Abstract—These This paper presents a new method for Sage filtering considering the model systematic errors. This method adopts Sage filtering established to estimate the model systematic error as well as the covariance matrices of observation residual vector, predicted residual vector, and predicted state vector within a moving time window. Experiment results and comparison analysis with the existing methods demonstrate that the proposed method of Sage filtering considering the model systematic errors can effectively resist the disturbances of the model error. The achieved navigation accuracy is much higher than the kalman filtering and Sage filtering methods.
Index Terms—Errors, adaptive estimation, predicted residual vectors, covariance matrix.
Yi Gao is with School of Electronic Engineering Xian Shi You University, Xi'an 710065, China (e-mail:gaoyi_nwpu@163.com).
Ya Gao is with School of Electronic Information Engineering Xian Technological University, Xi’an, 710032 China (e-mail: leave.999@163.com).
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Cite: Yi Gao and Ya Gao, "Research on Algorithm of Sage Filtering Considering the Model Systematic Errors," International Journal of Engineering and Technology vol. 9, no. 1, pp. 7-11, 2017.
