Advanced Algorithms for Radar Classification of Airborne Targets

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: 140D04-19-C-0024
Agency Tracking Number: F183-002-1285
Amount: $74,967.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF183-002
Solicitation Number: 2018.3
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-01-31
Award End Date (Contract End Date): 2020-01-31
Small Business Information
940 South 2000 West #140, Springville, UT, 84663
DUNS: 626017854
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr. Ivan Ashcraft
 (801) 798-8440
Business Contact
 Doug Yarrington
Phone: (801) 798-8440
Research Institution
The proliferation of Unmanned Aircraft Systems (UAS) provides many benefits and opportunities, but also introduces a number of new challenges and threats.These UAS are widely commercially available and can be operated by individuals with little training.These UAS present a threat in both commercial and military environments.For example, near airports or forest fires, low flying aircraft could collide with a small UAS.Enemy operators in a military base or outpost could use a low cost UAS for surveillance or other nefarious purposes.In addition to the problems that UAS can create in terms of a tactical threat, they can also have issues of colliding with birds.Not only can UAS have problems with birds, but birds can be nuisance on airfields as well and can interfere with the taking off and landing of various aircrafts.A key to mitigating the risks presented by these low-cost UAS and birds is the ability to detect them and initiate proper actions.Radar, with its all weather, day and night operation is an excellent option for providing this capability. IMSAR proposes leveraging it's proven capability with machine learning to automatically distinguish between UAS and birds.

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

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