A Bayesian framework for ATR decision-level fusion experiments

Douglas R. Morgan; Timothy D. Ross

doi:10.1117/12.719766

9 April 2007 A Bayesian framework for ATR decision-level fusion experiments

Douglas R. Morgan, Timothy D. Ross

Proceedings Volume 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007; 65710C (2007) https://doi.org/10.1117/12.719766
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The US Air Force Research Laboratory (AFRL) Fusion for Identifying Targets Experiment (FITE) program aims to determine the benefits of decision-level fusion (DLF) of Automatic Target Recognition (ATR) products. This paper describes the Bayesian framework used to characterize the trade-space for DLF approaches and applications. The overall fusion context is represented as a Bayesian network and the fusion algorithms use Bayesian probability computations. Bayesian networks conveniently organize the large sets of random variables and distributions appearing in fusion system models, including models of operating conditions, prior knowledge, ATR performance, and fusion algorithms. The relationship between fuser performance and these models may be analytically stated (the FITE equation), but must be solved via stochastic system modeling and Monte Carlo simulation. A key element of the DLF trade-space is the degree to which the various models depend on ATR operating conditions, since these will determine the fuser's complexity and performance and will suggest new requirements on source ATRs.

Citation Download Citation

Douglas R. Morgan and Timothy D. Ross "A Bayesian framework for ATR decision-level fusion experiments", Proc. SPIE 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007, 65710C (9 April 2007); https://doi.org/10.1117/12.719766

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