紧耦合INS/视觉相对位姿测量方法

研究了一种紧耦合INS/视觉相对位姿测量方法在无人机自主空中加油中的应用。该方法直接将特征点的图像坐标作为滤波器输入,避免了求解复杂的非线性位姿方程,尤其是特征点提取不全时,该方法具有较强的鲁棒性。引入相对惯导误差建立了增广状态模型,根据杆臂效应详细推导了紧、松两种耦合模式的量测方程。采用扩展卡尔曼滤波算法估计误差状态,并校正惯导输出获取精确的相对位姿信息。仿真结果表明,与松耦合模式相比,紧耦合在提高系统实时性的同时可获得更高的测量精度,位置误差小于0.1 m,姿态角误差小于3'。     

Convergence of Calabi–Yau manifolds

In this paper, we study the convergence of Calabi–Yau manifolds under Kähler degeneration to orbifold singularities and complex degeneration to canonical singularities (including the conifold singularities), and the collapsing of a family of Calabi–Yau manifolds.     

线性过程方差变点的平方累计和SCUSUM估计(英文)

This paper considers the estimation problem of a variance change-point in linear process.Consistency of a SCUSUM type change-point estimator is proved and its rate of convergence is established.The mean-unknown case is also considered.     

带跳的随机比例微分方程的半隐式Euler数值解法(英文)

In this paper,we present the semi-implicit Euler（SIE）numerical solution for stochastic pantograph equations with jumps and prove that the SIE approximation solution converges to the exact solution in the mean-square sense under the Local Lipschitz condition.     

线性随机微分方程的离散波形松弛方法的几乎必然收敛性

提出了随机微分方程的离散型波形松弛方法,证明了它是几乎必然收敛的.此外,通过数值实验验证了所得结果.     

Two new least-squares mixed finite element procedures for convection-dominated Sobolev equations

Two new least-squares mixed finite element procedures are formulated for solving convection-dominated Sobolev equations. Optimal H(div;Ω)×H ¹(Ω) norms error estimates are derived under the standard mixed finite spaces. Moreover, these two schemes provide the approximate solutions with first-order and second-order accuracy in time increment, respectively.     

基于纵向数据下连续型单参数指数族参数的经验贝叶斯检验(英文)

本文讨论了在纵向数据下,运用非参数估计方法构造了连续型单参数指数族参数的经验贝叶斯检验函数,证明了所提出的经验贝叶斯检验函数的渐近最优性,并获得了它的收敛速度.     

无约束优化的一个充分下降共轭梯度算法

在修正PRP共轭梯度法的基础上,提出了求解无约束优化问题的一个充分下降共轭梯度算法,证明了算法在Wolfe线搜索下全局收敛,并用数值实验表明该算法具有较好的数值结果.     

关于迭代数列的审敛法

从迭代数列及其基本性质出发,给出单调有界定理、压缩映象原理、Cauchy收敛准则和上(下)极限四种判别迭代数列收敛的方法.     

根值判别法的两个推广

讨论正项级数的根值判别法．若将判别极限lim↓n→∞ n√an更改为lim↓n→∞或lim↓n→∞^n√am^n＋i,则相应结果在一定条件下将比原判别方法更为精细,且应用范围也有所推广．