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Unmanned Aerial Systems UAS, i.e. drones, revolutionize the market for mobility based services and enable more efficient defence and security operations. Also this rapidly developing technology, however, proves to be Janus-faced. Despite their unquestionable benefits, UAS increasingly pose serious safety and security threats. Detection, tracking, and classification of small and highly agile drones, however, is one of the most challenging surveillance tasks. Only a properly designed suite of heterogeneous and mutually complementary sensor provides the required sensor data. On this basis, the key technology proves to advanced multiple sensor data fusion that provides situational awareness and the key information for assigning appropriate counter measures. We in particular focus on a novel approach and highly promising approach which has the potential of a paradigm shift in sensor data fusion: tensor decomposition based multiple sensor tracking filters. This new methodology for fusion engines is able to efficiently represent the full informational content of advanced sensors and sophisticated dynamic models for drone motion. Powerful multilinear decomposition methods for tensors are drastically reducing the computational efforts for producing high-quality tracks for dim and agile drones. Moreover, the deterministic performance characteristics of tensor decomposition based fusion have beneficial implications for systems design aspects. These advanced algorithms of multiple sensor data fusion play a key role in designing counter drone systems. In the context of C5ISR systems (Command, Control, Communications, Computer, Cyber, Intelligence, Surveillance and Reconnaissance), the technological challenges can be met, but require close cooperation between the military and police forces, research institutes and the relevant industries. In the protection of stationary equipment and mobile units in urban or open terrain, the integration of drone detection / tracking / classification in decision support systems is crucial.
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