The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
The authors establish the approximations to the distribution of M-estimates in a linear model by the bootstrap and the linear representation of bootstrap M-estimation, and prove that the approximation is valid in prob...The authors establish the approximations to the distribution of M-estimates in a linear model by the bootstrap and the linear representation of bootstrap M-estimation, and prove that the approximation is valid in probability 1. A simulation is made to show the effects of bootstrap approximation, randomly weighted approximation and normal approximation.展开更多
The paper presents a numerical method for solving a class of high-dimensional stochastic control systems based on tensor decomposition and parallel computing.The HJB solution provides a globally optimal controller to ...The paper presents a numerical method for solving a class of high-dimensional stochastic control systems based on tensor decomposition and parallel computing.The HJB solution provides a globally optimal controller to the associated dynamical system.Variable substitution is used to simplify the nonlinear HJB equation.The curse of dimensionality is avoided by representing the HJB equation using separated representation.Alternating least squares(ALS)is used to reduced the separation rank.The experiment is conducted and the numerical solution is obtained.A high-performance algorithm is designed to reduce the separation rank in the parallel environment,solving the high-dimensional HJB equation with high efficiency.展开更多
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
基金supported by Fund of 211 Program of SHUFEFund of Educational Committee of Shanghai
文摘The authors establish the approximations to the distribution of M-estimates in a linear model by the bootstrap and the linear representation of bootstrap M-estimation, and prove that the approximation is valid in probability 1. A simulation is made to show the effects of bootstrap approximation, randomly weighted approximation and normal approximation.
基金supported by the National Natural Science Foundation of China under Grant No.61873254。
文摘The paper presents a numerical method for solving a class of high-dimensional stochastic control systems based on tensor decomposition and parallel computing.The HJB solution provides a globally optimal controller to the associated dynamical system.Variable substitution is used to simplify the nonlinear HJB equation.The curse of dimensionality is avoided by representing the HJB equation using separated representation.Alternating least squares(ALS)is used to reduced the separation rank.The experiment is conducted and the numerical solution is obtained.A high-performance algorithm is designed to reduce the separation rank in the parallel environment,solving the high-dimensional HJB equation with high efficiency.
