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adaptive Channelized greedy algorithm for Analog Signal Compressive Sensing

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第11期67卷 10645-10659页

作者： Xu, Hongyi Zhang, Chaozhu Kim, Il-Min Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada

In the development of analog signal compressive sensing (CS), the degradation of reconstruction performance under noise is the main bottleneck because the CS framework is very sensitive to noise. This paper proposes an adaptive channelization-based orthogonal matching pursuit algorithm (C-OMP) combining the channelization and the adaptive iteration methods. The proposed C-OMP has two steps: channel screening and global iteration. Based on the proposed method, the original signal can be recovered adaptively in high probability of success with fewer observations under the noise background. Simultaneously, the noise can be reduced as much as possible to enhance the output signal-tonoise ratio (SNR) by excluding the noise channel during the channel screening and separating noise atoms during the global iteration. The relationship between the probability of successful reconstruction and the number of observations is mathematically analyzed. Furthermore, the parameter settings, computational complexity, and output SNR are analytically evaluated. The simulation results confirm the analytical results and further demonstrate the effectiveness and advantages of the C-OMP in the noise environment. Overall, the proposed algorithm considerably improves the performance of the analog signal CS in the practical noisy environment.

关键词： adaptive greedy algorithm analog signal compressive sensing channelization

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AUV Path Planning Based on Improved Sparrow Search algorithm 23

AUV Path Planning Based on Improved Sparrow Search Algorithm

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Wang, Siyuan Sun, Qian Ning, Beixi Zhou, Xingyu College of Information and Communication Harbin Engineering University Key Laboratory of Advanced Marine Communication and Information Technology Harbin China

ISBN: (纸本)9798350325959

An improved sparrow search algorithm (ISSA) is used to study the three-dimensional path-planning problem of automatic underwater vehicle (AUV). Use mathematical models to build the environment and construct an energy consumption model based on the AUV and environmental conditions. Firstly, based on the upper confidence bound algorithm (UCB), the iterative population size is improved to utilize search resources at different times fully;Secondly, considering the undirected search of the discoverer, an adaptive greedy algorithm is adopted to enhance convergence accuracy;Finally, improve the evolutionary strategy of discoverers and threatened sparrows to reduce the algorithm falling into local optima. The simulation results show that ISSA has better global optimization capability, making it more suitable for AUV 3D path planning. © 2023 IEEE.

关键词： adaptive greedy algorithm Automatic underwater vehicle Sparrow search algorithm Upper confidence bound

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Cooperative MASS path planning for marine man overboard search

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OCEAN ENGINEERING 2021年 235卷 109376-109376页

作者： Mou, Junmin Hu, Tao Chen, Pengfei Chen, Linying Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China

In a Man Overboard (MOB) incident, a quick and effective Search and Rescue (SAR) operation is crucial to increase the survival probability of the victim. Determining the search area and planning paths for the rescue ships are essential for efficient SAR operation. The search area's determination requires the prediction of the missing person, facing the challenges of lacking information about the accurate position and time of falling and the influence of environmental disturbances. Two main aims of path planning for SAR operation are quick arrival at the search area and coverage search with high cumulative Possibility of Success (POS). Many path planning algorithms have been proposed. Most of them aim at finding the shortest paths, which meet the goal of quick arrival. However, the path planning for maximizing POS of finding the person is still lacking. Besides, compared to an individual ship, a fleet of cooperative Maritime Autonomous Surface Ships (MASS) can significantly increase the POS and reduce SAR personnel's risk in a time-sensitive SAR operation. Therefore, in this paper, we propose a cooperative path planning framework to search for the missing person in a MOB incident using a fleet of fully autonomous MASS. The framework is divided into three modules, i.e., position prediction, target tracking, and coverage search. Firstly, the stochastic particle simulation method is used to predict the missing person's position considering the environment forecasting data, which determines the search area. Secondly, an adaptive greedy search algorithm is applied to tracking the drifting predicted area. Thirdly, the coverage search algorithm is designed with an adaptive neighborhood and evaluation function for increasing the cumulative POS in a limited time. Moreover, the path is smoothed by the Line-of-Sight algorithm and the kinematic interpolation method. Simulation experiments and sensitivity analysis are carried out to demonstrate the effectiveness of the proposed f

关键词： Cooperative path planning Man overboard Multiple MASS adaptive greedy algorithm Search and rescue

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adaptive block greedy algorithms for receiving multi-narrowband signal in compressive sensing radar reconnaissance receiver

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Journal of Systems Engineering and Electronics 2018年第6期29卷 1158-1169页

作者： ZHANG Chaozhu XU Hongyi JIANG Haiqing School of Information and Communication Engineering Harbin Engineering University School of Information and Electronics Beijing Institute of Technology

This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown *** simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.

