Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling and Antenna Designs
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77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadays’ development of advanced automotive radar systems. One of the main challenges in automotive radar simulation is to simulate the complex scattering phenomenon from various targets in real-time. This thesis first develops physical-based statistical models for the radar cross-section (RCS) of parts of or the entire targets for various types of radar, then those statistical models are utilized to generate real-time automotive radar scene simulation in the 3D simulation platform Unreal Engine 4 with parallel computing technologies. An analytic iterative multiple-source angle-of-arrival (AOA) estimation algorithm for automotive radar is developed to accurately estimate the positions of targets, and the proposed approach is much more efficient compared to traditional optimization-based algorithm. To improve the safety of autonomous vehicles in a wide variety of complex environments, target classification with radar is studied and developed. The aforementioned statistical models and the radar response or radar images created by the real-time radar simulation are applied in radar target classification with machine learning models such as the artificial neural network (ANN) and convolutional neural network (CNN). The foliage effect on the mmWave vehicular communication is analyzed, and a broadband omnidirectional horizontally polarized and a circularly polarized antenna to support 5G communication are studied in this thesis as well.
Chair: Professor Kamal Sarabandi
Remote Access: https://bluejeans.com/305566860