You are here


Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA120P0017
Agency Tracking Number: T192-002-0040
Amount: $162,216.68
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DTRA192-002
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-12-17
Award End Date (Contract End Date): 2020-07-16
Small Business Information
3600 Green Court Suite 600
Ann Arbor, MI 48105
United States
DUNS: 009485124
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Chris Kawatsu
 Research Scientist
 (734) 887-7625
Business Contact
 Laura Schwennesen
Phone: (734) 887-7683
Research Institution

SoarTech proposes DeepCounter: a system that automatically learns UAS and C-UAS behaviors across multiple tasks using DRL within a 3D simulation environment. Our system addresses several gaps in applying state of the art DRL to learn C-UAS and UAS behaviors. First, our existing DRL framework extends the state of the art to work with variable numbers of heterogenous agents. Our work in Phase I will extend the framework to scale across multiple tasks and multiple agents. Second, we will design efficient state and action representations which improve data efficiency, allowing DeepCounter to deliver behaviors faster than conventional DRL approaches. Third, we will use flexible exploration algorithms (agnostic to simulation type) which allow DeepCounter to learn complex behaviors with an infrequent reward. On the Phase I effort, we will develop a 3D simulation environment with C-UAS scenarios, modify our existing DRL framework to simultaneously learn multiple tasks in the new simulation, learn both UAS and C-UAS behaviors within the simulation, and evaluate learned behaviors across multiple dimensions.

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

US Flag An Official Website of the United States Government