레이다 애플리케이션을 위한 MIMO 신호처리

Abstract

There have been several significant developments in radio-frequency (RF) technology, which now offer more compact, energy-efficient, and cost-effective radar sensors. Consequently, there is a significant surge in interest regarding the utilization of radar sensors for a diverse range of applications. In the context of autonomous driving, radar’s ability to ”see” through adverse weather conditions provides a crucial advantage over other sensor technologies. Unlike cameras and lidar, which may struggle in inclement weather, radar sensors reliably detect objects by emitting radio waves and analyzing the reflections. This ensures that autonomous vehicles maintain a high level of perception regardless of environmental challenges. On the other hand, in indoor sensing, radar sensors are non-invasive and do not require a direct line of sight, making them suitable for innovative applications where privacy is important, such as healthcare, security systems, and smart homes. This dissertation contributes to the advancement of multiple-input multiple-output radar signal processing techniques, particularly in the context of autonomous vehicle and indoor environmental sensing. The proposed frameworks offer practical solutions to the challenges posed by diverse vehicle types in the road and indoor multipath propagation, paving the way for improved radar applications in autonomous driving and smart indoor monitoring, respectively.