Mapping-Guided Optimization Framework for Low-Sidelobe Sparse Antenna Arrays via Quadrant Amplitude Mapping

Large-Aperture antenna arrays promise fine angular resolution, but their dense layouts usually lead to prohibitive cost and design complexity. Sparse arrays present a compelling alternative, yet their design typically depends on computationally intensive and numerically unstable optimization methods that are prone to local minima. In this article, we first propose a deterministic and region-partitioning algorithm, referred to as quadrant-Amplitude mapping (QAM), which recursively divides the aperture into quadrants and directly maps a given amplitude taper to element positions. In 30 independent trials, QAM reduces the standard deviation (STD) of the peak sidelobe level (SLL) by 67.4% relative to conventional density-weighted (DW) mapping, effectively eliminating stochastic variability. Building on QAM, we further develop a mapping-guided sparse-Array optimization framework that embeds position selection within the amplitude-weight refinement loop. Compared with direct position optimization, the proposed framework achieves 9.9 dB lower sidelobes within 60 min of computation, and outperforms the existing weight-first mapping approach by 4.97 dB after 20 iterations. The proposed strategy offers a fast and robust solution for designing low-sidelobe sparse arrays. Moreover, it can be extended to the design of arrays with arbitrary or nonuniform apertures, offering a cost-effective solution for next-generation high-resolution sensing systems.