一种基于信号合成的无线电干涉测量方法

针对后续深空探测任务更高测量精度需求,文章提出了一种基于信号合成的无线电干涉测量方案,并对方案性能进行了理论分析;结合目前主流信号合成算法,提出了一种基于相位补偿的Sumple算法,该算法不仅合成效率高,且合成信号与参考信号具有相同的相位特性,这是天线组阵技术应用于无线电干涉测量的前提;利用相控阵天线试验数据和DOR信号仿真数据对合成方案和合成算法进行了验证,结果表明信号合成处理可以提高无线电干涉测量时延估计精度,这在深空探测任务中具有重要意义。     

基于双目视觉的非合作目标自主姿态估计方法

针对在轨服务任务对于空间非合作目标的自主相对导航需求,提出了一种基于双目视觉的自主姿态估计方法。该方法以Markley变量描述目标的姿态运动,并利用双目相机对目标表面的特征点进行观测,利用特征点的运动规律与目标姿态运动的相关性,并通过容积卡尔曼滤波算法实现了对非合作目标角速度与自旋轴方向的估计。数学仿真验证了所给出的姿态估计方法,并分析了估计精度的影响因素。     

多工艺路线可视化设计与评估方法

根据CAPP系统人机工程和工艺路线方案选择的需要,提出了一种基于图形控件和过程网的多工艺路线可视化设计与评估方法.该方法根据工艺路线的构成要素,设计建立了具有标示、工艺、图形和评估四个方面属性的工序控件、机床控件、特征控件和搬运控件等图形控件,采用图形控件的拖拽方式和网络图的表达方式构建工艺路线,实现了多工艺路线的可视化设计,并采用Dijkstra算法对过程网中的多条工艺路线进行评估,从而选择最优工艺路线.最后给出了某飞机结构件的应用实例.该方法能够更好地体现工艺师的设计思路,有效地进行工艺方案论证和决策.     

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。     

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。     

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。     

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。     

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.