A Fast 2D Super-resolution Imaging Method via Bayesian Compressive Sensing for mmWave Automotive radar

Millimeter-wave (mmW) automotive radar imaging technology is widely applied to advanced driver assistance systems (ADAS). Existing super-resolution imaging methods can improve angular resolution for automotive radar with a limited aperture. However, these super-resolution methods have high computational complexity, meanwhile have poor imaging performance in single-snapshot. To address these problems. we propose a fast 2D super-resolution imaging method for real-time and high-quality automotive radar imaging. First, a novel Bayesian compressive sensing with the Kailath-Variant (BCS-KV) imaging method is proposed to achieve superior angular super-resolution in single-snapshot. And the K-V is used to reduce the complexity of matrix inversion. Then, in the range dimension, a Multi-Channel Accumulation (MCA) is utilized to detect the effective range unit to further reduce the 2D imaging computational complexity. Finally, both simulated and experimental results demonstrate that the proposed method has lower computational complexity and compelling imaging performance than other imaging methods.