27 July 1999 Application of unified evidence accrual methods to robust SAR ATR
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Proceedings Volume 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII; (1999); doi: 10.1117/12.357194
Event: AeroSense '99, 1999, Orlando, FL, United States
Abstract
During the last two decades I.R. Goodman, H.T. Nguyen and others have shown that several basic aspects of expert- systems theory-fuzzy logic, Dempster-Shafer evidence theory, and rule-based inference-can be subsumed within a completely probabilistic framework based on random set theory. In addition, it has been shown that this body of research can be rigorously integrated with multisensor, multitarget filtering and estimation using a special case of random set theory called `Finite-Set Statistics' (FISST). In particular, FISST allows the basis for standard tracking and I.D. algorithms--nonlinear filtering theory and estimation theory--to be extended to the case when evidence can be highly `ambiguous' (imprecise, vague, contingent, etc.). This paper summarizes preliminary results in applying the FISST filtering approach to the problem of identifying ground targets from Synthetic Aperture Radar data that is `ambiguous' because of Extended Operating Conditions, e.g. when images are corrupted by effects such as dents, mud, etc.
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Ronald P. S. Mahler, Ssu-Hsin Yu, Raman K. Mehra, Ravi B. Ravichandran, Stanton Musick, "Application of unified evidence accrual methods to robust SAR ATR", Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357194; https://doi.org/10.1117/12.357194
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KEYWORDS
Automatic target recognition

Synthetic aperture radar

Systems modeling

Model-based design

Fuzzy logic

Performance modeling

Detection and tracking algorithms

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