GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

Asogbon, Mojisola Grace; Lu, Yu; Samuel, Oluwarotimi Williams; Jing, Liwen; Miller, Alice; Li, Guanglin; Wong, Kelvin Kian Loong

doi:10.1016/j.compeleceng.2021.107189

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Therefore, compressed sensing is widely used in image compression [5,6], medical imaging [7,8], communication system [9] and many other fields [10,11]. The recovery algorithms of compressed sensing model mainly include greedy algorithm and convex optimization algorithm [12]. When no noise exists, the greedy algorithm and its optimization algorithm Electronics 2023, 12, 162 2 of 15 need too many measurements and have low recovery accuracy, which cannot guarantee the global optimal solution.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Recovery Algorithm Based on Arithmetic Optimization

2022

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At present, the sparse recovery problem is mainly solved by convx optimization algorithm and greedy tracking method. However, the former has defects in recovery efficiency and the latter in recovery ability, and neither of them can obtain effective recovery under large sparsity or small observation degree. In this paper, we propose a new sparse recovery algorithm based on arithmetic optimization algorithm and combine the ideas of greedy tracking method. The proposed algorithm uses arithmetic optimization algorithm to solve the sparse coefficient of the signal in the transform domain, so as to reconstruct the original signal. At the same time, the greedy tracking technique is combined to design the initial position of the operator before solving, so that it can be searched better. Experiments show that compared with other methods, the proposed algorithm can not only obtain more effective recovery, but also run faster under general conditions of observation number. At the same time, It can also recover the signal better in the presence of noise.

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Section: Introductionmentioning

confidence: 99%

A Sparse Recovery Algorithm Based on Arithmetic Optimization

2022

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“…23 Recently, many scholars have replaced the random generation method with the orthogonal experiment design to initialize the population. 24,25 The orthogonal experiment design method takes advantage of the orthogonality to get initially well-distributed population in the search domain. 26 In this study, the orthogonal experiment design-based initialization method is exploited to NSGA-II to optimize the inherent parameters of the HIS system.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization method for hydraulically interconnected suspension system

Zhou

Cheng

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To enhance the ride comfort and roll stability of the special vehicle, this study concentrates on the inherent parameters optimization of the hydraulically interconnected suspension (HIS) system. A road-tire-HIS-body model of a special vehicle is established based on the AMESim/Simulink co-simulation platform. Then the sensitivity of the HIS system is analyzed to determine the design variables to be optimized through the AMESim/Simulink/Isight co-simulation. Two objectives including improving the ride comfort and roll stability of the vehicle are proposed. In order to enhance the population diversity and accelerate the convergence velocity, the improved non-dominated sorting genetic algorithm II (NSGA-II) based on orthogonal experimental design method is used for multi-objective optimization problem of the HIS system. The numerical simulations indicate that the improved NSGA-II (INSGA-II) can generate better distributed Pareto solutions than traditional NSGA-II. Furthermore, simulation results indicate that both of the ride comfort and roll stability of the vehicle are improved based on the HIS system optimized by INSGA-II than NSGA-II. Compared with the NSGA-II method, the vehicle ride comfort and roll stability of HIS system optimized by INSGA-II are increased by 17.62% and 8.56% in sinusoidal conditions, respectively.

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“…The fast developed Compressive Sensing (CS) technique has been widely used in the fields of sparse signal reconstruction [1,2], communication channel estimation [3,4], and underwater source localization [5]. Among the well-known CS algorithms, the two representative methods are the convex relaxation-based algorithm and the greedy algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sparse signal recovery from noisy measurements via searching forward OMP

Sun

Yang

et al. 2021

Electronics Letters

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Recovering sparse signals from compressed measurements has received much attention in recent years. Considering that measurement errors always exist, an improved orthogonal matching pursuit (OMP) method which is called Searching Forward OMP (SFOMP), is proposed in this letter. The proposed SFOMP method is designed for compressive sensing and sparse signal recovery in the noisy environment. To improve the recovery performance, the SFOMP method incorporates a searching forward strategy to find the column leading to a minimum norm of residual error among the added candidates in each iteration. Numerical results show that, compared with other commonly used methods, this method provides a higher recovery signal‐to‐noise ratio, more accurate reconstruction of support set, and a competitive computational complexity with noisy measurements.

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GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

Cited by 10 publications

References 21 publications

A Sparse Recovery Algorithm Based on Arithmetic Optimization

A Sparse Recovery Algorithm Based on Arithmetic Optimization

A multi-objective optimization method for hydraulically interconnected suspension system

Sparse signal recovery from noisy measurements via searching forward OMP

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