Internship: Phase-Noise-Resilient Architectures for Large MIMO FMCW Automotive Radars
Core
Research and evaluate next-generation automotive radar architectures combining large-scale MIMO imaging and phased-array beam steering to address phase noise and multipath propagation.
Role type
Research intern (automotive radar systems)
Builds
Simulation frameworks and mathematical models for hybrid radar architectures
Domain
Automotive radar, wireless communications, signal processing
Deliverable
production ML models | research
Required skills
FMCW radar, MIMO radar, phased-array beamforming, array signal processing, FFT-based processing, MATLAB, Python
Preferred skills
DDMA processing, direction-of-arrival estimation, structured software development, Git
Technologies
MATLAB, Python, Git
Responsibilities
Investigate phase noise accumulation and multipath propagation in large MIMO FMCW radar systems; Develop mathematical models and simulation frameworks to analyze radar system performance; Research and evaluate hybrid radar architectures combining MIMO imaging with directional phased-array transmission; Explore beam-scanning strategies, DDMA-based transmission schemes, and subarray-based beamforming concepts; Design and perform simulations to compare alternative radar architectures and quantify system trade-offs.
Seniority
Intern