Real-time clock comparison and monitoring with multi-GNSS precise point positioning: GPS,GLONASS and Galileo

The precise point positioning (PPP) technique is widely used in time and frequency applications. Because of the real-time service (RTS) project of the International GNSS Service, we can use the PPP technique for real-time clock comparison and monitoring. As a participant in the RTS, the Centre National d’Etudes Spatiales (CNES) implements the PPPWIZARD (Precise Point Positioning with Integer and Zero-difference Ambiguity Resolution Demonstrator) project to validate carrier phase ambiguity resolution. Unlike the Integer-PPP (IPPP) of the CNES, fixing ambiguities in the post-processing mode, the PPPWIZARD operates in the real-time mode, which is also called real-time IPPP (RT-IPPP). This paper focuses on applying the RT-IPPP for real-time clock comparison and monitoring. We review the principle of real-time clock comparison and monitoring, and introduce the methodology of the RT-IPPP technique. The observations of GPS, GLONASS and Galileo were processed for the experiments. Five processing modes were provided in the experiment to analyze the benefits of ambiguity resolution and multi-GNSS. In the clock comparison experiment, the average reduction ratios of standard deviations with respect to the G PPP mode range from 9.7% to 35.0%. In the clock monitoring experiment, G PPP mode can detect clock jumps whose magnitudes are larger than 0.9 ns. The RT-IPPP technique with GRE PPP AR (G) mode allows for the detection of any clock jumps larger than 0.6 ns. For frequency monitoring, G PPP mode allows detection of frequency changes larger than 1.1 × 10⁻¹⁴. When the RT-IPPP technique is applied, monitoring with GRE PPP AR (G) mode can detect frequency changes larger than 6.1 × 10⁻¹⁵.     

Comparison of GPS derived TEC with the TEC predicted by IRI 2012 model in the southern Equatorial Ionization Anomaly crest within the Eastern Africa region

We have compared the TEC obtained from the IRI-2012 model with the GPS derived TEC data recorded within southern crest of the EIA in the Eastern Africa region using the monthly means of the 5 international quiet days for equinoxes and solstices months for the period of 2012 – 2013. GPS-derived TEC data have been obtained from the Africa array and IGS network of ground based dual-frequency GPS receivers from four stations (Kigali (1.95°S, 30.09°E; Geom. Lat. 11.63°S), Malindi (2.99°S, 40.19°E; Geom. Lat. 12.42°S), Mbarara (0.60°S, 30.74°E; Geom. Lat. 10.22°S) and Nairobi (1.22°S, 36.89°E; Geom. Lat. 10.69°S)) located within the EIA crest in this region. All the three options for topside Ne of IRI-2012 model and ABT-2009 for bottomside thickness have been used to compute the IRI TEC. Also URSI coefficients were considered in this study. These results are compared with the TEC estimated from GPS measurements. Correlation Coefficients between the two sets of data, the Root-Mean Square Errors (RMSE) of the IRI-TEC from the GPS-TEC, and the percentage RMSE of the IRI-TEC from the GPS-TEC have been computed. Our general results show that IRI-2012 model with all three options overestimates the GPS-TEC for all seasons and at all stations, and IRI-2001 overestimates GPS-TEC more compared with other options. IRI-Neq and IRI-01-corr are closely matching in most of the time. The observation also shows that, GPS TEC are underestimated by TEC from IRI model during noon hours, especially during equinoctial months. Further, GPS-TEC values and IRI-TEC values using all the three topside Ne options show very good correlation (above 0.8). On the other hand, the TEC using IRI-Neq and IRI-01- corr had smaller deviations from the GPS-TEC compared to the IRI-2001.     

Analysis of the data processing strategies of spherical harmonic expansion model on global ionosphere mapping for moderate solar activity

