Determination of optimal observable subspace for strapdown inertial navigation systems through observability analysis
The determination of unobservable states is important in consideration of system performance during initial alignment. The aim of observability analysis is concluded as follows: 1) finding the observable states or linear combinations of these states; 2) finding those states whose measurements turn the system into a completely observable; 3) separating the system into observable and unobservable subsystems. In this paper, the system equation and measurement equation of SINS for Kalman filtering are given. The Observability of initial alignment process of SINS is analyzed by means of singular value decomposition method. Degree of observability for every state can be computed by preceding method, the three unobservable states of INS are obtained; therefore optimal observable subspace is determined by structure decomposition method. For proving the correctness and effectiveness of this proposed method, a Kalman filter is designed. The Kalman filtering results are obtained. By comparing these results with results of observability analysis, the uniform conclusion is obtained. Before designing a Kalman filter, the observability of every systematic state can be known by use of the singular value decomposition method of the observable matrix, i.e. degree of observability for every state can be computed. Therefore observable vector and optimal observable subspace may be determined. The correctness and effectiveness of this proposed method was proven by analyzing results of Kalman filtering. This method can be used for direct design of a Kalman filter.
strapdown inertial navigation system observability analysis singular value Kalman filter initial alignment
Yafei Yang Xin Huo
Control and simulation center, Harbin Institute of Technology, 150080, Harbin, China
国际会议
第五届仪器科学与技术国际学术会议(ISIST 2008)Fifth International Symposium on Instrmentation Science and Technology
沈阳
英文
1-6
2008-09-15（万方平台首次上网日期，不代表论文的发表时间）