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Proceedings Paper

Application of unified evidence accrual methods to robust SAR ATR
Author(s): Ronald P. S. Mahler; Ssu-Hsin Yu; Raman K. Mehra; Ravi B. Ravichandran; Stanton Musick
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Paper 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.

Paper Details

Date Published: 27 July 1999
PDF: 9 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357194
Show Author Affiliations
Ronald P. S. Mahler, Lockheed Martin Tactical Defense Systems (United States)
Ssu-Hsin Yu, Scientific Systems Co., Inc. (United States)
Raman K. Mehra, Scientific Systems Co., Inc. (United States)
Ravi B. Ravichandran, Scientific Systems Co., Inc. (United States)
Stanton Musick, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

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