This document describes the architecture and algorithms of a high fidelity fixed wing flight simulator intended to test and validate novel guidance, navigation, and control (GNC) algorithms for autonomous aircraft. It aims to replicate the influence of as many factors as possible on the aircraft performances, the Earth model, the physics of flight and the associated equations of motion, and in particular the behavior of the onboard sensors, limiting the assumptions to the bare minimum, and including multiple relatively minor effects not usually considered in simulation that may play a role in the GNC algorithms not performing as intended. The author releases the flight simulator C ++ implementation as open-source software. The simulator modular design enables the replacement of the standard GNC algorithms with the objective of evaluating their performances when subject to specific missions and meteorological conditions (atmospheric properties, wind field, air turbulence). The testing and evaluation is performed by means of Monte Carlo simulations, as most simulation modules (such as the aircraft mission, the meteorological conditions, the errors introduced by the sensors, and the initial conditions) are defined stochastically and hence vary in a pseudo-random way from one execution to the next according to certain user-defined input parameters, ensuring that the results are valid for a wide range of conditions. In addition to modeling the outputs of all sensors usually present onboard a fixed wing platform, such as accelerometers, gyroscopes, magnetometers, Pitot tube, air vanes, and a Global Navigation Satellite System (GNCC) receiver, the simulator is also capable of generating realistic images of the Earth surface that resemble what an onboard camera would record if following the resulting trajectory, enabling the use and evaluation of visual and visual inertial navigation systems.

翻译：本文档描述了一种高保真固定翼飞机模拟器的架构与算法，旨在测试和验证自主飞行器的新型制导、导航与控制（GNC）算法。该模拟器力求在最小化假设的前提下，尽可能多地复现影响飞机性能的因素，包括地球模型、飞行动力学及相关运动方程，特别是机载传感器的行为，并纳入通常模拟中未考虑但可能影响GNC算法预期性能的多种相对次要效应。作者将飞行模拟器的C++实现以开源软件形式发布。该模拟器采用模块化设计，可替换标准GNC算法，旨在评估其在特定任务和气象条件（大气特性、风场、空气湍流）下的性能。测试与评估通过蒙特卡洛模拟执行，由于大多数模拟模块（如飞机任务、气象条件、传感器误差及初始条件）均被随机定义，因而会根据用户定义的特定输入参数以伪随机方式逐次变化，确保结果在广泛条件下均具有有效性。除模拟固定翼平台通常配备的所有传感器输出（如加速度计、陀螺仪、磁力计、皮托管、风向标及全球导航卫星系统（GNSS）接收器）外，该模拟器还能生成逼真的地球表面图像，模拟机载摄像头在沿结果轨迹飞行时可能记录的景象，从而支持视觉导航系统及视觉惯性导航系统的使用与评估。