Extendible Sensor-software Platform for Detection, Classification, and Tracking of UAS (ESP for DeteCT)

Unmanned aerial systems (UAS) pose one of the greatest threats to the future fighting forces. The number of nation-states employing UAS has risen by 70% since 2010, and military analysts are calling this proliferation the ōsecond drone age.ö UAS are inexpensive, readily available, and easy to operate. Additionally, very few resources are needed to effectively weaponize small UAS, making these systems an ideal attack vector for terrorists and insurgents. Because of the danger UAS pose, several military and national security organizations are investing in S&T to address this rising threat, including the Army Futures Command and the newly formed Joint Counter-small Unmanned Aircraft Systems Office. One major challenge in counter-UAS (C-UAS) operations is that these systems are hard to detect, classify, and track. UAS come in various form factors, ranging from quadrotor toy drones to full-sized aircraft. As such, no single sensor or algorithm can enable robust C-UAS technologies. Future C-UAS operations will require a suite of sensors and algorithms that adapt in real-time to adjust for mission-specific conditions. A viable C-UAS technology necessitates sensors and software algorithms that change which sensors, sensor modalities, and algorithms are employed based on the operational environment. With minimal operator input, this sensor-software platform must adjust for operational conditions, including environment type (desert, forest, urban, etc.) and environmental conditions (rain, snow, etc.).Ā Assured Information Security (AIS) and the University of Buffalo (UB) propose the Extendible Sensor-software Platform for Detection, Classification, and Tracking of UAS (ESP for DeteCT) project to address the need for cutting-edge C-UAS technologies. The ESP for DeteCT project will research the state-of-the-art in multi-sensor solutions for UAS detection, classification, and tracking. ESP for DeteCT will also investigate the feasibility of an open platform for integrating sensors and algorithms for C-UAS operations. The results of these studies will include observations on the potential performance of this system across environments and its capabilities for detecting, classifying, and tracking UAS. The final ESP for DeteCT white paper will include the following Chapters: 1. An overview of the C-UAS problem. 2. An overview of multi-sensor approaches to object detection, classification, and tracking that highlights emergent techniques in AI/ML and context-aware sensor fusion. 3. A technical report on the sensors and algorithms currently used for C-UAS operations. 4. An analysis of the predicted performance of an open sensor platform for C-UAS based on the findings of the preceding chapters. 5. An analysis of the predicted capabilities of an open sensor platform for detection, tracking, and classification of a range of UAS platforms. 6. A discussion on developing, enhancing, and fielding an open sensor platform and algorithms for near-future C-UAS operations.