用于卫星状态估计的物理信息神经网络
Physics-Informed Neural Networks for Satellite State Estimation
AI总结：
本文提出将物理信息神经网络（PINNs）结合物理模型与深度神经网络，用于估计卫星轨道状态及连续低幅度异常加速度，在GEO卫星仿真中其观测残差和传播精度均显著优于纯物理模型。
摘要：
空间域感知（SDA）社区通常通过将轨道状态拟合到空间监视网络（SSN）的观测数据来跟踪在轨卫星。为了拟合这些轨道，需要对作用在卫星上的力建立准确的模型。在过去几十年中，人们开发了高质量的基于物理的模型用于卫星状态估计和传播。这些模型在估计和传播非机动卫星的轨道状态方面表现极为出色；然而，卫星可能会经历几类建模效果不佳的异常加速度，例如使用低推力电推进来改变轨道的卫星。物理信息神经网络（PINNs）是处理这些类别卫星的有价值工具，因为它们将物理模型与深度神经网络（DNNs）相结合，后者是极具表达能力和通用性的函数近似器。通过将物理模型与DNN结合，机器学习模型无需学习天体动力学，从而更高效、更有效地利用机器学习资源。本文详细介绍了应用PINNs来估计卫星的轨道状态以及连续的低幅度异常加速度分布。PINN通过最小化观测的均方误差来训练学习未知加速度。我们评估了纯物理模型与PINNs在观测残差和超出观测拟合跨度的传播精度方面的性能。对于在未建模加速度分布（量级为$10^{-8} \ ext{ km/s}^2$）下进行的为期两天的GEO卫星仿真，PINN在观测残差（123角秒对比1.00角秒）和传播精度（五天后3860公里对比164公里）上均比最佳拟合物理模型高出数个数量级。
英文摘要：
The Space Domain Awareness (SDA) community routinely tracks satellites in orbit by fitting an orbital state to observations made by the Space Surveillance Network (SSN). In order to fit such orbits, an accurate model of the forces that are acting on the satellite is required. Over the past several decades, high-quality, physics-based models have been developed for satellite state estimation and propagation. These models are exceedingly good at estimating and propagating orbital states for non-maneuvering satellites; however, there are several classes of anomalous accelerations that a satellite might experience which are not well-modeled, such as satellites that use low-thrust electric propulsion to modify their orbit. Physics-Informed Neural Networks (PINNs) are a valuable tool for these classes of satellites as they combine physics models with Deep Neural Networks (DNNs), which are highly expressive and versatile function approximators. By combining a physics model with a DNN, the machine learning model need not learn astrodynamics, which results in more efficient and effective utilization of machine learning resources. This paper details the application of PINNs to estimate the orbital state and a continuous, low-amplitude anomalous acceleration profile for satellites. The PINN is trained to learn the unknown acceleration by minimizing the mean square error of observations. We evaluate the performance of pure physics models with PINNs in terms of their observation residuals and their propagation accuracy beyond the fit span of the observations. For a two-day simulation of a GEO satellite using an unmodeled acceleration profile on the order of $10^{-8} \text{ km/s}^2$, the PINN outperformed the best-fit physics model by orders of magnitude for both observation residuals (123 arcsec vs 1.00 arcsec) as well as propagation accuracy (3860 km vs 164 km after five days).
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