Adaptive Subspace-Based User Localization in Near-Field Regime Using Sub-Array Architecture

Locating transmitted signals in antenna arrays has been a widely studied topic in signal processing literature. The near-field setting, particularly relevant when considering an extremely large antenna array (ELAA), poses a significant challenge as it deviates from the conventional steering vector model often used in array signal processing. In this paper, we propose an innovative method that combines adaptive subspace estimation with sub-array architecture to accurately locate users in near-field scenarios. The integration of sub-array techniques for ELAA and adaptive subspace approaches provides remarkable advantages, such as reduced computational complexity and improved performance. Our method is compared to well-established techniques in the literature, with evaluations based on root mean square error (RMSE) and cumulative distribution functions (CDFs) to characterize the statistical behavior of each method. The results demonstrate that our proposed method outperforms existing approaches in terms of precision.