  In this article we analyze the state-of-the-art in multilateration - the
family of localization methods enabled by the range difference observations.
These methods are computationally efficient, signal-independent, and flexible
with regards to the number of sensing nodes and their spatial arrangement.
However, the multilateration problem does not admit a closed-form solution in
the general case, and the localization performance is conditioned on the
accuracy of range difference estimates. For that reason, we consider a
simplified use case where multiple distributed microphones capture the signal
coming from a near field sound source, and discuss their robustness to the
estimation errors. In addition to surveying the relevant bibliography, we
present the results of a small-scale benchmark of few "mainstream"
multilateration algorithms, based on an in-house Room Impulse Response dataset.
