标题： 无人驾驶飞行器（UAV）数据驱动建模软件，集成九轴IMU GPS传感器融合和数据过滤算法 显示英文标题

标题： Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMUGPS Sensor Fusion and Data Filtering Algorithm

摘要： 无人驾驶飞行器（UAV）已成为多功能平台，推动了对准确建模的需求以支持开发测试。 本文提出了用于UAV的数据驱动建模软件。 强调利用成本效益高的传感器获取方向和位置数据，随后通过应用数据过滤算法和传感器融合技术来提高数据质量，在软件中实现精确的模型可视化。 UAV的方向是通过处理后的惯性测量单元（IMU）数据获得，并使用四元数表示以避免万向节锁问题。 UAV的位置是通过结合全球定位系统（GPS）的数据确定的，GPS提供稳定的地理坐标但数据更新频率较慢，而加速度计具有较高的数据更新频率，但由于其累积误差，将其积分以获得位置数据不稳定。 通过结合这两种传感器的数据，软件能够在飞行操作过程中计算并持续更新UAV的实时位置。 结果表明，该软件能够以高精度和流畅性渲染UAV的方向和位置。 显示英文摘要

摘要： Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity