Matching Pursuit Approach Research Articles

Modeling and field demonstration of water-to-ice wireless optical communication system based on highly-sensitive detectors.

In this paper, the first water-to-ice (W2I) wireless optical communication (WOC) system model is proposed and verified by laboratory and field experiments. The Monte Carlo (MC) approach is used to simulate the optical characteristics of ice and water, resulting in the channel impulse response and received optical power (ROP) distribution. The simulation results demonstrate that the substantial absorption and scattering of the ice and ice-water interface significantly affect the cross-medium communication. A comparative study in the laboratory validated the channel characteristics obtained from the simulation. Following this, a W2I WOC system based on photomultiplier tubes (PMTs) was established. Using the maximum ratio combining (MRC) technique, a net data rate of 400 Mbps was achieved in a 1-m laboratory tank, and a net data rate of 320 Mbps was achieved across a 1-m transmission distance in the reservoir. To reduce the computational complexity and realize practical system deployment, the orthogonal matching pursuit (OMP) approach is employed to compress the equalizer. The number of kernels in the Volterra equalizer is reduced by 36% in the laboratory experiment and 36.9% in the field experiment, respectively. The results of this study can serve as a reference for future deployment of W2I WOC systems.

A matching pursuit approach to the geophysical inverse problem of seismic traveltime tomography under the ray theory approximation

SUMMARYSeismic traveltime tomography is a geophysical imaging method to infer the 3-D interior structure of the solid Earth. Most commonly formulated as a linearized inverse problem, it maps differences between observed and expected wave traveltimes to interior regions where waves propagate faster or slower than the expected average. The Earth’s interior is typically parametrized by a single kind of localized basis function. Here we present an alternative approach that uses matching pursuits on large dictionaries of basis functions.Within the past decade the (Learning) Inverse Problem Matching Pursuits [(L)IPMPs] have been developed. They combine global and local trial functions. An approximation is built in a so-called best basis, chosen iteratively from an intentionally overcomplete set or dictionary. In each iteration, the choice for the next best basis element reduces the Tikhonov–Phillips functional. This is in contrast to classical methods that use either global or local basis functions. The LIPMPs have proven their applicability in inverse problems like the downward continuation of the gravitational potential as well as the MEG-/EEG-problem from medical imaging. Here, we remodel the Learning Regularized Functional Matching Pursuit (LRFMP), which is one of the LIPMPs, for traveltime tomography in a ray theoretical setting. In particular, we introduce the operator, some possible trial functions and the regularization. We show a numerical proof of concept for artificial traveltime delays obtained from a contrived model for velocity differences. The corresponding code is available online.

Second Order Rectified Parallel Factor Model Based Cascaded Channel Estimation in IRS-Assisted SWIPT System

This paper investigates cascaded channel estimation in intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) system. Multiple-input multiple-output (MIMO) transceiver structures combined with transmitter active beamforming and IRS passive beamforming are studied. Cascaded channel estimation is transformed into sparse signal reconstruction problem, and compressed sensing (CS) is applied to solve this problem. Only a small amount of training overhead provides reliable channel estimation gains as well as better beamforming gains. Second order rectified parallel factor (PARAFA) model is implemented in IRS-assisted SWIPT system, which is described by tensor decomposition method. The received signal can be represented by PARAFA model with algebraic structure and IRS phase shift. Two cascaded channel estimation approaches, namely, bilinear alternating least squares (BALS) and least squares Khatri-Rao factorization (LSKRF), are proposed respectively. Simulation results show that, compared with orthogonal matching pursuit (OMP) approach, the proposed BALS cascaded channel estimation approach obtains better normalized mean square error (NMSE) and convergence performance with the least parameter constraints.

RIS-Aided mmWave MIMO Channel Estimation Using Deep Learning and Compressive Sensing

Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve both the bandwidth and energy efficiency. However, CSI acquisition is non-trivial for two reasons: 1) the passive nature of RIS does not allow transceiving and processing pilot signals, and 2) the dimensions of the cascaded channel between transceivers increases with the large number of RIS elements, which yields high training overhead and computational complexity. While prior art has mainly focused on frequency-flat channel estimation, this paper proposes novel data-driven and compressive sensing based approaches for estimating both frequency-flat and frequency-selective cascaded channels of RIS-assisted multi-user millimeter-wave large multiple input multiple output (MIMO) systems with limited training overhead. The proposed methods exploit the common sparsity property among the different subcarriers and the double-structured sparsity property of the angular cascaded channel matrices as different angular cascaded channels observed by different users share completely common non-zero rows and user-specific column supports. The proposed data-driven cascaded channel estimation approaches use denoising neural networks to accurately detect channel supports. Alternatively, when data-training capabilities are not available, the compressive sensing based orthogonal matching pursuit (OMP) approach relies on sparsity properties and applies simultaneous OMP to detect the channel supports. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes. When compared to other OMP approaches that achieve an NMSE gap of 5 to 6 dB with respect to the Oracle least square lower bound, the proposed algorithms reduce the lower bound gap to only 1 dB, while reducing complexity by more than two orders of magnitude.

