Observable degree analysis for inertial navigation in-flight alignment based on information theory

A method of analyzing the observable degree of time-varying linear systems as piece-wise constant systems(PWCS) is applied to the analysis of in-flight alignment(IFA) of inertial navigation systems(INS) whose estimability is known to be enhanced by maneuvers. It is shown that through the method based on mutual information, we can not only determine which state is observable and which is not, but also calculate the exact degree of observability of the system states. Simulation results demonstrate the validity of the method.

[1]  Hyung Suck Cho,et al.  A Neural Net-based Assembly Algorithm for Flexible Parts Assembly , 2000, J. Intell. Robotic Syst..

[2]  I. Rhee,et al.  Observability of an integrated GPS/INS during maneuvers , 2002, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Sinpyo Hong,et al.  Observability of error States in GPS/INS integration , 2005, IEEE Transactions on Vehicular Technology.

[4]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems. I. Theory , 1992 .

[5]  J. Speyer,et al.  Observability of an integrated GPS/INS during maneuvers , 2004 .

[6]  F. Ham,et al.  Observability, Eigenvalues, and Kalman Filtering , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[7]  S. Sukkarieh,et al.  Observability analysis and active control for airborne SLAM , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Yuanxin Wu,et al.  INS/GPS Integration: Global Observability Analysis , 2009, IEEE Trans. Veh. Technol..

[9]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems. II. Application to inertial navigation in-flight alignment (military applications) , 1992 .

[10]  Salah Sukkarieh,et al.  Improving the real-time efficiency of inertial SLAM and understanding its observability , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[11]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .