Neural networks for ATR parameters adaptation

Hossien Amehdi; Hatem N. Nasr

doi:10.1117/12.45740

1 July 1991 Neural networks for ATR parameters adaptation

Hossien Amehdi, Hatem N. Nasr

Proceedings Volume 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling; (1991) https://doi.org/10.1117/12.45740
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

The performance of complex signal processing systems such as Automated Target Recognition (ATR) systems can be dramatically improved by adjusting the system parameters in a dynamic fashion. One of the critical problems in ATR systems is their inability to adapt to changes in the scene and the environment. ATR parameters adaptation techniques have been the focus of many ATR researchers. In this paper a back-propagation neural network (NN) architecture for automatically adapting certain critical parameters in an ATR system is described. The NN uses as input certain image and scene descriptors called 'metrics.' The output of the NN is the suggested values of the ATR parameters. The authors show some preliminary results of their NN approach and discuss the trade-offs between that approach and alternative approaches.

Citation Download Citation

Hossien Amehdi and Hatem N. Nasr "Neural networks for ATR parameters adaptation", Proc. SPIE 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling, (1 July 1991); https://doi.org/10.1117/12.45740

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