Adaptive Orthogonal Matching Pursuit Research Articles

Enhancing Spectral and Energy Efficiencies in a Cognitive Radio CRAN with PUEAs

In a cloud radio access network, malicious users mitigate the attributes of primary users in order to occupy a specific idle spectrum band by sending false signals or carry out a denial of service attack. Moreover, with the increase in number of users and limited spectral and energy resources, the malicious users will compete for the spectrum with legitimate users, thus resulting in increase in spectrum scarcity problem. The most widely used defense approach against malicious users is the received signal strength method. However, harmful users can still imitate signal attributes and transmit powers of the primary users. Therefore, in order to elaborate the best method to tackle this vulnerability and hence make more spectrum available, the modified adaptive orthogonal matching pursuit localization algorithm is proposed to detect harmful users existing in the network. However, in order to elaborate the convergence speed of the proposed method, the regularized particle filter algorithm is applied to evaluate the performance of the modified adaptive orthogonal matching pursuit under real-time conditions. The restricted isometry property is used for the performance evaluation. Further, spectral and energy efficiencies are used in the simulation results for performance evaluation, in order to observe spectrum and energy utilization efficiencies. The simulation results show that the proposed method is better in terms of computational complexity, spectral efficiency, and energy efficiency compared to other matching pursuit approaches.

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.

Valid Inertial Gait Data Recovery for Gait Recognition: A Multi-mode Adaptive Orthogonal Matching Pursuit

Gait recognition based on inertial sensor data develops rapidly. Recently, more and more studies are based on the data collected in-the-wild, where the data quality is limited. Thus, the requirement for data preprocessing is higher. In this work, an adaptive preprocessing algorithm, MAOMP, is proposed to extract the effective components in the gait data. Different from traditional denoising methods, MAOMP recovers valid data from scratch using sinusoidal bases adaptively by projecting signals and bases into the Hilbert space. It can remove invalid data to smooth the signals but highlight the essential extremums at the same time. Finally, MAOMP is evaluated on four publicly available datasets of different grades and three different neural networks. The quantization of SNR shows the data recovered by MAOMP is at a higher level. Compared to the two commonly used preprocessing methods, the performance of MAOMP can be more pronounced as the quality of the datasets decreases. The improvements of the recognition performance are more apparent in the ConvLSTM network compared to the CNN with the data recovered by MAOMP.

A Joint Low-Rank and Sparse Method for Reference Signal Purification in DTMB-Based Passive Bistatic Radar.

In a digital terrestrial multimedia broadcasting (DTMB)-based passive bistatic radar (PBR) system, the received reference signal often suffers from serious multipath effect, which decreases the detection ability of low-observable targets in urban environments. In order to improve the target detection performance, a novel reference signal purification method based on the low-rank and sparse feature is proposed in this paper. Specifically, this method firstly performs synchronization operations to the received reference signal and thus obtains the corresponding pseudo-noise (PN) sequences. Then, by innovatively exploiting the inherent low-rank structure of DTMB signals, the noise component in PN sequences is reduced. After that, a temporal correlation (TC)-based adaptive orthogonal matching pursuit (OMP) method, i.e., TC-AOMP, is performed to acquire the reliable channel estimation, whereby the previous noise-reduced PN sequences and a new halting criterion are utilized to improve channel estimation accuracy. Finally, the purification reference signal is obtained via equalization operation. The advantage of the proposed method is that it can obtain superior channel estimation performance and is more efficient compared to existing methods. Numerical and experimental results collected from the DTMB-based PBR system are presented to demonstrate the effectiveness of the proposed method.

A-OMP: An Adaptive OMP Algorithm for Underwater Acoustic OFDM Channel Estimation

A fundamentally difficult problem to apply the orthogonal matching pursuit (OMP) to estimate the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel is designing a proper iteration termination condition. However, existing OMP-based algorithms that design termination conditions based on the physical sparsity suffer the limited estimation accuracy in high-level noise cases and the difficult balance between the accuracy and computational costs in low-level noise cases. We find that it is the recovery sparsity, a quantity related to but possibly significantly different from the physical sparsity, that should be utilized to devise termination conditions. Based on this observation, we first exploit the recovery sparsity to derive a closed-form expression for the termination condition to improve the estimation accuracy in high-level noise cases. Then, to balance the estimation accuracy and computational costs in low-level noise cases, we design another termination condition based on the residual vector and the observation vector. By embedding these two conditions into the OMP structure, we propose an innovative channel estimation algorithm for UWA-OFDM communication systems, referred to as the adaptive OMP (A-OMP). Simulation results show that, compared with the OMP, the A-OMP can achieve comparable or even higher estimation accuracy with smaller computational costs in all considered noise cases. Specially, the A-OMP can increase the estimation accuracy by 71.31% with only 13.48% CPU running time in high-level noise cases.

