GPS不完整星座定位方法研究

为了提高钟差预测模型辅助接收机定位解算的效果,从灰色系统的角度对接收机钟差序列进行探讨,提出了基于灰色理论的钟差预测模型。文中首先给出了新模型的基本思想和具体实现步骤,然后将基于该模型的钟差预测值引入到GPS接收机中,辅助接收机在不完整星座条件下实现定位解算。基于GPS实测数据的验证分析表明,新模型不仅对钟差序列具有很好的预测效果;而且提高了接收机定位的连续性和可靠性,在仅有3颗卫星的条件下可以实现三维定位。同时,新方法无需增加额外设备,辅助方式简单方便,经济灵活。     

The ground GNSS tomography – unconstrained approach

The GNSS signal along its more than 20,000 km line of sight is bended, attenuated and delayed. These effects are results of the Earth’s atmosphere, and Sun radiation. Amongst all mentioned effects the one considered in this paper is a signal phase delay in troposphere.     

GNSS reflectometry and remote sensing: New objectives and results

The Global Navigation Satellite System (GNSS) has been a very powerful and important contributor to all scientific questions related to precise positioning on Earth’s surface, particularly as a mature technique in geodesy and geosciences. With the development of GNSS as a satellite microwave (L-band) technique, more and wider applications and new potentials are explored and utilized. The versatile and available GNSS signals can image the Earth’s surface environments as a new, highly precise, continuous, all-weather and near-real-time remote sensing tool. The refracted signals from GNSS radio occultation satellites together with ground GNSS observations can provide the high-resolution tropospheric water vapor, temperature and pressure, tropopause parameters and ionospheric total electron content (TEC) and electron density profile as well. The GNSS reflected signals from the ocean and land surface could determine the ocean height, wind speed and wind direction of ocean surface, soil moisture, ice and snow thickness. In this paper, GNSS remote sensing applications in the atmosphere, oceans, land and hydrology are presented as well as new objectives and results discussed.     

SINS/GNSS组合导航测试系统用惯导模拟源技术研究

针对光纤陀螺SINS(捷联惯性导航系统)与GNSS(全球卫星导航系统)组合导航产品高动态性能测试难的问题,本文研究了一种组合导航测试系统,并对捷联惯导模拟源进行了重点研究.首先以捷联惯导解算算法为基础逆推出了捷联惯导模拟源算法,然后对捷联惯导模拟源进行了功能实现,可以与导航卫星信号模拟源同步向组合导航计算机发送数据用于组合导航解算.最后对捷联惯导模拟源的功能与性能进行了验证.结果表明,捷联惯导模拟源功能正常,模拟数据的精度达到设计要求.利用此惯导模拟源与导航卫星信号模拟源配套使用,将可满足后续SINS/GNSS组合导航系统的相关性能测试或验证要求.     

Near-real time monitoring of the TEC fluctuations over the northern hemisphere using GNSS permanent networks

Since the early 1990s, global positioning system measurements have been used to study of the state and rapid changes of the Total Electron Content in the ionosphere. Currently, the increasing number of permanent stations makes it possible to generate maps of the irregularities in the ionosphere for specified regions with sub-daily resolution. The main goal of this work was to apply global navigation satellite system observations to obtain information about ionospheric variability around the North Geomagnetic Pole. In order to detect the ionospheric disturbances, 30-s observation data was used. The Rate of Total Electron Content Index was applied as a measure of the variability in the ionosphere. The first analyses were executed using more than 100 permanent stations. The results show two kinds of products: 2-hour maps in spherical geomagnetic coordinates and daily maps presenting the occurrence of the strong Total Electron Content fluctuations as a magnetic local time function, for the most disturbed days of April 2010. Apart from the main product of the algorithm, the Rate of Total Electron Content time series for individual satellite tracks was presented. The results demonstrated very good sensitivity of the obtained maps, which can detect even quite weak disturbances. The presented algorithm developed at the Geodynamic Research Laboratory of the University of Warmia and Mazury, in cooperation with Institute of Terrestrial Magnetism, Ionosphere and Radiowave Propagation, will be applied in the near future to create near-real time service of the conditions in the ionosphere based on the Global Navigation Satellite Systems observations.     

北斗多模多频导航SoC芯片技术研究

介绍了一款北斗多模多频导航SoC芯片—“OTrack-128”,该芯片实现了多模多频兼容导航定位,采用完全国产化的卫星导航核心芯片,在定位精度、动态性能等关键指标上达到了国际先进水平,最后给出了基于该芯片的各类主要应用技术指标。     

自动校相技术在现代测控雷达中的实现

介绍自动校相技术的程序设计及其在工程中的应用，详细叙述了该软件关键部分的设计技术。     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.     

Ionospheric total electron content response to annular solar eclipse on June 21, 2020

The effects of physical events on the ionosphere structure is an important field of study, especially for navigation and radio communication. The paper presents the spatio-temporal ionospheric TEC response to the recent annular solar eclipse on June 21, 2020, which spans across two continents, Africa and Asia, and 14 countries. This eclipse took place on the same day as the June Solstice. The Global Navigation Satellite System (GNSS) based TEC data of the Global Ionosphere Maps (GIMs), 9 International GNSS Service (IGS) stations and FORMOSAT-7/COSMIC-2 (F7/C2) were utilized to analyze TEC response during the eclipse. The phases of the TEC time series were determined by taking the difference of the observed TEC values on eclipse day from the previous 5-day median TEC values. The results showed clear depletions in the TEC time series on June 21. These decreases were between 1 and 9 TECU (15–60%) depending on the location of IGS stations. The depletions are relatively higher at the stations close to the path of annular eclipse than those farther away. Furthermore, a reduction of about ?10 TECU in the form of an equatorial plasma bubble (EPB) was observed in GIMs at ~20° away from the equator towards northpole, between 08:00–11:00 UT where its maximum phase is located in southeast Japan. Additionally, an overall depletion of ~10% was observed in F7/C2 derived TEC at an altitude of 240 km (hmF2) in all regions affected by the solar eclipse, whereas, significant TEC fluctuations between the altitudes of 100 km ? 140 km were analyzed using the Savitzky-Golay smoothing filter. To prove TEC depletions are not caused by space weather, the variation of the sunspot number (SSN), solar wind (V_SW), disturbance storm-time (Dst), and Kp indices were investigated from 16th to 22nd June. The quiet space weather before and during the solar eclipse proved that the observed depletions in the TEC time series and profiles were caused by the annular solar eclipse.