Huan, M., Liang, J., Ma, Y., Liu, W., Wu, Y., & Zeng, Y. (2024). Optimization of High-resolution and Ambiguity-free Sparse Planar Array Geometry for Automotive MIMO Radar. IEEE Transactions on Signal Processing, 1–16. https://doi.org/10.1109/tsp.2024.3404888
Abstract:
The next-generation 4D imaging automotive radar is
characterized by high angular resolution, unambiguous detection,
low latency, low cost, and small size. This study provides an
enhanced analysis of the angular ambiguity function (AAF)
for planar MIMO arrays, and pioneers a method for a more
accurate evaluation of angular resolution using the main lobe
width (MLW). Then the 2D expanded beam pattern (EBP) is
introduced to assess the field-of-view (FOV), region of interest
(ROI), sidelobe level (SLL), and normalized resolution intuitively
and precisely. After constructing the sophisticated 2D element
spacing and aperture constraints for planar MIMO arrays, the
optimization of array geometry is creatively formulated as a novel
Domino sparse optimization problem aiming to minimize the
MLW while sufficiently suppressing the SLL, which is inspired
by the sequential fall of dominoes. This non-convex and nonsmooth
constrained problem is efficiently solved by a hybrid
optimization framework, which integrates the alternating direction
multiplier method (ADMM), aggregate function, modified
real genetic algorithm (MGA), and non-uniform fast Fourier
transform (NUFFT). Numerical simulations demonstrate that
angular resolution varies with array geometry, even under the
same aperture size. The proposed arrays outperform others
with equal aperture size, exhibiting narrower MLW and lower
Cram´er-Rao bound (CRB), thereby enhancing angular resolution
with fewer antennas and without preprocessing in standard
single-snapshot 2D DOA estimation methods.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development - Future Communications Research & Development Program
Grant Reference no. : FCP-NUS-RG-2022-018
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development - Future Communications Research & Development Program
Grant Reference no. : FCP-ASTAR-TG-2022-003