Source localization using a network of sensors is a classical problem
with applications in tracking, habitat monitoring, etc.  A small
number of transmitters in the plane broadcast sine waves, a small
number of receivers record the signal, and we want to recover the
transmitters' locations from the receivers' observations.  A solution
to this estimation problem must satisfy a number of competing resource
constraints, such as estimation accuracy, communication and energy
costs, signal sampling requirements and computational complexity.
This paper exploits recent developments in sparse approximation and
compressive sensing to efficiently perform localization in a sensor
network.