequency brings significant challenges to the analog-to-digital converter and other test systems. To address this issue, in this study, an improved compressed sensing (CS) method is proposed for the measurement of impact signals based on cubic spline interpolation (CSI). According to the characteristics of the dynamic impact signal, a random non-uniform sampling strategy combining CS and CSI is presented. The CSI obviously reduces the number of observation points required by the traditional CS. To resolve the problem that the traditional orthogonal matching pursuit (OMP) algorithm can only guarantee the local optimal solution but cannot obtain the global optimal solution, an improved orthogonal matching pursuit (IOMP) algorithm is proposed. First, n atoms related to residuals are selected to build a local atomic dictionary. Subsequently, the atom most relevant to the signal observation result is selected from the local atomic dictionary. The iteration process is repeated until enough atoms are selected. The IOMP algorithm effectively improves the success rate of reconstruction. Finally, an impact signals test platform based on the Machete hammer is established. The results of theoretical simulations and several experiments indicate that the data reconstruction error of the proposed improved CS method for impact signals is approximately 5.0%.

An Orthogonal Matching Pursuit Variable Screening Algorithm for High-Dimensional Linear Regression Models

Variable selection plays an important role in data mining. It is crucial to filter useful variables and extract useful information in a high-dimensional setup when the number of predictor variables d tends to be much larger than the sample size n . Statistical inferences can be more precise after irrelevant variables are moved out by the screening method. This article proposes an orthogonal matching pursuit algorithm for variable screening under the high-dimensional setup. The proposed orthogonal matching pursuit method demonstrates good performance in variable screening. In particular, if the dimension of the true model is finite, OMP might discover all relevant predictors within a finite number of steps. Throughout theoretical analysis and simulations, it is confirmed that the orthogonal matching pursuit algorithm can identify relevant predictors to ensure screening consistency in variable selection. Given the sure screening property, the BIC criterion can be used to practically select the best candidate from the models generated by the OMP algorithm. Compared with the traditional orthogonal matching pursuit method, the resulting model can improve prediction accuracy and reduce computational cost by screening out the relevant variables.

Channel Estimation for Intelligent Reflecting Surface Assisted Massive MIMO Systems—A Deep Learning Approach

The benefits of intelligent reflecting surfaces (IRS) assisted massive multiple-input multiple-output systems are based on the accurate acquisition of channel state information but at the cost of the high pilot overhead. In this work, the sparse structure in angular domain is revealed, and then the uplink cascaded channel estimation is converted into a compressive sensing (CS) problem. However, the angle of arrival and angle of departure are fundamentally continuous values, and thus they usually do not fall precisely on the discrete grids, resulting in grid mismatch. In this case, the typical CS solution usually yields the compromised reconstruction performance. To address this issue, we proposed a deep learning-based approach with the traditional orthogonal matching pursuit followed by the residual network to improve the performance. Furthermore, a straightforward network structure is proposed to reduce computational complexity. Simulation results demonstrate that the proposed solutions achieve a better estimation performance and require lower pilot overhead compared with the state-of-the-art ones.

Indoor Passive Localization With Channel State Information Using a Single Access Point

With the development of multi-antenna technology, the localization technology based on Wi-Fi has made progress. However, the single station passive localization is still confronted with the problem of low accuracy. Towards this end, this paper proposes an indoor localization system with a single Access Point (AP). Firstly, we present a novel Channel State Information (CSI) phase error correction method to eliminate the phase error that caused by the Symbol Time Offset (STO) and the Sample Frequency Offset (SFO) due to the lack of rigid time synchronization between Wi-Fi transceivers. Then, the Time of Flight (ToF) of the reflected signal is obtained by the discrete correlation estimation algorithm. On this basis, we propose a Regionalized Orthogonal Matching Pursuit (R-OMP) algorithm to estimate the Angle of Arrival (AoA) of the reflected signal. The proposed R-OMP algorithm reduces the complexity of the traditional Orthogonal Matching Pursuit (OMP) algorithm via reducing the search area. Finally, we get the location of the target by building a single station localization model. Experiments show that the median localization error of the system can reach 0.75 meters in a typical indoor environment, which is significantly improved compared with the previous single station localization system.

Inter-carrier interference-aware sparse time-varying underwater acoustic channel estimation based on fast reconstruction algorithm

In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.

Direction of Arrival Estimation by Matching Pursuit Algorithm With Subspace Information

Traditional orthogonal matching pursuit (OMP) algorithms for direction of arrival (DOA) estimation suffer from poor angular resolution and noise suppression. In this paper, we analyze the reason why the OMP algorithm has difficulties in resolving closely separated DOAs and conclude that it lies in the rules of support detection. Moreover, we propose a solution to this problem via developing the connection between the sparse reconstruction class algorithm and the subspace algorithm from the structure of the redundant dictionary. Based on the framework of the matching pursuit (MP) algorithm, the effective information of the signal and noise subspaces is integrated, and a noise subspace reprojection orthogonal matching pursuit (NSRomp) algorithm for DOA estimation is proposed. By adopting signal subspaces to reconstruct the original signal, the proposed NSRomp can reduce both the influence of noise on the selection of the support set and the computing time. By implementing the minimum norm method to optimize the noise subspace into a vector, which corrects the selection rules of the support set during each iteration, the angular resolution of the proposed algorithm is improved. From the simulation results, when the signal to noise ratio (SNR) is lower than or near 0, the angular resolution can be improved by > 15° using OMP algorithms to by > 5° using the proposed NSRomp algorithm.

