Ifan Wiranto, Wrastawa Ridwan
In this paper the Kalman Filter and the Fuzzy Inference System hybrid algorithm has developed to get more accurate estimation result for maneuvering target tracking. Fuzzy Logic has used to adjust the process covariance error and measurement covariance error of the Kalman Filter process in the system model. The state space model used for estimation is a constant acceleration motion model, and the measurement model is a three-dimensional Cartesian coordinatmodel. The measurement result of the sensor containing noise estimated using the Kalman Filter (KF) algorithm. Then, the covariance error resulting from the KF process is used as input to the Fuzzy Inference System (FIS) for correction based on the mismatch between innovation vector and innovation covariance. The result of this correction used to obtain the optimal Kalman gain. The proposed system model leads to improved accuracy in the simulation case. ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.
Department of Electrical Engineering, State University of Gorontalo, Gorontalo, Indonesia
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