A Fast Lq Sparsity-Driven Method With Adaptive-Focusing Framework for mmWave Automotive Radar Super-Resolution Imaging

Millimeter-wave (mmW) automotive radar imaging technology shows significant promise in advanced driver assistance systems (ADAS). Super-resolution imaging methods can be employed the limited aperture length of automotive radar to improve azimuth (angular) resolution. However, automotive radar images typically exhibit large dynamic range (LDR) and large scene (LS), leading to pay extensive computational complexity and storage demands when striving for higher image quality. To tackle this challenge, a fast $l_{q}$ sparsity-driven imaging method with adaptive-focusing framework (FLSD-AF) for mmWave automotive radar super-resolution imaging in this article. First, in AF framework, a detect-before-imaging (DBI) is proposed to make echo data to adaptive focused on potential target area (PTR), thereby reducing the dimension of the effective data to reduce computational complexity and storage demands. Second, a subspace-phase-compensation (SPC) is proposed to reduces storage demands of the measurement matrix by addressing the imaging model mismatch in near-field under LS. Finally, a fast $l_{q}$ sparsity-driven (FLSD) imaging method is proposed. It employs $l_{q}$ -norm nonconvex penalty function to address the biased problem to improve imaging quality under LDR, meanwhile the computational complexity of the matrix operation is greatly reduced by utilizing joint Kailath-Variant (K-V) formula and Gohberg-Semencul (G-S) factorization. In summary, the proposed FLSD-AF not only substantially enhances the imaging performance, but also significantly diminishes the storage demands and computational complexity under LDR and LS. The results of simulations and experimental data all verify the proposed method.