Scientific performance metrics for data fusion: new results

Last year at this conference we described initial result in the practical implementation of a unified, scientific approach to performance measurement for data fusion algorithms. The proposed approach is based on 'finite-set statistics' (FISST), a generalization of conventional statistics to multisource, multitarget problems. Finite-set statistics makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems in such a way that 'information' can be defined and measured even though any given end-user may have conflicting or even subjective definitions of what 'information' means. In this follow-on paper we describe progress on this work completed over the last year. We describe the performance of additional FISST metrics, including metrics which estimate the amount of information attributable to specific algorithm functions and which include the classification performance of the fusion algorithm. In addition we consider metrics that can be applied when ground truth is not known, based on comparisons to complete uncertainty.