Off-grid DOA estimation via deep learning framework

In radar perception tasks, it is necessary to estimate the direction of arrival (DOA) of the sources to achieve various applications such as target detection, tracking, and imaging. Especially for automotive millimeter-wave radars, the requirement is to achieve long-distance target detection at a high speed. Therefore, there are higher requirements for real-time performance and super-resolution ability of DOA estimation.

applications automotive deep learning deep learning framework detection framework grid imaging machine learning & ai perception performance radar real-time requirements speed tasks tracking