某型装备轴承件无失效时的可靠性参数估计

针对寿命服从威布尔分布的某型装备轴承件在无失效数据时的可靠性参数估计问题,首先,以失效概率为待估参数,取其先验分布为Beta分布,在2个超参数的先验密度函数为2种不同情形下,建立了失效概率的2种EBayes估计模型,分析了稳健性、渐近性和单调性;然后,对威布尔分布参数进行了加权最小二乘估计,进而建立了可靠度的2种E-Bayes估计模型,分析了稳健性和单调性;最后,结合实例数据进行了可靠性参数估计与分析,指出第2种估计模型更加合理有效。     

Vision-based pose estimation for cooperative space objects

Imaging sensors are widely used in aerospace recently. In this paper, a vision-based approach for estimating the pose of cooperative space objects is proposed. We learn generative model for each space object based on homeomorphic manifold analysis. Conceptual manifold is used to represent pose variation of captured images of the object in visual space, and nonlinear functions mapping between conceptual manifold representation and visual inputs are learned. Given such learned model, we estimate the pose of a new image by minimizing a reconstruction error via a traversal procedure along the conceptual manifold. Experimental results on the simulated image dataset show that our approach is effective for 1D and 2D pose estimation.     

根据两颗导航星信息估计捷联惯导系统误差的一种方法

针对高机动短时间飞行的飞行器,介绍了一种根据它相对2颗导航卫星的伪距离和伪速度信息,估计捷联惯导系统定位和定速度误差的方法.考虑到由确定飞行器相对每颗星的径向距离和速度的非线性方程施加的约束,根据这些误差矢量长度最小值的条件确定误差值.用空间解析几何法得出了计算捷联惯导系统误差估值的最终算法,这种算法能给出非线性最小值问题的精确解.     

A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with real-time updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.     

现代飞机俯仰跟踪时诱发振荡预测分析

根据飞机纵向ＰＩＯ的特点，提出了预测分析ＰＩＯ所用的人－机闭环系统数学模型，其中飞机采用等效系统数学模型，驾驶员操纵采用ＭｃＲｕｅｒ模型，参数按地面人－机模拟飞行跟踪试验确定，集中介绍了四种飞机纵向ＰＩＯ预测准则，Ｓｍｉｔｈ－Ｇｅｄｄｅｓ准则，修正带宽准则，增益，相位裕度准则，Ｎｅａｌ－Ｓｍｉｔｈ原则，并提供了相应的计算方法。     

基于距离像特征的复合速度估计算法研究

首先分析了步进频率宽带信号混频处理原理和目标速度对距离像的影响。在此基础上，提出了基于距离像熵和距离像对比度的速度估计算法，详细分析了这两种算法的数学原理、步骤、效果、适用条件等。最后，基于这两种算法又提出了复合速度估计算法，大大提高了速度估计精度。     

航天型号项目成本估算方法研究与实现

介绍了国内外航天型号研制项目成本估算的研究现状,分析了航天型号研制项目成本估算的作用与特点。研究了参数成本估算法、工程成本估算法、类比成本估算法、外推成本估算法、仿真模型法和综合成本估算法等方法。给出了用综合成本估算法估算的风云四号气象卫星研制成本算例。     

目标函数误差估算及网格自适应处理

发展了一种基于目标函数误差估算的网格自适应准则,进而通过网格自适应处理提高目标函数计算的效率和准确性。首先描述了目标函数的误差估算及修正的方法,该方法通过伴随方程将原方程的残值误差与目标函数联系起来。然后,基于误差估算建立网格自适应准则,以减少目标函数修正后的剩余误差,提高目标函数计算结果的准确性,并将方法进一步发展至多目标问题。最后将该准则应用于Euler方程控制的NACA0012翼型无粘可压流动的网格自适应模拟。数值计算成功地捕获了与升力、阻力和力矩等目标函数相关的特征流动区域,计算出符合指定精度要求的目标函数值,验证了本文所发展的方法。     

基于2D-LDV技术的船舶螺旋桨三维流场结构分析(英文)

利用2D-LDV设备,通过优化布置测试点,获得了均匀来流下螺旋桨尾流场三维流动数据。展示了宏观的流场动态以及微观的流动信息,如尾涡和梢涡在流场中的分布情况、尾涡部流动的剪切效应、梢涡部的绕流及流动分离状态。计算了水动力螺距角以及螺旋桨附着涡的总环量。试验显示了LDV技术在流动细节测试方面的优势,获得的定量信息可以为相关研究人员提供技术支持。     

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.