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。     

基于分数阶Fourier变换的微弱LFM信号检测

为了提高侦察系统对微弱LFM信号的检测能力,文章提出了一种基于分数阶Fourier变换的微弱LFM的检测方法。首先,分析LFM信号在离散分数阶Fourier变换计算条件下的输出信噪比,得出当观测信号的采样点数增大时,分数阶Fourier变换可有效改善输出信噪比。利用该结论,提出了通过提高信号的采样频率或者对已采样信号作插值的方法,改善分数阶Fourier变换对微弱LFM信号的检测。最后,仿真验证该方法是有效的。     

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.     

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...