Improving DOA Estimation Accuracy Using Combination of U-Net Model and MUSIC Algorithm for Uniform Circular Arrays With Inactive Elements

In this study, a novel method is proposed to improve the accuracy of direction of arrival (DOA) estimation for radio signal sources when a uniform circular antenna array (UCA) has inactive elements. Specifically, the full-rank covariance matrix is reconstructed by integrating a U-Net deep learning model with the multiple signal classification (MUSIC) algorithm, even with incomplete array elements. To restore essential correlation information lost due to the inactive elements, a subspace-based full-rank recovery technique is employed. The reconstructed covariance matrix is then utilized by the MUSIC algorithm for accurate DOA estimation. Experimental results demonstrate significant improvements in accuracy, especially under low signal-to-noise ratio (SNR) conditions and with incomplete antenna arrays. Therefore, this approach ensures stable and precise DOA estimation even under non-ideal operating scenarios, offering a practical solution when antenna arrays experience element failures or physical obstructions.