17 July 1998 Information for fusion management and performance estimation
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Abstract
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.
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Ronald P. S. Mahler, "Information for fusion management and performance estimation", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327137; https://doi.org/10.1117/12.327137
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