  Hybrid localization in GNSS-challenged environments using measured ranges and
angles is becoming increasingly popular, in particular with the advent of
multimodal communication systems. Here, we address the hybrid network
localization problem using ranges and bearings to jointly determine the
positions of a number of agents through a single maximum-likelihood (ML)
optimization problem that seamlessly fuses all the available pairwise range and
angle measurements. We propose a tight convex surrogate to the ML estimator, we
examine practical measures for the accuracy of the relaxation, and we
comprehensively characterize its behavior in simulation. We found that our
relaxation outperforms a state of the art SDP relaxation by one order of
magnitude in terms of localization error, and is amenable to much more
lightweight solution algorithms.
