A testbed for architecture and fidelity trade studies in the Bayesian decision-level fusion of ATR products

Kyle J. Erickson; Timothy D. Ross

doi:10.1117/12.719620

7 May 2007 A testbed for architecture and fidelity trade studies in the Bayesian decision-level fusion of ATR products

Kyle J. Erickson, Timothy D. Ross

Proceedings Volume 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI; 65670U (2007) https://doi.org/10.1117/12.719620
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Decision-level fusion is an appealing extension to automatic/assisted target recognition (ATR) as it is a low-bandwidth technique bolstered by a strong theoretical foundation that requires no modification of the source algorithms. Despite the relative simplicity of decision-level fusion, there are many options for fusion application and fusion algorithm specifications. This paper describes a tool that allows trade studies and optimizations across these many options, by feeding an actual fusion algorithm via models of the system environment. Models and fusion algorithms can be specified and then exercised many times, with accumulated results used to compute performance metrics such as probability of correct identification. Performance differences between the best of the contributing sources and the fused result constitute examples of "gain." The tool, constructed as part of the Fusion for Identifying Targets Experiment (FITE) within the Air Force Research Laboratory (AFRL) Sensors Directorate ATR Thrust, finds its main use in examining the relationships among conditions affecting the target, prior information, fusion algorithm complexity, and fusion gain. ATR as an unsolved problem provides the main challenges to fusion in its high cost and relative scarcity of training data, its variability in application, the inability to produce truly random samples, and its sensitivity to context. This paper summarizes the mathematics underlying decision-level fusion in the ATR domain and describes a MATLAB-based architecture for exploring the trade space thus defined. Specific dimensions within this trade space are delineated, providing the raw material necessary to define experiments suitable for multi-look and multi-sensor ATR systems.

Citation Download Citation

Kyle J. Erickson and Timothy D. Ross "A testbed for architecture and fidelity trade studies in the Bayesian decision-level fusion of ATR products", Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670U (7 May 2007); https://doi.org/10.1117/12.719620

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