Generalized Optimization of Sparse Antenna Arrays for High-Resolution Automotive Radar Imaging.

Automotive radar with sparse arrays are highly desired, as a sparse array has a smaller number of elements compared to a uniform linear array (ULA) of the same physical aperture size, resulting in lower system cost, increased flexibility, and reduced mutual coupling between antennas. However, this leads to an increase in sidelobes in the angle spectrum and higher signal processing complexity. Interpolation techniques can help reduce sidelobe levels, mitigating ambiguity in angle estimation with sparse arrays, and improving the ability to distinguish desired signals from interference. In this paper, we investigate transform matrix optimization technique to interpolate a virtual sparse array (VSA) synthesized by automotive multi-input multi-output (MIMO) radar to a ULA so that high-resolution direction-of-arrival estimation algorithms that are designed for ULA can be applied to VSA to achieve high-resolution radar imaging. Our simulation results in the one dimensional (1D) sparse arrays demonstrate the feasibility and effectiveness of this technique.