Using Classify-While-Scan (CWS) Technology to Enhance Unmanned Air Traffic Management (UTM)

• Published in: Drones (Basel) Vol. 6; no. 9; p. 224
• Main Authors: Gong, Jiangkun, Li, Deren, Yan, Jun, Hu, Huiping, Kong, Deyong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Air traffic control; Air traffic management; Aircraft; Airports; Algorithms; Automatic target recognition; automatic target recognition (ATR); Classification; classify while scan (CWS); Coastal environments; detection response time (DRT); drone detection radar; Drones; Machine learning; Pattern recognition; Radar beams; Radar data; Radar detection; Radar equipment; Radar systems; Response time; Situational awareness; Superhigh frequencies; Surveillance; Surveillance radar; Target recognition; Unmanned aerial vehicles; unmanned air traffic management (UTM)
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones6090224

Drone detection radar systems have been verified for supporting unmanned air traffic management (UTM). Here, we propose the concept of classify while scan (CWS) technology to improve the detection performance of drone detection radar systems and then to enhance UTM application. The CWS recognizes the radar data of each radar cell in the radar beam using advanced automatic target recognition (ATR) algorithm and then integrates the recognized results into the tracking unit to obtain the real-time situational awareness results of the whole surveillance area. Real X-band radar data collected in a coastal environment demonstrate significant advancement in a powerful situational awareness scenario in which birds were chasing a ship to feed on fish. CWS technology turns a drone detection radar into a sense-and-alert planform that revolutionizes UTM systems by reducing the Detection Response Time (DRT) in the detection unit.