<title>Exact maximum likelihood registration approach for data fusion</title>

Abstract

This paper discusses the problem of registration which is a prerequisite process of a data fusion system to accurately estimate and correct systematic errors. An exact maximum likelihood (EML) registration algorithm is presented. The likelihood criterion is formulated by transforming the measurement data from local sensors to a common system plane. The algorithm is implemented by applying a recursive two-step optimization which involves a modified Gauss-Newton procedure to ensure fast convergence. Numerical simulation studies are conducted to show the effectiveness of the algorithm and comparisons with other registration approaches are provided.