Array Partitioning Based Near-Field Attitude and Location Estimation

This paper studies a passive source localization system, where a single base station (BS) is employed to estimate the positions and attitudes of multiple mobile stations (MSs). The BS and the MSs are equipped with uniform rectangular arrays, and the MSs are located in the near-field region of the BS array. To avoid the difficulty of tackling the 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 each MS is in the far-field region of each BS subarray. By exploiting the angles of arrival (AoAs) of an MS antenna 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. An estimation-error lower bound is obtained as a performance benchmark of the proposed algorithm. Numerical results demonstrate that the proposed APPLE algorithm outperforms other baseline methods in the accuracy of position and attitude estimation.