Yapay sinir ağları ile genişletilmiş kalman filtresinin bütünleştirilmiş ANS/KKS ve manyetometre ile modellenmesi simülasyonu ve test edilmesi.

The objective of this thesis is to investigate a hybrid Artificial Intelligence/ Kalman Filter (AI/KF) system to determine 3D attitude, velocity and position of a vehicle in challenging GPS environment. In navigation problem, the aim is to determine the position and velocity of the host vehicle from initial conditions. By using Inertial Measurement Unit (IMU), it is possible to calculate position and velocity with an error. In other words, during the integration stage of the IMU measurement, errors will be accumulated throughout the time. In literature to eliminate the divergent characteristic of integral calculation, other sensor measurements are combined with navigation calculation process. The traditional complementary technique to calculate the vehicle position, velocity and attitude is integrated Inertial Navigation System (INS) and Global Positing System (GPS). The integrated INS/GPS shows greater accuracy with respect to standalone INS. To achieve this accuracy it is common to use Kalman Filter as an integration technique. The Kalman Filter approach has been used widely as the standard optimal estimation technique. However because of the acquiring an accurate stochastic model and prior knowledge of the measurement error for the precision of the estimation KF has several shortcomings. Based on these shortcomings Artificial Intelligence (AI) based techniques are motivated. In this thesis, navigation mechanization algorithm and sensors mathematical models were studied. Accelerometer, gyroscope, GPS and magnetometer were selected for the sensor fusion integration. With the help of accelerometer and gyroscope, position and velocity of the host vehicle were realized through integration process of mechanization algorithm. Also GPS and magnetometer measurements were used for position-velocity and heading determination independent from IMU respectively. The design of sensor fusion algorithm is based on Extended Kalman Filter (EKF). The EKF linearized mathematical model relies on the error propagation model of the mechanization equations. As for the Neural Network structure Multilayer Perceptron Neural Network (MLPNN) were used to improve the integration results during GPS outages. After modelling and simulating the results in simulation environment, real test data were used in AI/KF based prediction algorithm. The measurement results were logged in computer to be used in algorithm. The results show how AI/KF based algorithm is more accurate during GPS outages with respect to standalone Kalman Filter algorithm.M.S. - Master of Scienc