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This paper addresses challenges in agricultural unmanned aerial vehicle (A-UAV) positioning, emphasizing the significance of accurate position estimation for applications like coverage path planning under depended noises. The study introduces a solution involving a PCA-based maximum correntropy Kalman filter (PCA-MCKF) to mitigate issues such as lowaltitude flight control, inaccurate position estimation due to coloured noise, and non-Gaussian distribution, including wind effects. Comparative analysis with traditional methods, such as Kalman filter (KF), PCA-KF, and PCA-MCKF, is conducted using four rotor-wing UAVs with linear and nonlinear dynamical models. The paper employs interval type-2 Fuzzy PID as an intelligent controller method and constant acceleration and constant velocity manoeuvre models for estimation. Root mean square error is used as the accuracy metric, and real-time simulations in Webots demonstrate the superiority of the proposed PCA-MCKF in enhancing agricultural UAV applications.