Automated Generation of Electronic Warfare Libraries

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00024-13-P-4583
Agency Tracking Number: N131-036-0948
Amount: $79,996.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solitcitation Year: 2013
Solitcitation Topic Code: N131-036
Solitcitation Number: 2013.1
Small Business Information
Lakota Technical Solutions, Inc.
PO Box 2309, Columbia, MD, -
Duns: 040326220
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 William Farrell
 Director of Information F
 (410) 381-9780
 jim.farrell@lakota-tsi.com
Business Contact
 J. Pence
Title: President
Phone: (410) 381-9780
Email: rob.pence@lakota-tsi.com
Research Institution
N/A
Abstract
Metrological differences between tactical sensors result in a wide range of accuracy, completeness, and correctness of features used for object classification. Thus, it is typical practice for Subject Matter Experts (SMEs) to develop the required reference libraries so that they are tailored to each tactical sensor type and, in some cases, sensing environments. The process of developing this tailored library is often called"coloring"the reference library. The SMEs objective is to optimize the classification performance (w.r.t. some metric(s)) using a feature-based classifier. The proposed Hierarchical Emitter Library Optimization (HELO) technology mimics the coloring process in order to generate an Emitter Library that optimizes the classifier performance while avoiding the need for a labor-intensive manual process that requires SME knowledge. HELO employs a general Hierarchical Evolutionary Programming (H-EP) based upon the Genetic Programming (GP) optimization paradigm to achieve this optimization. This approach provides a computationally scalable process that rigorously quantifies the performance of the classification algorithms without knowledge of its algorithms. Using the performance assessment of the classifier, a large set of potential emitter libraries (population) is iteratively refined (evolved) until an optimal (or sufficiently good) emitter library is generated. The solution is hierarchical because the evolution of the population is achieved jointly in two stages: (1) evolution of the coloring functions to generate a parameter library for a particular parameter type and (2) evolution of the set of parameters used by the classifier.

* information listed above is at the time of submission.

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