专用捷变频信号源校准系统研制

捷变频末制导雷达可有效提高武器系统的抗干扰能力,但是作为其专用测试设备的捷变频信号源的计量检定问题却难以得到有效解决。通过分析现有的测试技术,使用通用仪器设备构建测试平台,可实现专用捷变频信号源的自动计量校准。系统采用两路差频形成中频频差的检测方法进行瞬时频率测量,采用单脉冲引频激励测试技术,利用通用测试仪器实现捷变频参数的校准,将捷变频计量技术推向工程应用阶段,可避免通过研制生产专用的计量设备造成其本身难以计量的问题。     

基于GPS的新型二级频标锁定系统

提出了一种基于GPS的新型二级频标锁定系统的设计方案。利用信号的时延稳定性和群相位差变化的规律性,产生一种基于长度游标的高精度时间间隔测量方法。将该方法应用于二级频标锁定系统中,通过对被测时间间隔进行多尺度卡尔曼滤波,在MCU控制下算出GPS与二级频标分频信号之间的相对频差;根据二级频标的频-压控制特性得到补偿电压,将该电压进行D/A转换后送到二级频标的压控端,调整输出频率,形成二级频标锁定系统。实验结果表明其锁定精度可达10 -12 /s 量级,与传统频标锁定系统相比具有电路简单,成本低廉,附加噪声小,锁定精度高等特点。
     

AD9852在小型铷频标频率综合电路中的应用

文章概述了一种利用DDS芯片AD9852实现铷频标频率综合电路的方案,给出了具体结果。这种电路方案具有较高的调节能力,可用于小型铷原子频率标准。     

基于KZK方程的声爆频域预测法

建立了一种快速预测声爆传播特性的频域方法,基于传统Khohklov-Zabolotskaya-Kuznetsov (KZK)方程,描述声爆沿激波波阵面法线方向的传播。为了验证模型正确性,以NASA TD N-161中试件C为对象,首先基于不同网格,利用计算流体力学(CFD)方法得到超音速流场;将CFD结果作傅立叶变换后代入模型方程,快速求解声爆传播特性。预测结果与NASA实验结果符合很好,研究表明：预测方法能够捕捉声爆的非线性传播,声衍射项使得声场在远场趋于轴对称分布,远距离传播后声能量集中于低频分量。     

车辙图像频域分析及星球车车轮滑转率估计方法

在月面环境下车轮滑转影响月球车的运动性能和定位精度,针对星球车车轮滑转率检测问题,提出一种基于车辙图像频域特征的滑转率估计方法。通过分析车轮与土壤相互作用过程与车辙形成机理,建立车辙的时域参数化解析模型。并在车辙构造时域解析模型基础上,通过对车辙图像进行频域分析,建立了基于车辙图像基频特征的车轮滑转率估计模型。此外,介绍了一种适用于该方法的车辙频域特征提取和图像处理技术。通过试验证明,该方法只需检测车辙图像基频便可以实现车轮滑转率非接触式精确估计。采用本方法估计车轮滑转率,相对精度可达3%。     

H-450 X射线参考辐射质的研建及验证

根据PTB和ISO 4037-1:2019要求,关于高空气比释动能系列（H系列）X射线参考辐射质的推荐数据,外推得出H-450的附加过滤为6.24 mm Cu,第一半值层（HVL_1st）为(5.39~5.52)mm Cu,平均光子能量为(214.25~214.78)keV,剂量率为11.51 Gy/h（距离焦斑为1 m处,管电流为10 mA）。随后,使用PTW32005电离室作为测量器具,采用半值层法建立H-450 X射线参考辐射质,得出HVL_1st为5.37 mm Cu,第二半值层（HVL_2nd）为5.97 mm Cu,同质系数为0.9。最后,使用PTW30013次级标准电离室测量得出H-450 X射线参考辐射质下距离焦斑1 m,管电流为10 mA时的剂量率值为11.98 Gy/h,与理论值的相对误差为4.07 %。采用MCNP 5软件对H-450 X射线参考辐射质进行能谱模拟并计算得出其平均光子能量为200.4 keV。     

面向无人集群的通信定位一体化方法研究

针对无人集群协同作战通信/定位的集约化设计需求,提出了一种单载波频域均衡(SCFDE)与直接序列扩频(DSSS)技术有机结合的通信定位一体化波形。一方面,利用SCFDE中频域均衡易于克服频率选择性衰落且便于与DSSS结合的优势,使系统具有良好的适应复杂场景的通信能力;另一方面,利用恒包络零自相关导频序列的优良自相关和互相关特性,将其作为导频序列并计算与本地导频符号的循环移位相关,检测相关峰值即可实现整数符号周期信号到达时间估计。同时,结合用于信道估计的导频序列,构建差分延时相关模型,有效解决了小数采样周期信号到达时间估计问题,实现了高精度的信号到达时间估计。     

多通道GPS共视法时频传递接收机的研制

GPS共视法是国际上流行的远距离时间频率传递技术,核心是共视法接收机。我们成功研制了多通道GPS共视法时频传递接收机系统,硬件部分主要由自主研制的高精度时间间隔计数器和Motorola生产的VPONCORE GPS引擎组成,软件符合时间频率咨询委员会(CCTF)发布的GPS共视法数据处理软件标准化指南的要求,与单通道GPS定时接收机相比,界面更友好,操作更方便,具有很强的分析处理数据功能。经测试证明多通道GPS接收机零基线共钟共视时间比对的不确定度小于4 ns(仰角40°),与国外报道基本相同。     

Force measurement using strain-gauge balance in shock tunnel based on deep learning

When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering.     

Oblique shock train motion based on schlieren image processing

In this paper, shock train motion in a Mach number 2.7 duct is studied experimentally, and large numbers of schlieren images are obtained by a high-speed camera. An image processing method based on Maximum Correlation Detection (MCD) is proposed to detect shock train motion from the schlieren images, based on which the key structures, e.g., separation positions and separation shock angles on the top and bottom walls, can be analysed in detail. The oscillations of the shock train are generated by rhombus and ellipse shafts at various excitation frequencies. According to the analysis of MCD results, the distributions of the frequency components of shock train oscillation generated by the two shafts are distinctly different, in which the motion generated by the ellipse shaft is much smoother; shock train motion is mainly characterized by the oscillation of separation position while the separation shock strength is not so sensitive to downstream disturbance; there is a hysteresis loop relation between the downstream pressure and separation position.