BSA-OMP: Beam-Split-Aware Orthogonal Matching Pursuit for THz Channel Estimation

Terahertz (THz)-band has been envisioned for the sixth generation wireless networks thanks to its ultra-wide bandwidth and very narrow beamwidth. Nevertheless, THz-band transmission faces several unique challenges, one of which is beam-split which occurs due to the usage of subcarrier-independent analog beamformers and causes the generated beams at different subcarriers split, and point to different directions. Unlike the prior works dealing with beam-split by employing additional complex hardware components, e.g., time-delayer networks, a beam-split-aware orthogonal matching pursuit (BSA-OMP) approach is introduced to efficiently estimate the THz channel and beamformer design without any additional hardware. Specifically, we design a BSA dictionary comprised of beam-split-corrected steering vectors which inherently include the effect of beam-split so that the proposed BSA-OMP solution automatically yields the beam-split-corrected physical channel directions. Numerical results demonstrate the superior performance of BSA-OMP approach against the existing state-of-the-art techniques.

An effective combination of terahertz band NOMA and MIMO system for power efficiency enhancement

SummaryTerahertz (THz) band communication is extensively used to reach future communication needs for large capacity. Moreover, the multiple input multiple output (MIMO) and Non‐orthogonal multiple access (NOMA) approach with multi‐antenna enable users to share more data with good spectrum efficiency. Due to its various applications, different research works are done in MIMO and NOMA. But the existing approach used in NOMA‐MIMO has a major limitation in power efficiency. Hence, this research work is presented for maximizing power efficiency. The power efficiency is improved effectively using the THz‐NOMA‐MIMO system in the presented approach. Initially, users are clustered effectively by utilizing the enhanced fuzzy c‐means clustering (EFCM) approach. Afterwards, hybrid precoding with threshold orthogonal matching pursuit (TOMP) approach is presented for generating the hybrid precoding vectors. Here, optimal precoding vectors are generated using the geometric distance‐based TOMP approach. This process eliminates the interference present in the data communication, thus improving power and spectral efficiency. Finally, the weight‐based distributed power efficient algorithm (WDPEA) achieves the finest power allocation. These combinations of effective approaches enhance the power and spectral efficiency. The presented methodology is implemented in the working platform of MATLAB. The performance of the presented approach is examined with the different existing approaches in regards to spectral efficiency, energy efficiency, effective capacity, and the mean squared error. When the users are at 20, the sum spectral efficiency of existing zero forcing (ZF) is 21, regularized zero forcing (RZF) is 54, maximum ratio transmission (MRT) is 71, and the proposed method is 77 bit/s/Hz. When the signal to noise ratio at 25 dB, the energy efficiency of existing time division multiple access (TDMA) is 1.24, digital MIMO is 1.21, orthogonal multiple access (OMA) is 1.23, weighted NOMA is 1.35, and the proposed method is 1.38 bit/joule/Hz. The proposed method gets better results when compared to other methods.

Exploiting Rapidly Time-Varying Sparsity for Underwater Acoustic Communication

Due to the combined effects of different acoustic propagation, reflection patterns in different time-scale, time-varying underwater acoustic (UWA) channel generally exhibits hybrid sparsity, i.e., contains not only rapidly time-varying elements, but also stationary or slowly time-varying ones. While the classic sparse reconstruction algorithms such as orthogonal matching pursuit (OMP) generally do not take it into account, the existence of static multipath will unavoidably hinder the tracking of rapid time variations. In this paper, a sequential adaptive observation length orthogonal matching pursuit (SAOLOMP) approach is proposed to sequentially explore rapidly time-varying sparsity in a two-step sequential manner. Specifically, the static components are firstly separated via simultaneous orthogonal matching pursuit (SOMP) among continuous measurements. Upon the residual error of the SOMP, the measurement-wise OMP is applied for estimating the remained dynamic components. As the variation of the SOMP residual error indicates how rapidly the remained dynamic multipath components vary, the observation length of the OMP is adjusted according to the gradient of residual error to adapt to rapidly time-varying components. Finally, both types of sparse components are summed up to obtain the whole channel response. Numerical simulations as well as experimental results from field UWA communication data show that the proposed algorithm exhibits better performance in exploiting rapidly time-varying sparsity than the state-of-the-art compressive methods do under UWA channel.

