Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).     

基于适航的通航飞机左边界飞行安全研究

失速/尾旋一直是通航飞机面临的最严重的左边界飞行安全问题，失速/尾旋事故统计结果表明，具备尾旋改出能力，不能避免失速/尾旋造成灾难性后果。为了避免飞机出现无意偏离可控飞行的趋势，通过对“多重防御”理念和基于此理念修订的失速/尾旋条款及其符合性验证方法的解析，提出通过提升飞行员的状态感知、增强飞机抗偏离特性和降低失控危害程度等层面的通航飞机左边界飞行的多重防御体系构建方法。本文提出的方法有助于通航飞机更好的符合新规章中失速尾旋适航条款的要求，达到提升左边界飞行安全的目的。     

民航运行风险的灰色神经网络模型

采用灰色神经网络的方法，建立了中国民航运行风险的非线性在线模型。模型以风险监测指标作为输入，以评价民航安全的综合指数作为输出。编写了模型计算软件进行仿真计算，计算结果表明，模型预测值与实际安全综合指数值吻合较好，验证了方法的正确性。利用该模型既可以确定风险监测指标中的主要影响指标，为民航降低运行风险提出合理的建议；又可以对民航安全的综合指数进行分析，为行业的安全运行提供预警。     

基于注意力机制特征重建网络的舰船目标检测

深度学习为遥感领域诸多应用提供了重要的技术支撑,光学遥感图像的舰船目标检测对国防侦察和预警具有重要意义。真实场景中的舰船往往呈不同方向任意排列,且小目标的占比大,经典的深度学习目标检测算法在这种复杂条件下精度低、易漏检。为此,本文设计了基于注意力机制特征重建网络的舰船目标检测算法。首先,通过引入注意力机制对多尺度特征融合网络模型进行训练,以高召回率产生水平锚框;然后,旋转锚框以缓解密集排列目标引起的噪声问题,并利用特征重建模块来缓解特征不对齐的问题,实现模型精炼。在HRSC2016和DOTA数据集上的测试结果表明:舰船目标检测平均精度分别达到90.20和87.52,相比经典的深度学习目标检测算法得到了有效提升,并在模拟星载嵌入式智能图像处理平台上验证了算法在轨应用的可行性。