Classification of Methods in the SINS/CNS Integration Navigation System
Author(s) -
Yanqiang Yang,
Chunxi Zhang,
Jiazhen Lu,
Hao Zhang
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2787424
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The known methods used for strapdown inertial navigation system (SINS)/celestial navigation system (CNS) integration are classified based on two categories of measurement in this paper. One category is called the"attitude observation method," in which the measurement is derived by the difference between the optimal attitude information of the star sensor and the SINS. The other category is called the "star vector observation method," in which the measurement is derived by the difference between the original star vector information of the star sensor. The attitude angle observation equation of the first category is generally obtained by using the relationship between the attitude angle errors and the Phi-angle (or tilt errors), and the attitude matrix observation equation is obtained by using the relationship between the attitude matrix and the Psi-angle (or platform errors). However, the interrelationship between these two observation equations has not been developed in previous studies. A simpler attitude angle observation method based on the Psi-angle instead of the Phi-angle is proposed to reveal the interrelationship between these two methods. This proposed method is basically the principle behind the SINS/CNS integration and depicts the physical meaning clearly. In addition, the internal relationships of the second category and the interrelationship of these two categories are also analyzed to show their equivalence to each other. Numerical simulations verify the correctness of the analysis. Experimental studies indicate that the integration accuracy of the two categories is also exactly equivalent.
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