Direction of Arrival Estimation with Virtual Antenna Array Using FMCW Radar Simulated Data

The FMCW radars are widely used for automotive radar systems. The basic idea for FMCW radars is to generate a linear frequency ramp as transmit signal. The difference frequency, (i.e., beat frequency) between the transmitted and received signal is determined after down conversion. The FFT operation on beat frequency signal can recognize targets at different range and velocity. Increasing demand on safety functionality leads to the Direction of Arrival (DOA) estimation to resolve two closely located targets. Consequently, the problem of angle estimation for 77GHz FMCW automotive radar simulated data has been investigated in this term project. In particular, we examined the performances of FFT, MUSIC and compressed sensing in angle estimation task, and it was found that although FFT is the fastest algorithm, it has very poor angular resolution when compared with others which are both super resolution algorithms. The code for this project report is available at https://github.com/ekurtgl/FMCW-MIMO-Radar-Simulation.

ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User Interfaces

The past decade has seen great advancements in speech recognition for control of interactive devices, personal assistants, and computer interfaces. However, Deaf and hard-ofhearing (HoH) individuals, whose primary mode of communication is sign language, cannot use voice-controlled interfaces. Although there has been significant work in video-based sign language recognition, video is not effective in the dark and has raised privacy concerns in the Deaf community when used in the context of human ambient intelligence. RF sensors have been recently proposed as a new modality that can be effective under the circumstances where video is not. This paper considers the problem of recognizing a trigger sign (wake word) in the context of daily living, where gross motor activities are interwoven with signing sequences. The proposed approach exploits multiple RF data domain representations (time-frequency, range-Doppler, and range-angle) for sequential classification of mixed motion data streams. The recognition accuracy of signs with varying kinematic properties is compared and used to make recommendations on appropriate trigger sign selection for RFsensor based user interfaces. The proposed approach achieves a trigger sign detection rate of 98.9% and a classification accuracy of 92% for 15 ASL words and 3 gross motor activities.