You are here

AI-assisted Clutter Mitigation for Standoff LIDAR Plume Detection

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: W911SR-21-C-0012
Agency Tracking Number: C202-002-0018
Amount: $167,490.70
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CBD202-002
Solicitation Number: 20.2
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-11-23
Award End Date (Contract End Date): 2021-05-03
Small Business Information
20 New England Business Center
Andover, MA 01810-1111
United States
DUNS: 073800062
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christian Smith
 (978) 738-8269
Business Contact
 B. David Green
Phone: (978) 689-0003
Research Institution

Physical Sciences Inc. (PSI) proposes to develop a suite of artificial intelligence algorithms designed to discriminate airborne chemical/biological warfare agent plumes from battlefield clutter in standoff LIDAR data. The AI-assisted LIDAR clutter mitigation (ALCM) system will track all plume-type objects within the LIDAR field of regard, and employ a two-stage classification algorithm to quantify the probabilistic threat level of each plume. The ALCM will utilize a convolutional neural network to identify and characterize plumes in each LIDAR scan based on shape and concentration profile, and additional confidence refinement will be achieved through characterization of plume properties such as airborne mass and dissipation rate by performing temporal analysis of subsequent LIDAR scans with DisperseNET, PSI’s real-time dispersion modeling algorithm. The ALCM system is designed to quantify threat/non-threat confidences for each plume-like object, provide these outputs to the user in real-time, and achieve a greater than 90% threat classification probability at an operationally relevant false classification rate of 1 in 240 hours. The Phase I program will develop the CNN plume classification model, integrate the CNN model outputs to DisperseNET, and culminate in the performance characterization of the prototype ALCM system using government provided historical LIDAR data.

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

US Flag An Official Website of the United States Government