Effective fault diagnosis based on strong tracking UKF

Abstract

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 x 2 -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 x 2 -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.

Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

“…The STSSRCKF combines the advantages of STF and SSRCKF. Then the STSSRCKF has strong robustness against model uncertainties and good real-time state tracking capability [ 28 ]. Moreover, the STSSRCKF algorithm eliminates the cumbersome evaluation of Jacobian/Hessian matrices, its numerical stability and estimated accuracy are significantly improved.…”

New fault-tolerant federated Kalman filters for integrated navigation

“…More details of the AKF can be found in Hajiyev (2012) and the references therein. The other one is the so-called strong tracking filter (STF) (Han et al, 2011;Jwo and Lai, 2009;Zhou et al, 1993;Zhou and Frank, 1996). In this method, based on the orthogonality principle, the innovations at different sampling times are forced to be approximately orthogonal to each other by designing single or multiple fading factors.…”