亚分米级GPS基线解算新算法

当双差模糊度固定解的可靠性下降时,三差基线解成为当前情况下的最优选择,但通常情况下三差基线解精度只能达到分米级,难以满足高精度的要求.通过对三差测量值进行误差分析,给出三差观测量信噪比模型,结合实际观测数据分析,指出噪声对三差观测量影响很大.借鉴均值漂移理论出了精度为亚分米级的基线解算的新算法,给出了适用于单频三差基线解算的均值漂移模型,所述算法的精度可以达到10cm,即亚分米级.     

广义逆的计算与最小二乘估计

给出了某些广义逆的计算方法,将这些结果应用于观测系统的参数估计,给出了系统无加权和加权最小二乘的最新估计结果,推广了[1]的工作,同时简化了[2]中求多个相容矩阵方程公共解的算法。     

一类有理三次样条的区域控制和逼近性质

对一类分母为二次的有理三次插值样条曲线的区域控制问题进行了研究 ,给出了将其约束于给定区域的充分条件及充分必要条件 ,然后给出了当被插值函数为一阶连续时插值函数的逼近性质 .     

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.     

混沌干扰背景下的正弦频率估计新方法

提出一种基于单变量驱动误差滤波反馈混沌同步的强混沌背景下正弦参数估计的方法。该方法利用采样的混合信号(混沌加白噪声和正弦)通过某一类型滤波器,用滤波后信号驱动一新构建的同类响应混沌系统,若混沌同步发生,则驱动响应信号的误差序列中应含有正弦成分,对误差序列采用互谱MUSIC算法估计正弦频率。该方法适于强混沌信号加强白噪声为背景的正弦参数估计。仿真实验表明,该方法简单有效。     

双线性系统参数估计中的泰勒级数方法

本文给出了一种利用泰勒级数估计双线性系统参数的方法。通过将输入输出进行泰勒级数展开,把原微分方程模型转化成一个代数方程,进而利用最小二乘原理得到参数的估值。     

一种非线性动态系统参数估计方法

从非线性动态系统的参数估计问题出发,介绍了一种新的估计方法——粒子滤波估计法,该方法是一种基于贝叶斯推理和蒙特卡罗方法递推算法,通过动态模型推导了粒子的数值,通过实例分析了该估计方法的性能.结果表明,粒子滤波方法能显著提高估计性能,且精度高,比较灵活.     

On a new class of finite dimensional estimation algebras

We introduce a new class of solvable finite dimensional estimation algebras that contains both the linear and the Bene cases. The condition for finite dimensionality is expressed in terms of two matrices. They play an important role in understanding the structure of estimation algebras.