Accuracy Analysis of SINS/CNS Integrated Attitude Determination Based on Simplified Spatio-Temporal Model

For ground-based Celestial Navigation System/Strapdown Inertial Navigation System (CNS/SINS) integrated navigation with arcsecond-level accuracy, the current spatio-temporal transformation model involves a considerable amount of astronomical knowledge, making it difficult for ordinary navigation professionals to quickly master and operate. There has been no strict argumentation on which parameters can be simplified in the calculation process. Under the premise of ensuring that the attitude accuracy of ground integrated navigation meets the requirement of 5 arcseconds, through argumentation and quantitative analysis, the complex links in the spatio-temporal transformation model that contribute minimally to the final attitude measurement accuracy can be eliminated, significantly reducing the complexity of the model and lowering the threshold for its use. The factors considered in this paper include proper motion, annual parallax, light deflection, aberration of light, details of the precession-nutation model, details of the time system, and calibration parameters. Factors contributing less than 0.1 arcsecond to the accuracy during the coordinate transformation process are ignored or approximately simplified. Error analysis shows that the corrections for annual parallax and light deflection have negligible effects on accuracy. Except for the calculation of the Earth's rotation angle, which requires a relatively precise UT1-UTC time, the time input in the calculation process of other astronomical parameters can directly use UTC time. Experimental measurements show that the calibration parameters obtained by the method in this paper have high robustness, and the parameter accuracy meets the requirements of attitude calculation. The proposed simplified spatio-temporal model reduces the computational load by 90%, can meet the arcsecond-level attitude measurement accuracy requirements of ground-based CNS/INS integrated navigation, and has the potential to be extended to more general dynamic or air/space-based intelligent navigation scenarios.