Enhanced motion and sizing of bank in moving-bank MMAE

Abstract

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. An online parameter discretization method is developed based on the probabilities associated with the generalized Chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.