Abstract
We propose a new automotive radar architecture that achieves high resolution in range, range rate, azimuth and elevation angles while requiring smaller antenna aperture and fewer reception channels as compared to conventional digital beamforming planar arrays. This is achieved by leveraging two orthogonally-placed digital beamforming arrays using the frequency modulation continuous waveform. The high-resolution range-Doppler images generated by azimuth and elevation beams of the two arrays isolate each physical scatterer, thus, the azimuth and elevation angles can be precisely measured. To match the measurements of an object from azimuth and elevation beams, a deep learning based beam matching method is proposed, which converts the beam matching problem to an image patch matching problem in the range-Doppler domain. Furthermore, a new radar resource management algorithm is proposed, which schedules radar jobs by their time urgency as well as beam locations. Jobs fall into the same beams are scheduled together to optimally use the radar time resource and also reduces the computation introduced by the beam matching procedure. The advantage of the proposed radar is demonstrated by simulations.
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