Date of Award
Doctor of Philosophy (PhD)
Department of Electrical and Computer Engineering
Peter S. Maybeck, PhD
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. A new moving-bank MMAE algorithm is developed based on exploitation of the density data available from the MMAE. The methods used to exploit this information include various measures of the density data and a decision-making logic used to move, expand, and contract the MMAE bank of filters. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters. A new 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. Modifications to an existing discretization method are also presented, permitting application of this method in real time and to nonlinear system models or linear/linearized models that are unstable or astable. These new algorithms are validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.
DTIC Accession Number
Vasquez, Juan R., "New Algorithms for Moving-Bank Multiple Model Adaptive Estimation" (1998). Theses and Dissertations. 5520.