OMP Algorithm Research Articles

DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning.

Through extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector-SBL). Firstly, vector hydrophones capture both sound pressure and particle velocity, enabling the acquisition of multidimensional sound field information. Secondly, SBL accurately reconstructs the received vector signal, addressing challenges like low signal-to-noise ratio (SNR), limited snapshots, and coherent sources. Finally, precise DOA estimation is achieved for multiple sources without prior knowledge of their number. Simulation experiments have shown that compared with the OMP, MUSIC, and CBF algorithms, the proposed method exhibits higher DOA estimation accuracy under conditions of low SNR, small snapshots, multiple sources, and coherent sources. Furthermore, it demonstrates superior resolution when dealing with closely spaced signal sources.

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.

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.

Low Complexity Hybrid-Field Channel Estimation Based on Simultaneous Weighted OMP Algorithm in Extreme Large-Scale MIMO Systems

Low Complexity Hybrid-Field Channel Estimation Based on Simultaneous Weighted OMP Algorithm in Extreme Large-Scale MIMO Systems

URBAN 3D RECONSTRUCTION OF VHR SAR IMAGES USING ITERATIVE OPTIMIZATION ALGORITHM AND LAYOVER FIXED-ORDER MODEL

Abstract. Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is a three-dimensional SAR imaging technique that uses multiple passes to process complex SAR images and obtain three-dimensional spatial scattering information to derive the elevation scattering distribution. Due to its own shortcomings, the elevation obtained by the traditional spectral estimation method has low resolution in the elevation direction and is affected by noise. The imaging algorithm based on compressed sensing can achieve super-resolution reconstruction in the elevation direction while reducing the number of observations. However, the CS algorithm still faces challenges when applied to real-world tomographic SAR imaging. In particular, it often requires numerous iterations to achieve satisfactory results, which significantly reduces its processing efficiency in large-scale tomography. To address the above issues, in this paper, we proposed an urban 3D reconstruction of VHR SAR images using an iterative optimization algorithm and layover fixed-order model. The iterative optimization algorithm and the layover fixed-order model consist of two parts: The TomoSAR imaging equation is solved by the two-step iterative shrinkage/thresholding (TwIST) algorithm, and the number of scatterers K is estimated by the Bayesian Information Criterion (BIC). In this paper, the effectiveness of TwIST-BIC in TomoSAR imaging in urban areas is verified with real TerraSARX data. By comparing with the OMP algorithm based on matching tracking and the FISTA algorithm based on gradient descent. The TWIST-BIC method is less complex, converges faster, and combines both execution speed and super-resolution, which can effectively solve the processing efficiency problem in large-area tomography and acquire high-resolution tomographic analysis.

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.

A fast threshold OMP based on self-learning dictionary for propeller signal reconstruction

The acquisition of propeller signals can be applied to localization and fault diagnosis, etc. However, recording a large amount of propeller signals results in higher costs. Compressive sensing (CS) is an effective approach to processing large data. CS algorithms work well for sparse signals, but propeller signals are non-sparse in both the time and frequency domains. Therefore, the traditional fixed dictionary, such as discrete cosine transform (DCT), has a limited sparse representation of propeller signals. We propose a self-learning dictionary in this study, the self-learning dictionary uses the propeller signal as input to train the dictionary, and the trained dictionary can be stored and used for sparse representation of propeller signals. Based on the self-learning dictionary, we propose a threshold orthogonal matching pursuit based on QR decomposition (TOMP-QR) algorithm. We use the ratio of two energy thresholds as the stopping condition of the TOMP-QR algorithm, which is more robust under arbitrary noise. Then, we use QR decomposition to reduce the complexity of the algorithm. The simulation results verify the effectiveness of the proposed method in signal reconstruction accuracy and running time. Based on DCT, discrete wavelet transform (DWT) and self-learning dictionary, we compare the TOMP-QR algorithm with TOMP, OMP, sparsity adaptive matching pursuit (SAMP) and stage wise OMP (StOMP) algorithm to reconstruct propeller signals in the experiment, the results show that the effectiveness and superiority of the self-learning dictionary and TOMP-QR algorithm.

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene.

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25-30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm's robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm's estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.

Hybrid-Field Channel Estimation for Extremely Large-Scale Massive MIMO System

Hybrid-field channel estimation for extremely large-scale massive MIMO (XL-MIMO) system is discussed in this letter. By exploiting the structural characteristics of far-field path components in the angle domain and the sparsity of near-field path components in the polar domain, a channel estimation algorithm combining support detection and orthogonal matching pursuit (SD-OMP) is proposed. Specifically, the supports of the far-field path components are firstly detected according to components structure characteristics of the angle domain in the XL-MIMO system and then can be used to obtain the far-field path components. Next, effect on the XL-MIMO system induced by far-field path components can be removed firstly and the OMP algorithm is employed to obtain the near-field path components by exploiting its polar domain sparsity. Finally, the hybrid-field channel is recovered by superposing the given far-field path components and near-field path components. Experiment results show that the proposed algorithm can accurately recover channel with relatively low pilot overhead and computation complexity comparing with some classical channel estimation algorithms.

