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N-BEATS的单站对流层天顶总延迟预报

高精度对流层延迟先验值有助于加速精密单点定位的快速收敛。基于高精度高分辨率气象数据库，采用深度学习N BEATS算法，进行了单站对流层天顶总延迟的预报试验。试验选取了9个IGS跟踪站，试验弧段从2002年1月至2019年6月共185a。首先基于N BEATS算法，设计了3种预报策略，然后基于前175a针对不同预报策略进行模型训练，并对最后365d的对流层天顶总延迟进行预报。试验结果表明，以该气象数据库为基准，12h以内预报弧段的预报残差均值量级大多可达亚毫米，2h、4h、6h的预报残差的标准差分别约为5mm、9mm、13mm。     

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.     

卫星动量轮灵敏性变异分析

基于模糊理论提出一种非线性卫星动量轮灵敏性变异分析的数值方法,以工程实际中的模糊相似关系转化为空间向量的模糊等价关系,来评估稳定系统的总体变异本质特征.并通过仿真均匀分布、线性分布以及周期分布的时间数据序列验证了该模型的可行性；以3套卫星动量轮实际稳态运转实验见证了该方法的实用性和有效性.其中动量轮A的最小灵敏系数为0.678，大于0.5阈值,表明其运转期间灵敏性十分良好；动量轮B的最小灵敏系数为0.439，小于0.5阈值,其运转期间灵敏性有所变异；动量轮C的最小灵敏系数等于0.5阈值,其关系最为模糊并介于稳定与变异之间.该模型实时预测并描述了动量轮灵敏性变异过程,且适用于诸多航天领域的非线性乏信息问题.      

Smooth free-cycle dynamic soaring in unspecified shear wind via quadratic programming

Harvesting wind energy is promising for extending long-endurance flights, which can be greatly facilitated by a flight technique called dynamic soaring. The presented study is concerned with generating model-based trajectories with smooth control histories for dynamic soaring maneuvers exploiting wind gradients. The desired smoothness is achieved by introducing a trigonometric series parameterization for the controls, which are formulated with respect to the normalized time.Specifically, the per...