Low-Complexity High-Resolution Parameter Estimation for Automotive MIMO Radars

In this paper, we propose a parameter estimation method for multiple-input-multiple-output (MIMO) automotive radars that consists of two stages. The first stage is a low-complexity three-dimensional (3D) constant false alarm rate (CFAR) detection technique that exploits spatial filtering to extend radar coverage, and it performs low-complexity peak detection. The second stage is an ESPRIT-based direction-of-arrival (DOA) estimation technique that adopts time-frequency resource division to generate high-quality snapshots and it performs DOA estimation of targets without the knowledge of the target number. Computer simulations reveal that the proposed method achieves the performance of the two-dimensional ordered statistic CFAR (2D OS-CFAR) while having much lower computational complexity, and it offers the higher resolution DOA estimation compared to the conventional MIMO radars.