Weak Orthogonal Matching Pursuit Research Articles

A method of sound source recognition based on two stagewise weak orthogonal matching pursuit

The stagewise weak orthogonal matching pursuit (SWOMP) algorithm solves the problem that the orthogonal matching pursuit (OMP) algorithm relies too much on the sparsity of sound sources in the process of sound source identification. However, compared with OMP algorithm, SWOMP algorithm has some disadvantages, such as lower spatial resolution and worse identification accuracy. To solve the problem of poor reconstruction effect of SWOMP algorithm, a sound source identification algorithm based on two stagewise weak orthogonal matching pursuit (TSWOMP) is proposed. In this method, the initial atom set is obtained by threshold selection, the reliability of the primary atom set is detected, and the wrong atoms in the primary atom set are further deleted. Finally, the position and size of the sound source signal are identified according to the obtained final atom set. Under different frequencies and signal-to-noise ratios, the simulation results show that compared with SWOMP algorithm, TSWOMP algorithm can effectively reduce the main lobe width, suppress the influence of side lobes and significantly improve the spatial resolution. Moreover, even if the sparsity of sound source is unknown, it can achieve the same recognition accuracy as OMP algorithm and has high recognition stability. Finally, the validity and reliability of TSWOMP algorithm are further verified by experiments.

IGBT Gate Waveform Acquisition Based on Compressed Sensing Technology

As the core device of power electronic equipment, IGBT (Insulated Gate Bipolar Transistor) is related to the reliability of the system, so its online health monitoring is particularly important. Due to the large amount of data and high sampling rate for IGBT online health monitoring, data transmission has become a major problem, the method of effectively reconstructing the original signal has become one of the current research hotspots. Based on the theory of compressed sensing, the acquisition of IGBT gate waveforms is studied in this paper. First, the theoretical principle of compressed sensing is expounded and analyzed, and the evaluation criteria for signal reconstruction performance are given. Then, from the theory and simulation, the sparseness of IGBT gate drive signals under different sparse bases and different measurement matrices are discussed and analyzed. Next, aiming at the shortcomings of the stagewise weak orthogonal matching pursuit algorithm, a SWOMP algorithm based on the backtracking strategy of different Sigmoid functions is proposed. Simulation and experimental results show that the improved SWOMP4 algorithm has the best reconstruction effect. Finally, the SWOMP4 algorithm is applied to the process of IGBT gate drive signal reconstruction.This paper mainly collects and reconstructs the IGBT gate drive signal through the improved SWOMP4 algorithm. The designed algorithm has high reconstruction accuracy and can be used in the IGBT module online health status system.

A deconvolution sound source identification method based on twice weak selection orthogonal matching pursuit

The deconvolution sound source identification algorithm based on orthogonal matching pursuit has high identification accuracy and spatial resolution. Still, it has a significant defect in that the sparse degree of sound source needs to be known in advance, so it often has significant limitations in practical engineering applications. In this paper, a deconvolution sound source identification algorithm with twice weak selection orthogonal matching pursuit (TWSOMP-DAMAS) is proposed. It can delete the wrong atoms according to twice weak selection criteria in the iterative process, gradually narrow the range of sound sources, and finally find the location of real sound sources. The simulation results show that the TWSOMP-DAMAS algorithm can effectively reduce the main lobe width and has a higher spatial resolution than the deconvolution algorithm with sparse constraints (SC-DAMAS). And the deconvolution sound source identification algorithm with orthogonal matching pursuit (OMP-DAMAS) has the same identification effect; The TWSOMP-DAMAS algorithm is proved to have good adaptability to noise environment, and the recognition results show that the algorithm has high recognition stability.

A method of sound source recognition based on tow weak selection compressed sensing

The current compression sensing sound source recognition algorithm is mainly based on prior knowledge of exact sparsity. This paper proposes the two stagewise weak orthogonal matching pursuit (TSWOMP) compressed sensing reconstruction algorithm for sound source recognition, which selects atoms and detects the reliability of the selected atoms, and deletes the wrong atoms to obtain the final set of atoms. The results of TSWOMP, stagewise weak orthogonal matching pursuit (SWOMP) and conventional beamforming under different frequencies and different SNR are compared through numerical simulation methods. The results show that TSWOMP can effectively reduce the width of the main lobe, suppressing the influence of the side lobes, and improve the resolution. The recognition accuracy of TSWOMP is significantly higher than that of SWOMP. In addition, it can effectively reduce the algorithm's dependence on sparsity.

Weak fault signature identification of rolling bearings based on improved adaptive compressed sensing method

In order to extract the weak fault feature mixed in the background noise and escape the constraints of traditional sampling theorems on large-capacity data collection, transmission and storage, an improved compressed sensing (CS) algorithm, in which an adaptive sparse representation based on learning dictionary and an improved reconstruction scheme are integrated, is presented in this paper. The adaptive sparse representation method includes the K-singular value decomposition based dictionary learning algorithm, and the stagewise weak orthogonal matching pursuit reconstruction is improved by the regularization principle, backtracking mechanism, adaptive mechanism and relevant stop criteria. The compressive measurement was completed through Gaussian random matrix. One simulation signal and two different experimental data sets of rolling bearing are utilized to validate the effectiveness of the proposed method, and the results have shown that the reconstruction accuracy and calculation efficiency can be improved simultaneously. Most importantly, the weak fault feature shaded by strong background noise can be effectively identified, which cannot be obtained by traditional CS method.

