Expert System for Radar Measurement Contamination Identification

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9101-19-P-0014
Agency Tracking Number: F182-019-0669
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF182-019
Solicitation Number: 2018.2
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-11-30
Award End Date (Contract End Date): 2019-11-30
Small Business Information
601 Hutton St, Raleigh, NC, 27606
DUNS: 148551653
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Eric Fails
 (919) 341-8241
 eric.fails@vaduminc.com
Business Contact
 Gary Edge
Phone: (919) 341-8241
Email: gary.edge@vaduminc.com
Research Institution
N/A
Abstract
Vadum will develop a target agnostic Expert Analyst Software Engine (EASE) to identify contamination in multi-band RCS measurements. EASE consists of a novel hybrid machine learning architecture leveraging a combination of data-driven deep learning and state-of-the-art computer vision features to automate and expedite the detection, localization and classification of contaminates. The envisioned machine learning architecture is a unique combination of convolutional networks, ensemble learning, and information fusion to address the specific detection, localization, classification and regression tasks associated with identifying RCS contaminants. EASE will post-process multi-band RF phase history data (frequency vs azimuth RCS data) from 60MHz through 18 GHz. Spatiotemporal correlation of the birds/bats and clutter contaminates will be exploited across simultaneous RF band range profiles. RFI contamination will be exploited independently across each RF band phase history.

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

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