  Accurate estimation of the position of network nodes is essential, e.g., in
localization, geographic routing, and vehicular networks. Unfortunately,
typical positioning techniques based on ranging or on velocity and angular
measurements are inherently limited. To overcome the limitations of specific
positioning techniques, the fusion of multiple and heterogeneous sensor
information is an appealing strategy. In this paper, we investigate the
fundamental performance of linear fusion of multiple measurements of the
position of mobile nodes, and propose a new distributed recursive position
estimator. The Cram\'er-Rao lower bounds for the parametric and a-posteriori
cases are investigated. The proposed estimator combines information coming from
ranging, speed, and angular measurements, which is jointly fused by a Pareto
optimization problem where the mean and the variance of the localization error
are simultaneously minimized. A distinguished feature of the method is that it
assumes a very simple dynamical model of the mobility and therefore it is
applicable to a large number of scenarios providing good performance. The main
challenge is the characterization of the statistical information needed to
model the Fisher information matrix and the Pareto optimization problem. The
proposed analysis is validated by Monte Carlo simulations, and the performance
is compared to several Kalman-based filters, commonly employed for localization
and sensor fusion. Simulation results show that the proposed estimator
outperforms the traditional approaches that are based on the extended Kalman
filter when no assumption on the model of motion is used. In such a scenario,
better performance is achieved by the proposed method, but at the price of an
increased computational complexity.
