DCD-MUSIC: Deep-Learning-Aided Cascaded Differentiable MUSIC Algorithm for Near-Field Localization of Multiple Sources

Future wireless technologies will require accurate localization of multiple users in the radiative near-field. A leading approach employs subspace decomposition of the input covariance and localizes by peak-finding over the MUltiple SIgnal Classification (MUSIC) spectrum, which is suitable for non-coherent sources with sufficient snapshots and calibrated arrays. This work introduces deep-learning-aided cascaded differentiable MUSIC (DCD-MUSIC) that augments MUSIC near-field localization with dedicated deep neural networks (DNNs), allowing it to operate reliably and interpretably. DCD-MUSIC utilizes two DNNs trained to produce surrogate covariances, one from which the angles and number of sources are recovered, and one to compute the range MUSIC spectrum. This is achieved via a novel learning method that (i) facilitates division into signal and noise subspaces; and (ii) converts MUSIC into a differentiable machine learning model. Our results show that DCD-MUSIC successfully localizes multiple coherent near- and far-field sources.