Recurrent neural network approaches for nonlinear filters of navigation systems
Liguo Zhang, Hai-Bo Ma, Yangzhou Chen, Nikos E. Mastorakis · 2007
Abstract: For vehicle integrated navigation systems, real-time estimating states of the dead reckoning (DR) unit is much more difficult than that of the other measuring sensors under the indefinite noises and nonlinear characteristics. Compared with the well known extended Kalman filter (EKF), a recurrent neural network is 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 are used to estimate states of the vehicle DR navigation system. Simulation results show the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation. Key words: dead reckoning; extended Kalman filter; recurrent neural network; vehicle integrated navigation systems 1.