In the near future, a large number of unmanned aerial vehicles, also known as drones, will pervade populated areas' skies, serving millions of people worldwide for goods transportation, construction, agriculture, medical, surveillance, search-and-rescue operations, and a variety of other applications. Daily tasks such as food or packet delivery, grocery shopping, and surveillance, among others, will be carried out by autonomous UAVs in densely populated areas, resulting in profound changes in day-to-day human life.
The LUCAT project, funded by IKTPLUSS-INDNOR (Joint Indo-Norwegian researcher projects in Information and Communication Technology by NFR and DST), is working to develop technology for accurate sensing, precise tracking, and communication of both manned and unmanned aerial vehicles operating in low-altitude corridors. We, the Autonomous and Cyber-Physical Systems Research Group at the University of Agder, Campus Grimstad, Norway, will design, develop, and implement the proposed technology in collaboration with the Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India, and the Department of Computational and Data Sciences, Indian Institute of Sciences, Bangalore, India.
Using mmWave radar sensors and other sensors, as well as novel signal processing and machine learning models, wireless communication algorithms, this project aims to sense/detect and precisely track multiple rapidly moving unmanned aerial vehicles. New methods will also be developed to classify objects in flight corridors, and communication modules located within unmanned aerial vehicles will include advanced software-defined radio modules with the ability to sense the radio-frequency environment on-the-fly, leading to the discovery of communications opportunities (what is called spectrum cognizant communications). Sensing, tracking, and communication tasks will collaborate and enhance each other, significantly improving sensing, tracking, and classification performance when compared to currently available solutions for low-altitude traffic management systems.
New techniques for the detection, localization, and classification of UAVs are developed using ground station mmWave radars. Hybrid communication schemes are explored for UAV-UAV and UAV-Ground station communications.
The LUCAT project deals with designing, development and implementation of an integrated technology of spectrum cognizant communication and tracking system for low-altitude autonomous operations in densely populated areas for both manned and unmanned aerial vehicles (UAVs).
Key objectives of LUCAT is to develop advanced, robust and computationally efficient radar signal processing algorithms to detect and precisely track rapidly moving UAVs through a flight corridor that spans multiple radar sensors and spectrum-aware communication system. A major novelty of LUCAT is to cross-fertilize these two research areas (spectrum-cognizant communication and tracking system) towards mutual performance enhancements such that location can be used to improve performance metrics of communications, whereas communications may facilitate tracking UAVs operating at low altitudes.
The project idea consists of 4 major research domains: 1) precise detection and tracking of UAVs through radar; 2) classification of objects in the flight corridor; 3) 3D spectrum cartography for ground-air-ground channels; and 4) spectrum-cognizant ground-air-ground communications.
LUCAT will tackle the challenging requirements of the tracking and communication system for low-altitude corridors and provide 1) precise tracking of high speed UAVs and multiple UAVs; 2) very low latency, in the order of 15 milliseconds to match the fast dynamics of UAVs; 3) wide coverage and very high reliability, since the loss of communication may entail risks for security; 4) support for fast mobility, and 5) fast re-synchronization if the connection is lost. These are achieved by: 1) designing reduced complexity algorithms for detection and tracking, and 2) spectrum cognizant ground-air-ground communications.