Predicting Aircraft Intent in the Terminal Area using ATC Operations Knowledge and Run-time Information

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-11-M-3125
Agency Tracking Number: F103-007-2183
Amount: $99,991.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2010
Solicitation Topic Code: AF103-007
Solicitation Number: 2010.3
Small Business Information
Barron Associates, Inc.
1410 Sachem Place, Suite 202, Charlottesville, VA, -
DUNS: 120839477
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Alec Bateman
 Principal Research Scientist
 (434) 973-1215
 bateman@bainet.com
Business Contact
 Connie Hoover
Title: General Manager
Phone: (434) 973-1215
Email: barron@bainet.com
Research Institution
 Stub
Abstract
A key barrier to expanding the missions performed by UASs is ensuring safe interoperation with other manned and unmanned vehicles. In critical avoidance scenarios, UASs in autonomous flight do not currently employ the same level of situational awareness and decision-making offered by a human pilot in a manned aircraft. A number of research groups have been working to develop sensors and algorithms to provide UASs with an autonomous sense and avoid (SAA) capability. Many of these approaches are based on extrapolating trajectories of nearby vehicles from current and recent state measurements. An opportunity exists to enhance the effectiveness of current SAA technologies in the terminal area by leveraging knowledge from sources such as published procedures, current conditions, and ATC instructions, in addition to the current trajectories of nearby aircraft. Barron Associates proposes a Phase I research effort to develop intent analysis algorithms that combine run-time observations with prior models of expected behavior based on detailed knowledge of standard terminal area operating procedures. The proposed approach will use statistically rigorous methods to detect when aircraft are violating the rules, so that intent predictions can be revised accordingly. The result will be reliable detection of conflicts with low false alarm rates. BENEFIT: The proposed intent prediction methods represent a key enabling technology for integrating UASs into terminal area operations both in the national airspace and in war zones. Safely integrating UASs in airspace with manned vehicles as well as other unmanned craft will greatly expand the missions that can be performed by UASs, both for military and non-military operators. The greater autonomy afforded by the technology will also reduce operator workload, allowing an operator to manage more vehicles and/or perform higher level tasks such as analyzing data collected by an UAS. The potential market for the technology is quite large. As of 2008, the Department of Defense was reported to have nearly 6,000 UASs in its inventory and the number continues to increase. Any of these vehicles that operate from airfields potentially shared with other air vehicles (most medium and large UASs, and some small vehicles) will benefit from the technology.

* information listed above is at the time of submission.

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