The AI based compensators for a rapid and accurate alignment procedure: An open loop approach

Inertial navigation systems are commonly used in several applications such as aerospace systems and land vehicle navigation. The navigation parameters including position, velocity and attitude of a moving platform are determined by processing the measurement of inertial sensors. In general, the accuracy of the navigation solutions provided by an INS depends on the initial attitude angles of the body frame where the measurements of specific forces and angular rate are sensed by the inertial measurement unit and the navigation frame applied. Therefore, those initial angles have to be estimated accurately prior to switching the INS into navigation mode. The techniques to estimate those initial attitude angles are known as the process of alignment. An optimal estimator, the Kalman filter, takes about 10 to 15 minutes to converge then achieve the alignment process due to measurement errors. Those errors increase the alignment time and deteriorate the overall accuracy of initial attitude angels estimated. Therefore, this article suggested an intelligent alignment scheme that combines an Artificial neural network and Kalman filter to improve the accuracy of initial attitude angles and reduce the consumption of time. In this study, a navigation grade inertial measurement unit was applied to verify the performance of proposed scheme. The preliminary results presented in this article indicate that a faster alignment procedure with higher accuracy can be achieved through the use of proposed scheme.