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4 August 2000 Scientific performance evaluation for sensor management
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Last year at this conference we described initial result in the practical implementation of a unified, scientific approach to performance measurement for 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 multi-source, 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 last year's paper, we described scientific performance evaluation for Level 1 data fusion. In this follow-on paper we describe a generalization of the FISST approach to Level 4 data fusion, specifically sensor management. Our Level 4 MoEs are based on the fact that sensor management is a support function: its purpose is to redirect collection assets in order to improve the input data into- and therefore the output performance of a Level 1 fusion algorithm. Accordingly, our basic MoE is 'excess information'. By using a sensor scheduler to simulate various sensor management algorithms, we established the effectiveness and intuitiveness of two different sensor management MoEs: the multitarget Kullback-Leibler information metric, and the Hausdorff multitarget miss-distance metric.
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Adel I. El-Fallah, Ronald P. S. Mahler, Tim Zajic, E. Sorensen, Mark G. Alford, and Raman K. Mehra "Scientific performance evaluation for sensor management", Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000);

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