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

-- Unmanned Aerial Vehicle s (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. Unmanned Aerial Vehicle s (UAV) have rapidly evolved as a versatile platform for various applications [ 1 ] . The increasing demand for UAV development to solve complex environment s necessitates raising the need to develop accurate and reliable simulation models that faithfully represent the dynamic behavior of the UAV. An accurate simulation model of UAV that has been tested allows developers to perform cost - effective analysis and evaluation while also validating the performance of UAV under real - world scenarios.