Quantum and classical metrology for noise radar
Doctoral thesis, 2024

We are in an era of rapid advancements in quantum technology, exploring the potential of exploiting quantum phenomena for technological solutions across a wide range of applications. Quantum technologies show promise in areas such as computing, optimisation, communication, sensing, and more. Among these emerging quantum technologies, sensing has perhaps reached the highest level of maturity, with practical applications already available. Quantum radar, a concept from quantum sensing, has garnered significant attention within the radar community, due to the potential of enhancing detection sensitivity compared to classical radar.

This thesis and the appended papers explore measurement protocols for radar-like scenarios. The research spans across two areas, from the classical world to the quantum domain. On the quantum side, the viability and practicality of quantum-enhanced radar is investigated, shedding light on the origin of its potential advantages and the challenges of its realisation. Furthermore, using the tools of quantum metrology, optimal probes for radar-like parameter estimation tasks are established. On the classical side, the development and implementation of an experimental bistatic noise radar system is detailed in terms of a series of signal processing methods.

Clutter suppression

Model-based Signal Processing

Quantum Radar

Noise Radar

Quantum Fisher Information

Quantum Metrology

Kollektorn (MC2), Kemivägen 9, Göteborg
Opponent: Associate Professor Gheorghe-Sorin Paraoanu, Aalto University, Finland

Author

Robert Jonsson

Chalmers, Microtechnology and Nanoscience (MC2), Applied Quantum Physics

Experimental Evaluation of Moving Target Compensation in High Time-Bandwidth Noise Radar

20th European Radar Conference, EuRAD 2023,;(2023)p. 213-216

Paper in proceeding

Experimental Analysis of a Clutter Suppression Algorithm for High Time-Bandwidth Noise Radar

Proceedings of the IEEE Radar Conference,;(2023)

Paper in proceeding

Bistatic noise radar: Demonstration of correlation noise suppression

IET Radar, Sonar and Navigation,;Vol. 17(2023)p. 351-361

Journal article

Gaussian quantum estimation of the loss parameter in a thermal environment

Journal of Physics A: Mathematical and Theoretical,;Vol. 55(2022)

Journal article

Quantum Radar-What is it good for?

IEEE National Radar Conference - Proceedings,;Vol. 2021-May(2021)

Paper in proceeding

A comparison between quantum and classical noise radar sources

IEEE National Radar Conference - Proceedings,;Vol. 2020-September(2020)

Paper in proceeding

M. Ankel, R.S.Jonsson, M. Tholén, T. Bryllert, L.M.H. Ulander, P. Delsing, Real-Time Bistatic Noise Radar with Adaptive Beamforming

R.S. Jonsson and G Johansson, Applications of tri-squeezed states for quantum sensing of two-photon absorption

Understanding the sensitivity of the equipment and accuracy of the result are fundamental concepts in any quantitative research. Thus, metrology, the science of measurement, is central to practically all fields of technology. It tells us how to systematically estimate unknown quantities, how to quantify the uncertainties, and how to evaluate the results.

This thesis studies various theoretical aspects in the analysis of estimation and discrimination with applications to radar, addressing two research topics. The first area covers the development of an experimental noise radar system implementing model-based signal processing techniques. The second area studies quantum metrology, where the special features of quantum mechanics are exploited to enhance radar-like measurement protocols.

Wallenberg Centre for Quantum Technology (WACQT)

Knut and Alice Wallenberg Foundation (KAW 2017.0449, KAW2021.0009, KAW2022.0006), 2018-01-01 -- 2030-03-31.

Subject Categories

Other Physics Topics

Signal Processing

ISBN

978-91-8103-093-8

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5551

Publisher

Chalmers

Kollektorn (MC2), Kemivägen 9, Göteborg

Opponent: Associate Professor Gheorghe-Sorin Paraoanu, Aalto University, Finland

More information

Latest update

12/19/2024