空天飞行器弹道/轨道一体化设计

弹道/轨道一体化设计是解决空天飞行器发射入轨和轨道转移问题的一种全新思路。针对目前存在的空天飞行器弹道/轨道一体化设计问题,通过改进非开普勒轨道方程的方法建立飞行器在连续推力、气动力、引力以及摄动力等多种力作用下的弹道/轨道一体化设计动力学模型;提出基于轨道设计反方法的弹道/轨道一体化设计方法。其创新点主要体现在：通过整合连续推力、气动力、引力以及摄动力等多种作用力达到了统一弹道/轨道模型的目的;提出了基于傅里叶级数形状方法的轨道设计方法,该方法相比于之前的逆多项式法,可以处理带推力约束的轨道设计问题;由于在弹道段采用类似于轨道设计反方法的设计思想设计弹道,使得弹道和轨道两段轨迹的设计方法也达到了统一,致使从模型和设计方法的角度都体现了弹道/轨道设计的统一性,解决了传统分段设计方法是在不同段采用不同的模型和方法,很难体现出一体化设计思想的问题。仿真分析表明本文提出的弹道/轨道一体化设计方法是可行和有效的。     

基于级数展开的位置误差加权定位方法

随着对海洋探索的愈加深入,对水下目标探测能力的要求也愈发严格。针对噪声干扰严重时算法性能大幅度下降的问题,文章主要研究仅利用分布式矢量水听器的波达方向信息对未知目标进行融合定位。在已有的基于位置误差加权方法的基础上,利用几何二次约束减小波达方位角量测误差,并结合泰勒级数展开,将一阶权值推导至高阶,解决在低信噪比情况下定位精度差的问题。仿真结果表明:相对于传统的基于方差倒数的自适应加权算法,该方法在定位精度上有很大提高,尤其在低信噪比的情况下效果显著。     

双基固体推进剂药柱非线性蠕变特性实验研究

研究了某双基固体推进剂药柱的非线性蠕变性能。依据固体推进剂药柱试件在不同温度、应力水平条件下的蠕变实验,分析其蠕变行为的非线性特性。实验结果表明,其力学性能与时间、温度、应力强烈相关。构建了参考温度20℃和参考应力5 MPa水平条件下的蠕变柔量主曲线。理论和数值计算表明,较高温度和应力水平下的固体推进剂药柱短期蠕变实验,可用于预测其在较低温度和应力水平下的长期蠕变行为。利用5级Prony级数模拟的蠕变柔量函数与实验叠合主曲线能很好吻合。     

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。     

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...