NewSense project evaluates the use of low-cost innovative sensors for use in A-SMGCS and proposes two Surveillance solutions; the 5G Surveillance solution, including a cooperative 5G signal based surveillance sensor and a non-cooperative Synthetic-aperture radar (SAR) imaging with 5G signals sensor, and the mmWave Surveillance solution augmented with Artificial Intelligence for non-cooperative targets positioning and classification.
The proposed 5G Surveillance Solution works in two configurations:
• An uplink (UL) configuration, with base stations equipped with 3D VA antennas and positioning of the user equipment (UE) done at the base station side.
• A downlink (DL) configuration, with UE equipped with 3D VA antennas and positioning of the user equipment (UE) done at the UE side.
5G positioning using both angle and time estimates from different 5G reference signals in both DL and UL directions are promising for secondary airport surveillance systems. For the timing estimates, correlation results of reference signals are to be used with the assumption of receiver system having some prior information about these reference signals. For the angle estimates, 3D-VA antenna structures are used due to their direction detection capabilities. The output of these antenna structures is processed by subspace-based MUSIC AoA estimation algorithm to estimate the angles. 5G positioning explored the combination of 3D-VA and innovative signal processing techniques based on time and angle measurements, as well as Machine Learning based Line of Sight (LOS) detection mechanisms for use in airport surveillance. The performances obtained with the 5G Surveillance solution are promising especially for LOS scenario that concerns mainly the Manoeuvring area and taxiways as there are less obstacles and thus less multi-path near these areas (depending on the position of the Base Station relative to these areas). The performances decrease significantly for Non LOS (NLOS) which could concern mainly apron and stands area.
The use of mmWave radar for positioning is a promising complementary tool in future airport surveillance. The mmWave Radar Surveillance Solution is based on the use of a non-cooperative mmWave radar to position and detect the type of targets (e.g. aircraft, vehicle and person) on the airport surface using mmWave signals and Machine Learning. The technology is based on a Frequency Modulated Continuous Wave (FMCW) mmWave radar operating in the 77 – 81 GHz frequency band. The mmWave radar includes the RF front-end, the ADC and processing modules used for positioning, velocity calculation and classification using Machine Learning algorithms. The maximum range of the radar depends on the radar configuration, the Radar Cross Section (RCS) of the target and the radar transmit power. The mmWav radar used within this study has a transmit power up to 12 dBm (16 mW) and it was able to detect a truck up to 137 meters (as example, typical Surface Movement Radar (SMR) has a transmitting power superior to 180 Watt and its coverage is between 150m to 2500m). We estimate that Commercial aircraft could be detected at a range of 200 meters within the same conditions and we assume that a maximum range of 500 meters can be reached with a transmitted power of 1 Watt. Measurements demonstrate that the mmWave radar offer a high accuracy which makes possible to differentiate two objects that are close and to detect accurately some events such as ATOT, ALDT, AIBT, AOBT, etc. Machine Learning applied to radar data makes possible to classify the target. An accuracy of 90% could be achieved using the range-angle heatmaps with YOLOv4 network.
NewSense project participated to multiple dissemination events to present the project objectives and results (D5.3).
