Machine Learning for Robust Automatic Target Recognition

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-04-M-1659
Agency Tracking Number: F041-230-2192
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2004
Solicitation Year: 2004
Solicitation Topic Code: AF04-230
Solicitation Number: 2004.1
Small Business Information
Suite A, 75 Aero Camino, Goleta, CA, 93117
DUNS: 054672662
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Kevin Sullivan
 Vice President
 (805) 968-6787
 ksullivan@toyon.com
Business Contact
 Marcella Lindbery
Title: Director of Contracts
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
N/A
Abstract
Toyon Research Corporation and Dr. David Miller from The Pennsylvania State University propose to develop robust ATR algorithms for the classification of ground vehicles. Additionally, we propose to apply the same underlying technology to improve the robustness of feature-aided tracking (FAT). Our approach is based on recent advances in robust classifier design using semisupervised learning. That is, the classifier processes a set of data where some items have labels and some do not. Our approach can automatically recognize targets that are not part of the dataset used to train an ATR. Furthermore, it can make mappings between labeled data, such as synthetic signatures, and unlabeled measured signatures that are collected in the field. For FAT, our algorithms apply to both class-dependent and class-independent approaches and they allow for the development of new approaches which combine the benefits of both. In Phase I, we will focus on the application of our algorithms to high-range-resolution GMTI range profiles. We will train an ATR and evaluate its performance including the ability of the ATR to recognize targets that are not in the training set. We will finish with a final report and a plan for a Phase II effort.

* Information listed above is at the time of submission. *

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