31 July 2002 Scientific performance estimation of robustness and threat
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Abstract
For the last three years at this conference we have been describing the implementation of a unified, scientific approach to performance estimation for various aspects of data fusion: multitarget detection, tracking, and identification algorithms; sensor management algorithms; and adaptive 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 informative means. In this presentation, we will show how to extend our previous results to two new problems. First, that of evaluating the robustness of multisensor, multitarget algorithms. Second, that of evaluating the performance of multisource-multitarget threat assessment algorithms.
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John R. Hoffman, John R. Hoffman, Eric Sorensen, Eric Sorensen, Chad A. Stelzig, Chad A. Stelzig, Ronald P. S. Mahler, Ronald P. S. Mahler, Adel I. El-Fallah, Adel I. El-Fallah, Mark G. Alford, Mark G. Alford, } "Scientific performance estimation of robustness and threat", Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477610; https://doi.org/10.1117/12.477610
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