Through the coherent accumulation of target echoes, inverse synthetic aperture radar （ISAR） imaging achieves high azimuth resolution. However, because of the instability of the radar system, the echoes of the 1SAR w...Through the coherent accumulation of target echoes, inverse synthetic aperture radar （ISAR） imaging achieves high azimuth resolution. However, because of the instability of the radar system, the echoes of the 1SAR will be randomly lost. The conventional FFT processing methods can cause image blur and high sidelobes or other issues. A novel algorithm for ISAR missing-data imaging based on the Iterative Adaptive Approach （IAA） is proposed. The algorithm enjoys global convergence properties and does not need to set the parameters in advance. The missing-data ISAR imaging results for simulated and measured data illustrate the effectiveness of the algorithm.展开更多
Extreme wave is highly nonlinear and may occur due to diverse reasons unexpectedly.The simulated results of extreme wave based on wave focusing,which were generated using high order spectrum method,are presented.The i...Extreme wave is highly nonlinear and may occur due to diverse reasons unexpectedly.The simulated results of extreme wave based on wave focusing,which were generated using high order spectrum method,are presented.The influences of the steepness,frequency bandwidth as well as frequency spectrum on focusing position shift were examined,showing that they can affect the wave focusing significantly.Hence,controlled accurate generation of extreme wave at a predefined position in wave flume is a difficult but important task.In this paper,an iterative adaptive approach is applied using linear dispersion theory to optimize the control signal of the wavemaker.The performance of the proposed approach is numerically investigated for a wide variety of scenarios.The results demonstrate that this approach can reproduce accurate wave focusing effectively.展开更多
This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in R n.In each iteration,a subset of the sampling data （n-points,where n is the number of features） is adap...This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in R n.In each iteration,a subset of the sampling data （n-points,where n is the number of features） is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.展开更多
对于频率交叠严重且频率成分接近的多分量信号,常用的短时傅里叶变换(Short Time Fourier Transform,STFT)和S方法(S-Method,SM)频率分辨能力不足,重构精度低.针对该问题,本文结合逆Radon变换提出了基于短时迭代自适应-逆Radon变换(Shor...对于频率交叠严重且频率成分接近的多分量信号,常用的短时傅里叶变换(Short Time Fourier Transform,STFT)和S方法(S-Method,SM)频率分辨能力不足,重构精度低.针对该问题,本文结合逆Radon变换提出了基于短时迭代自适应-逆Radon变换(Short Time Iterative Adaptive Approach-Inverse Radon Transform,STIAA-IRT)的微多普勒特征提取方法.首先采用基于加权迭代自适应的STIAA时频分析方法分析了散射点模型的微多普勒特性,然后利用逆Radon变换分离重构不同散射点的微多普勒分量.该方法在低信噪比、邻近时频分布情况下能获得高分辨的多分量信号的完整微多普勒信息,性能分析显示STIAA-IRT重构精度较高,明显优于STFT-IRT(Short Time Fourier Transform-Inverse Radon Transform)和SM-IRT(S-Method-Inverse Radon Transform)特征提取方法.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.61471149 and 61622107)
文摘Through the coherent accumulation of target echoes, inverse synthetic aperture radar （ISAR） imaging achieves high azimuth resolution. However, because of the instability of the radar system, the echoes of the 1SAR will be randomly lost. The conventional FFT processing methods can cause image blur and high sidelobes or other issues. A novel algorithm for ISAR missing-data imaging based on the Iterative Adaptive Approach （IAA） is proposed. The algorithm enjoys global convergence properties and does not need to set the parameters in advance. The missing-data ISAR imaging results for simulated and measured data illustrate the effectiveness of the algorithm.
基金supported by the Basic Research Program of Dalian Maritime University(Grant No.3132019112)the Open Fund Program of State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology(Grant No.LP1910).
文摘Extreme wave is highly nonlinear and may occur due to diverse reasons unexpectedly.The simulated results of extreme wave based on wave focusing,which were generated using high order spectrum method,are presented.The influences of the steepness,frequency bandwidth as well as frequency spectrum on focusing position shift were examined,showing that they can affect the wave focusing significantly.Hence,controlled accurate generation of extreme wave at a predefined position in wave flume is a difficult but important task.In this paper,an iterative adaptive approach is applied using linear dispersion theory to optimize the control signal of the wavemaker.The performance of the proposed approach is numerically investigated for a wide variety of scenarios.The results demonstrate that this approach can reproduce accurate wave focusing effectively.
文摘This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in R n.In each iteration,a subset of the sampling data （n-points,where n is the number of features） is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.
基金supported by National Natural Science Foundation of China(Nos.60834001,60974040,61120106009)the Fundamental Research Funds for the Central Universities(No.2011JBM201)
