  A fully-digital massive MIMO receive array is promising to meet the
high-resolution requirement of near-field (NF) emitter localization, but it
also results in the significantly increasing of hardware costs and algorithm
complexity. In order to meet the future demand for green communication while
maintaining high performance, the grouped hybrid analog and digital (HAD)
structure is proposed for NF DOA estimation, which divides the large-scale
receive array into small-scale groups and each group contains several
subarrays. Thus the NF direction-of-arrival (DOA) estimation problem is viewed
as far-field (FF) within each group, and some existing methods such as MUSIC,
Root-MUSIC, ESPRIT, etc., can be adopted. Then by angle calibration, a
candidate position set is generated. To eliminate the phase ambiguity arising
from the HAD structure and obtain the emitter position, two low-complexity
clustering-based methods, minimum sample distance clustering (MSDC) and range
scatter diagram (RSD) - angle scatter diagram (ASD)-based DBSCAN
(RSD-ASD-DBSCAN), are proposed based on the distribution features of samples in
the candidate position set. Then to further improve the localization accuracy,
a model-driven regression network (RegNet) is designed, which consists of a
multi-layer neural network (MLNN) for false solution elimination and a
perceptron for angle fusion. Finally, the Cramer-Rao lower bound (CRLB) of NF
emitter localization for the proposed grouped HAD structure is also derived.
The simulation results show that the proposed methods can achieve CRLB at
different SNR regions, the RegNet has great performance advantages at low SNR
regions and the clustering-based methods have much lower complexity.
