Decoupled Observability Analyses of Error States in INS/GPS Integration

Abstract

The observability of error states in Inertial Navigation System/Global Positioning System (INS/GPS) integration is of great importance. Rank tests or null space tests of the observability matrix have been adopted by previous works, however, for a time-varying system with a high-dimension error state vector, it is very difficult to analyse the observability matrix by these traditional methods. In this paper, the decoupled observability analysis method is proposed for an 18-dimensional INS/GPS integration system. By reducing the dimension of coupling error states, several six-dimensional decoupled observability sub-matrices are obtained, which make the observability analyses easier. The observability results of error states are obtained by the proposed method. Covariance simulation with an Extended Kalman filter (EKF) and a flying test were performed which confirmed the theoretical results.K E Y WO R D S 1. Decoupled observability.

Initial fine alignment based on self‐contained measurement in erection manoeuvre

“…Observability is an important aspect of the state estimation in an integration system. The observability of the system can be analysed according to piecewise linear constant system (PWCS) theory [21]. The observability degree for state variables can be computed by means of singular value decomposition (SVD) method [22].…”

Fast Fine Initial Self-alignment of INS in Erecting Process on Stationary Base

“…Observability is important to the fine initial alignment, which requires careful investigation. Since the system is a time-varying system, its observability can be analysed according to the observability analysis of a Piecewise Linear Constant System (PWCS) theory (Ma et al, 2014). The PWCS theory can determine whether the system is completely observable or not, but it cannot analyse the observability degree.…”

Observation data processing methods in SINS/multi-GNSS integrated navigation simulation

“…For instance, Luo et al 2 used double-filter model in ultra-tight GPS/INS integration; Chang et al 3 applied marginal unscented KF in INS/GPS integration; Zhou et al 4 improved unscented particle filter in INS/GPS integration. These studies focus on either revising the state transition model, the observation model 2 or the error models, 5,6 or improving the KF algorithm itself. 3,4,7,8 Other relevant research efforts include the modifications of coupling modes of the SINS and GNSS.…”