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Aircraft Intent Inference based on Real-Time ADS-B Data Processing

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0566
Agency Tracking Number: N193-A01-0210
Amount: $1,599,971.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N193-A01
Solicitation Number: 19.3
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-04-29
Award End Date (Contract End Date): 2021-11-01
Small Business Information
2360 SW Chelmsford Ave.
Portland, OR 97201-2265
United States
DUNS: 802036496
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Jimmy Krozel
 (503) 863-0012
Business Contact
 Michelle M. Camarda
Phone: (503) 242-1761
Research Institution

This effort develops Artificial Intelligence (AI)/Machine Learning (ML) capabilities to address a variety of use cases that expand outside the current field of focus of the Department of the Navy (DON). AI/ML algorithms are developed to enable analyses of massive quantities of data in a multitude of applications with a shared focus on program and fleet success.  This effort develops solutions to the following Navy Focus Area: Integration of Automatic Dependent Surveillance – Broadcast (ADS-B) data through AI/ML Applications.  The Navy seeks to develop models and algorithms through AI/ML processes to autonomously characterize behaviors of self-reporting aircraft using ADS-B data. The behavior models and data will be used to (1) identify apparent air corridors and (2) detect anomalous behavior in support of determining aircraft intent. The required processes include pre-mission, mission deployment, and real-time monitoring.  Only during during the pre-mission phase does ML have access to massive quantities of historical data.  The appropriate ML data structures are then passed over to the Navy Application for use in the Mission Deployment phase.  During deployment, a limited amount of adaptation of the ML data structures is possible.  The deployed capability provides timely input to a process where Real-Time Monitoring can apply ML anomalous behavior detection to thereafter invoke AI models for aircraft intent inferences.

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

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