In normal operation conditions of an UAV (Unmanned Aerial Vehicle), Optimal Kalman Filter gives sufficiently good estimation results. However, in case, where measurements are faulty, filter outputs become inaccurate and even the filter may fail. This study, introduces an Adaptive Kalman Filter algorithm with the filter gain correction for the case of measurement malfunctions. By the use of a defined variable named as the adaptive factor, faulty measurements are taken into consideration with small weight and the estimations are corrected without affecting the characteristic of the accurate ones. Efficiency of the presented algorithm is tested by the simulations for implementation on a UAV platform. Behavior of the filter algorithm is investigated for various types of measurement errors.