  Source localization is the process of estimating the location of signal
sources based on the signals received at different antennas of an antenna
array. It has diverse applications, ranging from radar systems and underwater
acoustics to wireless communication networks. Subspace-based approaches are
among the most effective techniques for source localization due to their high
accuracy, with Multiple SIgnal Classification (MUSIC) and Estimation of Signal
Parameters by Rotational Invariance Techniques (ESPRIT) being two prominent
methods in this category. These techniques leverage the fact that the space
spanned by the eigenvectors of the covariance matrix of the received signals
can be divided into signal and noise subspaces, which are mutually orthogonal.
Originally designed for far-field source localization, these methods have
undergone several modifications to accommodate near-field scenarios as well.
This chapter aims to present the foundations of MUSIC and ESPRIT algorithms and
introduce some of their variations for both far-field and near-field
localization by a single array of antennas. We further provide numerical
examples to demonstrate the performance of the presented methods.