On the sparsity bound for the existence of a unique solution in compressive sensing by the Gershgorin theorem

Since compressive sensing deals with a signal reconstruction using a reduced set of measurements, the existence of a unique solution is of crucial importance. The most important approach to this problem is based on the restricted isometry property which is computationally unfeasible. The coherence index-based uniqueness criteria are computationally efficient, however, they are pessimistic. An approach to alleviating this problem has been recently introduced by relaxing the coherence index condition for the unique signal reconstruction using the orthogonal matching pursuit approach. This approach can be further relaxed and the sparsity bound improved if we consider only the solution existence rather than its reconstruction. One such improved bound for the sparsity limit is derived in this paper using the Gershgorin disk theorem.

Blind and Semi-blind Anechoic Mixing System Identification Using Multichannel Matching Pursuit

Sparse component analysis techniques have been successfully applied to the separation of speech sources. This paper presents an efficient algorithm based on the matching pursuit approach to deal with multichannel records. The proposed algorithm explicitly employs spatial constraints among different channels to express mixed signals as linear combinations of delayed components selected from an overcomplete dictionary. We present a new procedure for estimating the mixing system parameters (attenuations and delays), which can be applied to more than two mixtures and is not restricted to non-negative attenuation coefficients. The proposed mixing system estimation method can accommodate delays of greater magnitude than traditional approaches. In addition, learned dictionaries that improve the identification step can be used when excerpts from sources (exogenous to mixtures) are available. The simulation results show that semi-blind dictionaries perform better than those used in blind configurations.

Compressive sensing based PAPR reduction in OFDM systems: Modified orthogonal matching pursuit approach

Abstract Orthogonal frequency division multiplexing (OFDM) has been proposed to achieve high data rate transmission in wireless communications. The OFDM system usually suffers from the high envelope fluctuations called peak-to-average power ratio (PAPR). The high PAPR causes a signal clipping distortion, and consequently, the performance is degraded. To mitigate the PAPR, we introduce a new PAPR reduction approach using a compressive sensing approach at the transmitter side and an orthogonal matching pursuit (OMP) reconstruction algorithm at the receiver end. Numerical results show the significant reduction of the PAPR compared with the traditional OFDM system without degrading the bit error rate performance.

Multiple Batches of Motion History Images (MB-MHIs) for Multi-view Human Action Recognition

The recognition of human actions recorded in a multi-camera environment faces the challenging issue of viewpoint variation. Multi-view methods employ videos from different views to generate a compact view-invariant representation of human actions. This paper proposes a novel multi-view human action recognition approach that uses multiple low-dimensional temporal templates and a reconstruction-based encoding scheme. The proposed approach is based upon the extraction of multiple 2D motion history images (MHIs) of human action videos over non-overlapping temporal windows, constructing multiple batches of motion history images (MB-MHIs). Then, two kinds of descriptions are computed for these MHIs batches based on (1) a deep residual network (ResNet) and (2) histogram of oriented gradients (HOG) to effectively quantify a change in gradient. ResNet descriptions are average pooled at each batch. HOG descriptions are processed independently at each batch to learn a class-based dictionary using a K-spectral value decomposition algorithm. Later, the sparse codes of feature descriptions are obtained using an orthogonal matching pursuit approach. These sparse codes are average pooled to extract encoded feature vectors. Then, encoded feature vectors at each batch are fused to form a final view-invariant feature representation. Finally, a linear support vector machine classifier is trained for action recognition. Experimental results are given on three versions of a multi-view dataset: MuHAVi-8, MuHAVi-14, and MuHAVi-uncut. The proposed approach shows promising results when tested for a novel camera. Results on deep features indicate that action representation by MB-MHIs is more view-invariant than single MHIs.

