Improved Sensing and Positioning via 5G and mmWave radar for Airport Surveillance

This paper explores an integrated approach for improved sensing and positioning with applications in air traffic management (ATM) and in the Advanced Surface Movement Guidance & Control System (A-SMGCS). The integrated approach includes the synergy of 3D Vector Antenna with the novel time-of-arrival and angle-of-arrival estimate methods for accurate positioning, combining the sensing on the sub-6GHz and mmWave spectrum for the enhanced non-cooperative surveillance. For the positioning scope, both uplink and downlink 5G reference signals are investigated and their performance is evaluated. For the non-cooperative sensing scope, a novel 5G-signal-based imaging function is proposed and verified with realistic airport radio-propagation modelling and the AI-based targets tracking-and-motion recognition are investigated. The 5G-based imaging and mmWave radar based detection can be potentially fused to enhance surveillance in the airport. The work is being done within the European-funded project NewSense and it delves into the 5G, Vector Antennas, and mmWave capabilities for future ATM solutions.acceptedVersionPeer reviewe

Use of 5G and mmWave radar for positioning, sensing, and line-of-sight detection in airport areas

acceptedVersionPeer reviewe

A runway exit prediction model with visually explainable machine decisions

A growing number of machine learning (ML) enabled tools and prototypes have been developed to assist air traffic controllers (ATCOs) in their decision-making process. These ML tools can facilitate faster and more consistent decisions for traffic monitoring and management. However, many of these tools utilize models, where machine made decisions are not readily compre- hensible to ATCO. Hence, it is pertinent to develop explainable ML model-based tools for ATCO to manage the inherent risks of using ML model-based decisions. This research investigates visually- explainable ML models for runway exit prediction for better runway management. Specifically, this research adopts local interpretable model-agnostic explanations (LIME) on XGBoost, where machine- made decisions for runway exit prediction are visualized. XGBoost achieved a classification accuracy of 94.35%, 94.17% and 80.87% on the three types of aircraft studied here, respectively. When the LIME parameters are analyzed, Lime shows the contribution of the features for each aircraft corresponding to a particular runway exit. Furthermore, the visual analysis can inform decision makers about the sources of uncertainty in runway exit prediction. Thus, this work paves the way to explainable ML-based prediction of runway exits, where the visually explainable machine decisions can provide insights to ATCO for effective runway management and planning of arrivals and departures. An interactive interface which visualizes machine decisions for runway exit prediction is also developed as a prototype in this paper.Civil Aviation Authority of Singapore (CAAS)National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and the Civil Aviation Authority of Singapore

Use of 5G and mmWave radar for positioning, sensing, and line-of-sight detection in airport areas

International audienceThis paper explores innovative low-cost technologies, widely used outside of Air Traffic Management (ATM), for use in airport surface surveillance. These technologies consist of a 5G-signal-based surveillance solution and a millimeter wave (mmWave) radar augmented with artificial intelligence (AI). The 5G solution is based on the combination of 3D Vector Antenna, innovative signal processing techniques, and hybridization techniques based on time-of-arrival and angle-ofarrival estimates with uplink and downlink 5G signals, as well as Machine Learning (ML)-based Line of Sight (LOS) detection algorithms. The mmWave solution is based on mmWave radar for non-cooperative target's positioning and sensing, combined with deep learning for objects classification. Standalone 5G positioning accuracy reaches m-level accuracy in LOS scenarios and it is better with downlink reference signals than with uplink ones, while it deteriorates quite drastically in NLOS scenarios. LOS detection accuracies above 84% average accuracy can be achieved with ML. The mmWave radar is tested in different scenarios (short, medium and long range) and it provides cost-effective surface surveillance up to few hundred meters (depending on the object radar cross section RCS) with ±60°field of view. The work is being conducted within the H2020 European-funded project NewSense and it delves into the 5G, Vector Antennas, mmWave, and ML/AI capabilities for future ATM solutions