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Online Calibration of Installation Errors of SINS/OD Integrated Navigation System Based on Improved NHC.
- Source :
- IEEE Sensors Journal; Jul2022, Vol. 22 Issue 13, p12602-12612, 11p
- Publication Year :
- 2022
-
Abstract
- The integrated navigation system of strapdown inertial navigation system (SINS) and odometer (OD) is a commonly used vehicle positioning system. The installation errors caused by the difference between the installation positions of SINS and OD mainly include the installation deflection angle and lever arm, which will reduce the positioning accuracy. The traditional calibration method is to expand the installation deflection angle error and lever arm error into state variables, the velocity difference under nonholonomic constraint (NHC) is taken as the measurement to establish the Kalman filter model for online calibration, but this method seriously depends on the sensor accuracy. In order to calibrate the installation errors of low-precision SINS, this paper improves NHC, adds the difference between the centripetal acceleration measured by odometer and calculated by SINS as measurement on the basis of the original measurement, and introduces a low-pass filter to reduce the interference of vehicle vibration on the accelerometer.The experimental results show that the proposed algorithm has better convergence of the installation deflection angle, and the error between the estimation results of the forward lever arm and the actual measurement results is less than 0.1m; After compensating the installation errors of the improved NHC, the maximum positioning errors in the east and north directions are reduced by 63.19% and 43.51% respectively, the height error is also greatly reduced, and the positioning accuracy of the integrated navigation system is significantly improved. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1530437X
- Volume :
- 22
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- IEEE Sensors Journal
- Publication Type :
- Academic Journal
- Accession number :
- 157765340
- Full Text :
- https://doi.org/10.1109/JSEN.2022.3170707