Real time FPGA implemnation of SAR radar reconstruction system based on adaptive OMP compressive sensing

<p><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Synthetic Aperture Radar (SAR) is an imaging system based on the processing of radar echoes. The produced images have a huge amount of data which will be stored onboard or transmitted as a digital signal to the ground station via downlink to be processed. Therefore, some methods of compression on the raw images provides an attractive option for SAR systems design. One of these techniques which used for image reconstruction is the Orthogonal Matching Pursuit (OMP). OMP is an iterative algorithm which need high computational operations. The computational complexity of the iterative algorithms is high due to updating operations of the measurement vector and large number of iterations that are used to reconstruct the images successfully. This paper presents a new adaptive OMP algorithm to overcome this issue by using certain threshold. The new adaptive OMP algorithm is compared with the classical OMP algorithm using the Receiver Operating Characteristic (ROC) curves. The MATLAB simulations show that the new adaptive OMP algorithm improves the probability of detection at lower SNRs, reduce the computational operations as well as the number of required iterations. FPGA implementation of both the classical OMP and the adaptive OMP algorithm are also presented in this paper.</span></p>

Denoising Method for Underwater Acoustic Signals Based on Sparse Decomposition

In order to improve the problem of relatively low signal-to-noise in the extraction of underwater acoustic signals under the background of strong interference, a sparse decomposition-based underwater acoustic signal denoising method is proposed. The main work is as follows: First, the signal is decomposed into a complete dictionary that can reflect the characteristics of the signal environment through the singular value decomposition method. Then, a cyclic shift is used to construct a signal matrix and an initial dictionary. A newly generated super-complete dictionary is obtained through training and updating. The atoms in the dictionary matrix are correlated and orthogonalized by an adaptive orthogonal matching pursuit method. Finally, the linear combination of the atoms that can best reflect the characteristic information of the underwater acoustic signal is used to reconstruct the underwater acoustic signal to achieve the purpose of denoising. The noise filtering underwater noise signal is simulated and compared with the traditional filtering method. The simulation results show that this method improves the signal-to-noise ratio of the signal after sparse decomposition and reconstruction of the original underwater acoustic signal, and has a certain denoising ability under strong noise and various types of reverberation interference.

Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The number of selected atoms is the integral multiple of initial step size. In respect of sparsity estimation, the proposed method introduces the exponential function to sparsity estimation. It uses a larger step size to estimate sparsity at the beginning of iteration to accelerate the algorithm convergence speed and a smaller step size to improve the reconstruction accuracy. Simulations show that the proposed method has better performance in terms of convergence speed and reconstruction accuracy for one-dimension signal and two-dimension signal.

Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm

Aiming at the disadvantages of greedy algorithms in sparse solution, a modified adaptive orthogonal matching pursuit algorithm (MAOMP) is proposed in this paper. It is obviously improved to introduce sparsity and variable step size for the MAOMP. The algorithm estimates the initial value of sparsity by matching test, and will decrease the number of subsequent iterations. Finally, the step size is adjusted to select atoms and approximate the true sparsity at different stages. The simulation results show that the algorithm which has proposed improves the recognition accuracy and efficiency comparing with other greedy algorithms.

Structured Compressive Sensing-Based Channel Estimation for Time Frequency Training OFDM Systems Over Doubly Selective Channel

This letter proposes a novel doubly selective channel estimation scheme based on structured compressive sensing (SCS) for time-frequency training orthogonal frequency division multiplexing systems. By exploiting the proposed pilot pattern, an SCS model is built by utilizing the jointly sparse property of the coefficient vectors corresponding to the channel expansion bases. The doubly selective channel can be recovered through the proposed adaptive support-aware block orthogonal matching pursuit algorithm. Simulation results demonstrate that the proposed scheme has superior performance than conventional counterparts over doubly selective channel with lower computational complexity.

Compressed sensing-based time-domain channel estimator for full-duplex OFDM systems with IQ-imbalances

In full-duplex orthogonal frequency-division multiplexing (OFDM) systems with in-phase and quadrature (IQ) imbalances, a time-domain least squares (TD-LS) channel estimator is proposed for estimating both the source-to-destination (intended) and the destination-to-destination (self-interference) channels. To further improve the performance, an adaptive orthogonal matching pursuit (OMP) is proposed and its sparsity is estimated by a threshold method. Finally, the full-duplex interference is removed using serial interference cancellation and sphere decoding is used to complete the maximum likelihood detection. Simulation results demonstrate that in terms of both the mean square error (MSE) and the bit error rate (BER), the proposed adaptive OMP performs better than the TD-LS by exploiting the sparse property of the channel. Additionally, compared to the gradient projection, the proposed adaptive OMP is better in the low and medium signal-to-noise ratio (SNR) regions and marginally worse in the high SNR region.

