MATHEMATICAL MODELLING OF AN ENHANCED REAL-TIME NAVIGATION AND TRAJECTORY CORRECTION OF AERIAL VEHICLES USING EXTENDED KALMAN FILTERS (EKP) AND STATE SPACE-BASED ADAPTIVE CONTROL STRATEGY APPLICATION

Abstract

The research article gives an overview of the enhancing real-time navigation and trajectory correction of aerial objects using extended Kalman filter with a computational study using the simulation results carried out in the Matlab environment. Accurate and real-time correction of flight trajectories is a fundamental requirement for reliable aerial navigation, particularly in scenarios where the environment is unpredictable and external disturbances are common. In this research, a robust computational approach has been developed by combining the strengths of the Extended Kalman Filter (EKF), a Raspberry Pi Pico microcontroller, and a gyroscope sensor. The EKF plays a crucial role by filtering out noise from the sensor data and providing a continuous estimation of the object’s actual position and motion. This approach enables the detection of any deviations in the flight path as soon as they occur and allows for timely corrective actions, thereby maintaining the intended trajectory. To thoroughly assess the effectiveness of this method, a dedicated simulation environment was created to mimic a variety of motion patterns and introduce realistic disturbances. The EKF-based system successfully corrected these deviations in the simulated scenarios. For further validation, the setup was implemented on actual hardware using the Raspberry Pi Pico platform. During these real-time tests, the system consistently achieved a root mean square error (RMSE) as low as 0.05 meters, with rapid response times below 10 milliseconds. The outcomes clearly highlight the system’s ability to deliver both stability and precision in trajectory correction, making it highly suitable for aerial navigation and defense applications where compact, cost-effective, and reliable solutions are needed.