Optimizing GPS and Gyroscope Data for Military Missions

Abstract

The use of unmanned aerial vehicles (UAVs) in military missions has greatly increased in recent years. To ensure precise and efficient movements, it is essential to estimate the UAV's orientation accurately. Two commonly used methods for this estimation are the Kalman filter and the Madgwick filter. In this article, we present a method to combine Kalman and Madgwick filters to optimize the orientation estimation of a UAV in military missions. The Kalman filter estimates the orientation with low noise and high accuracy. In contrast, the Madgwick filter is used to correct the orientation quickly in the presence of rapid changes. Our results show that combining these two filters leads to better orientation estimation than using either filter separately. This improved orientation estimation leads to more precise and efficient UAV movements, making it an essential tool for military missions. The proposed method is verified through simulations and experiments, including an Arduino model with a gyroscope and GPS tracker. This method can be easily integrated into the existing control systems of UAVs. It can be adapted to different missions, providing valuable insights for researchers and engineers in the UAV control field. The combination of the Kalman and Madgwick filters offers a robust solution to the challenge of orientation estimation, providing a reliable and efficient tool for a specific purpose