基于北斗导航检测平台的时间量值传递系统研究

研究了基于北斗卫星的北京市导航检测平台的时间量值传递系统,它以卫星共视法作为数据比对的条件,采用修正值预估技术和相位补偿技术来提高铷钟的准确度与稳定度。本系统可实时地将地方原子时标与UTC（NIM）进行比对溯源,实现北斗卫星导航检测平台的时间量值可靠传递,保证地方原子时标与国家时间基准UTC（NIM）同步差在±10ns以内,以满足北斗导航产品研制、开发和应用的需要。     

基于长短期记忆网络时序数据趋势预测及应用

为了研究状态监测大数据对设备运行状态的估计和预测,提出了一种人工经验与主成分分析相结合的长短期记忆网络方法（AEPCA-LSTM）,利用运行过程中的监测时序数据对设备运行趋势进行预测。首先,通过基于人工经验的主要成分分析方法（AEPCA）从状态监测系统中提取与目标变量最相关的状态变量作为输入;其次,利用长短期记忆网络（LSTM）对目标变量趋势变化进行预测,并考虑运行过程中新数据样本的持续产生,对模型进行定期更新,以提高模型的动态适应性。最后,将所提出的方法应用于船舶副机系统的涡轮增压器转速预测中,结果表明该方法相对于传统的PCA-LSTM和LSTM,具有更小的预测平均误差0.18037,即展现了其在时序数据趋势预测的优势。     

调频调相应答机距离零值测量新方法

叙述了分频调制度变换器的工作原理和时延测量。提出了根据测量FM信号源大频偏调制与小频偏调制的时延差来挑选出恒时延FM信号源,采用恒时延FM调制器构成FM—PM应答机（或FM—PM测距地面站）距离零值测量方法和设备,这是FM—PM体制侧音测距系统的一种距离零值测量新方法;提出了采用分频调制度变换器和非恒时延FM信号源构成FM—PM应答机（或FM-PM测距地面站）距离零值测量方法及设备。     

基于匹配滤波和时间恢复算法的B(AC)码数字解调技术

针对靶场时间统一系统中B（AC）码解调的现状和存在的问题,本文从匹配滤波和时间恢复两个方面入手,阐述了一种B（AC）码在噪声和波形失真条件下的数字解调技术。     

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.     

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.     

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.