Low-altitude UAV Communication and Tracking (LUCAT)

The LUCAT project aims to develop advanced and robust algorithms to detect and accurately trace rapidly moving UAVs, popularly called drones. Most of the research will take place at the University of Agder’s WISENET Center and the Robert Bosch Centre for Cyber-Physical systems, IISc Bangalore, India. This is the only project where the prestigious Indian University IISc collaborates with a Norwegian university in relation to the areas of signal processing, communication technology and machine learning.

Full project name: Low-altitude UAV Communication and Tracking (LUCAT)

Funding: Researcher Project, INDNOR Program, Research Council of Norway

Principal Investigators: Prof. Linga Cenkeramaddi, Prof. Baltasar Beferull-Lozano, Prof. Daniel Romero.

Topic: The LUCAT project funded by IKTPLUSS-INDNOR (Joint Indo-Norwegian researcher projects within Information and Communication Technology) develops the technology for both communication and precise tracking of both manned and unmanned aerial vehicles operating in low-altitude corridors. The Intelligent Signal Processing & Wireless Networks (WISENET) group at University of Agder, Campus Grimstad, Norway in collaboration with Robert Bosch Centre for cyber-physical systems at the Indian Institute of Science, Bangalore, India, will jointly design, develop and implement the proposed technology. This project aims to detect and precisely track multiple rapidly moving unmanned aerial vehicles using smart radar sensors, as well as novel signal processing and wireless communication algorithms. New methods will be developed also to classify the objects in the flight corridors and the communication modules located within the unmanned aerial vehicles will be advanced software defined radio modules with the ability to sense on-the-fly the radio-frequency environment, leading to discovery of opportunities of communications (what is called spectrum cognizant communications). The tasks of tracking and communication will be cooperating and enhancing each other, improving substantially the performance of tracking and classification, as compared to the currently existing solutions.

Participant Institutions: WISENET-UiA, IISc Bangalore, University of Texas, Austin

Period: 2018 – 2021