Spherical harmonic (SH) expansion is widely used to model the global ionosphere map (GIM) of vertical total electron content (VTEC). According to the impact of different data processing methods of the SH expansion model on the VTEC maps, we specifically performed comprehensive analysis in terms of the data sampling rate, the time resolution, the spherical harmonic degree, and the relative constraint. One month of GPS data (January in 2016) from the International GNSS (Global Navigation Satellite System) Service (IGS) network in a moderate ionospheric activity period at the descending phase of Solar Cycle 24 was processed. To improve the computational efficiency of the daily GIM generation, the data sampling rate of 5?min was recommended allowing the GIM precision loss within 0.10 TECU (total electron content unit). The global VTEC map could be better represented in temporal and spatial domains with higher time resolution and higher spherical harmonic degree, especially at low latitude bands and in the southern hemisphere. The GIM precision improvement was about 10.91% for 1-h and about 15.15% for 0.5-h compared with the commonly used 2-h time resolution. The use of spherical harmonic degree 17 or 20 instead of 15 could improve the precision by 3.19% or 6.06%. We also found that an optimal relative constraint had to be found experimentally considering both the GIM precision and the GIM root mean square (RMS) map.     

TEC differences for the mid-latitude ionosphere in both sides of the longitudes with zero declination

Based on measurements of ground-based GPS station network, differences of the mid-latitude ionospheric TEC in the east and west sides of North America, South America and Oceania have been analyzed in this paper. Results show that for nearly all seasons from 2001 to 2010 and in both sides of the longitudes with zero declination, there exist systematic differences for the mid-latitude ionospheric TEC in the regions mentioned above and the features of these differences markedly depend upon the local time but less depend upon seasons and the level of solar activity. Theory analysis shows that the longitude variations of both declination and zonal thermospheric winds are one of important factors to cause differences of the mid-latitude ionospheric TEC in both sides of the longitudes with zero declination.     

Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.     

Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

月球表层采样样品智能确认方法

表层采样是月球采样探测的重要方式，样品智能确认有助于提升工作效率与复杂问题处理能力。结合月球表层采样铲挖工作过程，分析了铲挖过程中臂载相机图像的特点，模仿有人参与识别过程，提出了层次解耦的月球样品智能识别流程，利用深度学习方法构建了一类深度卷积识别网络，完整地描述了图像、特征、标记在网络中的正反传递关系，并在月球表层采样地面试验中进行了验证，结果表明该方法对不同光照、不同背景、不同过程、不同形态的样品，具有较好的泛化识别能力，误识别率优于8.1%，平均单幅识别时间约0.7 s。     

基于改进的卷积神经网络的步态识别

为了提高惯性传感器采集到的序列数据中步态识别的准确率,建立了一个激励层改进的卷积神经网络(CNN)模型。针对三轴加速度传感器对运动太过敏感导致步态周期划分不准确的问题,采用加速度传感器与弯曲度传感器组合获取人体运动信息。将CNN模型中激励层的线性整流函数(ReLU)改进为带泄露线性整流函数(Leaky ReLU),以解决遇到卷积输出数据小于0时神经元被抑制的问题,进而达到提高步态识别准确率的目的。实验结果表明激励层优化的CNN模型在行走、上下楼和上下坡五种步态模式下识别率达到了95.79%,与未采用弯曲度传感器的改进CNN模型和未进行激励层改进的CNN模型相比,步态识别率有所提高。     

神经网络模型在压气机通流特性分析中的应用

为了解决通流特性分析程序中原始模型对压气机性能预测精度不足的问题,提高压气机通流特性分析过程的可靠性,基于对大量多圆弧叶栅的数值模拟结果建立了压气机叶栅性能数据库,并以该数据库为依托,采用神经网络建模方法建立了压气机叶栅基准损失系数和基准落后角模型。结果显示:两模型对叶栅基准损失系数和基准落后角的预测精度均满足工程应用要求,其精度分别为±0.002和±1°。在对采用神经网络模型的通流特性分析程序校验过程中发现,其无论对压气机整机性能还是对流动细节的预测精度上都获得了显著提高,尤其是在主流区。此外从压气机整体特性上看,基准损失系数和基准落后角精度的提高对非设计工况损失系数和落后角的预测精度影响是积极的。      

利用GPS进行姿态估计的一种算法

首先建立了全球定位系统（ＧＰＳ）姿态确定的观测方程;然后给出了利用ＧＰＳ进行飞行器姿态估计的模型,并对该模型进行了线性处理;最后利用攻推广卡尔曼滤波技术,针对某飞行器进行了仿真计算。计算结果表明,对于不同的测量噪声和系统噪声,滤波器都有较好的估计,姿态估计的精度明显高于单纯ＧＰＳ姿态确定的精度,可以满足大多数飞行器对姿态确定的要求,证实了模型和算法可用性。