Date of Award


Degree Type


Degree Name

Master of Science in Electrical Engineering (MSEE)


Electrical, Computer, and Biomedical Engineering

First Advisor

Qing Yang


Currently available automotive radars are designed to stream real-time 2D image data over high-speed links to a central ADAS (Advance Driver-Assistance System) computer for object recognition, which considerably contributes to the system’s power consumption and complexity. This thesis presents a preliminary work for the implementation of a new in-sensor computer architecture to extract representative features from raw sensor data to detect and identify objects with radar signals. Such new architecture makes it possible to reduce the data transferred between sensors and the central ADAS computer significantly, giving rise to significant energy savings and latency reductions, while still maintaining sufficient accuracy and preserving image details. An experimental prototype has been built using the Texas Instruments AWR1243 Frequency-Modulated Continuous Wave (FMCW) radar board and commodity computer hardware. We carried out experiments using the prototype to collect radar images, to preprocess raw data, and to transfer feature vectors to the simulated central ADAS computer for processing. A vanilla autoencoder will demonstrate the possibility of data reduction on radar signals. We will show that the reconstruction of Range-Angle Heatmaps can be achieved with a very high accuracy by leveraging deep learning architectures. Implementation of such a deep learning architecture on the sensor board can reduce the amount of data transferred from sensors to the central ADAS computer implying great potential for an energy efficient deep learning architecture in such environments.



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