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A robust integration algorithm for Strapdown Inertial Navigation System/Celestial Navigation System (SINS/CNS) is designed and investigated in this paper. The main objective of the developed integration scheme is the estimation and correction of unacceptable SINS' errors in long time navigation missions. A Narrow Field of View (NFOV) star tracker is used as a CNS aid in this study. A mathematical model is designed to estimate the platform misalignment angles and transformations of different frames are exercised. Linearized Kalman Filter (LKF), with a modified measurement model, encompassing the position errors, is proposed for fusing the data of SINS and CNS. Finally, simulation results are produced to show the integrity and validity of the integration algorithm for estimation and correction of the SINS errors in extended time flights.

In this paper, a method for real-time fault detection and isolation is presented, which has been tested successfully for unmanned aerial vehicles. In this method, two parity vectors are constructed to achieve the fault detection and isolation. In addition, the maximum likelihood estimate is used to estimate and correct the fault vector. Simulation results show that not only can the faulty system be detected accurately in time by this method, but also the fault can be repaired efficiently. Thus, the fault tolerance of the integrated system is improved remarkably by this method.

The two-stage Kalman filter requires the accurate information of unknown random bias. Unfortunately, this algebraic constraint is seldom satisfied for practical systems. The adaptive solution of estimating a set of dynamic state in the presence of a random bias employing a two-stage Kalman estimator is addressed. The solution performed well when the information of unknown random bias was inaccurate. The validity of the solution is verified with a simulative example.