High Angular Resolution Method Based on Deep Learning for FMCW MIMO Radar

In this article, we investigate the feasibility of a high angular resolution method based on deep learning for estimating the angle of arrival (AoA) using an automotive frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar. The deep learning approach takes advantage of the 2-D signal structure from the range-Doppler (RD) maps to determine AoA of targets. To achieve this, we use a neural network architecture that is based on the frequency-representation module of the DeepFreq model. We call it FRNet12, since it uses 12 virtual antennas, which are derived from three transmit and four receive antennas using our FMCW MIMO radar. Furthermore, we propose a cascaded neural network system to further improve the performance of the FRNet12 model. This system consists of an extrapolation neural network (ETPNet) and FRNet18. The ETPNet extrapolates six additional samples from the 12 virtual antenna inputs, and the output is then used to train the FRNet18. The cascaded system provides an improvement of
$33{\%}$
in angular resolution compared to FRNet12. Additionally, it maintains the probability of resolution (PoR) at nearly
$100{\%}$
, even when two targets with different amplitudes are within the theoretical angular resolution region. The proposed method is verified using simulation and measurement data from a 77-GHz FMCW MIMO radar with three transmit and four receive antennas. The results of this research demonstrate the potential of applying deep learning to estimate the AoA in an automotive radar system, and the proposed cascaded neural network system represents a significant improvement over the FRNet12 model.