Master thesis project
Share
Job Location:
Eindhoven - Netherlands
Monthly Salary:
Not Disclosed
Posted on:
04-11-2025
Vacancies:
1 Vacancy
Job Summary
Radar Data Compression through Model-based Deep Learning
Introduction
Automotive radar sensors are required to adhere to the functional and safety standards meaning that these sensors are required to operate with high update rates (>25 Hz) and high resolution in range Doppler and angular domains. The significant dynamic range of radar signals promotes the use of high-performance analog-to-digital converters (ADCs). All these factors lead to an enormous amount of data collection for a single radar frame i.e. generally tens of gigabits per second (Gb/s). This increases both the on-chip memory and data transfer expenses. The student will look into enhanced deep learning methods that can encode and decode radar data having real-time and memory constraints.
Scope
Data compression is being applied in a broad range of applications. Each application may use different approaches to compress data by exploiting application-specific features in the contrast JPEG for RGB images use for example down-sampling block splitting and a discrete cosine transform to enable a lossy compression. Deep learning is commonly applied nowadays and has shown outstanding performance in some tasks; hence also in the context of data compression in the following domains: communications 1 medical imaging 2 seismic sensing 3 and SAR imaging 4. Likewise in automotive radar sensing similar and more sophisticated/pruned methods can be applied. The student is free to define the architecture of the encoding and decoding modeland exploit radar-specific featuresto further reduce the data rate.
#LI-0d06
Key Skills
- Dermatology
- Communication
- Excel
- Furniture
- Airlines
- Jboss
About Company
NXP is a global semiconductor company creating solutions that enable secure connections for a smarter world.