High-precision DOA estimation for underwater acoustic signals based on sparsity adaptation

The direction of arrival (DOA) estimation technique is to obtain the direction information of the source when it reaches the array by processing and analyzing the received signal. In recent years, the DOA estimation of an array signal has been a research hotspot. For application scenarios with a small number of snapshots and a low signal-to-noise ratio, the compressive sensing theory has been commonly used to estimate the DOA of an array signal to achieve better estimation performance. However, the DOA estimation methods based on compressive sensing theory require information on source sparsity. Moreover, the influence of a complex underwater acoustic environment limits the accuracy of estimation algorithms. To address this limitation, this study proposes a high-precision DOA estimation model for underwater acoustic signals based on sparsity adaptation. The proposed model includes mainly two parts. In the first part, a source sparsity adaptive model based on a causal convolutional neural network is proposed. The model is used to address the constraint that the source sparsity should be known a priori when compressed sensing is used for DOA estimation. In the second part, a differential combination matching pursuit (DCMP) algorithm is adopted. First, a differentiated path filtering strategy is employed to reduce algorithm complexity and avoid the problem of invalid filtering. In addition, the combined optimization strategy is used to improve the prediction accuracy of the algorithm, providing an efficient error correction idea for the compressed sensing application to DOA estimation. The results of simulations conducted under seven different signal-to-noise ratios and using three different array types show that the proposed source sparsity adaptive model can reach an average prediction accuracy of 89.6%. In addition, compared with the other reconstruction algorithm accuracy, on the basis of ensuring low time complexity, the proposed DCMP algorithm can achieve an accuracy improvement of 9.99%–19.94% under seven different signal-to-noise ratio values. Moreover, the mean absolute error of the proposed DCMP algorithm is lower by approximately 0.05°–14° than those of the OMP and MMP algorithms.

Spectral Efficiency Enhancement using Hybrid Pre-Coder Based Spectrum Handover Mechanism

The use of Millimeter-wave (mm-Wave) has recently immensely enhanced in various communication applications due to massive technological developments in wireless communications. Furthermore, mm-Wave consists of a high bandwidth spectrum that can handle large demands of data transmission and internet services. However, high interference is observed in previous research at the time of spectrum handover from secondary (unlicensed) users to primary (licensed) users. Thus, interference reduction by achieving high spectral efficiency and an easy spectrum handoff process with minimum delay is an important research area. Therefore, a Hybrid Pre-coder Design based Spectrum Handoff (HPDSH) Algorithm is proposed in this article to increase spectrum efficiency in Cognitive Radio Networks(CRNs) and to access the large bandwidth spectrum of mm-Wave systems to meet the high data rate demands of current cellular networks. Moreover, a HPDSH Algorithm is presented to enhance spectral efficiency and this algorithm is utilized to take handover decisions and select backup channels. Here, different scenarios and parameters are considered to evaluate the performance efficiency of the proposed HPDSH Algorithm in terms of spectral efficiency and Signal to Noise (SNR) ratio. The proposed HPDSH Algorithm is compared against precoding methods like the OMP algorithm and SIC based methods.

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.

Bearing Fault Feature Extraction Based on Adaptive OMP and Improved K-SVD

The condition of the bearing is closely related to the condition and remaining life of the rotating machine. Targeting the problem of the large number of harmonic signals and noise signals during the operation of rolling bearings, and given that it is difficult to identify the fault in time, an adaptive orthogonal matching pursuit algorithm (OMP) and an improved K-singular value decomposition (K-SVD) for bearing fault feature extraction are proposed. An adaptive OMP algorithm is applied, which uses the Fourier dictionary to improve the solution method of the OMP algorithm so that it can separate the harmonic components in the signal faster and more accurately. At the same time, the stopping criterion of the adaptive sparsity is improved in dictionary learning. There is no need to manually set the sparsity in the algorithm initialization process, which avoids the problem of algorithm performance degradation due to improper sparsity settings, and improves the efficiency of the K-SVD algorithm. As shown by theoretical verification, algorithm comparison, and experimental comparisons, the algorithm has certain advantages in fault feature extraction during rolling bearing operation, and the algorithm still has considerable practical value in long-duration and strong noise environments.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

A Generalized Seismic Attenuation Compensation Operator Optimized by 2-D Mathematical Morphology Filtering

