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AI-Assisted Clutter Mitigation for Standoff LIDAR Plume Detection

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: W911SR-22-C-0004
Agency Tracking Number: C2-0597
Amount: $599,533.73
Phase: Phase II
Program: SBIR
Solicitation Topic Code: CBD202-002
Solicitation Number: 20.2
Solicitation Year: 2020
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-24
Award End Date (Contract End Date): 2024-02-05
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. proposes to mature a suite of artificial intelligence algorithms developed to discriminate airborne chemical/biological warfare agent plumes from battlefield clutter in standoff light detection and ranging (LIDAR) data. The AI-assisted LIDAR clutter mitigation (ALCM) system tracks all plume-type objects within the LIDAR field of regard, and employs a two-stage classification algorithm to quantify the probabilistic threat level of each plume. The ALCM utilizes a convolutional neural network to identify and characterize plumes in each LIDAR scan based on shape and concentration profile. Additional confidence refinement is achieved through characterization of plume properties such as airborne mass and dissipation rate by performing temporal analysis of subsequent LIDAR scans using DisperseNET. The DisperseNET algorithm is PSI’s real-time dispersion modeling algorithm. The prototype ALCM system demonstrated a 92% probability of threat detection and classification at an average of 301 LIDAR scans between false track output in the Phase I. The primary objective of the proposed effort is to improve and operationalize the ALCM capability through enhancement of the DisperseNET simulation engine, expansion of the training and validation datasets, and demonstration of the ALCM capability through the production of a ruggedized processing module for LIDAR integration.

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

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