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17 July 1998 Information for fusion management and performance estimation
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);


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