This research obtains the optimal estimation and data fusion for linear and nonlinear systems suffering from uncertain observations (missing measurements). The noise from the different data sources are considered to correlated. The derivation of the robust Kalman filter for systems subject to aditional uncertainties in the modelling parameters is presented
Notes
Submitted to the Centre for Intelligent Systems Research of the Institute for Technology Research and Innovation, Deakin University
Thesis (Ph.D.)--Deakin University, Victoria, 2009
Bibliography
Includes bibliographical references (leaves 153-166)
