多航天器协同探测效能影响参数不确定性分析

为解决传统基于蒙特卡洛仿真的探测效能不确定性分析评价方法中时间效率低，参数关系映射单一的问题，提出一种基于神经网络的不确定性分析方法，利用人工神经网络在拟合回归分析上的非线性特性，设计了能够替代复杂系统的神经网络结构，能够通过少量仿真计算结果作为训练样本实现模型的收敛并能有效反映被替代系统的原有特性。选择以一个多航天器的天文观测任务作为典型用例，建立了仿真分析模型，并与蒙特卡洛仿真方法结果进行了对比，验证了此方法的准确性和计算效率。

Performance Uncertainty Analysis of Multi Spacecraft Mission

Uncertainty analysis plays an important role in early phases of a space mission. The uncertainty of parameters, such as the accuracy of positioning, timing and error in payloads, broadly exists in space mission. When it comes to a distributed satellite system, this problem turns to be more complicated. Traditional method is Monte Carlo simulation, which is time consuming and hard to recognize the coupling relationship between parameters. In this paper, we propose a method which uses a neural network as the model to replace the MCS process. By training the network model using small number of samples, this method can build a model and have advantages in time consuming. We use a multi-spacecraft astronomical observation mission as the use case, And the comparison results between the two methods verify the feasibility of the proposed method.