In this work, a Robust Worst-Case (RWC) estimation is presented for recovering sparse reflectivity series from uniformly quantized seismic signals. First, the <i>Orthogonal Matching Pursuit</i> (OMP) algorithm is applied on a quantized seismic trace. A set of conservative estimates of the reflectivity impulses and a dimension-reduced system are accordingly obtained. Second, the error induced by the quantization is modeled as a systemic uncertainty in a multiplication way. This modeling imposes a greater model uncertainty on the estimated reflectivity impulses with small values and vice versa. Finally, a RWC deconvolution scheme is designed for the dimension-reduced system. Among those roughly estimated impulses from the OMP algorithm, the ones statistically less affected by the quantization process are assigned with higher confidence weights and the ones statistically with higher magnitudes of quantization error are assigned with lower confidence weights. By rescaling the quantization error, the proposed scheme significantly increases the robustness of the solution to the quantization error and hence better improves the visual saliency of seismic signals than that of the OMP algorithm. This scheme is tested on both synthetic and real seismic data and the performance is evaluated by compared to that of the OMP algorithm. The results shows that the new scheme significantly outperforms the OMP algorithm by observing the following two aspects: first, the falsely or overly estimated impulses are significantly suppressed in the RWC estimation compared with that of the OMP algorithm, and second, the RWC estimation exhibits enhanced robustness to the change of the quantization interval.

DOA Estimation of Underwater Acoustic Targets Based on Bayesian Compressed Sensing

Aiming at the performance degradation or even failure of existing underwater acoustic target direction of arrial (DOA) estimation methods under the conditions of low signal-to-noise ratio and low snapshot, a underwater acoustic target DOA estimation method based on sparse Bayesian compressed sensing theory is studied, and simulation experiments are carried out by MATLAB to verify the feasibility and effectiveness of the proposed method. Simulation results show that compared with CBF , MUSIC and OMP algorithms, the proposed method can improve the estimation accuracy in the case of lower snapshot number, lower number of array elements and smaller array element spacing, and can achieve high resolution DOA estimation in the case of low signal-to-noise ratio and close position of multiple target signals.

Feature Extraction Method of Helicopter Target Based on Flicker Phenomenon Combined With Phase Compensation

For the typical multi-component periodic micro-Doppler signal parameter estimation, a helicopter rotor feature extraction method based on flicker phenomenon and phase compensation is proposed in this paper. The method firstly analyzes the formation mechanism of helicopter rotor flicker phenomenon in time domain and time-frequency domain through the integral model. Then the demodulation operator of the micro-Doppler signal is constructed for the phase compensation of the echoes with respect to the time-frequency flicker characteristics and the phase information of the slow time dimension of the target echoes. The parameter combination of the possible number of blades and rotational speed is predicted using the time domain flicker interval, and the phase compensation process is evaluated and optimized to significantly reduce the amount of computation. Finally, the parameter estimation of rotor blade number, blade length and rotational speed is acquired by calculating whether the center frequency of each flicker after phase compensation is periodically focused to zero frequency. The simulation results show that the method can effectively estimate the main parameters of helicopter rotor blades, and the processing results of the measured data show that the proposed method has more stable performance compared with OMP algorithm and Hough transform, which provides a new technical way for helicopter rotor blade feature extraction.

Noise Reduction in Electrical Signal Using OMP Algorithm Based on DCT and DSC Dictionaries

The noise is the most common issue present in all signals. Depending on the process or application, the reduction of the noise is mandatory mainly in areas such as signal classification, pattern recognition, and training process, among others. In recent years, diverse methodologies for noise reduction have been proposed, but they have left some issues with no solution. Such are the noise reduction in signals immersed in white noise without changes in their amplitude or phase or the high performance of recovering components of the desire signals distributed among the frequency spectrum. Also, filtering techniques without significant changes in signal phase and amplitude are necessary. This article proposes a methodology for noise reduction based on the orthogonal matching pursuit algorithm with dictionaries constructed from kernel functions of the discrete cosine transform and discrete sine transform. The methodology is proved in synthetic signals and real electrical signals, reaching noise reduction of white noise (~34 dB in electrical signals) and recovering without distortion.

Low pilot overhead channel estimation for CP‐OFDM‐based massive MIMO OTFS system

In high-speed mobile scenarios, due to the high-speed relative motion between transmitter and receiver, the high Doppler frequency shift interferes with the inter-subcarrier orthogonality in orthogonal frequency-division multiplexing (OFDM) systems. Therefore its performance is significantly degraded. Recently, orthogonal time–frequency space (OTFS) is considered as an effective alternative scheme to OFDM for time-varying channels. As with OFDM-massive multiple input multiple output (MIMO) systems, downlink channel estimation is necessary for OTFS-massive MIMO systems to improve the spectral efficiency in frequency-division duplex (FDD) mode without channel reciprocity. Here, first the cyclic prefix -OFDM-based massive MIMO OTFS system channel with antenna directivity pattern is analyzed, and transform the burst sparsity in the angle domain into block sparsity by using non-uniform Fourier transform (NUFT). Furthermore, to solve the problem that the pilot overhead grows linearly with the number of antennas, we propose a three-dimensional (3D) dynamic support detect (DSD) algorithm. Compared with the traditional OMP algorithm, and the 3D-structured orthogonal matching pursuit algorithm, simulation results demonstrate the proposed DSD algorithm has higher channel estimation accuracy, and lower pilot overhead.