Accurate Integrated Navigation Method Based on Medium Precision Strapdown Inertial Navigation System

Abstract

A method of accurate integrated navigation for high-altitude aerocraft by medium precision strapdown inertial navigation system (SINS), star sensor, and global navigation satellite system (GNSS) is researched in this paper. The system error sources of SINS and star sensor are analyzed and modeled, and then system errors of SINS and star sensor are chosen as system states of integrated navigation. Considering that the output of star sensor is attitude quaternion, it can be regarded as an attitude matrix, then the equivalent attitude matrix is constructed by using the output of SINS, and the calculating equation of the equivalent attitude matrix is designed. Thus, one of the measurements of integrated navigation can be constructed by using the equivalent attitude matrix and the attitude matrix output of star sensor. According to the constraint conditions of the attitude matrix, the diagonal elements are selected as one of the measurements of integrated navigation, and the corresponding measurement equation is derived. At the same time, the velocity output and position output difference between SINS and GNSS is selected as the other measurement, and the corresponding measurement equation is also derived. On this basis, the Kalman filter is used to design an integrated navigation filtering algorithm. Simulation results show that although the medium precision SINS is used, the heading accuracy of this integrated navigation method is better than ±1.5′, the pitch and roll accuracy are better than ±0.9’, the velocity accuracy is better than ±0.05 m/s, and the position accuracy is better than ±3.8 m. Therefore, the integrated navigation effect is very significant.

An initial alignment algorithm of strapdown inertial navigation system assisted by Doppler radar based on adaptive filtering

“…However, satellite signals are vulnerable to external interference and frequency blocking, and are easy to lose signals in buildings and tunnels, which cannot be applied to complex environments [5][6] . Doppler radar uses Doppler effect to measure the motion velocity of objects, with advantages such as high output frequency, strong autonomy, and difficulty in being interfered [7][8][9][10][11] . Yang Bo [10] uses the speed measurement information of Doppler radar to assist the SINS to achieve precise alignment in the air during the flight, and uses the speed output of Doppler radar and the SINS output to construct measurement, which can further improve the alignment accuracy by using aircraft maneuvering.…”

“…Doppler radar uses Doppler effect to measure the motion velocity of objects, with advantages such as high output frequency, strong autonomy, and difficulty in being interfered [7][8][9][10][11] . Yang Bo [10] uses the speed measurement information of Doppler radar to assist the SINS to achieve precise alignment in the air during the flight, and uses the speed output of Doppler radar and the SINS output to construct measurement, which can further improve the alignment accuracy by using aircraft maneuvering. However, when the vehicle-mounted Doppler radar is on the road surface with many potholes or the road surface after rain, the condition of the millimeter wave reflection of Doppler radar is poor, the signal instability of Doppler radar is caused, and the velocity measurement noise is large and difficult to determine the problem.…”

Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

“…Because the global positioning system (GPS) is invalid when the AUV operates underwater, it is necessary to develop underwater autonomous navigation technology. The main navigation methods at present include inertial navigation [ 3 , 4 ], acoustic navigation [ 5 , 6 ], simultaneous localization and mapping (SLAM) [ 7 , 8 ], geophysical map-based navigation [ 9 ], and integrated navigation [ 10 ]. Among the above methods, integrated navigation is the most widely used navigation method for AUVs.…”