基于改进多模型的火星大气进入自适应估计方法

针对火星进入过程中大气密度等不确定参数对导航系统状态估计精度的影响,提出了一种基于改进混合专家框架的多模型自适应估计方法。该方法对进入过程中不同的测量信息进行规范化处理,以克服传统多模型自适应估计方法稳定性差、数值下溢等固有缺陷,进一步提高状态估计精度。将其应用于火星不同进入探测方式下的导航场景进行仿真分析。仿真结果表明:该方法在动力学系统模型参数存在不确定扰动时能获得精确的状态估计,可以满足未来定点着陆探测对导航系统的精度需求。     

GO-FLOW法在飞机EHA可靠性分析中的应用

应用GO-FLOW法分析了飞机电静液作动器(EHA)的可靠性。首先在EHA单元功能合理划分的基础上,建立了EHA的GO-FLOW可靠性分析模型,采用布尔代数求解描述反馈环的布尔方程,解决了GO-FLOW图不允许存在循环的难题;其次进行了GO-FLOW运算,得到EHA系统在各时间点的可靠度;再次与GO法的结果比较,验证了GO-FLOW法的可行性和准确性;最后通过MATLAB曲线拟合,得到系统可靠度随时间的变化规律,以便及时对系统进行检修和维护。结果表明GO-FLOW法只需一次运算,就可得到系统在各时间点的可靠度,在减小计算复杂度方面较GO法有优势。     

基于稀疏重构的混叠脉冲序列的周期估计

针对于以往方法在克服抖动大和缺失率高的混叠脉冲信号周期估计上的不足,提出了一种基于稀疏重构的混叠脉冲序列的隐藏子周期估计新方法。该方法首先针对由多个具有不同周期的脉冲串混合而成脉冲序列进行插值重采样,然后利用由Ramanujan构造的周期字典,建立了混叠脉冲序列的稀疏表示模型,最后采用联合l_2,0混合范数算法求得混叠脉冲序列的周期估计。方法的优点是具有较强抗噪能力和抗脉冲缺失能力,并且不受初相的改变影响。所需要较少的脉冲数据长度就能得到准确的周期稀疏解。仿真实验表明,所提方法具有更好的估计性能。     

基于遗传算法优化的人工神经网络下高速滚动轴承的疲劳可靠性

考虑到高速滚动轴承中热弹流润滑效应的影响,提出一种人工智能方法进行航空轴承疲劳可靠性分析。通过带交叉项的二次多项式近似拟合温度场效应,并将热应力映射到滚动轴承赫兹接触区内,完成热弹流润滑效应下航空轴承接触应力分析模型的建立,同时考虑热弹流润滑效应、材料属性以及疲劳强度修正系数的随机性,运用人工神经网络法完成热弹流润滑效应下航空滚动轴承疲劳可靠性分析。采用遗传算法完成最小可靠性指标寻优和惩罚函数最佳设计点。基于改进的一次二阶矩法完成可靠性灵敏度分析。数值算例表明,所建立的可靠性分析模型能正确反映热弹流润滑效应对航空轴承接触疲劳的影响。与传统的Monte Carlo法相比,两种计算结果的失效概率之差为2.0×10-4,相对误差为23.8%,而所提方法耗时只有蒙特卡洛方法的0.15%,具有良好的全局搜索能力和高效的计算性能。      

Rapid and robust initialization for monocular visual inertial navigation within multi-state Kalman filter

Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper presents an improved monocular visual inertial navigation architecture within the Multi-State Constraint Kalman Filter (MSCKF). In addition, to alleviate the initialization demands by appending enough stable poses in MSCKF, a rapid and robust Initialization MSCKF (I-MSCKF) navigation method is proposed in the paper. Based on the trifocal tensor and sigma-point filter, the initialization of the integrated navigation can be accomplished within three consecutive visual frames. Thus, the proposed I-MSCKF method can improve the navigation performance when suffered from shocks at the initial stage. Moreover, the sigma-point filter is applied at initial stage to improve the accuracy for state estimation. The state vector generated at initial stage from the proposed method is consistent with MSCKF, and thus a seamless transition can be achieved between the initialization and the subsequent navigation in I-MSCKF. Finally, the experimental results show that the proposed I-MSCKF method can improve the robustness and accuracy for monocular visual inertial navigations.     