Multimeasurement aliased-noise attenuation for sparse land seismic acquisition using compressed sensing

Land data are contaminated by energetic surface waves that have low apparent velocities relative to the seismic signal. The conventional land acquisition approach to noise attenuation is to deploy arrays of hard-wired geophones whose combined response acts as a spatial filter. Besides the shortcomings of a fixed array response that is sensitive to perturbations, using a large amount of equipment limits the field efficiency of array-based acquisition. The advent of point-receiver acquisition technology addresses some of these limitations by enabling measurement of each single geophone along a receiver line and removing the slowest energetic noise by velocity-based filtering. However, according to the classic sampling theorem, the proper sampling of noise requires sampling the shortest wavelength twice. Because surface waves have slow propagation velocities, it is not feasible to sample the noise properly and a compromise is made between quality and efficiency. We have developed two different methods to improve the efficiency of point-receiver technology by allowing for wider receiver spacing without compromising the coherent noise attenuation capabilities. First, we combine compressed sensing-based methods such as the matching pursuit approach with the gradient measurement inferred from a novel 5C sensor to dealias and attenuate source-generated noise. Second, we have developed a method to design optimized acquisition geometries by using the spectral characteristics of the surface waves, where the objective is to place a limited number of receivers at the most favorable locations to help attenuate the coherent noise. We determine the effectiveness of our methods through synthetic and field data examples in which we find that spatially aliased coherent noise can be reconstructed to a denser grid and attenuated.

Low Complexity FFT Factorization for CS Reconstruction

In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compressive sensing (CS) reconstruction. Among various methodologies used for CS reconstruction algorithms, Greedy-based orthogonal matching pursuit (OMP) approach provides better solution in terms of accurate implementation with complex computations overhead. Several computationally intensive arithmetic operations like complex matrix multiplication are required to formulate correlative vectors making this algorithm highly complex and power consuming hardware implementation. Computational complexity becomes very important especially in complex FFT models to meet different operational standards and system requirements. In general, for real time applications, FFT transforms are required for high speed computations as well as with least possible complexity overhead in order to support wide range of applications. This paper presents an hardware efficient FFT computation technique with twiddle factor normalization for correlation optimization in orthogonal matching pursuit (OMP). Experimental results are provided to validate the performance metrics of the proposed normalization techniques against complexity and energy related issues. The proposed method is verified by FPGA synthesizer, and validated with appropriate currently available comparative analyzes.

Radar Imaging based on Orthogonal Matching Pursuit via Sparse Constraint

This paper presents constraint generalized orthogonal matching pursuit (C-gOMP) approach to tackle the problem of clutter mitigation for radar imaging in the compressive sensing context. The generalized orthogonal matching pursuit algorithm (gOMP) in radar imaging process is susceptible to environmental noise interference. Therefore, a particular constraint condition is added to the recovery signal, and the noise component is restrained after the greedy iteration. The C-gOMP algorithm uses the cost function to impose more constraints on each coefficient to ensure the convergence of the whole function. To further analyze the performance of C-gOMP algorithm, the effectiveness of this algorithm is demonstrated by simulations. The results show that the proposed approach is very obvious at suppressing unwanted clutter and enhancing the desired targets, and C-gOMP algorithm brings an evident performance improvement and application prospect.

Underlying Trend Extraction via Joint Ensemble Intrinsic Timescale Decomposition Algorithm and Matching Pursuit Approach

Time series usually consist of an underlying trend and the irregularities. Therefore, the underlying trend extraction plays an important role in the analysis of the time series. This paper proposes a method which combines the ensemble intrinsic timescale decomposition (EITD) algorithm and the matching pursuit (MP) approach for performing the underlying trend extraction. In order to extract the underlying trend, the EITD algorithm is applied to obtain a set of components. Then, the first component which contains most of the noise is removed. Next, some appropriate components are selected by the MP approach. In particular, the total number of components that composites of the underlying trend is minimized. To guarantee that the underlying trend tracks the signal, it is required to impose a constraint on the maximum absolute error between the underlying trend and the denoised signal. As the selection of the EITD components is binary, this component selection problem is formulated as an $$ L_{0} $$ -norm binary programming problem subject to the specification on the maximum absolute error between the denoised signal and the underlying trend. This optimization problem is further solved by the MP approach. Finally, the underlying trend is constructed. Compared with the conventional empirical mode decomposition algorithm and the ensemble empirical mode decomposition algorithm, our proposed approach could extract a better underlying trend for some random time series.

