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.     

Star angle/ double-differenced pulse time of arrival integrated navigation method for Jupiter exploration

Pulsar navigation is a promising autonomous navigation system for spacecraft, which is applicable to the entire solar system. However, the pulsar’s directional error and the onboard clock error are two types of systematic errors that seriously reduce navigation accuracy. To solve this problem, a star angle/double-differenced pulse time of arrival(SA/DDTOA) integrated navigation method is proposed. Since measurements obtained by observing different pulsars contain the same clock errors, the measurements can be differed to eliminate the common clock error. Then, the pulsar-differenced measurements at neighbor filtering time can be differed to suppress the effect of the pulsar’s directional error on navigation precision. Star angle is used to obtain absolute navigation information, which denotes the angles between the light of sight of Jupiter and that of its background stars. Simulation results demonstrate that the proposed method can eliminate the influence of the onboard clock error and greatly weaken the effects of the pulsar’s directional error. The navigation accuracy is better than the traditional star angle/pulse time of arrival integrated navigation method and star angle/pulse time difference of arrival integrated navigation method. In addition, the navigation accuracy of the SA/DDTOA integrated navigation method is less affected by Jupiter’s ephemeris error. This work greatly reduces the influence of common systematic errors in pulsar navigation on navigation accuracy.     

基于北斗三号的多区域实时动态授时服务系统

面向新一代移动通信和物联网等领域对高精度、低成本授时的需求,针对典型的实时动态授时服务只能单区域覆盖的问题,提出了一种基于北斗的多区域实时动态精密授时服务系统.该系统由标准时间、时间基准站、高精度时间频率实时传递链路、数据中心和时间用户等部分组成,可实现标准时间实时动态授时服务的全国多区域覆盖.依托中国科学院国家授时中心时间频率和卫星导航平台建立了原型系统,并基于多天的北斗三号新体制信号观测数据开展了试验验证.试验结果表明,短基线授时精度优于0.2ns,频率稳定度万秒稳在10-15量级;零基线授时精度可达0.02ns,频率稳定度万秒稳在10-16量级.基于北斗的多区域实时动态精密授时服务系统具备技术可行性,可为北斗精密时间应用提供参考.     

HPM对伪码脉冲无线电引信的压制干扰效应仿真

为了对高功率微波作用于伪码脉冲无线电引信的干扰效果进行分析,建立了引信信号处理模型并进行了计算机仿真。仿真结果表明,在频率对准的情况下,当 HPM进入引信距离门对回波信号造成压制时,由于 HPM的信号频带较宽,覆盖了多普勒滤波器带宽,多普勒滤波器的输出信号幅度仍可达到启动门限电平,压制干扰无效。     

基于永久散射体合成孔径雷达干涉测量技术的地面沉降监测

城市施工建设及地下水开采活动日益频繁，导致城市地面沉降问题日益严重。虽然传统地面沉降监测技术的精度较高，但成本也高。近年来，星载合成孔径雷达干涉测量(InSAR)技术发展起来，已被用于监测大范围地表形变，具有低成本、大面积、高精度等特点。首次采用永久散射体合成孔径雷达干涉测量(PSInSAR)技术，监测上海西南某区域2017—2021年间的地面沉降。结果显示:除一处楼房外，该区域大部分地区未发生沉降，最大沉降速率约-7.10 mm/a，最大沉降量约25.43 mm。采用该技术监测沉降，一方面节省了大量的人力和物力;另一方面，本研究成果对城市地表沉降的科学防治具有重要的参考意义。     

An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series

The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNN-LSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %～52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %～54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series.     

基于小波神经网络的PIM功率时间序列预测

无源互调（passive intermodulation，PIM）是影响通信系统性能的关键因素之一。研究表明，PIM信号并非一个定值，而是随时间变化的非平稳信号。基于PIM功率信号的时间序列特性，文章提出基于小波神经网络的PIM功率时间序列预测方法。首先，详细介绍小波神经网络预测模型及其预测方法；其次，以同轴连接器为验证对象，通过PIM实验测试系统获得3阶PIM功率的时间序列；最后，依据获得的PIM功率时间序列，结合构建的小波神经网络预测模型对后续的时间序列进行预测分析，并将预测结果与实验结果进行比较，从而验证小波神经网络在预测PIM功率时间序列方面的有效性。该研究对于开展PIM抑制技术具有一定的参考价值。     

基于发射时间序列的反舰导弹联合突防方法

为了使多型反舰导弹达到饱和攻击的目的,最理想的情况是同批次的异型导弹在同一时刻飞行到同一目标的防空区域。研究了单横队队形舰艇编队的异型导弹联合突防问题。针对舰艇编队时各个舰艇与目标的距离不同、反舰导弹的飞行速度不同、飞行航向不同等因素,建模求解了同批次异型导弹的发射时间序列。     

基于iGMAS的国家标准时间精密授时系统

UTC是国际标准时间.UTC(NTSC)是UTC的物理实现之一,是我国的国家标准时间,也是我国一切授时业务的基础.目前,广泛应用的GNSS授时精度可达10～50ns.随着现代信息社会的快速发展,数十纳秒的授时精度及事后处理的工作模式已无法满足需求.针对上述问题,设计了基于iGMAS的国家标准时间精密授时系统(PTS).PTS的基本原理为:服务端基于iGMAS平台生成实时轨道及以UTC(NTSC)为参考的实时卫星钟差,用户端结合实时产品及伪距、载波相位观测数据,解算本地钟与UTC(NTSC)偏差.此外,搭建了PTS原型系统并展开测试分析,测试结果显示,基于PTS原型系统,各用户站授时精度均优于1ns.与GNSS授时技术相比,PTS将授时精度提高了1～2个量级,且基于国家标准时间授时,成本低廉,易于实现,具有应用前景.