A counter-drone visualisation platform incorporating sensor-data fusion

This technical report describes the development of a counter-unmanned aircraft system platform for sensor-data fusion and visualisation. The report outlines the design, implementation and integration of sensor inputs for the effective detection, identification and tracking of both cooperative and non-cooperative unmanned aircraft, commonly known as drones, in airspace. With a focus on the direct remote identification sensing, the platform architecture is based on commonly used technologies, i.e., message broker, Python data processing backend, and Redis in-memory database. The objective was to develop a base platform that can be extended to deploy and test more sophisticated sensor-data fusion algorithms on real-time data. A first version of a sensor-data fusion algorithm was implemented for data from direct remote identification sensors using dynamic time warping (DTW). The Python implementation of DTW is discussed, highlighting its efficiency and applicability in real-time operations. Additionally, the frontend interface of the platform is described, offering a user-friendly graphical user interface for monitoring and controlling drone activity. The work concludes with the successful creation of a full-stack infrastructure utilizing open-source tools for data handling, processing, and visualisation. The frontend application incorporates the visualisation of the official Belgian geo-zones, and future developments are discussed, such as implementing geo-zoning and geo-fencing alerts.