  For a group of cooperating UAVs, localizing each other is often a key task.
This paper studies the localization problem for a group of UAVs flying in 3D
space with very limited information, i.e., when noisy distance measurements are
the only type of inter-agent sensing that is available, and when only one UAV
knows a global coordinate basis, the others being GPS-denied. Initially for a
two-agent problem, but easily generalized to some multi-agent problems,
constraints are established on the minimum number of required distance
measurements required to achieve the localization. The paper also proposes an
algorithm based on semidefinite programming (SDP), followed by maximum
likelihood estimation using a gradient descent initialized from the SDP
calculation. The efficacy of the algorithm is verified with experimental noisy
flight data.
