Project
Mitigation of Multipath Ghosts for Indoor MIMO Radars - Signal Modeling and Deep Learning
Since World War II, Radars have gone a long way from bulky and expensive military/space radars to miniaturized radars for various applications in the consumer, automotive and smart environments domains. The trend over the last decade has been to move to millimeter wave, supported by the constant improvement in semiconductor technology and supported by a strong demand in the automotive domain. IMEC started several years ago the development of mm-wave radars and has now a strong mm-wave radar program covering frequencies of 60GHz, 79 GHz and 140GHz, all implemented in bulk CMOS technology. The IMEC radars can be used in several application domains such as smart home/building, smart cities, security and automotive.
In order to provide additional functionality and performance to miniaturized mm-wave radars, this PhD project will focus on advanced signal processing algorithms, resulting in strong differentiators in the following areas:
Radar performance (resolution in range, Doppler, angle)
Advanced multi-antenna architectures and concepts (e.g. beamforming, MIMO, SAR)
Advanced target detection, tracking and feature extraction
Multipath mitigation in indoor environment for human monitoring
Machine learning algorithms for radar signal processing