AI-Assisted Clutter Mitigation for Standoff LIDAR Plume Detection

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.