Multi-Sensor Placement and Information Fusion Analysis to Enable Beyond Visual Line of Sight Operations for Small Uncrewed Aerial Vehicles

This paper describes the development of a multi-sensor placement and information fusion testbed for detection and tracking of small uncrewed aerial vehicles (sUAV). This testbed is called Mason's UAV Systems and Cyber Analysis Testbed (MUSCAT) and combines JDL Data Fusion model theoretic foundations with systems engineering principles applied to sUAV corridors design. Recent results are focused on analyzing sensor coverage with terrain models as a function of drone altitude, with a goal to identify optimal placement of multiple sensors and provide cost-performance trade-offs. The methodology is composed of four main phases: setting up the computational environment, defining sensor attributes and region of interest, computing sensor coverage for individual sensor types and respective combinations, and displaying results. An instance considering a drone corridor is used to analyze the optimal placement of short-range radars and remote identification (RID) receivers to maximize the total coverage and improve the system's resilience by track fusion with varied sensors. Results show that MUSCAT is able to generate a wide range of metrics and spatial insights, important to identify the most suitable solution based on user requirements.