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A Metadata Management and Visualization System for Radio Frequency Activity Modeling and Pattern Recognition

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0306
Agency Tracking Number: N182-138-0114
Amount: $749,999.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N182-138
Solicitation Number: 18.2
Timeline
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-25
Award End Date (Contract End Date): 2021-09-30
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Genshe Chen
 CTO
 (301) 515-7261
 gchen@intfusiontech.com
Business Contact
 Yingli Wu
Phone: (949) 596-0057
Email: yingliwu@intfusiontech.com
Research Institution
N/A
Abstract

In current Naval Communications Intelligence operations, significant volumes of potentially valuable, “non-analyzable”, intercepted data are discarded. By using an Automated Radio Frequency Activity Modeling and Pattern Recognition (RF-AMPR) system, operators may be able to gain critical insights into RF activities from this discarded data. This capability would be a key enabler for Naval Intelligence. It allows radio fault detection, spectrum interference monitoring, dynamic RF pattern recognition, clustering and tracking of emitters, and anomaly detection. The main objective of this effort is to develop technical underpinnings and methodologies, as well as a workable prototype, for RF activity modeling and pattern recognition to address the challenging problems in the dynamic and dense RF environments. Specific technical objectives of the proposed metadata analysis, management, and visualization system (MDAMS) include: develop a generative model for the RF activities to generate metadata; design a metadata management system to efficiently retrieve, organize, and display the RF activity; cluster detections into contacts for emitters localization and tracking; develop a pattern learning and recognition module for anomaly detection; develop a friendly operator interface for situation monitoring; and demonstrate the performance of the proposed system with realistic and pressing scenarios using simulated and real metadata.

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

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