scholarly journals Filtering-Based Regularized Sparsity Variable Step-Size Matching Pursuit and Its Applications in Vehicle Health Monitoring

2021 ◽  
Vol 11 (11) ◽  
pp. 4816
Author(s):  
Haoqiang Liu ◽  
Hongbo Zhao ◽  
Wenquan Feng
Keyword(s):  
Health Monitoring ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Superior Performance ◽  
Sparse Signal ◽  
Variable Step Size ◽  
Step Size ◽  
Signal Sampling ◽  
Reconstruction Performance ◽  
Sparsity Level

Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods.

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

2021 ◽  
Vol 11 (4) ◽  
pp. 1435
Author(s):  
Xue Bi ◽  
Lu Leng ◽  
Cheonshik Kim ◽  
Xinwen Liu ◽  
Yajun Du ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Energy Criterion ◽  
Step Size ◽  
Relationship Analysis ◽  
Support Set ◽  
Reconstruction Performance ◽  
Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

scholarly journals Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yigang Cen ◽  
Fangfei Wang ◽  
Ruizhen Zhao ◽  
Lihong Cui ◽  
Lihui Cen ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Selection Process ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Search Space ◽  
Orthogonal Matching Pursuit ◽  
Superior Performance ◽  
Sparse Signal ◽  
Tree Model

Compressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursuit (TBOMP) algorithm is presented with the idea of the tree model in wavelet domain. The algorithm can convert the wavelet tree structure to the corresponding relations of candidate atoms without any prior information of signal sparsity. Thus, the atom selection process will be more structural and the search space can be narrowed. Moreover, according to the backtracking process, the previous chosen atoms’ reliability can be detected and the unreliable atoms can be deleted at each iteration, which leads to an accurate reconstruction of the signal ultimately. Compared with other compressed sensing algorithms, simulation results show the proposed algorithm’s superior performance to that of several other OMP-type algorithms.

scholarly journals Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4153 ◽  
Author(s):  
Adeel Feroz Mirza ◽  
Majad Mansoor ◽  
Qiang Ling ◽  
Muhammad Imran Khan ◽  
Omar M. Aldossary
Keyword(s):  
Steady State ◽  
Pid Controller ◽  
Superior Performance ◽  
Variable Step Size ◽  
Step Size ◽  
Pv System ◽  
Fast Tracking ◽  
Point Tracking ◽  
Incremental Conductance ◽  
Variable Step

In this article, a novel maximum power point tracking (MPPT) controller for the fast-changing irradiance of photovoltaic (PV) systems is introduced. Our technique utilizes a modified incremental conductance (IC) algorithm for the efficient and fast tracking of MPP. The proposed system has a simple implementation, fast tracking, and achieved steady-state oscillation. Traditional MPPT techniques use a tradeoff between steady-state and transition-state parameters. The shortfalls of various techniques are studied. A comprehensive comparative study is done to test various existing techniques against the proposed technique. The common parameters discussed in this study are fast convergence, efficiency, and reduced oscillations. The proposed method successfully addresses these issues and improves the results significantly by using a proportional integral deferential (PID) controller with a genetic algorithm (GA) to predict the variable step size of the IC-based MPPT technique. The system is designed and tested against the perturbation and observation (P&O)-based MPPT technique. Our technique effectively detects global maxima (GM) for fast-changing irradiance due to the adopted GA-based tuning of the controller. A comparative analysis of the results proves the superior performance and capabilities to track GM in fewer iterations.

scholarly journals Splitting Matching Pursuit Method for Reconstructing Sparse Signal in Compressed Sensing

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Liu Jing ◽  
Han ChongZhao ◽  
Yao XiangHua ◽  
Lian Feng
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Sparse Signal ◽  
Ratio Test ◽  
Correlation Vector ◽  
Precise Estimation ◽  
Novel Method ◽  
Sparsity Level

In this paper, a novel method named as splitting matching pursuit (SMP) is proposed to reconstructK-sparse signal in compressed sensing. The proposed method selectsFl  (Fl>2K)largest components of the correlation vectorc, which are divided intoFsplit sets with equal lengthl. The searching area is thus expanded to incorporate more candidate components, which increases the probability of finding the true components at one iteration. The proposed method does not require the sparsity levelKto be known in prior. The Merging, Estimation and Pruning steps are carried out for each split set independently, which makes it especially suitable for parallel computation. The proposed SMP method is then extended to more practical condition, e.g. the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, outperforming other OMP-type methods. The proposed method also obtains more precise estimation of DOA angle using one snapshot compared with the traditional estimation methods such as Capon, APES (amplitude and phase estimation) and GLRT (generalized likelihood ratio test) based on hundreds of snapshots.

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