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Passive Radar for UAV Detection using Machine Learning

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Passive radar via machine learning project.

Short Description

In order to satisfy the growing demands to reliably detect illegally used unmanned aerial vehicles (UAVs), this project will investigate the feasibility of UAV detection by passive radar techniques. The idea is to exploit existing long-term evolution (LTE) downlink signals (or other communication signals, such as DAB or DVB-T) and use machine learning techniques to detect the presence of UAVs without actively transmitting any signals. The project will be carried out in collaboration with the Swiss startup company YOTASYS [2] and develop signal processing as well as machine learning algorithms for a software-defined radio passive radar system. The project also comprises preliminary experimental results and a fundamental analysis of the concept with the goal of verifying the effectiveness and practicability of such a system, which will answer the question of whether reliable UAV detection is indeed possible via machine-learning-based passive radar.

[1] Y. Dan et al., “LTE-Based Passive Radar for Drone Detection and its Experimental Results,” Journal of Engineering, Nov. 2019


Status: Available

Looking for 1-2 Semester/Bachelor/Master students
Contact: Christoph Studer


Basic understanding of radar and signal processing
Basic understanding of wireless communication
Basic understanding of machine learning


20% Literature research
30% Theory
20% System-level simulation
30% Practical setup and tests


Christoph Studer

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Detailed Task Description

Practical Details


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