Integrated navigation filtering algorithm based on MEP-UKF

A method of combining model error prediction (MEP) and unscented Kalman filter (UKF) was put forward to solve the problems of low accuracy and poor real time, which consist in extended Kalman filter (EKF) applied in SINS/GPS integrated navigation system. MEP-UKF filtering algorithm considers the measurement error resulted from the inertial unit as the model error and predicts it in time through MEP. Then it uses UKF to estimate the vehicle error information, including attitude, velocity and position, which are fed back to SINS to correct the navigation parameters. Consequently, MEP-UKF not only avoids the limitation that UKF has to assume the measurement error as the Gaussian white noise, but also decreases the state dimension of SINS/GPS integrated navigation system, further shorten the navigation calculation time. Simulation results show that MEP-UKF is superior to EKF in convergence and precision, and satisfies the requirements of navigation precision and real time which is emphasized in project.