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Proceedings Paper

Scientific performance estimation of robustness and threat
Author(s): John R. Hoffman; Eric Sorensen; Chad A. Stelzig; Ronald P. S. Mahler; Adel I. El-Fallah; Mark G. Alford
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Paper 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.

Paper Details

Date Published: 31 July 2002
PDF: 11 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477610
Show Author Affiliations
John R. Hoffman, Lockheed Martin Tactical Systems (United States)
Eric Sorensen, Lockheed Martin Tactical Systems (United States)
Chad A. Stelzig, Lockheed Martin Tactical Systems (United States)
Ronald P. S. Mahler, Lockheed Martin Tactical Systems (United States)
Adel I. El-Fallah, Scientific Systems Co., Inc. (United States)
Mark G. Alford, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4729:
Signal Processing, Sensor Fusion, and Target Recognition XI
Ivan Kadar, Editor(s)

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