Today's ATR is constructed via inefficient and suboptimal system configuration and training. The process of configuring an ATR is currently very labor intensive, subjective, and inaccurate, as is the process of training an ATR for a particular mission. To cure this deficiency, what is desired is an automated method of configuration and training which is capable of searching the N-dimensional space of modules, algorithms, and parameter values to produce ATR algorithm suites which perform best in each trained scenario. Also, today's ATR is only capable of a limited amount of adaptation to sensed (or otherwise obtained) changes in the environment. To improve the adaptibility of ATR processing and thereby improve accuracy and robustness, what is desired is a high-level control structure which enables system adaptation via changes in parameter values and changes in algorithms (at the component and at the 'suite' level). The Honeywell effort is producing a system for Adaptive Configuration and Control (ACC) of an ATR system which addresses the above described problems. The software system is using the machine learning technique of Genetic Algorithms to autonomously and optimally perform configuration and training and it is using case-based reasoning to provide run-time configuration and control of the ATR system. This paper provides an overview of the ACC system, describes its operation, and describes the benefits it provides to ATR systems.

Date Published: 20 June 1995
PDF: 12 pages
Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); doi: 10.1117/12.211338

Published in SPIE Proceedings Vol. 2496:
Detection Technologies for Mines and Minelike Targets
Abinash C. Dubey; Ivan Cindrich; James M. Ralston; Kelly A. Rigano, Editor(s)