Adaptive Kalman Filter with Multiple Fading Factors for UAV State Estimation

In general case, as an algorithm for estimating the parameters of a linear system, Kalman filter can be utilized without any problem. However, when there is a malfunction in the estimation system, the filter fails and the outputs become inaccurate. In this paper, an Adaptive Kalman Filter with multiple fading factors based gain correction for the case of malfunctions in the estimation system is presented. By the use of an adaptive matrix constituted of multiple fading factors, faulty measurements are taken into consideration with small weight and the estimation errors are corrected without affecting the good estimation characteristic of the remaining process. Adaptive Kalman Filter algorithm is tested by simulations for the implementation in the navigation system of an UAV platform. The filter performance has been evaluated for different kinds of measurement malfunctions.