The Embedded GPS/INS System (EGI) has been used more widely as central navigation equipment of aircraft. For certain cases needing high attitude accuracy, star sensor can be integrated with EGI to improve attitude performance. Since the filtering‐correction loop has already built in finished EGI product, centralized or federated Kalman filter is not applicable for integrating EGI with star sensor; it is a challenge to design multi‐sensor information fusion algorithm suitable for this situation. The purpose of this paper is to present a double‐layer fusion scheme and algorithms to meet the practical need of constructing integrated multi‐sensor navigation system by star sensor assisting finished EGI unit.
The alternate fusion algorithms for asynchronous measurements and the sequential fusion algorithms for synchronous measurements are presented. By combining alternate filtering and sequential filtering algorithms, a kind of double‐layer fusion algorithms for multi‐sensors is proposed and validated by semi‐physical test in this paper.
The double‐layer fusion algorithms represent a filtering strategy for multiple non‐identical parallel sensors to assist INS, while the independent estimation‐correction loop in EGI is still maintained. It has significant benefits in updating original navigation system by integrating new sensors.
The approach described in this paper can be used in designing similar multi‐sensor information fusion navigation system composed by EGI and various kinds of sensors, so as to improve the navigation performance.
Compared with conventional approach, in the situation that centralized and federated Kalman filter are not applicable, the double‐layer fusion scheme and algorithms give an external filtering strategy for measurements of finished EGI unit and star sensors.
Wang, R., Liu, J., Xiong, Z. and Zeng, Q. (2013), "Double‐layer fusion algorithm for EGI‐based system", Aircraft Engineering and Aerospace Technology, Vol. 85 No. 4, pp. 258-266. https://doi.org/10.1108/AEAT-Apr-2012-0052Download as .RIS
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