The growth of the Unmanned Aerial Vehicle (UAV) has become greater than before due to the presence of inexpensive sensors and improved computer technology. The state estimation of the UAV has faced challenges such as inaccurate estimation and delay created by the imperfect controllers in the Global Positioning System. So, in this paper, Extended Kalman Filter (EKF) with Modified Artificial Bee Colony (MABC) algorithm is proposed to estimate the state of UAV. The position of the vehicle is estimated by computing the Yaw angle (psi(Y)), pitch angle (theta(P)), and roll angle (phi(R)). Furthermore, the absolute deviation between the measured angles and the actual angle is estimated by computing the error (Root Mean Square Error, Mean Absolute Percentage Error, Mean Absolute Error, and L-1-Norm) and also analysis of the convergence of the proposed method. Thus the proposed EKF-MABC shows its superior performance in calculating all three angles, when compared with KF, EKF, EKF-artificial bee colony (ABC), EKF-ABC-Inversely Proportional Mutation, EKF-Quick artificial bee colony, EKF-ABC-Multi-Point, and EKF-Adaptive and Hybrid-ABC methods and accurately estimates the final position of the UAV system.
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