Rotation Matrix Method Based on Ambiguity Function for GNSS Attitude Determination
AbstractGlobal navigation satellite systems (GNSS) are well suited for attitude determination. In this study, we use the rotation matrix method to resolve the attitude angle. This method achieves better performance in reducing computational complexity and selecting satellites. The condition of the baseline length is combined with the ambiguity function method (AFM) to search for integer ambiguity, and it is validated in reducing the span of candidates. The noise error is always the key factor to the success rate. It is closely related to the satellite geometry model. In contrast to the AFM, the LAMBDA (Least-squares AMBiguity Decorrelation Adjustment) method gets better results in solving the relationship of the geometric model and the noise error. Although the AFM is more flexible, it is lack of analysis on this aspect. In this study, the influence of the satellite geometry model on the success rate is analyzed in detail. The computation error and the noise error are effectively treated. Not only is the flexibility of the AFM inherited, but the success rate is also increased. An experiment is conducted in a selected campus, and the performance is proved to be effective. Our results are based on simulated and real-time GNSS data and are applied on single-frequency processing, which is known as one of the challenging case of GNSS attitude determination. View Full-Text
Share & Cite This Article
Yang, Y.; Mao, X.; Tian, W. Rotation Matrix Method Based on Ambiguity Function for GNSS Attitude Determination. Sensors 2016, 16, 841.
Yang Y, Mao X, Tian W. Rotation Matrix Method Based on Ambiguity Function for GNSS Attitude Determination. Sensors. 2016; 16(6):841.Chicago/Turabian Style
Yang, Yingdong; Mao, Xuchu; Tian, Weifeng. 2016. "Rotation Matrix Method Based on Ambiguity Function for GNSS Attitude Determination." Sensors 16, no. 6: 841.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.