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。     

大入射余角海杂波相关特性分析及幅度拟合

对入射余角44°到74°的海杂波实测数据进行时间相关性、空间相关性分析,并提出了一种新的海杂波幅度拟合混合分布。实验表明,海杂波相关时间维持在毫秒量级,且随着入射余角增加,先增加后减小,时间相关性最强的入射余角在60°左右,而平均相关距离单元个数一直增加。通过KK分布、Rayleigh分布与K分布的对比,表明新的混合分布对大入射余角海杂波的拖尾特性具有良好的拟合效果。     

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。     

民用飞机机翼下壁板连接强度试验方案研究

机翼的连接是飞机结构设计中最重要的环节之一,在理论分析的基础上,往往需要通过试验验证其强度性能。在机翼下壁板的连接试验中,试验件的设计往往忽略了翼根肋腹板的支持和上反角的影响。通过有限元软件,建立三个模型,就翼根肋腹板及上反角对下壁板连接的影响进行对比分析。结果表明,若试验件忽略翼根肋腹板和上反角,其变形、应力分布和紧固件载荷分配均与实际不相符。建议机翼下壁板的连接试验件考虑实际构型,设计合理的夹具进行加载试验。     

同步发电机励磁功率单元中可控整流桥的故障工况研究

分析了三相全控桥式整流电路的各种故障工况,得出故障下触发角与输出励磁电压之间的定量关系,并提出了相应的解决措施。通过对MATLAB/Simulink搭建的同步发电机励磁功率单元模型进行仿真,结果表明,所提措施能有效减轻故障对系统的不利影响,在脉冲丢失时,可以为备用设备切换赢得时间;晶闸管发生故障时,有利于调控发电机平稳退出运行以避免产生稳定性问题。     

Adaptive robust cubature Kalman filtering for satellite attitude estimation

This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.     

An estimation of Envisat’s rotational state accounting for the precession of its rotational axis caused by gravity-gradient torque

The rotational state of Envisat is re-estimated using the specular glint times in optical observation data obtained from 2013 to 2015. The model is simplified to a uniaxial symmetric model with the first order variation of its angular momentum subject to a gravity-gradient torque causing precession around the normal of the orbital plane. The sense of Envisat’s rotation can be derived from observational data, and is found to be opposite to the sense of its orbital motion. The rotational period is estimated to be $(120.674\pm 0.068)·\exp \left((4.5095\pm 0.0096)\times {10}^{-4}·t\right)\text{s}$, where t is measured in days from the beginning of 2013. The standard deviation is 0.760?s, making this the best fit obtained for Envisat in the literature to date. The results demonstrate that the angle between the angular momentum vector and the negative normal of the orbital plane librates around a mean value of $8.53°\pm 0.42°$ with an amplitude from about $0.7°$ (in 2013) to $0.5°$ (in 2015), with the libration period equal to the precession period of the angular momentum, from about 4.8?days (in 2013) to 3.4?days (in 2015). The ratio of the minimum to maximum principal moments of inertia is estimated to be $0.0818\pm 0.0011$, and the initial longitude of the angular momentum in the orbital coordinate system is $40.5°\pm 9.3°$. The direction of the rotation axis derived from our results at September 23, 2013, UTC 20:57 is similar to the results obtained from satellite laser ranging data but about $20°$ closer to the negative normal of the orbital plane.     

面向装备RUL预测的平行仿真技术

装备平行仿真是系统建模与仿真领域的新兴仿真技术,已经成为研究热点。在装备维修保障领域中,分析了装备剩余寿命（RUL）预测存在的突出问题,即模型参数固定、不具备自适应演化能力,成为阻碍实现装备剩余寿命自适应预测的首要因素。结合装备平行仿真理论,在建模分析的基础上提出了面向装备剩余寿命预测的平行仿真框架,该框架以Wiener状态空间模型为基础仿真模型,在动态注入的装备退化观测数据驱动下,利用期望最大化（EM）算法在线更新模型参数,并利用卡尔曼滤波（KF）算法实现仿真输出数据与观测数据的同化（DA）,从而实现仿真模型动态演化,使得仿真输出不断逼近装备真实退化状态,为准确预测剩余寿命提供高逼真度仿真模型和数据输出。以某轴承性能退化数据为数据驱动源,对该框架进行了验证,仿真结果表明平行仿真方法能准确仿真装备性能退化过程,在提高预测精度的基础上实现了装备剩余寿命的自适应预测,有力证明了平行仿真方法的可行性和有效性。