In this work, we introduce a variable window size (VWS) spatial smoothing framework that enhances coarray-based direction of arrival (DOA) estimation for sparse linear arrays. By compressing the smoothing aperture, the proposed VWS Coarray MUSIC (VWS-CA-MUSIC) and VWS Coarray root-MUSIC (VWS-CA-rMUSIC) algorithms replace part of the perturbed rank-one outer products in the smoothed coarray data with unperturbed low-rank additional terms, increasing the separation between signal and noise subspaces, while preserving the signal subspace span. We also derive the bounds that guarantees identifiability, by limiting the values that can be assumed by the compression parameter. Simulations with sparse geometries reveal significant performance improvements and complexity savings relative to the fixed-window coarray MUSIC method.

翻译：本文提出了一种可变窗口尺寸空间平滑框架，用于增强基于协阵列的稀疏线性阵列波达方向估计性能。通过压缩平滑孔径，所提出的VWS协阵列MUSIC算法与VWS协阵列根MUSIC算法将平滑协阵列数据中部分受扰动的秩一外积替换为未扰动的低秩附加项，在保持信号子空间张成的条件下，增大了信号子空间与噪声子空间的分离度。我们通过限制压缩参数的取值范围，推导了保证可辨识性的边界条件。稀疏阵列构型的仿真实验表明，相较于固定窗口协阵列MUSIC方法，所提算法在性能提升与计算复杂度降低方面均取得显著效果。