E-HIPA: An Energy-Efficient Framework for High-Precision Multi-Target-Adaptive Device-Free Localization

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an e nergy-efficient framework for high-precision multi-target-a daptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

Acoustic Modeling of Bangla Words using Deep Belief Network

Reconstruction of a sparse signal from fewer observations require compressive sensing based recovery algorithm for saving memory storage. Various sparse recovery techniques including minimization, greedy pursuit approaches and non-convex optimization requires sparsity to be known in advance. This article presents the generalized adaptive orthogonal matching pursuit with forward-backward movement under the cumulative coherence property; which removes the need of knowledge of sparsity prior to implementation. In this technique, the forward step increases the size of support set and backward step eliminates the misidentified elements. It selects multiple indices on the basis of maximum correlation by forward-backward movement. The size of backward step is kept smaller than the forward one. These forwardbackward steps then iterate and increment through the algorithm adaptively and terminate with stopping condition to ensure the identification of significant components. Recovery performance of proposed algorithm is demonstrated via simulation results including reconstruction of sparse signals in noisy and noise free environment. The algorithm has major advantage that it does not require the knowledge of sparsity in advance in contrast to the earlier reconstruction techniques. The evaluation and comparative analysis of result shows that algorithm leads to the increment in recovery performance and efficiency considerably.

Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm.

In an effort to improve compressive sensing and spare signal reconstruction by way of the backtracking-based adaptive orthogonal matching pursuit (BAOMP), a new sparse coding algorithm called improved adaptive backtracking-based OMP (ABOMP) is proposed in this study. Many aspects have been improved compared to the original BAOMP method, including replacing the fixed threshold with an adaptive one, adding residual feedback and support set verification, and others. Because of these ameliorations, the proposed algorithm can more precisely choose the atoms. By adding the adaptive step-size mechanism, it requires much less iteration and thus executes more efficiently. Additionally, a simple but effective contrast enhancement method is also adopted to further improve the denoising results and visual effect. By combining the IABOMP algorithm with the state-of-art dictionary learning algorithm K-SVD, the proposed algorithm achieves better denoising effects for astronomical images. Numerous experimental results show that the proposed algorithm performs successfully and effectively on Gaussian and Poisson noise removal.

Compressed video sensing method based on motion estimation and backtracking-based adaptive orthogonal matching pursuit

In order to remove the image blurring caused by reconstructing video frames independently frame by frame using traditional compressed video sensing method, this paper proposed a new approach to video compressed sensing based on motion estimation and motion compensation by combining the compressed sensing theory with related technology of MPEG standard video coding, so as to remove the spatial and temporal redundancy of video signal. This method took full account of the temporal correlations of video sequences and firstly compensated video frames using forward, backward and bidirectional prediction, then adopted the Backtracking-based Adaptive Orthogonal Matching Pursuit( BAOMP) algorithm to reconstruct the motion prediction residuals and finally reconstructed current frames. The experimental results indicate that the proposed method can gain a better video image quality compared with frame-by-frame reconstruction method and achieve a higher Peak Signal-to-Noise Ratio( PSNR).

Compressive spectrum sensing in the cognitive radio networks by exploiting the sparsity of active radios

Spectrum sensing is a key technology to detect spectrum holes in cognitive network. It has been demonstrated that collaboration among cognitive users can improve the probability of detecting the primary users, but the fusion center is the bottleneck when a lot of collaborative information is transmitted. In this paper, we consider the cognitive radio users only transmit part of sensing information to relieve the transmission load. Besides, the sensing information will be inevitably influenced by various noise in the process of transmission. Therefore, the challenge is how we can detect spectrum holes successfully from these incomplete and inexact measurements. Most recently, there are some research results on this but the detection performance is not satisfactory. In this paper, we firstly formulate the collaborative spectrum sensing as an optimization model and then present a novel adaptive orthogonal matching pursuit algorithm by exploiting the sparsity of active primary users. Statistical property of the sensing data plays a crucial role in spectrum sensing. Theoretical analysis shows the presented scheme can detect active primary users rapidly and efficiently. Simulation results verify that the proposed method can obtain better detection performance with stronger noise background, which is more attractive in real applications.

Backtracking-Based Matching Pursuit Method for Sparse Signal Reconstruction

This letter presents a variant of Orthogonal Matching Pursuit (OMP) method, called Backtracking-based Adaptive OMP (BAOMP), for compressive sensing and sparse signal reconstruction. As an extension of the OMP algorithm, the BAOMP method incorporates a simple backtracking technique to detect the previous chosen atoms' reliability and then deletes the unreliable atoms at each iteration. Through this modification, the BAOMP method achieves superior performance while maintaining the low complexity of OMP-type methods. Also, unlike its several predecessors, the BAOMP method does not require the sparsity level to be known a priori. The experiments demonstrate the proposed method's superior performance to that of several other OMP-type and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> optimization methods.