  We propose a simple, stable and distributed algorithm which directly
optimizes the nonconvex maximum likelihood criterion for sensor network
localization, with no need to tune any free parameter. We reformulate the
problem to obtain a gradient Lipschitz cost; by shifting to this cost function
we enable a Majorization-Minimization (MM) approach based on quadratic upper
bounds that decouple across nodes; the resulting algorithm happens to be
distributed, with all nodes working in parallel. Our method inherits the MM
stability: each communication cuts down the cost function. Numerical
simulations indicate that the proposed approach tops the performance of the
state of the art algorithm, both in accuracy and communication cost.
