Scientific performance metrics for data fusion: new results

Tim Zajic; John L. Hoffman; Ronald P. S. Mahler

doi:10.1117/12.395068

4 August 2000 Scientific performance metrics for data fusion: new results

Tim Zajic, John L. Hoffman, Ronald P. S. Mahler

Author Affiliations +

Proceedings Volume 4052, Signal Processing, Sensor Fusion, and Target Recognition IX; (2000) https://doi.org/10.1117/12.395068
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

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 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 'information' means. In this follow-on paper we describe progress on this work completed over the last year. We describe the performance of additional FISST metrics, including metrics which estimate the amount of information attributable to specific algorithm functions and which include the classification performance of the fusion algorithm. In addition we consider metrics that can be applied when ground truth is not known, based on comparisons to complete uncertainty.

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Tim Zajic, John L. Hoffman, and Ronald P. S. Mahler "Scientific performance metrics for data fusion: new results", Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); https://doi.org/10.1117/12.395068

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