Optimised two‐dimensional orthogonal matching pursuit algorithm via singular value decomposition

The reconstruction algorithm is one of the core issues in applying compressed sensing theory to practical applications. Two-dimensional orthogonal matching pursuit (2DOMP) algorithm, as an extension of the traditional orthogonal matching pursuit algorithm, can be used directly for the reconstruction of two-dimensional signals. With 2D separable sampling, the memory requirements and the complexity of 2DOMP are exponentially reduced. However, in 2DOMP algorithm, the requirement of reconstruction matrix is not taken into consideration, merely measurement matrix is used directly. In this study, singular value decomposition is introduced into 2DOMP algorithm, and 2DOMP algorithm based on singular value decomposition (2DOMP-SVD) is proposed. Singular value decomposition of separable measurement matrices is used to obtain optimised separable reconstruction matrices and optimised measurements. Numerical experiments demonstrate that the proposed 2DOMP-SVD algorithm can significantly improve the success rate and robustness of reconstruction. Moreover, the separation design of the matrix can satisfy the requirements for both the measurement matrix and the reconstruction matrix individually, and is suitable for general separable linear system.

Efficient interpolation based OMP for sparse channel estimation in underwater acoustic OFDM

To achieve higher accuracy of path delay estimation with lower computational complexity, the orthogonal matching pursuit (OMP) method with interpolation algorithm has been adopted for the sparse channel estimation, such as underwater acoustic (UWA) communication channels. For an orthogonal frequency division multiplexing (OFDM) system with uniform pilots, estimating the path delay by OMP in each its iteration is equivalent to the frequency estimation of single-tone signals with discrete Fourier transform (DFT). In this paper, based on the existing frequency estimation algorithms, we propose two novel interpolation based OMP methods for baseband sampling grid and over-sampling grid respectively, aiming at improving path delay estimation efficiency. Moreover, the closed-form of Cramer-Rao lower bound (CRLB) for single path delay estimation is derived to serve as a benchmark for simulations. The performances of the two proposed methods are verified by both simulations and UWA communication experiment in Yellow Sea, China, which show a higher estimation accuracy than that of the traditional OMP method, and the similar estimation performance but lower computational complexity than that of the existing high-accuracy interpolation based OMP method.

Gram–Schmidt orthogonalisation aided hybrid precoding in millimetre‐wave massive MIMO systems

The authors introduce a novel hybrid precoding algorithm based on Gram–Schmidt orthogonalisation (GSO) in millimetre-wave massive MIMO systems. Specifically, the columns of array response matrix orthogonalised by the GSO process are considered as a set of candidate analogue precoders, then traditional orthogonal matching pursuit (OMP) is utilised to find the optimal analogue and digital precoders. Since GSO is a recursive process that depends on the order in which the matrix columns are selected. A heuristic solution to the order of columns selection is suggested according to the array response vector along which the fully-digital precoder has the maximum projection. The proposed algorithm, not only constrained to uniform linear arrays, can avoid the matrix inversion in designing the digital precoder compared to OMP. Simulation results show that the spectral efficiency and bit error rate of the proposed hybrid precoding solutions are close to that obtained with fully digital architectures. Furthermore, the results indicate that the proposed hybrid precoding solutions outperform the orthogonality-based matching pursuit, which uses the columns of the DFT matrix as a set of candidate analogue precoders.

Target Localization Algorithm of Wireless Sensor Network Based on Fast Orthogonal Matching Pursuit in

Efficient localization of multiple targets is one of the basic technical problems in wireless sensor networks (WSN). The traditional sparse representation method based on greedy class is not efficient in multi-target positioning. Aiming at this problem, a multi-target localization algorithm based on QR decomposition for fast orthogonal matching pursuit is proposed. The algorithm meshes the wireless sensor coverage area to design an over-complete dictionary, which transforms the multi-target localization problem into a sparse signal recovery problem. The method utilizes the sensor to receive the sparse characteristics of the target signal strength, and then uses fast orthogonal matching pursuit to recover the measured values, thereby localization the target by sparsity. Through the QR decomposition of the column full rank matrix, the recursive form is used to invert the sub-dictionary matrix, thus avoiding the direct inversion of the matrix in the traditional method, so that the computational complexity is greatly reduced. The simulation results show that compared with the traditional orthogonal matching pursuit compressed sensing reconstruction method, this method does not lose the localization accuracy and improves the computational efficiency.

Deep learning based channel estimation in fog radio access networks

As a promising paradigm of the fifth generation networks, fog radio access network (F-RAN) has attracted lots of attention nowadays. To fully utilize the promising gain of F-RANs, the acquisition of accurate channel state information is significant. However, conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead. In this paper, we consider the channel estimation in F-RANs with fog access point (F-AP) equipped with massive antennas. Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain, Gated Recurrent Unit (GRU), a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead. The GRU-based method can capture the hidden sparsity structure automatically through the network training. Moreover, to further improve the channel estimation, a bidirectional GRU based method is proposed, whose target channel structure is decided by previous and subsequent structures. We compare the performance of our proposed channel estimation with traditional methods (Orthogonal Matching Pursuit (OMP) and Simultaneous OMP (SOMP)). Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.

An Efficient Precoding Algorithm for mmWave Massive MIMO Systems

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.

Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

In order to extract fault impulse feature of large-scale rotating machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined multiresolution generalized S transform is proposed in this paper. In this method, multiresolution generalized S transform is employed to find the optimal atom for every iteration, which firstly takes in to account the generalized S transform with discretized adjustment factors, then builds an atom corresponding to the maximum energy. The multiresolution generalized S transform has better accuracy compared to generalized S transform and faster searching speed compared to the orthogonal matching pursuit method in selecting the optimal atom. Then, the orthogonal matching pursuit method is used to decompose the signal into several optimal atoms. The proposed method is applied to analyze the simulated signal and vibration signals collected from experimental failure rolling bearings. The results prove that the proposed method has better performances such as high precision and fast decomposition speed than the traditional orthogonal matching pursuit method method and local mean decomposition method.