关键词： compressive sensing(CS) adaptive greedy algorithm block sparsity analog-to-information convertor(AIC) multinarrowband signal

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The adaptive algorithm for the selection of sources of the method of fundamental solutions

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2018年 95卷 154-159页

作者： Lin, Ji Lamichhane, A. R. Chen, C. S. Lu, Jun Hohai Univ Coll Mech & Mat Int Ctr Simulat Software Engn & Sci Nanjing 211100 Jiangsu Peoples R China Ohio Northern Univ Dept Math & Stat Ada OH 45810 USA Univ Southern Mississippi Dept Math Hattiesburg MS 39406 USA Nanjing Hydraul Res Inst Mat & Struct Engn Dept Hujuguan Rd 34 Nanjing 210024 Jiangsu Peoples R China State Key Lab Hydrol Water Resources & Hydraul En Xikang Rd 1 Nanjing 210098 Jiangsu Peoples R China

Despite all the efforts and success for finding the optimal location of the sources outside the domain for the method of fundamental solutions (MFS), this issue continues to attract the attention from researchers for seeking more efficient and reliable algorithms. In this paper, we propose to extend the adaptive greedy technique which applies the primal-dual formulation for the selection of source nodes in the MFS for Laplace equation with nonharmonic boundary conditions. Such approach is a data-dependent algorithm which adaptively selects the suitable source nodes based on the specific adaptive procedure. Both 2D and 3D examples are provided. Moreover, the proposed algorithm is easy to implement with high accuracy.

关键词： Method of fundamental solutions Source nodes adaptive greedy algorithm Meshless method

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Channelized-Based Denoising Generalized Orthogonal Matching Pursuit for Reconstructing Structural Sparse Signal Under Noise Background

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IEEE ACCESS 2018年 6卷 66105-66122页

作者： Jiang, Haiqing Xu, Hongyi Xu, Chengfa Beijing Inst Technol Dept Informat & Elect Beijing 100081 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China

This paper proposed a channelized-based denoising generalized orthogonal matching pursuit algorithm (gOMP) for reconstructing structural sparse signal in engineering application. The algorithm combines the compressive sensing channelization method, the pre-estimation-based adaptive method with the gOMP. By channelizing the observation matrix, the algorithm first eliminates most of the channels that only contain noise with a residual-based detection method. Then, according to a pre-estimated sparsity level, the signal can be accurately and adaptively reconstructed by re-screening the redundant support obtained by the gOMP. The two steps of the algorithm effectively reduce the deterioration of the reconstruction caused by noise, thereby significantly improving the output signal-to-noise ratio (SNR). A mathematical derivation of the reconstruction conditions is given. Also, the computational complexity and the theoretical SNR improvement are discussed. Besides, the upper bound of the reconstruction error in the noise environment is mathematically analyzed. Finally, the experiments verified the performance analysis and detailedly demonstrated the advantages of the proposed algorithm for recovering structural sparsity signals under noise interference. The results show that the proposed scheme considerably outperforms any existing adaptive and denoising greedy algorithm in the sense of the reconstruction accuracy and the output SNR.

关键词： adaptive greedy algorithm channelization compressive sensing denoising

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A FAST BLOCK-greedy algorithm FOR QUASI-OPTIMAL MESHLESS TRIAL SUBSPACE SELECTION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2016年第2期38卷 A1224-A1250页

作者： Ling, Leevan Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China

Meshless collocation methods are often seen as a flexible alternative to overcome difficulties that may occur with other methods. As various meshless collocation methods gain popularity, finding appropriate settings becomes an important open question. Previously, we proposed a series of sequential-greedy algorithms for selecting quasi-optimal meshless trial subspaces that guarantee stable solutions from meshless methods, all of which were designed to solve a more general problem: " Let A be an M x N matrix with full rank M;choose a large M x K submatrix formed by K <= M columns of A such that it is numerically of full rank." In this paper, we propose a block-greedy algorithm based on a primal/dual residual criterion. Similar to all algorithms in the series, the block-greedy algorithm can be implemented in a matrix-free fashion to reduce the storage requirement. Most significantly, the proposed algorithm reduces the computational cost from the previous O(K-4+NK2) to at most O(NK2). Numerical examples are given to demonstrate how this efficient and ready-to-use approach can benefit the stability and applicability of meshless collocation methods.

