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.     

超宽带变频系统中的本振产生技术研究

本振源是超宽带变频系统中的核心部件，本文以超宽带上变频系统为例，介绍变频系统中本振信号的产生技术。本文所设计的超宽带变频系统，经过三次上变频，可将(0.8~2.8)GHz 的低频信号变换至（0.5~40）GHz。变频过程中需要一系列点频本振和一组宽带本振，基于PLL+DFS 的方法产生点频本振，基于DDS+DFS 的方式产生宽带本振。     

DDS中相位截断效应分析及其改善方法

分析了在直接数字频率合成（ＤＤＳ）中引起杂散的相位截断效应,针对其引起的杂散特点讨论了一种通过加入伪随机数改善杂散谱分布的方法,并通过计算机仿真进行了验证。     

数据中继卫星的频率和轨道

描述了典型数据中继卫星系统的通信链路;给出了这些系统在用的和计划使用的无线电频谱;讨论了影响数据中继卫星频段选择的因素。为了防止其他中继卫星系统对一个中继卫星系统的有害干扰,可以使用如频率错开、极化分辨、经度分离,甚至采用伪码分辨的方法。     

对旋轴流风机降噪措施的实验研究

针对一台自行设计的高效对旋轴流风机噪声高的缺点, 分析了主要噪声源及噪声的频率特征;并用消声声衬及适当增加管道长度的方法进行了大量的实验研究, 取得了良好的效果。总声压级由101dB(A)降至88dB(A)。      

机载32位计算机指令系统结构军标分析

随着国际形势的缓和,美国国防工业及半导体制造业发生着微妙的变化,于是美国防部果断地提出军杯改革。面对这种情况,国内机载计算机造型、指令系统结构标准的制定和选择目前变成一个热门话题。本文介绍了几种典型的32位计算机指令系统结构标准和美国此方面的应用现状和特点,给出了机载计算机指令系统结构标准确定的可行性与途径。     

数据收集系统下行频偏校准与点频值计算

风云二号（ＦＹ－２）卫星在轨运行期间本振源必定产生一定的频率偏移,这就影响了数据收集平台接收系统（ＤＣＰＲ）相应于各种平台的报文锁相环路的频率捕获范围,严重时将无法解调出各种平台收集的气象数据,整个系统也将无法正常工作。为解决上述问题,介绍了ＤＣＰＲ下行频偏校准及点频值计算的方法。通过ＦＹ－２卫星在轨测试表明,在随机干扰比较严重的情况下,ＤＣＰＲ对各种平台报文接收解调的成功率优于９８％,取得了令人满意的结果。     

文献“Software Testing in Space Programs”[1]点评

作者以他个人的观点,对该文献中重要部分进行了点评,指出:空间软件地面可靠性测试尽管困难重重且有诸多限制,但不能放松甚至放弃执行现有的软件地面测试标准;应从"精神号"火星探测器飞行失败中吸取教训,加强软件地面测试,包括传统软件黑盒和白盒测试方法中的典型测试项目(如:健壮测试、应力测试、极值测试、随机测试等);已经公布的新软件研发标准——Aerospace Report No.TOR-2004(3909)-3537,Software Development Standard for Space Systems值得我们参考。     

一种低复杂度的Multi-h CPM相干解调算法及FPGA实现

多调制指数连续相位调制（Multi-h Continuous Phase Modulation,Multi-h CPM）是一种恒包络调制,具有多个周期出现的调制指数,因此其频谱效率和功率效率比单调制指数CPM更高,但其接收机的复杂度也随之增加。本文在倾斜相位（Tilted Phase,TP）的基础上采用频率脉冲截断（Frequency Pulse Truncation,FPT）和状态空间分割（State Space Partition,SSP）相结合的方法来降低多调制指数连续相位调制的复杂度,使相位状态由512降低到16,理论损耗约为0.8 dB。同时,对降低复杂度的解调算法在现场可编程门阵列（Field Programmable Gate Arrays, FPGA）上进行了验证。仿真和在线测试的误码率曲线接近理论分析曲线,和常用的128状态解调算法相比,资源消耗降低了约59%。     

一种基于频移补偿的apFFT相位估计改进方法及应用

全相位快速傅里叶变换（all phase Fast Fourier Transform,apFFT)相位一致性特点使正弦信号相位估计不受频率估计影响,但由于频谱泄露和栅栏效应,apFFT相位估计性能受信号频率位置影响。针对该问题,提出了一种基于高精度频率估计和补偿的apFFT相位估计方法。首先,对信号进行高精度频率估计,并以此对信号进行频移补偿,然后对补偿信号进行apFFT,最后求解信号相位。蒙特卡洛仿真结果表明：所提算法的相位估计性能不受信号频率位置影响,相位估计误差性能与理论值一致,受两参数克拉美罗界（CRLB₂）约束,约为1.158 2倍的CRLB₂;相位估计性能和抗噪声能力明显优于经典apFFT算法和其他对比算法。更进一步,利用我国首次火星探测器TW-1（天问一号）近火捕获段干涉测量数据进行算法验证,结果表明：相位估计精度约4 mrad,干涉测量时延估计精度约50 ps。