基于噪声子空间重构的矢量阵MUSIC改进算法

Improved MUSIC Algorithm for Vector Sensor Arrays Based on Noise Subspace Reconstruction

摘要:

针对传统多重信号分类（Multiple Signal Classification，MUSIC）算法在矢量水听器（Acoustic Vector Sensor，AVS）阵列波达方向（Direction-of-arrival，DOA）估计中因声压与振速通道噪声功率不一致导致噪声子空间失准、空间谱出现虚假峰值的问题，提出一种基于噪声子空间重构的改进多重子空间分类 （Modified MUSIC，MMUSIC）算法。该方法在建立 AVS 接收信号模型的基础上，解析通道噪声差异引入“虚源”的机理，进而重构噪声子空间划分准则，并构造修正的谱峰搜索函数以抑制伪峰。仿真实验显示：在低信噪比、小角度间隔及有限快拍数条件下，所提算法相比常规波束形成（Conventional Beamforming，CBF）、 最小方差无畸变响应（Minimum Variance Distortionless Response，MVDR）和传统 MUSIC 等方法具有更低的均方根误差（Root Mean Square Error，RMSE）和更强的角度分辨能力。结果表明：所提方法能有效修复子空间正交结构、抑制虚源影响，在复杂条件下有效提升 AVS 阵列 DOA 估计的分辨率与鲁棒性。

Abstract:

In direction-of-arrival（DOA）estimation using acoustic vector sensor（AVS）arrays，the conventional multiple signal classification（MUSIC）algorithm suffers from noise subspace distortion due to unequal noise power between the pressure and particle velocity channels，leading to spurious peaks in the spatial spectrum. To address this issue，a modified MUSIC（MMUSIC）algorithm based on noise subspace reconstruction is proposed in this paper. By establishing a signal model for AVS arrays，the mechanism by which channel noise mismatch introduces “virtual sources” is analyzed，and then the noise subspace separation criterion is accordingly reformulated. A revised spatial spectrum function is then constructed to suppress pseudo-peaks. Simulation results demonstrate that，under challenging conditions，including low signal-to-noise ratio（SNR），closely spaced sources，and limited snapshots，the proposed MMUSIC algorithm achieves lower root mean square error（RMSE）and higher angular resolution compared to conventional beamforming（CBF），minimum variance distortionless response（MVDR）， and standard MUSIC. The results indicate that the proposed method effectively restores the orthogonality between signal and noise subspaces，mitigates virtual-source interference，and thereby enhances the resolution and robustness of DOA estimation for AVS arrays in complex environments.