State-estimation of vehicle dead-reckoning system based on recurrent neural network

With indefinite noises and nonlinear characteristics, real-time estimating states of the dead reckoning (DR) unit is much more difficult than that of the other measuring sensors, which are used in vehicle integrated navigation systems. Compared with the well known extended Kalman filter (EKF), a recurrent neural network was proposed for the solution, which not only improves the location precision, the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. In order to test the performances of the recurrent neural network, these two methods were used to estimate states of the vehicle DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.