Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network...

A Novel Approach to Unambiguous Doppler Beam sharpening for Forward-looking MIMO Radar

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the...

Matrix-Pencil Approach-Based Interference Mitigation for FMCW Radar Systems

A novel matrix-pencil (MP)-based interference mitigation approach for frequency-modulated continuous-wave (FMCW) radars is proposed in this article. The interference-contaminated segment of the beat signal is first cut out, and then, the signal samples in the cutout region are reconstructed by modeling the beat signal as a sum of complex...