Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.publishedVersionPeer reviewe

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

5G Positioning Via AoA and ToA Estimates in Secondary Airports

Positioning parts within Advanced-Surface Movement Guidance and Control Systems (A-SMGCS) is challenging for the current positioning technologies due to strict availability, reliability, and update rate requirements. In addition, the high cost of existing A-SMGCS solutions used in primary airports has made it challenging for secondary (small to medium-sized) airports to implement these systems. Therefore, cost-efficient alternative positioning techniques for secondary airports are an essential area of research in the aviation sector. The fifth-generation network 5G has significant potential as a low-cost solution for these airport positioning scenarios considering their large-scale deployment in the near future. This master’s thesis focuses on 5G time of arrival (TOA) and angle of arrival (AOA) positioning performance analysis for downlink (DL) and uplink (UL) direction through MATLAB simulations with various channel models combined with line-of-sight (LOS) and non-line-of-sight (NLOS) propagation scenarios. In the simulations, Positioning Reference Signal (PRS), Sounding Reference Signal (SRS), and Channel State Information Signal (CSI-RS) which are the reference signals standardized for 5G, are used. TOA estimates are calculated using correlation results with reference signals assuming the receiver has prior information about reference signal configurations. AOA estimates are computed by processing phased array antenna outputs by subspace-based Multiple Signal Classification (MUSIC) AOA estimation algorithm. To analyze the effect of noise and multipath on positioning performance, average white Gaussian noise (AWGN) channel, 5G Tapped Delay Line (TDL) models, 5G Clustered Delay Line (CDL) models, and WINNER II channel models are employed. The findings indicate that the designed solution based on the 5G reference signals has a significant potential for secondary airport positioning. Also, the impact of various factors such as 5G signal configurations, multipath, and NLOS transmission are analyzed

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers

Detection and Microwave Imaging of Conducting Objects Buried Very Closely to the Air-Soil Boundary

Down-looking Ground Penetrating Radar (GPR) is an ultra-wideband electromagnetic sensor which has important applications such as IED and landmine detection, locating people in earthquake rescue operations, detection of archeological sites, mapping ice thickness or quantification of sedimentary structures in geophysical applications. The very first and important step in target detection by GPR is the removal of ground reflections caused by the air-soil boundary as these undesired signals are usually much stronger than the signals reflected and scattered from the buried targets. Ground reflections are well-known for their deteriorating effects on detection rate and false alarm rate in GPR applications. When a target is buried in a reasonable depth such as five centimeters or more, the ground reflections and the first returns from the buried object can be well separated in time, thus the removal of ground reflections turns out to be a standard procedure. However, if the burial depth is very small, the early returns from the target may be mistakenly removed together with the ground reflections. In such a case, a shallowly buried conducting target may go completely unnoticed. In this study, we will investigate the problem of detection and imaging of various conducting targets which are buried only one centimeter below the air-soil interface. The test targets are chosen to be a water-filled rectangular prism made of plastic; a thin rectangular prism coated by aluminum foil; two metal rods of the same length one with circular cross-section and the other one with a square shaped cross-section. After GPR-based measurements are recorded for these targets, a preprocessing method based on energy features and background removal will be used to eliminate air-ground reflections from the raw GPR A-Scan signals. C-Scan data sets, which are the collections of measured A-Scan signals recorded in cross-track and down-track directions, will be used for subsurface microwave imaging to sense the presence of the buried targets, and to figure out their shapes, if possible

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