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Proceedings Paper

Bayesian probabilistic inference for target recognition
Author(s): Kuo-Chu Chang; Jun Liu; Jing Zhou
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Paper Abstract

The objectives of the current research in target recognition are to determine techniques for understanding the nature and special features of a target and use those to develop specific identification techniques. Bayesian networks have received much attention as an efficient way of combining evidences from different sources and reasoning under uncertainty. For target recognition, a Bayesian network built from the models involves both discrete and continuous variables. In this paper, an efficient algorithm based on stochastic simulation is proposed which has the following important features: (1) it can handle a generic network with non-linear, non-Gaussian, discrete-continuous, and arbitrary topology; (2) it can pre-compute and store evidence likelihood functions for a set of Bayesnets in the library; and (3) it can efficiently compute the results incrementally with the capability of cache. A method to construct a Bayesian network from a given training database is also introduced. Simulation examples with SAR data for ATR are presented.

Paper Details

Date Published: 14 June 1996
PDF: 8 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243157
Show Author Affiliations
Kuo-Chu Chang, George Mason Univ. (United States)
Jun Liu, George Mason Univ. (United States)
Jing Zhou, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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