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To solve the highly nonlinear and non-Gaussian recursive state estimation problem in geomagnetic navigation,the unscented particle filter (UPF) was introduced to navigation system.The simulation indicates that geomagnetic navigation using UPF could complete the position estimation with large initial horizontal position errors.However,this navigation system could only provide the position information.To provide all the kinematics states estimation of aircraft,a novel autonomous navigation algorithm,named unscented particle and Kalman hybrid navigation algorithm (UPKHNA),was proposed for geomagnetic navigation.The UPKHNA used the output of UPF and barometric altimeter as position measurement,and employed the Kalman filter to estimate the kinematics states of aircraft.The simulation shows that geomagnetic navigation using UPKHNA could provide all the kinematics states estimation of aircraft continuously,and the horizontal positioning performance is better than that only using the UPF.
To solve the highly nonlinear and non-Gaussian recursive state estimation problem in geomagnetic navigation, the unscented particle filter (UPF) was introduced to navigation system. The simulation indicates that geomagnetic navigation using UPF could complete the position estimation with large initial horizontal position errors. However, this navigation system could only provide the position information.To provide all the kinematics states estimation of aircraft, a novel autonomous navigation algorithm, named unscented particle and Kalman hybrid navigation algorithm (UPKHNA), was proposed for geomagnetic navigation. The UPKHNA used the output of UPF and barometric altimeter as position measurement, and employed the Kalman filter to estimate the kinematics states of aircraft.The simulation shows that geomagnetic navigation using UPKHNA could provide all the kinematics states estimation of aircraft continuously, and the horizontal positioning performance is better than that only using the UPF.