A New Integrated Algorithm for Spacecraft Attitude Determination

Paper number

IAC-06-C1.P.1.02

Author

Prof. Xiaokui Yun, Northwestern Polytechnical University, China

Coauthor

Prof. Jianping Yuan, Northwestern Polytechnical University, China

Year

2006

Abstract

Along with the development of space technology, more to more space operations require to determining the attitude information of space vehicle firstly. In the past ten years, many researches have focused on attitude determination using Global Positioning System (GPS). Certainly, with the potential capability to provide the angular rate information, GPS is really a full-capability sensor for attitude determination. But, the space environment is various and complex, the availability of GPS in space is not so enough, for example, when one spacecraft runs near to another big space platform (space station), GPS system is not available sometimes. So, GPS can be not used as the unique navigation sensor in space now.

GPS/Inertial Navigation System (INS) integrated navigation system is being given much attention, it is widely used in several positioning and navigation fields. Where, Kalman filtering theory is one of the best methods for incorporating of INS with GPS. But, it has also some drawbacks in terms of stability, computation load, immunity to noise effects, observability, and son on. Specially, Kalman filters perform adequately only under certain predefined dynamic models. 

Neuron computing, one of the technologies of Artificial Neural Network (ANN), is a useful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. It is also suitable to be used for integrating GPS and INS. In this paper, a new adaptive Kalman filtering based on Back Propagation Neural Network (BPNN) is studied, which is used for integration between GPS and INS data. When GPS information exists, the integrated model is established and its internal structure is tuned to mimic the present vehicle dynamics. During periods of GPS signal blockage, the studied algorithm works in the prediction mode to estimate the position changes based on the INS velocity and azimuth information. Through some theoretic analysis, this algorithm not only has good estimation performance, but also has better robustness to the system model and noise than the traditional Kalman algorithm. Finally, to assess the performance of the proposed integrated model more deeply, according to the correlative conditions in space environment, some simulation is done to compare against the traditional Kalman filter model. The results indicate that the proposed model provides a significant improvement in some performance for spacecraft attitude determination, such as accuracy, stability, robustness, and so on.

Abstract document

IAC-06-C1.P.1.02.pdf

Manuscript document

IAC-06-C1.P.1.02.pdf (🔒 authorized access only).

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