Stagewise Weak Orthogonal Matching Pursuit Algorithm Based on Adaptive Weak Threshold and Arithmetic Mean

Stagewise Weak Orthogonal Matching Pursuit Algorithm Based on Adaptive Weak Threshold and Arithmetic Mean

A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition.

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

Research on Improvement of Stagewise Weak Orthogonal Matching Pursuit Algorithm

One of the key technologies of compressed sensing is the signal reconstruction. And the two important indicators of signal reconstruction are the reconstruction probability and the time consumed. The Stagewise Weak Orthogonal Matching Pursuit (SWOMP) is widely used because the sparsity does not need to be a priori condition. The use of fixed threshold parameter in the iterative process can easily lead to overestimation and underestimation. Inspired by the idea of “the initial stage is approaching quickly and the final stage is approaching gradually,” that is, the search rule of “firstly fast and then slow,” an improved algorithm replacing the fixed threshold selection with S-shaped function value in each iteration is proposed to overcome the shortcoming that the fixed threshold parameter is selected in every iteration of SWOMP algorithm. Through compared experiment of six different S-shaped functions, the results show that the influence of different S-shaped functions on the SWOMP algorithm is different, and the improved SWOMP algorithm with the sixth S-shaped function has the best reconstruction effect.

Block-sparse signal recovery based on orthogonal matching pursuit via stage-wise weak selection

For recovering the block-sparse signal with unknown block structures, this paper presents a Block Stage-wise Weak Orthogonal Matching Pursuit (BSWOMP) algorithm. Which partitions the original signal into different groups or segments and conducts the selection for these groups separately using weak selection method to update reconstructing support set. And it proves that the Restricted Isometry Property (RIP) of BSWOMP algorithm is slack than Stage-wise Weak Orthogonal Matching Pursuit (SWOMP) algorithm and Block Orthogonal Matching Pursuit (BOMP) algorithm, this means BSWOMP algorithm generates better convergence than traditional sparse signal recovery algorithm. To further analysis of the performance of BSWOMP, the effectiveness of this algorithm is demonstrated by simulations. Experiments results show that the proposed algorithm effectively selects the nonzero elements of the block-sparse signal by a threshold, and compared with the traditional algorithms, BSWOMP algorithm has higher recovering stability, expedites the approximate conjugate gradient update step and brings an evident performance improvement and application prospect.

A novel approach for Sparse Imaging of Through-wall Radar

Aiming at the problem of segmented weak orthogonal matching pursuit (SWOMP) imaging blur in the process of through-wall radar (TWR) imaging, a dynamic threshold weak orthogonal matching pursuit algorithm (DWOMP) is introduced in this paper, which can significantly improve the imaging performance. Firstly, the TWR compressed sensing simulation model and over-complete dictionary are established by using Chirp signal radar echo data. Then the specific process of DWOMP algorithm is proposed and used to reconstruct multi-sparse target scenes. Finally, the DWOMP algorithm is compared with Basis Pursuit (BP) algorithm and SWOMP algorithm through simulation experiments. The simulation results show that in the same experimental conditions, the imaging time of DWOMP algorithm is about 3/5 of that of BP algorithm, and the imaging resolution of DWOMP algorithm is better than that of SWOMP algorithm.

Stagewise Arithmetic Orthogonal Matching Pursuit

In order to improve the problems that stagewise weak orthogonal matching pursuit (SWOMP) has low reconstruction accuracy and imprecise choice of indexs selecting, an effective algorithm called stagewise arithmetic orthogonal matching pursuit (SAOMP) was proposed. As an extension of SWOMP algorithm, SAOMP algorithm first adopts an arithmetic threshold strategy to improve the accuracy of the selected indexs, and then introduces a backtracking step to flexibly remove some indexs wrongly selected at previous processing. Through these two modifications, SAOMP algorithm achieves superior reconstruction performance with low complexity. Simulation results demonstrate that, under the same condition, SAOMP algorithm can get better reconstruction result and higher exact reconstruction probability.

Sparse Approximation and Recovery by Greedy Algorithms

We study sparse approximation by greedy algorithms. Our contribution is twofold. First, we prove exact recovery with high probability of random \(K\) -sparse signals within \(\lceil K(1+\epsilon )\rceil \) iterations of the orthogonal matching pursuit (OMP). This result shows that in a probabilistic sense, the OMP is almost optimal for exact recovery. Second, we prove the Lebesgue-type inequalities for the weak Chebyshev greedy algorithm, a generalization of the weak orthogonal matching pursuit to the case of a Banach space. The main novelty of these results is a Banach space setting instead of a Hilbert space setting. However, even in the case of a Hilbert space, our results add some new elements to known results on the Lebesgue-type inequalities for the restricted isometry property dictionaries. Our technique is a development of the recent technique created by Zhang.

SPARSE APPROXIMATION AND RECOVERY BY GREEDY ALGORITHMS IN BANACH SPACES

AbstractWe study sparse approximation by greedy algorithms. We prove the Lebesgue-type inequalities for the weak Chebyshev greedy algorithm (WCGA), a generalization of the weak orthogonal matching pursuit to the case of a Banach space. The main novelty of these results is a Banach space setting instead of a Hilbert space setting. The results are proved for redundant dictionaries satisfying certain conditions. Then we apply these general results to the case of bases. In particular, we prove that the WCGA provides almost optimal sparse approximation for the trigonometric system in $L_p$, $2\le p<\infty $.