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Proceedings Paper

Developing a robust integrated learning system for the modern battlefield
Author(s): Robert E. Smith; B. Ravichandran; Avinash Gandhe; Kai Pak Chan; Raman Mehra
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Paper Abstract

This paper outlines our long-term vision for integrating robust machine learning as an approach to the modern battlefield. We will develop the architecture for an Integrated Learning System (ILS) that will enable representation tools to maximize the utility of data collected by distributed sensors. This project will suggest a system for data capture, processing, retrieval and analysis and focus on the development of semantic interoperability for ontology alignment and the ability to learn from experiences, so that performance improves as it accumulates knowledge resulting in the ability to learn new object/event classes and improve its classification accuracy. To illustrate the notion of robust learning from distinct representations of sensor data from a common source, we offer an application where a LCS addresses automatic target recognition (ATR) in extended operating conditions (EOCs). The LCS-based robust ATR system performed well, resulting in powerful ATR rules that generalize over multiple feature types, with accuracy over 99% and robustness over 80%. To illustrate the notion of ontology enabling learning, we outline preliminary experiments with a network of LCSs integrating ATR via a simple vehicle ontology.

Paper Details

Date Published: 20 May 2006
PDF: 13 pages
Proc. SPIE 6228, Modeling and Simulation for Military Applications, 62280R (20 May 2006); doi: 10.1117/12.669002
Show Author Affiliations
Robert E. Smith, RESystems, Inc. (United States)
B. Ravichandran, Scientific Systems, Inc. (United States)
Avinash Gandhe, Scientific Systems, Inc. (United States)
Kai Pak Chan, Scientific Systems, Inc. (United States)
Raman Mehra, Scientific Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 6228:
Modeling and Simulation for Military Applications
Kevin Schum; Alex F. Sisti, Editor(s)

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