This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy.The subpixel translational shift information is directly obtained from the phase of the...This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy.The subpixel translational shift information is directly obtained from the phase of the normalized cross power spectrum by using Maximum Likelihood Estimation(MLE).The proposed algorithm also has slighter time complexity.Experimental results show that the proposed algorithm yields superior registration precision on the Cramér-Rao Bound(CRB) in the presence of aliasing and noise.展开更多
X-ray pulsar navigation(XPNAV) is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the ge...X-ray pulsar navigation(XPNAV) is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile from the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood(ML) phase estimation method that directly utilizes the measured time of arrivals(TOAs) is presented. The X-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used Poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.展开更多
配电网受谐波和噪声干扰严重,电压相量的计算难度更大,传统的输电网相量测量精度不能满足配电网的要求。为提高配电网电压相量幅值和相角的测量精度,提出了一种基于条件最大似然估计法(conditional maximum likelihood estimation,CML)...配电网受谐波和噪声干扰严重,电压相量的计算难度更大,传统的输电网相量测量精度不能满足配电网的要求。为提高配电网电压相量幅值和相角的测量精度,提出了一种基于条件最大似然估计法(conditional maximum likelihood estimation,CML)的配电网微型同步相量测量单元(μPMU)的相量测量方法。该算法建立了三相不平衡系统的信号模型,在待测量最多包含两个未知量且待测量矩阵与单位矩阵正交不等于零时,利用三相矩阵与样本协方差矩阵的特征向量正交来获取待测量,进而通过几何特性推导出电压相量的幅值及相位表达式,降低了运算量。通过Matlab进行了仿真验证,仿真结果表明所提方法能够在一定程度上提高配电网电压相量幅值和相位的测量精度。展开更多
文摘This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy.The subpixel translational shift information is directly obtained from the phase of the normalized cross power spectrum by using Maximum Likelihood Estimation(MLE).The proposed algorithm also has slighter time complexity.Experimental results show that the proposed algorithm yields superior registration precision on the Cramér-Rao Bound(CRB) in the presence of aliasing and noise.
基金Project supported by the National Natural Science Foundation of China(No.61172138)the Fundamental Research Funds for the Central Universities(Nos.K5051302015 and K5051302040)+1 种基金the Natural Science Basic Research Plan in Shaanxi Province of China(No.2013JQ8040)the Research Fund for the Doctoral Program of Higher Education of China(No.20130203120004)
文摘X-ray pulsar navigation(XPNAV) is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile from the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood(ML) phase estimation method that directly utilizes the measured time of arrivals(TOAs) is presented. The X-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used Poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.
文摘配电网受谐波和噪声干扰严重,电压相量的计算难度更大,传统的输电网相量测量精度不能满足配电网的要求。为提高配电网电压相量幅值和相角的测量精度,提出了一种基于条件最大似然估计法(conditional maximum likelihood estimation,CML)的配电网微型同步相量测量单元(μPMU)的相量测量方法。该算法建立了三相不平衡系统的信号模型,在待测量最多包含两个未知量且待测量矩阵与单位矩阵正交不等于零时,利用三相矩阵与样本协方差矩阵的特征向量正交来获取待测量,进而通过几何特性推导出电压相量的幅值及相位表达式,降低了运算量。通过Matlab进行了仿真验证,仿真结果表明所提方法能够在一定程度上提高配电网电压相量幅值和相位的测量精度。
