You are here

AI/ML Techniques for ADS-B to Support Base Security and Defense

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-P-0588
Agency Tracking Number: FX20A-TCSO1-0109
Amount: $49,762.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF20A-TCSO1
Solicitation Number: X20.A
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-06
Award End Date (Contract End Date): 2020-06-04
Small Business Information
PO Box 3426
Lawrence, KS 66046-1111
United States
DUNS: 108339966
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Tom Sherwood
 (785) 979-1113
 tom.sherwood@kalscott.com
Business Contact
 Suman Saripalli
Phone: (785) 979-1116
Email: suman.saripalli@kalscott.com
Research Institution
 University of Missouri-Kansas City
 Jesse Beaudin
 
574 Flarsheim Hall, 5110 Rockhill
Kansas City, MO 64110-0000
United States

 (785) 341-9684
 Nonprofit college or university
Abstract

We are developing a low-cost, ad-hoc ground network of sensors to monitor air traffic around specific areas, for example, to support security and defense around air bases and other installations. This can be seen an air traffic monitoring system in a box, which can be rapidly deployed in an ad-hoc manner, both on the ground and on aerial (manned and unmanned) nodes. The system generates millions of data points per day, which can be mined using Artificial Intelligence and Machine Learning (AI/ML) techniques to identify anomalous behavior of manned and unmanned air traffic. For example, spoofed air traffic (ADS-B) messages (underlying an area denial attack), emergent air routes (for trafficking, illegal entry), etc. In addition, this can also be done from space-based nano satellite sensors, and other open/crowd-sourced data. 

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

US Flag An Official Website of the United States Government