Effective fault diagnosis based on strong tracking UKF

Purpose - The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault-tolerant re-entry navigation requirements of reusable boost vehicle (RBV). Design/methodology/approach - A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state IT?/ITUP2/UP-test-based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors. Findings - The comparisons with IT?/ITUP2/UP-test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method. Practical implications - This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles. Originality/value - A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV.