Bayesian probabilistic inference for target recognition

Kuo-Chu Chang; Jun Liu; Jing Zhou

doi:10.1117/12.243157

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.

Citation Download Citation

Kuo-Chu Chang, Jun Liu, and Jing Zhou "Bayesian probabilistic inference for target recognition", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243157; https://doi.org/10.1117/12.243157

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