环境因子的定义及其统计推断

利用不同环境下寿命之间的折算原则，导出了不同寿命分布类环境因子的定义，得到了环境因子的点估计和区间估计。     

用线性曲线拟合判断固态功率放大器饱和点

在自动化测量系统中,软件需要根据测量数据自动寻找并判断功率放大器的饱和点。在受噪声影响较大的在轨测试中,这是个难点。本文根据固态功率放大器AM/AM特性曲线的特点,提出一种使用多项式线性曲线拟合测量数据建立增益曲线预测关系式,并通过增益趋势线准确判断饱和点的方法。经过真星在轨测试验证,该方法与实际特性吻合很好。     

An improved approach to model ionospheric delays for single-frequency Precise Point Positioning

PPP with low-cost, single-frequency receivers has been receiving increasing interest in recent years because of its large amount of possible users. One crucial issue in single-frequency PPP is the mitigation of ionospheric delays which cannot be removed by combining observations on different frequencies. For this purpose, several approaches have been developed, such as, the approach using ionospheric model corrections with proper weight, the GRAPHIC (Group and Phase Ionosphere Calibration) approach, and the method to model ionospheric delays over a station with a low polynomial or stochastic process. From our investigation on the stochastic characteristics of the ionospheric delay over a station, it cannot be precisely represented by either a deterministic model in the form of a low-order polynomial or a stochastic process for each satellite, because of its strong irregular spatial and temporal variations. Therefore, a novel approach is developed accordingly in which the deterministic representation is further refined by a stochastic process for each satellite with an empirical model for its power density. Furthermore, ionospheric delay corrections from a constructed model using GNSS data are also included as pseudo-observations for a better solution. A large data set collected from about 200 IGS stations over one month in 2010 is processed with the new approach and several commonly adopted approaches for validation. The results show its significant improvements in terms of positioning accuracy and convergence time with a negligible extra processing time, which is also demonstrated by data collected with a low-cost, handheld, single-frequency receiver.     

Modeling and initial assessment of the inter-frequency clock bias for COMPASS GEO satellites

The COMPASS system is a project established by China to develop an independent global satellite navigation system, which has five GEO (Geostationary Orbit) satellites and thirty Non-GEO satellites. An apparent inter-frequency clock bias (IFCB) for COMPASS GEO satellites is investigated using the real data. The bias also is modeled by the different models. Based on the 15 months (DOY 121, 2011–214, 2012) single-day-estimated results, the periodic variation of IFCBs of the COMPASS GEO satellite is studied using a harmonic analysis. The notable periods of 12 h and 8 h are noted. The harmonics-based models with different periods and different orders and quadratic function based model are used to describe the IFCB. The performances show that the 4-order harmonics-based model with the periods of 24, 12, 8 and 6 h is most optimal than others for describing the IFCB of COMPASS GEO satellite. Its amplitudes and phases estimated from a least square fit are used to study the features of the IFCB. The results show that the current amplitudes and phases do not present special features. Although the irregular amplitudes and phases of the model are disadvantageous for the long-term prediction of IFCB, it is obvious that the modeling IFCB can simple its service and a few of coefficients can replace the IFCB series. The performance of the model in short-term prediction IFCB is tested using the ten-day data (DOY 215-224, 2012).     

A real-time ionospheric model based on GNSS Precise Point Positioning

This paper proposes a method of real-time monitoring and modeling the ionospheric Total Electron Content (TEC) by Precise Point Positioning (PPP). Firstly, the ionospheric TEC and receiver’s Differential Code Biases (DCB) are estimated with the undifferenced raw observation in real-time, then the ionospheric TEC model is established based on the Single Layer Model (SLM) assumption and the recovered ionospheric TEC. In this study, phase observations with high precision are directly used instead of phase smoothed code observations. In addition, the DCB estimation is separated from the establishment of the ionospheric model which will limit the impacts of the SLM assumption impacts. The ionospheric model is established at every epoch for real time application. The method is validated with three different GNSS networks on a local, regional, and global basis. The results show that the method is feasible and effective, the real-time ionosphere and DCB results are very consistent with the IGS final products, with a bias of 1–2 TECU and 0.4 ns respectively.     

基于小波变换的运动点目标检测方法

在运动点目标的检测问题中，当目标轨迹产生随机偏移时已有检测算法的效果很不理想。为解决这一难题，本文定义了一种新的小波变换——方向小波变换，提出了一种基于方向小波变换的多尺度点目标检测方法，能够稳定、有效地检测出各种作近似匀速直线运动的点目标轨迹     

筛选时间的确定

本文研究在威布尔分布下筛选时间的点估计和置信上限估计,分别包括图估计和数值估计。推导了为确定筛选时间的置信上限而建立的方程式。确定了IC高温动态老化的筛选时间。为制定筛选技术条件和确定整机老练时间提供理论依据。     

Inter-satellite link augmented BeiDou-3 orbit determination for precise point positioning

Precise Point Positioning(PPP) requires precise products, including high-accuracy satellite orbit and clock parameters. It is impossible to obtain an orbit solution that is sufficiently accurate for PPP services with a regional tracking network; therefore, satellite orbits are usually estimated by a global tracking network with a large number of ground stations. However, it is expensive to build globally distributed stations. Fortunately, BeiDou-3 satellites carry an InterSatellite Link(ISL) pay...     

A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional (3D) aerodynamics numerical simulations and overcome the limitations of the traditional parametric learning methods, a point cloud deep learning non-parametric metamodel method is proposed in this paper. The 3D geometric data, corresponding to the object boundaries, are chosen as point clouds and a deep learning neural network metamodel fed by the point clouds is further established based on the PointNet architecture. This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities. With the proposed aerodynamic metamodel approach, the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain, which can maintain the boundary smoothness and allow the network to detect small changes between geometries. Moreover, the point clouds are easily accessible from 3D sensors. The point cloud deep learning neural network, which employs re-sampling technique, the spatial transformer network and the fully connected layer, is developed to predict the aerodynamic characteristics of 3D geometry. The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing. The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.     

差分码偏差对PPP授时精度影响的研究

论文分析了不同机构差分码偏差(DCB)产品的天稳性.选取了2个外接氢原子钟的测站进行实验,以国际GNSS服务组织(IGS)发布的接收机钟差为参考值,分析了不同机构DCB产品对2个测站PPP授时精度的影响.实验结果表明:1)不同机构DCB产品的天稳性差异不大,中国科学院天稳性略优于德国宇航中心;2)2个测站使用不同机构的DCB产品估计钟差的均方差(RMS)和时间偏差(Bias)均优于0.4ns,其中中国科学院产品精度最高,RMS和Bias均优于0.2ns;德国宇航中心和欧洲定轨中心精度略差,但也能够达到亚纳秒级,可为下一步推广PPP授时应用提供一定的参考.