Near-Field Attitude and Location Estimation Based on Partitioned Large-Scale Antenna Array

This paper studies a passive localization system, where a single base station (BS) is employed to estimate the position and attitude of a mobile station (MS). The BS and the MS are equipped with uniform rectangular arrays (URAs), and the MS is located in the near-field region of the BS array. To avoid the difficulty of tackling this problem directly based on the near-field signal model, we establish a subarray-wise far-field received signal model. In this model, the entire BS array is divided into multiple subarrays to ensure that the MS is in the far-field region of each BS subarray. By exploiting the angles of arrival (AoAs) of the MS antennas at different BS subarrays, we formulate the attitude and location estimation problem under the Bayesian inference framework. Based on the factor graph representation of the probabilistic problem model, a message passing algorithm named array partitioning based pose and location estimation (APPLE) is developed to solve this problem. Numerical results demonstrate that the proposed APPLE algorithm outperforms other baseline methods in the estimation accuracy.