  Source localization is of pivotal importance in several areas such as
wireless sensor networks and Internet of Things (IoT), where the location
information can be used for a variety of purposes, e.g. surveillance,
monitoring, tracking, etc. Time Difference of Arrival (TDOA) is one of the
well-known localization approaches where the source broadcasts a signal and a
number of receivers record the arriving time of the transmitted signal. By
means of computing the time difference from various receivers, the source
location can be estimated. On the other hand, in the recent few years novel
optimization algorithms have appeared in the literature for $(i)$ processing
big data and for $(ii)$ training deep neural networks. Most of these techniques
are enhanced variants of the classical stochastic gradient descent (SGD) but
with additional features that promote faster convergence. In this paper, we
compare the performance of the classical SGD with the novel techniques
mentioned above. In addition, we propose an optimization procedure called
RMSProp+AF, which is based on RMSProp algorithm but with the advantage of
incorporating adaptation of the decaying factor. We show through simulations
that all of these techniques---which are commonly used in the machine learning
domain---can also be successfully applied to signal processing problems and are
capable of attaining improved convergence and stability. Finally, it is also
shown through simulations that the proposed method can outperform other
competing approaches as both its convergence and stability are superior.