Accurate ultrasonic measurement technique for crack sizing using envelope detection and differential evolution

In this paper, an ultrasonic non-destructive measurement technique is proposed to detect and size cracks in engineering materials. This measurement technique is based on Time of Flight Diffraction (TOFD) permitting the detection and location of cracks employing the compression waves diffraction phenomena. Then, the cracks are sized by the estimation of Time of Flight (TOF) of the diffracted waves. In the simulation and experimental investigations, the TOFs are estimated in two steps, the first step is the envelope extraction by Hilbert Transform (HT). The second step is the envelope estimation using a Matching Pursuit (MP) approach optimized by Differential Evolution (DE) in order to reach fast and accurate results. Consequently, the estimation results confirm the efficiency of the proposed measurement technique, which can be considered as an encouraging method for crack sizing in industrial materials.

High‐definition frequency decomposition

ABSTRACTSpectral decomposition is a widely used technique in analysis and interpretation of seismic data. According to the uncertainty principle, there exists a lower bound for the joint time–frequency resolution of seismic signals. The highest temporal resolution is achieved by a matching pursuit approach which uses waveforms from a dictionary of functions (atoms). This method, in its pure mathematical form can result in atoms whose shape and phase have no relation to the seismic trace. The high‐definition frequency decomposition algorithm presented in this paper interleaves iterations of atom matching and optimization. It divides the seismic trace into independent sections delineated by envelope troughs, and simultaneously matches atoms to all peaks. Co‐optimization of overlapping atoms ensures that the effects of interference between them are minimized. Finally, a second atom matching and optimization phase is performed in order to minimize the difference between the original and the reconstructed trace. The fully reconstructed traces can be used as inputs for a frequency‐based reconstruction and red–green–blue colour blending. Comparison with the results of the original matching pursuit frequency decomposition illustrates that high‐definition frequency decomposition based colour blends provide a very high temporal resolution, even in the low‐energy parts of the seismic data, enabling a precise analysis of geometrical variations of geological features.

An artificial bee colony optimization based matching pursuit approach for ultrasonic echo estimation

Ultrasonic echo estimation is important in ultrasonic non-destructive evaluation and material characterization. Matching pursuit is one of the most popular methods for the purpose of estimating ultrasonic echoes. In this paper, an artificial bee colony optimization based matching pursuit approach (ABC-MP) is proposed specifically for ultrasonic signal decomposition by integrating the artificial bee colony algorithm into the matching pursuit method. The optimal atoms are searched from a continuous parameter space over a tailored Gabor dictionary in ABC-MP instead of a discrete parameter space in matching pursuit. As a result, echoes characterized by a set of physical parameters can be estimated accurately and efficiently. The performance of ABC-MP is tested using both simulated signals and real ultrasonic signals, and compared with matching pursuit. Results clearly demonstrate the superior performance of the proposed ABC-MP approach over matching pursuit in ultrasonic echo estimation in terms of the shape and amplitude of the recovered echoes and the reconstructed signal, and the residue signal.

Deconvolution of azimuthal mode detection measurements

Unequally spaced transducer rings make it possible to extend the range of detectable azimuthal modes. The disadvantage is that the response of the mode detection algorithm to a single mode is distributed over all detectable modes, similarly to the Point Spread Function of Conventional Beamforming with microphone arrays. With multiple modes the response patterns interfere, leading to a relatively high “noise floor” of spurious modes in the detected mode spectrum, in other words, to a low dynamic range. In this paper a deconvolution strategy is proposed for increasing this dynamic range. It starts with separating the measured sound into shaft tones and broadband noise. For broadband noise modes, a standard Non-Negative Least Squares solver appeared to be a perfect deconvolution tool. For shaft tones a Matching Pursuit approach is proposed, taking advantage of the sparsity of dominant modes. The deconvolution methods were applied to mode detection measurements in a fan rig. An increase in dynamic range of typically 10–15 dB was found.

Efficient Computation of MSE Lower Bounds via Matching Pursuit

The classes of large-error bounds that are based on the covariance inequality, in both Bayesian and non-Bayesian approaches, are characterized as projection-based bounds. Tightening of bounds in these classes involves high computational complexity due to multidimensional optimization procedure. Consequently, projection-based large-error bounds have little popularity, while small-error bounds are frequently preferred, although they are not necessarily tight. In this letter, we first introduce a unified formulation for Bayesian and non-Bayesian projection-based lower bounds and set a general framework, which allows for their approximation via a greedy-based method. This framework is then used to propose the use of optimized orthogonal matching pursuit approach for computing projection-based large-error bounds. We analyze the complexity of the proposed algorithm and show that it is significantly lower than the complexity of the conventional approach. Finally, we apply the algorithm for the problem of multitone estimation and show that for fixed computational resources, the Weiss–Weinstein bound implemented with the proposed algorithm, provides a tighter bound compared to conventional approaches.