Advanced Algorithms for Aerosol Plume Characterization and Differentiation

In a battlefield or other active environment where hundreds or thousands of aerosol plumes may be generated every day, it is a challenge to identify the rare plume resulting from a deliberate dissemination. Machine learning (ML) algorithms can be trained to identify patterns and features based on training data and have the potential to distinguish between intentionally disseminated plumes that may represent a chemical/biological threat and typical environmental plumes or those generated by common movement of events that are prevalent in a battlefield setting. S3 has previously shown that a Support Vector Machine (SVM) supervised learning ML model can distinguish disseminated from non-disseminated plumes at a high rate of accuracy in a stable, low-clutter environment such as that typically found at Dugway Proving Ground (DPG). However, the SVM requires data from plumes of both types in its training dataset, and in a real-world, high-clutter environment, disseminated plumes will generally be unavailable for training the SVM. An alternative approach using an unsupervised learning anomaly detection algorithm has demonstrated promising results for both simulated and real lidar data with accurate identifications of disseminated plumes more than 90% at false positive rates <2%. In Phase I of this project, S3 teamed with Aeris to utilize their Joint Outdoor-indoor Urban Large-Eddy Simulation (JOULES) model for generating synthetic plume data from a variety of simulated sources in a realistic atmosphere. Algorithms were benchmarked using the simulated data before down-selecting for evaluation with real lidar data collected at DPG by the West Desert Lidar. S3 proposes to continue working with Aeris to generate higher fidelity simulated plume data in more complex scenarios to further evaluate the anomaly detection algorithm. Following optimization with simulated data, the algorithm will be tested with lidar data collected continuously for multiple weeks outside the S3 Albuquerque facility. This collection will allow for a range of conditions and provide data for a variety of plumes common to an urban environment. This low-cost approach will produce valuable test data for algorithm refinement and aid in the development of a real-time autonomous and adaptable training methodology. Autonomous retraining based on observed conditions and data characteristics is critical to deploying a battlefield capability rather than a post-processing detection capability. Finally, S3 will integrate this real-time anomalous plume detection computational system with the REVEAL data stream and demonstrate its operation in a relevant environment such as a port, industrial area, or other highly cluttered background. Algorithm performance will be evaluated as a function of environmental conditions and retraining parameters and reported in terms of false positive and true positive rates.