An Augmented Space Smoothing Method based on the Signal Space in Coherent Scenarios

In the field of coherent direction of arrival (DOA) estimation, traditional subspace-based algorithms encounter difficulties due to the loss of rank in the signal covariance matrix. To mitigate this concern, we introduce a novel technique called augmented space smoothing (ASS). The proposed method exploits the maximum eigenvectors and their corresponding eigenvalues of the signal subspace, which benefits the utilize both auto-correlation and inter-correlation information. By assigning distinct weights to the auto-correlation and cross-correlation information, our method enables accurate direction-finding estimation of two fully coherent signals located at varying angular intervals. Furthermore, we provide a rigorous proof that the proposed ASS matrix efficiently recovers the matrix rank matching the source quantity. By utilizing these fundamental properties, our approach demonstrates a de-coherence ability to address the rank deficiency issue in traditional subspace-based algorithms used for coherent signal processing tasks. Compared to existing spatial smoothing methods, such as spatial smoothing pre-processing (SSP), modified spatial smoothing pre-processing (MSSP), subarrays cross-correlation (SCC), improved spatial smoothing (ISS), enhanced spatial smoothing (ESS) and enhanced spatial smoothing pre-processing based on signal space (ESS-SS), our proposed algorithm demonstrates a superior estimation performance. Finally, we have verified the effectiveness of our algorithm through the simulation results of signal-to-noise ratio (SNR), the number of snapshots, and angular separation.