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Through Wall Radar Imaging using Machine Learning

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Through wall radar imaging project.

Short Description

This project will investigate approaches that have been proposed to detect activities inside a building using through-the-wall radar imaging [1]. First, the project will explore the main types of radar operating in the range of 1GHz to 3GHz, which can be used for the detection of activities behind walls. Second, the project will investigate and develop new machine learning (ML) algorithms for this purpose. Third, the project will assess the efficacy of such ML-based methods using real-world data and software simulations. The project will be carried out in collaboration with the Swiss startup company YOTASYS [2].

Stepped frequency and pulse compression radar are the most commonly used types of radar in through-the-wall radar imaging (TWRI). Even though the literature describes a wide range of potentially suitable methods, many open questions persist. In particular, the detectability of humans, wall modeling, and target differentiation have been identified as the main open questions in the field of TWRI. These challenges could potentially be addressed using ML algorithms—this project focuses on exactly this aspect.

[1] P. K. Nkwari, “Through-the-Wall Radar Imaging: A Review,” IETE Technical Review, Nov. 2018


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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