Current automotive radar sensors enhance the angular resolution using a multiple-input multiple-output approach. The often applied time-division multiplexing scheme has the drawback of a reduced unambiguous Doppler velocity proportional to the number of transmitters. In this paper, a signal processing scheme is proposed to regain the same unambiguous Doppler as in the single-input multiple-output case with only one transmit antenna. Simulation and measurement results are shown to prove that the signal processing leads to an enhanced unambiguous Doppler velocity estimation.

Abstract-Radar sensors are utilized for detection and classification purposes in various applications. In order to use deep learning techniques, lots of training data are required. Accordingly, lots of measurements and labelling tasks are then needed. For the purpose of pre-training or examining first ideas before bringing them into reality, synthetic radar data are of great help. In this paper, a workflow for automatically generating radar data of human gestures is presented, starting with creating the desired animations until synthesizing radar data and getting the final required dataset. The dataset could then be used for training deep learning models. A classification scenario applying this workflow is also introduced.