Super-resolution reconstruction of astronomical images using time-scale adaptive normalized convolution

In this work, we describe a new multiframe Super-Resolution (SR) framework based on time-scale adaptive Normalized Convolution (NC), and apply it to astronomical images. The method mainly uses the conceptual basis of NC where each neighborhood of a signal is expressed in terms of the corresponding subspace expanded by the chosen polynomial basis function. Instead of the conventional NC, the introduced spatially adaptive filtering kernel is utilized as the applicability function of shape-adaptive NC, which fits the local image structure information including shape and orientation. This makes it possible to obtain image patches with the same modality, which are collected for polynomial expansion to maximize the signal-to-noise ratio and suppress aliasing artifacts across lines and edges. The robust signal certainty takes the confidence value at each point into account before a local polynomial expansion to minimize the influence of outliers. Finally, the temporal scale applicability is considered to omit accurate motion estimation since it is easy to result in annoying registration errors in real astronomical applications. Excellent SR reconstruction capability of the time-scale adaptive NC is demonstrated through fundamental experiments on both synthetic images and real astronomical images when compared with other SR reconstruction methods.     

一种带随机时延和丢包的分布式多传感器融合估计方法

研究了分布式采样线性系统的最优信息融合问题。其中,传感器信息通过无线网络发送到中心单元,每个传感器的测量值受随机时延甚至丢包的影响,最优传感器融合设计为一个带有缓冲测量值的时变卡尔曼滤波器。进行了算例仿真与分析,表明了融合估计器的有效性。     

战备完好性评估建模与分析

战备完好性是影响舰空导弹作战效能的重要保障性综合参数。在分析了舰空导弹舰上使用剖面一般形式的基础上,给出了舰空导弹舰上使用阶段战备完好性的定义,建立了无定检和有定检 2种情况下的战备完好性评估模型,并分析了值班可靠度、定检时间、舰上贮存备用导弹数量等因素对战备完好性的影响关系。文章可为舰空导弹舰上使用保障决策提供重要的理论依据。     

带有冗余度的新型连续地月载荷转移系统动力学与能量分析

传统的连续地月载荷转移系统(CCPTS）存在可靠性低、转移效率差的问题。设计了带有冗余度的新型连续地月载荷转移系统(N-CCPTS）,其带有的一套冗余抓捕机构不但提高了系统的可靠性,而且可以运载载荷,将转移效率提高了1倍。计算和仿真结果表明,冗余机构对系统参数变化规律的影响是小量;在转移相同质量的载荷时,当椭圆轨道远地点的半径足够大时,N-CCPTS相比CCPTS更加节省能量。N-CCPTS可以为高效率的连续地月物资转移提供一种更加可行的方案。     

基于侧扫声纳图像的AUV侧向速度估计与DVL粗差检测

复杂水底环境和自主潜航器(AUV）机动会造成Doppler测速仪(DVL）速度测量结果中出现粗差,提出一种检测DVL速度粗差数据的方法。该方法利用侧扫声纳图像数据,结合SIFT特征匹配算法求取声纳图像的边缘特征点。根据声纳图像成像原理,计算出每一帧图像相对上一帧图像对应特征点像素的横向平移量;结合特征像素平移量与AUV实际横向位移的比例尺度关系以及声纳侧扫周期推算出AUV侧向速度。该侧向速度用于检测水下组合导航中DVL的速度粗差,在几十米水深的浅水区,该方法能够检测并剔除出大于0.2m/s的DVL侧向速度粗差,显著减小DVL前向速度误差。算法可实时实现,改善AUV水下组合导航性能。