  We propose an algorithm for the Wireless Sensor Network localization problem,
which is based on the well-known algorithmic framework of Alternating
Minimization. We start with a non-smooth and non-convex minimization, and
transform it into an equivalent smooth and non-convex problem, which stands at
the heart of our study. This paves the way to a new method which is globally
convergent: not only does the sequence of objective function values converge,
but the sequence of the location estimates also converges to a unique location
that is a critical point of the corresponding (original) objective function.
The proposed algorithm has a range of fully distributed to fully centralized
implementations, which all have the property of global convergence. The
algorithm is tested over several network configurations, and it is shown to
produce more accurate solutions within a shorter time relative to existing
methods.
