加油机尾流场建模与仿真分析

提出了等效气动效应法,建立了加油机尾流场的等效扰动模型。等效气动效应法采用逐点积分方法和加权平均方法,将尾流的影响等效为受油机运动方程直接可用的平均风速度和风梯度。对不同状态下受油机所受尾流的扰动作用进行了仿真。结果表明,扰动模型与实际情况比较符合。     

一种实用型报警装置的研制

本文综合地运用了５５５集成电路，ＴＷＨ９２４８，ＴＷＨ９２４９雷达式扫描探测传感集成电路的ＳＲ９Ｇ２６Ａ／Ｄ、Ｄ／Ａ转换语音存放电路等，中大规模集成电路。并利用一种简单的高频发射线路，而研制成的实用型报警装置。     

瞬时自相关信道化接收机频率测量研究

提出了一种新的信道化IFM接收机的实现方案,该方案基于多相滤波的信道化技术,利用瞬时自相关方法实现复信号的瞬时频率测量,不仅可以实现精确的大动态范围瞬时频率测量,而且具备多信号同时处理能力,仿真结果和性能分析验证了本方法的有效性.     

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。     

RNSS/INS深耦合高动态导航接收机测试方法研究CSCD

卫星导航和惯性导航采用深耦合方式实现导航作为世界上最先进的导航形式,在国防中的应用越来越广泛,但其在高动态下的性能需要验证。当前针对深耦合高动态下导航接收机的测试研究较少,本文介绍了基于卫星信号模拟器的测试系统组成及测试用高动态场景,运用模拟测试原理,通过对导航接收机在高动态下定位速度和跟踪灵敏度的测试及其结果分析,验证了导航接收机的深耦合功能,对此类接收机的测试方法进行了初步探究。     

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.