Information for fusion management and performance estimation
Ronald Mahler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
This paper describes a unified, theoretically rigorous approach for measuring the performance of data fusion algorithms, using information theory. The proposed approach is based on 'finite-set statistics' (FISST), a direct generalization of conventional statistics to multisource, multitarget problems. FISST makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems. This can be done, moreover, in such a way that mathematical 'information' can be defined and measured even though an evaluator/end-user may have conflicting or even subjective definitions of what 'informative' means. The result is a scientifically defensible means of (1) comparing the performance of two algorithms with respect to a 'level playing field' when ground truth is known; (2) estimating the internal on-the-fly effectiveness of a given algorithm when ground truth is not known; and (3) dynamically choosing between algorithms (or different modes of a multi-mode algorithm) on the basis of the information content they provide.