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PlumeSight: A machine learning based plume identification and classification method for LIDAR instrument data

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: W911SR-21-C-0013
Agency Tracking Number: C202-002-0001
Amount: $167,374.91
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CBD202-002
Solicitation Number: 20.2
Timeline
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-12-07
Award End Date (Contract End Date): 2021-06-13
Small Business Information
1777 Highland Drive Suite B
Ann Arbor, MI 48108-2285
United States
DUNS: 969868298
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Matthew Lewis
 (734) 276-8359
 mlewis@michaero.com
Business Contact
 John Dodds
Phone: (734) 975-8777
Email: jdodds@michaero.com
Research Institution
N/A
Abstract

LIDAR is a powerful tool for the measurement and characterization of aerosols in the atmosphere. In battlefield environments, a wide variety of aerosol constituents and plume types are present. Traditional LIDAR analysis methods aim to characterize plumes in well controlled environments with a minimum of atmospheric clutter. To extend the abilities of plume characterization and identification in cluttered environments, Michigan Aerospace Corporation proposes to combine its long history of experience in atmospheric LIDAR with its machine learning algorithm development capabilities to generate a robust and powerful real-time tool for use in the battlefield. This tool will provide real time analysis and classification of plume types as measured with atmospheric LIDAR units being operated in the field. Initial results using an in-house LIDAR demonstration system are encouraging; Phase I work will use government-furnished data as indicated in the topic description. The proposed work will allow us to validate measurement performance in more complex and relevant environments.

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

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