Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network

Abstract

The accurate estimation of motion state has been the key node in the control of motion systems. The traditional GPS/INS (Global Position System/Inertial Navigation System) integration may be invalid in the outage of GPS signals. The continuous estimation framework and method were proposed to estimate the location in different environments. Firstly, a continuous framework was designed combining the traditional GPS/INS integration and the intelligent neural network. The methods were switched according to the condition of sensors to realize the continuous estimation. Secondly, NARX (Nonlinear Autoregressive with Exogenous Inputs) neural network was built to model the nonlinear mapping relation between INS and GPS. The time series data were analyzed in NARX neural network to excavate the data features in the time dimension. Lastly, the experiment was conducted to verify the method proposed. And the results showed that the solution is valid in the motion state estimation with INS when GPS is in the outage.