为了有效地解决多跳频信号的盲源分离问题,提出了一种变步长的EASI(Equivariant Adaptive Separation via Independence)盲源分离算法。该算法在传统EASI算法的等变化性基础上,用性能指标(串音误差)作为准则,通过改变函数的取值范围及形...为了有效地解决多跳频信号的盲源分离问题,提出了一种变步长的EASI(Equivariant Adaptive Separation via Independence)盲源分离算法。该算法在传统EASI算法的等变化性基础上,用性能指标(串音误差)作为准则,通过改变函数的取值范围及形状,自适应更新步长,使其在一个固定小的范围内,达到算法收敛速度和稳定性能的一个较理想的平衡点,改善了当步长固定时存在的缺陷。经过实验仿真,证明该算法对步长有很好的调整能力,性能稳定且收敛速度较快,能很好地将多个跳频信号进行分离,较传统的EASI算法有更高的适用性。展开更多
An accurate and rapid method for solving radiative transfer equation is presented in this paper. According to the fact that the multiple scattering component of radiance is less sensitive to the error of phase functio...An accurate and rapid method for solving radiative transfer equation is presented in this paper. According to the fact that the multiple scattering component of radiance is less sensitive to the error of phase function than the single scattering component is,we calculate the multiple scattering component by using delta-Eddington approximation and the single scattering component by solving radiative transfer equation. On the ground, when multiple sattering component is small, for example, when the total optical depth T is small, the accurate radiance can be obtained with this method. For the need of the space remote sensing, the upward radiance at the top of the atmosphere is mainly studied, and an approximate expression is presented to correct the multiple scattering component. Compared with the more precise Gauss-Seidel method.the results from this method show an accuracy of better than 10% when zenith angle 0 < 50 掳 and T < 1. The computational speed of this method is, however, much faster than that of Gauss-Seidel method.展开更多
The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear programming probl...The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear programming problem (LPP). One of the important steps of the simplex algorithm is to convert all unequal constraints into equal form by adding slack variables then proceeds to basic solution. Our new algorithm i) solves the LPP without equalize the constraints and ii) leads to optimal solution definitely in lesser time. The goal of suggested algorithm is to improve the simplex algorithm so that the time of solving an LPP will be definitely lesser than the simplex algorithm. According to this Easy Simplex (AHA Simplex) Algorithm the use of Big M method is not required.展开更多
文摘为了有效地解决多跳频信号的盲源分离问题,提出了一种变步长的EASI(Equivariant Adaptive Separation via Independence)盲源分离算法。该算法在传统EASI算法的等变化性基础上,用性能指标(串音误差)作为准则,通过改变函数的取值范围及形状,自适应更新步长,使其在一个固定小的范围内,达到算法收敛速度和稳定性能的一个较理想的平衡点,改善了当步长固定时存在的缺陷。经过实验仿真,证明该算法对步长有很好的调整能力,性能稳定且收敛速度较快,能很好地将多个跳频信号进行分离,较传统的EASI算法有更高的适用性。
文摘An accurate and rapid method for solving radiative transfer equation is presented in this paper. According to the fact that the multiple scattering component of radiance is less sensitive to the error of phase function than the single scattering component is,we calculate the multiple scattering component by using delta-Eddington approximation and the single scattering component by solving radiative transfer equation. On the ground, when multiple sattering component is small, for example, when the total optical depth T is small, the accurate radiance can be obtained with this method. For the need of the space remote sensing, the upward radiance at the top of the atmosphere is mainly studied, and an approximate expression is presented to correct the multiple scattering component. Compared with the more precise Gauss-Seidel method.the results from this method show an accuracy of better than 10% when zenith angle 0 < 50 掳 and T < 1. The computational speed of this method is, however, much faster than that of Gauss-Seidel method.
文摘The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear programming problem (LPP). One of the important steps of the simplex algorithm is to convert all unequal constraints into equal form by adding slack variables then proceeds to basic solution. Our new algorithm i) solves the LPP without equalize the constraints and ii) leads to optimal solution definitely in lesser time. The goal of suggested algorithm is to improve the simplex algorithm so that the time of solving an LPP will be definitely lesser than the simplex algorithm. According to this Easy Simplex (AHA Simplex) Algorithm the use of Big M method is not required.
