Unmanned Aerial Systems (UASs) are becoming increasingly popular for amateur use, but their arbitrary deployment poses severe public safety threats to critical infrastructures, such as airports. Typically an Amateur Unmanned Aerial System (AUAS) communicates with a ground control station (GCS) through a telemetry radio, which keeps transmitting data in poor connection conditions. The accuracy of AUASs detection is of great significance. In this paper, we propose a novel surveillance framework which leverages Surveillance Unmanned Aerial Systems (SUASs) to detect AUASs. The approximate position of an AUAS is first estimated by Ground Surveillance Nodes (GSNs) with radio receivers, and SUASs are then activated to determine its precise position. Different from previous research, this framework not only leverages both ground and aerial surveillance capabilities, but also integrates both radio and image processing techniques, thus achieving enhanced AUAS detection capability. This platform has the potential to be integrated with other advanced technologies, providing the recognition of radio signals and imagery for a holistic solution of effective AUAS detection.
Unmanned aerial vehicles (UAVs), commonly known as drones, have the potential to enable a wide variety of beneficial applications in areas such as monitoring and inspection of physical infrastructure, smart emergency/disaster response, agriculture support, and observation and study of weather phenomena including severe storms, among others. However, the increasing deployment of amateur UAVs (AUAVs) places the public safety at risk. A promising solution is to deploy surveillance UAVs (SUAVs) for the detection, localization, tracking, jamming and hunting of AUAVs. Accurate localization and tracking of AUAV is the key to the success of AUAV surveillance. In this article, we propose a novel framework for accurate localization and tracking of AUAV enabled by cooperating SUAVs. At the heart of the framework is a localization algorithm called cooperation coordinate separation interactive multiple model extended Kalman filter (CoCS-IMMEKF). This algorithm simplifies the set of multiple models and eliminates the model competition of each motion direction by coordinate separation. At the same time, this algorithm leverages the advantages of fusing multi-SUAV cooperative detection to improve the algorithm accuracy. Compared with the classical interacting multiple model unscented Kalman filter (IMMUKF) algorithm, this algorithm achieves better target estimation accuracy and higher computational efficiency, and enables good adaptability in SUAV system target localization and tracking.
An intelligent transportation system (ITS) is one typical cyber-physical system (CPS) that aims to provide efficient,
effective, reliable, and safe driving experiences with minimal congestion and effective traffic flow management. In order
to achieve these goals, various ITS technologies need to work synergistically. Nonetheless, ITS’s reliance on wireless
connectivity makes it vulnerable to cyber threats. Thus, it is critical to understand the impact of cyber threats on ITS. In
this paper, using real-world transportation dataset, we evaluated the consequences of cyber threats – attacks against service
availability by jamming the communication channel of ITS. In this way, we can have a better understanding of the
importance of ensuring adequate security respecting safety and life-critical ITS applications before full and expensive real-world
deployments. Our experimental data shows that cyber threats against service availability could adversely affect
traffic efficiency and safety performances evidenced by exacerbated travel time, fuel consumed, and other evaluated
performance metrics as the communication network is compromised. Finally, we discuss a framework to make ITS secure
and more resilient against cyber threats.