共享半径互质圆阵的混合连续波达方向估计

中文摘要 AI 辅助

本文提出一种共享半径的互质圆阵，用于在三维空间中进行高分辨率、连续的二维波达方向（DoA）估计，联合估计方位角和仰角。所提出的架构由两个均匀圆形子阵列组成，其天线数量互质，并共享一个公共半径RR，该设计从根本上抑制了与密集均匀阵列相比的互耦泄漏。与现有依赖复杂相位模式变换将圆形结构映射到虚拟线性阵列的工作不同，我们引入了一种直接在物理空间域中运行的混合连续恢复框架。通过将快速的离散粗网格搜索与群智能连续细化阶段相结合，所提出的方法完全绕过了离散网格失配限制和计算昂贵的特征值分解。利用尼文定理进行的严格理论分析确立了真实源方向的空间唯一性，有效解决了相位模糊问题。仿真结果表明，与均匀配置相比，该混合方案在低信噪比（SNR）下实现了更高的分辨率和更低的均方根误差（RMSE），同时在高信噪比下渐近收敛于理论克拉美-罗界（CRB）。

英文摘要

This paper proposes a shared-radius co-prime circular array for high-resolution, continuous 2D Direction-of-Arrival (DoA) estimation in 3D space, jointly estimating azimuth and elevation angles. The proposed architecture consists of two uniform circular sub-arrays with co-prime antenna counts sharing a common radius RR, a design that intrinsically suppresses mutual coupling leakage compared to dense uniform arrays. Unlike existing works that rely on complex phase-mode transformations to map circular structures to virtual linear arrays, we introduce a hybrid continuous-recovery framework operating directly in the physical spatial domain. By integrating a fast, discrete coarse-grid search with a swarm-intelligence continuous refinement stage, the proposed method completely bypasses discrete grid-mismatch limitations and computationally expensive eigenvalue decompositions. A rigorous theoretical analysis using Nivens Theorem establishes the spatial uniqueness of the true source direction, effectively resolving phase ambiguities. Simulation results demonstrate that this hybrid scheme achieves superior resolution and lower Root Mean Square Error (RMSE) at low Signal-to-Noise Ratios (SNR) compared to uniform configurations, while asymptotically converging to the theoretical Cramer-Rao Bound (CRB) at high SNRs.

发表机构

• Universidad Miguel Hernández de Elche（埃尔切米格尔·埃尔南德斯大学）

机构由 AI 辅助整理，请以论文原文为准。