Deep Learning Based Localization of Near-Field Sources with Exact Spherical Wavefront Model
Wenyi Liu, Jingmin Xin, Weiliang Zuo, Jie Li, Nanning Zheng, Akira Sano · 2019
Source localization for near-field narrowband signal is an important topic in array signal processing. Deep neural network (DNN) based methods are data-driven and free of pre-assumptions about data model and are expected to learn the intricate nonlinear structure in large data sets. This paper proposes a framework of DNN where a regression layer is utilized to address the problem of near-field source localization. Unlike previous studies in which DOA estimation is modeled as a classification problem and have a relatively low resolution, we exploit a regression model and aim to improve the estimation accuracy. In the training stage, we propose a novel form of feature representation to take full advantage of the convolution networks. In addition, the architecture of deep neural networks is well designed taking in to consideration the trade-off between the expression ability and under-training risks. The simulation results show that the proposed approach has a rather high validation accuracy with a high resolution, and also outperforms some conventional methods in adverse environments such as low signal to noise ratio (SNR) or small number of snapshots.