一种高动态低信噪比卫星导航信号捕获方法

为提升高动态低信噪比环境下卫星导航信号的捕获性能,提出了一种基于分数阶傅里叶变换(FrFT)及部分匹配滤波(PMF)的捕获方法。在该方法中,接收机首先利用PMF对接收信号做分段相干积分,随后借助快速傅里叶变换(FFT)对分段积分结果做离散快速FrFT,最后通过检测FrFT输出的峰值完成信号的捕获。由于具有多普勒频率变化率的卫星导航信号在FrFT后呈现能量聚焦特性,所提方法能够显著提高信号的长时间相干积分增益。同时对所提算法的捕获概率、平均捕获时间以及算法复杂度等性能指标进行了理论分析及计算机仿真验证。仿真表明,与传统的PMF-FFT方法相比,所提方法能够通过延长相干积分时间的方式有效提升高动态低信噪比卫星导航信号的捕获概率、降低捕获时间。     

GNSS9916(GPS/GLONASS)接收机

介绍七○四所独立研制开发的 GNSS991 6(GPS/ GL ONASS)接收机性能、指标、组成和工作原理。     

惯性／卫星组合导航开发平台的可视化仿真和实现

对惯性/卫星组合导航开发平台的可视化仿真及其实现进行了研究。该开发平台采用Visual++6.0语言进行编程,可进行实时导航动画显示,对惯性导航、GNSS及多种组合模态进行仿真运行,其结构和参数可由操作者根据实际情况选择或自行设定;特别针对不同的应用对象,可灵活地设置航迹和航路点,因此有较高的应用价值和实用意义,不失为设计组合导航系统的有力工具,在实际应用中对惯性/卫星组合导航系统研制和设计具有前瞻性和指导性。同时,模块化设计使该软件中的核心算法已方便地移植应用于实际工程组合导航系统中,收到了很好的效果。     

Study of GNSS-measured ionospheric total electron content variations generated by powerful HF-heating

The purpose of this work is to report the experimental evidences for the influence of perturbations in the electron density in the dayside mid-latitude ionosphere, that are caused by high-frequency heating of the F2 layer, on the GNSS signals. The experiments were carried out at the Sura heater (Radio Physical Research Institute, N. Novgorod). During the sessions of ionospheric heating with different time modulations of the radiated power the rays linking the navigational satellites with the ground receiver intersected the heated region. Variations in the total electron content (TEC) were studied; these variations are proportional to the reduced phases of navigational signals. It is shown that with the square-wave modulation of the radiated power (with periods of 1, 6, 10 and 15 min), perturbations with periods of the main modulation of heating and its harmonics appear in the spectrum of TEC variations. Examples are presented of identification of the heating-induced variations in TEC, including determination of the amplitudes and time characteristics of these variations.     

The verification of GNSS tropospheric tomography model in a mountainous area

The GNSS (Global Navigation Satellite System) has not been developed as a meteorological data source provider, but with a careful and sophisticated processing strategy it might be used as one. The term GNSS tomography refers to the usage of the ray traced GNSS signal as scanning rays in the tomographic model input. The model is divided into a number of voxels. The system is inverted and value of refractivity is obtained. Typically, as in the most of the inverse processing, there is a problem of the undetermined system and as a consequence the cofactor matrix is close to singular. To avoid singularity additional conditions or constrains should be added to the system. Here, additional parameters are derived with the help of the air flow analysis in the Sudety mountains (south-west region of Poland), and special Slant Wet Delay (SWD) trimming procedure. The flow’s synthetic parameters like the Bruint-Väisälä frequency and the Froude number are determined. This way the type of the flow is recognized and the analysis of the impact of orographic barrier has been quantified. The SWDs from the GNSS observations were tested against, SWD from raytracing through the COAMPS model field. The modified GNSS tomography model was tested for the real GNSS observations delivered from the GNSS network Karkonosze located in the Sudety mountains and compared with the COAMPS model. The solution shows a considerable improvement in comparison with plain tomographic model results.     

GNSS Satellite Autonomous Integrity Monitoring (SAIM) using inter-satellite measurements

Integrity is the ability of Global Navigation Satellite Systems (GNSS) to detect faults in measurements and provide timely warnings to users and operators when the navigation system cannot meet the defined performance standards, which is of great importance for safety of life critical applications. Compared with both Receiver Autonomous Integrity Monitoring (RAIM) and ground based GNSS Integrity Channel (GIC) methods which are widely adopted nowadays, the Satellite Autonomous Integrity Monitoring (SAIM) method can be used to monitor orbit/ephemeris and clock errors, and has advantages in monitoring orbit and clock quality and providing instantaneous responses when faults happen.     

Evaluation and improvement of coastal GNSS reflectometry sea level variations from existing GNSS stations in Taiwan

Global sea level rise due to an increasingly warmer climate has begun to induce hazards, adversely affecting the lives and properties of people residing in low-lying coastal regions and islands. Therefore, it is important to monitor and understand variations in coastal sea level covering offshore regions. Signal-to-noise ratio (SNR) data of Global Navigation Satellite System (GNSS) have been successfully used to robustly derive sea level heights (SLHs). In Taiwan, there are a number of continuously operating GNSS stations, not originally installed for sea level monitoring. They were established in harbors or near coastal regions for monitoring land motion. This study utilizes existing SNR data from three GNSS stations (Kaohsiung, Suao, and TaiCOAST) in Taiwan to compute SLHs with two methods, namely, Lomb–Scargle Periodogram (LSP)-only, and LSP aided with tidal harmonic analysis developed in this study. The results of both methods are compared with co-located or nearby tide gauge records. Due to the poor quality of SNR data, the worst accuracy of SLHs derived from traditional LSP-only method exceeds 1?m at the TaiCOAST station. With our procedure, the standard deviations (STDs) of difference between GNSS-derived SLHs and tide gauge records in Kaohsiung and Suao stations decreased to 10?cm and the results show excellent agreement with tide gauge derived relative sea level records, with STD of differences of 7?cm and correlation coefficient of 0.96. In addition, the absolute GNSS-R sea level trend in Kaohsiung during 2006–2011 agrees well with that derived from satellite altimetry. We conclude that the coastal GNSS stations in Taiwan have the potential of monitoring absolute coastal sea level change accurately when our proposed methodology is used.     

High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/?0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.