Effective fault diagnosis based on strong tracking UKF

Abstract

PurposeThe 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/approachA kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ2‐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.FindingsThe comparisons with χ2‐test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method.Practical implicationsThis 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/valueA 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.