Initial alignment method based on Kalman/UKF integrated training neural network

The Unscented Kalman Filter (UKF) was studied as a state estimation method for the nonlinear system and was used to train multilayered neural network by augmenting the state with unknown connecting weights. Whereas the computer time of UKF depended on the dimension of the inertial navigation system model state vector. Any more number of states would take leave of real time. A learning algorithm for multiplayer neural network based on the Kalman/UKF integrated filter was studied. The theoretical procedure of the algorithm was described in detail. Then, it was used to the nonlinear initial alignment of inertial navigation system. Simulation results prove the availability of the neural network algorithm for nonlinear initial alignment of inertial navigation system. Not only can surely alignment accuracy and alignment time be obtained, which is similar to that of the UKF, but also the computation time is reduced considerably. Consequently, an available algorithm of neural network for the nonlinear initial alignment of inertial navigation system is discovered.