关键词： Kansa method kernel collocation radial basis function adaptive greedy algorithm basis selection

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l₀-norm based structural sparse least square regression for feature selection

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PATTERN RECOGNITION 2015年第12期48卷 3927-3940页

作者： Han, Jiuqi Sun, Zhengya Hao, Hongwei Chinese Acad Sci Inst Automat 95 Zhongguancun East Rd Beijing 100190 Peoples R China

This paper presents a novel approach for feature selection with regard to the problem of structural sparse least square regression (SSLSR). Rather than employing the l(1)-norm regularization to control the sparsity, we directly work with sparse solutions via l(o)-norm regularization. In particular, we develop an effective greedy algorithm, where the forward and backward steps are combined adaptively, to resolve the SSLSR problem with the intractable l(r,o)-norm. On the one hand, features with the strongest correlation to classes are selected in the forward steps. On the other hand, redundant features which contribute little to the improvement of the objective function are removed in the backward steps. Furthermore, we provide solid theoretical analysis to prove the effectiveness of the proposed method. Experimental results on synthetic and real world data sets from different domains also demonstrate the superiority of the proposed method over the state-of-the-arts. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Structural sparse learning l(o)-norm Least square regression Feature selection adaptive greedy algorithm

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A new algorithm for high-dimensional uncertainty quantification based on dimension-adaptive sparse grid approximation and reduced basis methods

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JOURNAL OF COMPUTATIONAL PHYSICS 2015年 298卷 176-193页

作者： Chen, Peng Quarteroni, Alfio ETH Seminar Appl Math CH-8092 Zurich Switzerland Ecole Polytech Fed Lausanne MATHICSE Math Inst Computat Sci & Engn Modelling & Sci CompCMCS CH-1015 Lausanne Switzerland

In this work we develop an adaptive and reduced computational algorithm based on dimension-adaptive sparse grid approximation and reduced basis methods for solving highdimensional uncertainty quantification (UQ) problems. In order to tackle the computational challenge of "curse of dimensionality" commonly faced by these problems, we employ a dimension-adaptive tensor-product algorithm [16] and propose a verified version to enable effective removal of the stagnation phenomenon besides automatically detecting the importance and interaction of different dimensions. To reduce the heavy computational cost of UQ problems modelled by partial differential equations (PDE), we adopt a weighted reduced basis method [7] and develop an adaptive greedy algorithm in combination with the previous verified algorithm for efficient construction of an accurate reduced basis approximation. The efficiency and accuracy of the proposed algorithm are demonstrated by several numerical experiments. (C) 2015 Elsevier Inc. Allrightsreserved.

关键词： Uncertainty quantification Curse of dimensionality Generalized sparse grid Hierarchical surpluses Reduced basis method adaptive greedy algorithm Weighted a posteriori error bound

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Group greedy RLS Sparsity Estimation via Information Theoretic Criteria

Group Greedy RLS Sparsity Estimation via Information Theoret...

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19th International Conference on Control Systems and Computer Science

作者： Onose, Alexandru Dumitrescu, Bogdan Tampere Univ Technol Dept Signal Proc FI-33101 Tampere Finland

ISBN: (纸本)9780769549804

This work introduces a group sparse adaptive greedy algorithm that uses information theoretic criteria (ITC) to estimate online the sparsity level. The algorithm selects a set of candidate groups using group neighbor permutations and maintains a partial QR decomposition to compute the solution. It contains a mechanism that allows group joining which, complementing the splitting of groups, produces a robust algorithm. We focus here on a study of the ITC use, namely the predictive least squares (PLS) and Bayesian information criterion (BIC), in conjunction with the group sparse algorithm. We propose several forms of group oriented ITC and evaluate them with extensive simulations for a time-varying channel identification problem. Compared to the non group aware counterparts, the performance is improved at the cost of higher complexity. The best results are given by a group PLS criterion directly generalizing the standard PLS.

关键词： adaptive greedy algorithm group sparse filters model selection channel identification

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