  Many signal processing algorithms for distributed sensors are capable of
improving their performance if the positions of sensors are known. In this
paper, we focus on estimators for inferring the relative geometry of
distributed arrays and sources, i.e. the setup geometry up to a scaling factor.
Firstly, we present the Maximum Likelihood estimator derived under the
assumption that the Direction of Arrival measurements follow the von
Mises-Fisher distribution. Secondly, using unified notation, we show the
relations between the cost functions of a number of state-of-the-art relative
geometry estimators. Thirdly, we derive a novel estimator that exploits the
concept of rays between the arrays and source event positions. Finally, we show
the evaluation results for the presented estimators in various conditions,
which indicate that major improvements in the probability of convergence to the
optimum solution over the existing approaches can be achieved by using the
proposed ray-based estimator.
