In order to obtain a more precise aircraft altitude, this paper applies an integrated navigation method based on inertial navigation algorithms, which uses drag derived altitude to aid the inertial navigation during the blackout zone.
The simulation and experimental results show that Kalman filter parameters can be obtained for GPS/SINS integrated navigation system precisely and efficiently based on Allan variance modeling method, and the algorithm has reference value for theoretical perfection and engineering applications.
The simulating environment is set up, which involves the schema trajectory designing, the simulating algorithm of refracted star map and the modeling of the complete autonomous SINS/RCNS (Strapdown Inertial Navigation System/Refraction Celestial Navigation System) integrated navigation system.
Aiming to improve positioning precision of the GPS/INS integrated navigation system during GPS outages, a novel model combined with strong tracking Kalman filter (STKF) and wavelet neural network (WNN) algorithms for INS errors compensation is proposed and tested.
IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm.
This paper studies three schemes of Mars entry navigation: inertial measurement unit (IMU) based dead reckoning (DR), IMU/orbiter based integrated navigation, and IMU/orbiter/Mars surface beacon (MSB) based integrated navigation.
To exemplify integrated navigation filter mechanization is the foremost aspiration of this research.
Then, the model of INS/CNS/GPS integrated navigation system in launch inertial coordinate is built.
Besides, the alignment parameters can be employed for precision INS/DVL integrated navigation.
