Multitarget Miss Distance and Its Applications

The concept of miss distance—Euclidean, Mahalanobis, etc.—is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-sensor, single-target systems. One might expect that multisensor, multitarget information fusion theory and applications would already rest upon a similarly fundamental concept—namely, miss distance between multi-object systems (i.e., systems in which not only individual objects can vary, but their number as well). However, this has not been the case. Consequently, in this paper we introduce a comprehensive theory of distance metrics for multitarget (and, more generally, multi-object) systems. We show that this theory extends an optimal-assignment approach proposed by O. Drummond. We describe tractable computational approaches for computing such metrics, as well as some potentially far-reaching implications for applications such as sensor management.