چكيده به لاتين
Bias is one of the inertial sensors errors, which, if not resolved, causes a significant error in determining the attitude. In this research, three methods have been designed to eliminate the bias of the used sensors consist of gyroscope, accelerometer and magnetometer. In all three methods, biases are considered as system states. In the first method, the Euler angles obtained from the compass and the tiltmeter and in the second method directly measured acceleration and magnetic field were considered as Kalman observations. In both methods, the attitude is also simultaneously estimated. Third method used the dynamical relation obtained from cross product of angular velocity with the acceleration vector and the magnetic field vector. In this method, the attitude is not estimated. In all three methods, the Kalman filter has been designed and simulated to estimate biases separately and togetherly. Based on the simulation results, all algorithms can estimate biases after a small amount of motion. Then the algorithms were compared on the basis of two criteria of estimation error and calculation time; among them, for bias estimation, an algorithm that estimates all three biases using the cross product method is best based on both of the two criteria, and for attitude estimation, two algorithms, either the only gyroscope bias Estimates or does not estimate any bias are the best. Accordingly, the best algorithms in attitude estimation and bias estimation were placed in series; first, sensors biases are estimated and eliminated by the estimation algorithm of all three bias with the third method, then corrected measurements are given to the attitude algorithm. Finally, the simulation results showed a precision of 0.08 degrees for attitude estimation by proposed method. This error is almost the same as the estimation error in the sense that the sensors are unbiased; so the proposed method can well eliminate the effect of biases.
