Soort opdracht: Stage-/Afstudeeropdracht
Studierichting(en): Elektrotechniek, Toegepaste Wiskunde, Technische Natuurkunde
Titel van de opdracht:
Information-based Processing in Radar and Communications: Compressive Sensing, Information Geometry and Machine/Deep Learning
The people we all rely on to make the world go round – they rely on Thales. In a world that is increasingly fast-moving, unpredictable and full of opportunities, they come to us with big ambitions: to make life better and to keep you safer. Combining a unique diversity of expertise, talents and cultures, our employees design and deliver extraordinary high-tech solutions. With 68.000 talents working in 54 countries, 2000 employees are based in the Netherlands. We are one of the biggest high-tech employers in the field of safety and security.
We help our customers think smarter and act faster in the fields of transportation, defence, space, aerospace and cyberspace, mastering ever-greater complexity and every decisive moment along the way. We are therefore leading the digital transformation, focusing on artificial intelligence, big-data & data analytics, connectivity, mobility and internet of things and cybersecurity.
In the Netherlands, we are located in four cities: Huizen, Delft, Eindhoven and Hengelo (HQ). Together with an extensive ecosystem of knowledge partners, customers and suppliers, we work on radars for naval vessels, cyber security solutions, transportation systems, communication equipment for land forces, cryogenic cooling solutions, research & development for radar tech (in collaboration with TU Delft) and research & development for serious gaming (in collaboration with the University of Twente).
Compressive Sensing (CS) is a recent paradigm in sensing (since 2004) that works with a reduced number of measurements for a comparable sensing result. It is based on the incoherence of the sensing and sparsity of the processing results. Its major parts are: compressive data acquisition and sparse-signal processing. Most promising benefits of CS in radar are high resolution and multi-target analysis. Other specific issues in applying CS in radar or communications are: coded and sparse sensing (via waveforms and antenna arrays), noise and clutter, grid design and match, real-time implementation, etc.
Information geometry (IG) raises a new approach to stochastic signal processing (since the eighties) as its main principle is that many important structures in the stochastic signal processing can be treated as structures in differential geometry. Most promising benefits of IG have been found in resolution bounds and parameter estimation.
The importance of information in data is stressed in both fields as the useful dimension of signals is much smaller than the data dimensionality. Accordingly, conventional processing can be improved if the demands of data acquisition and signal processing are optimized to the information content (which links it also to the information theory).
IG and (Bayesian) CS can also be used in understanding of machine/deep learning (MDL), especially in the stochastic analysis of the underlying processing layers for better performance. In particular, using information distances can improve the performance.
About the assignment
Thales NL proposes an internship project whose aim is to investigate applicability of the information-based processing with emphasis on practical issues in signal processing (SP). Simulated data are to be used to demonstrate the applicability in realistic cases.
Proposed project planning:
- selecting a practical issue and its SP application(s) of interest,
- studying CS, IG and MDL, and information-specific (radar) measurements and their SP,
- (theoretically) investigating practical links between CS- IG-MDL and the application;
- implementing the CS- IG-MDL analysis for particular measurements in MATLAB,
- testing and evaluating the CS- IG-MDL applicability with simulated data, and
- reporting the CS- IG-MDL applicability in the (radar/comms) SP in a report.
- Scientific education (University) in Electrical Engineering or Applied Mathematics or Applied Physics;
- Strong background in stochastic signal processing and geometry;
- Experience in MATLAB or Python;
- Good verbal and written communication skills in English
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Please keep in mind that we can only consider students who are enrolled at a school during the whole internship period for our internships and graduation assignments.