An Optimized Spatial Smoothing Technique with QR_ESPRIT Algorithm for DOA-Estimation

Direction of Arrival (DOA) estimation is a critical topic in the field of array signal processing. In this paper, an optimized spatial smoothing technique (OSS) is proposed. Compared to existing improved spatial smoothing techniques (ESS_SS, ASS, ISST), the proposed method significantly reduces computational complexity by optimizing the construction of the covariance matrix, while maintaining high estimation accuracy and improving operational efficiency. Furthermore, to further reduce computational overhead, QR decomposition is introduced to obtain the subspace before using the ESPRIT algorithm as a replacement for traditional eigenvalue decomposition (EVD). Experimental results demonstrate that OSS combined with QR decomposition-based ESPRIT achieves substantial computational load reduction with minimal performance loss, making it particularly suitable for real-time processing of large-scale array data. The role of Spatial smoothing in scenarios involving incoherent signals is also explored. The experiments show that by fully utilizing all eigenvalues and eigenvectors containing signal information to reconstruct the signal subspace, spatial smoothing can effectively improve DOA estimation accuracy, even under incoherent signal conditions.