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.

Date Published: 1 July 1991
PDF: 8 pages
Proc. SPIE 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling, (1 July 1991); doi: 10.1117/12.45740

Published in SPIE Proceedings Vol. 1483:
Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling
Hatem N. Nasr; Michael E. Bazakos, Editor(s)