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Existing near-field localization algorithms generally face a scalabilityissue when the number of antennas at the sensor array goes large. To addressthis issue, this paper studies a passive localization system, where anextremely large-scale antenna array (ELAA) is deployed at the base station (BS)to locate a user that transmits signals. The user is considered to be in thenear-field (Fresnel) region of the BS array. We propose a novel algorithm,named array partitioning based location estimation (APLE), for scalablenear-field localization. The APLE algorithm is developed based on the basicassumption that, by partitioning the ELAA into multiple subarrays, the user canbe approximated as in the far-field region of each subarray. The APLE algorithmdetermines the user's location by exploiting the differences in the angles ofarrival (AoAs) of the subarrays. Specifically, we establish a probability modelof the received signal based on the geometric constraints of the user'slocation and the observed AoAs. Then, a message-passing algorithm, i.e., theproposed APLE algorithm, is designed for user localization. APLE exhibitslinear computational complexity with the number of BS antennas, leading to asignificant reduction in complexity compared to the existing methods. Besides,numerical results demonstrate that the proposed APLE algorithm outperforms theexisting baselines in terms of localization accuracy.

Scalable Near-Field Localization Based on Array Partitioning and Angle-of-Arrival Fusion