起落架结构参数对飞机机轮摆振频率特性的影响

研究了四个起落架结构参数：稳定距、机轮转动惯量、支柱侧倾刚度和减摆器传动系统扭转刚度对飞机机轮摆振频率特性的影响。对“轮胎型”摆振和“结构型”摆振进行了严格区分并分别予以研究,针对所计算的型号飞机,归纳总结出发生“轮胎型”摆振和“结构型”摆振的频率范围。所得结论为新机防摆设计和现役飞机防摆维护提供了理论依据。     

原子钟时域频率稳定性计算方法

首先给出时域频率稳定性分析中各种方差的实用计算公式,并在此基础上分析各种方差特性,总结归纳其适用范围。分析表明：①阿仑方差是时域频率稳定性分析中最常用的方法,但不能区分调相白噪声和调相闪变噪声;②改进阿仑方差通过相位平滑运算能识别这2种噪声;③时间方差和时间总方差主要用于描述频率源的时间波动,主要用于分析调相噪声;④当频率漂移项较大或受甚低频噪声影响时,哈达玛系列方差能给出很好的测量特性。     

双混频时差测量系统设计中的理论和技术问题

从分析双混频时差(DMTD)测量系统的测量误差着手,对系统设计和使用中常磁到的一些理论和技术问题进行了探讨,这涉及到公共源的影响、移相器的作用、自校和测量中的其它问题。     

介质材料对频率选择表面传输特性的影响

通过理论计算和实验验证分析了介质材料对频率选择表面传输特性的影响规律,讨论了蒙皮的厚度、介电常数,芯层厚度、介电常数四个因素对FSS的通带位置、宽度、损耗的影响。结果表明,蒙皮和芯层厚度增厚会使通带位置向低频移动,厚度减小通带位置向高频移动;蒙皮和芯层介电增大会使通带位置向低频移动,介电减小通带位置向高频移动。并测试了同样结构的FSS平板传输特性,理论分析和实验结果一致性较好。     

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)。      

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。