LEARNING-BASED APPROACH FOR RELEVANT DATA EXTRACTION (LARDE)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-P-1186
Agency Tracking Number: N13A-016-0331
Amount: $79,862.00
Phase: Phase I
Program: STTR
Awards Year: 2013
Solicitation Year: 2013
Solicitation Topic Code: N13A-T016
Solicitation Number: 2013.A
Small Business Information
555 Quince Orchard Road, Suite 300, Gaithersburg, MD, -
DUNS: 121257443
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Alberto Lacaze
 President
 (240) 631-0008
 lacaze@roboticresearch.com
Business Contact
 Alberto Lacaze
Title: President
Phone: (240) 631-0008
Email: lacaze@roboticresearch.com
Research Institution
 Southwest Research Institute
 Michael Ladika
 6220 Culebra Rd.
San Antonio, TX, 78238-5166
 (210) 684-5111
 Domestic nonprofit research organization
Abstract
Autonomous systems continue to be outfitted with larger amounts of sensors that are capable of collecting extremely large amounts of data over the course of a mission. Even autonomous systems with high storage capacities can run into storage limitations when burdened with large amounts of sensor data over long mission durations. This proposal will develop a Learning-based Approach for Relevant Data Abstraction (LARDA) from a set of sensors that produce a large volume of raw data on-board an autonomous system. LARDA will generate a data abstraction and handling framework that is generic enough to be useful for a variety of current and future autonomous systems, but specific enough to directly support missions fielded with autonomous systems in the near-term. The core algorithms of this framework will comprise both supervised and unsupervised machine learning techniques to extract, cluster, and label relevant features from sensor data that can support planning and decision-making for future autonomous missions.

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

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