Biological aerosol field trial: Exploring a UAS biological collection point sensor and its interoperability with standoff technology

Defence Research and Development Canada (DRDC) is actively pursuing solutions in detection and identification of Chemical, Biological, and Radiological (CBR) agents utilizing a variety of techniques. The techniques used to provide advanced warning of a biological threat can range from fixed point sensor arrays, fixed stand-off technologies, and point sensors that have been integrated into Uncrewed Ground and Aerial Vehicles (UGV/UAV). Military personnel desire a rapid and concise understanding of the threat environment with field forward technologies in order to pursue the most appropriate path to mission success. The Suffield Research Center (SRC) held a biological aerosol field trial to explore the performance of a custom biological sampling point sensor designed for Uncrewed Aircraft System (UAS) operations and its interoperability with LIDAR-based standoff technology against biological threat simulants Bacillus atrophaeus (BG) and Ovalbumin (OV). In total, 25 releases of BG and OV in varying concentrations were performed over the four-day field trial to understand the interplay of tasking mobile point sensors from the standoff technology. Each specific trial also tried to determine the optimal placement of the UAV in the dispersal cloud, its duration in the cloud, and the relationship between the real-time information from the sensor on the UAV and the collection filter. The standoff capability detected and tracked each of the generated clouds, and relayed multiple waypoints corresponding to the clouds' highest concentration to the UAS operator. The UAV payload collected BG on the filters in each release in varying concentrations, however, the identification success rate using field-forward technologies varied between 50% and 100%. The approach to use total particle count rate as a method to infer total biological material collected without monitoring background levels was insufficient; further trials will determine the best method using particle size distribution and UltraViolet Laser-Induced Fluorescence (UV-LIF) data to optimize collection.