Measures of effectiveness for data fusion based on information entropy

Abstract

This thesis is concerned with measuring and predicting the performance and effectiveness of a data fusion process. Its central proposition is that information entropy may be used to quantify concisely the effectiveness of the process. The personal and original contribution to that subject which is contained in this thesis is summarised as follows: The mixture of performance behaviours that occur in a data fusion system are described and modelled as the states of an ergodic Markov process. An new analytic approach to combining the entropy of discrete and continuous information is defined. A new simple and accurate model of data association performance is proposed. A new model is proposed for the propagation of information entropy in an minimum mean square combination of track estimates. A new model is proposed for the propagation of the information entropy of object classification belief as new observations are incorporated in a recursive Bayesian classifier. A new model to quantify the information entropy of the penalty of ignorance is proposed. New formulations of the steady state solution of the matrix Riccati equation to model tracker performance are proposed