2 research outputs found
3D Mobile Localization Using Distance-only Measurements
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.Comment: Submitted to IEEE Transactions on Aerospace and Electronic System
Range-based Coordinate Alignment for Cooperative Mobile Sensor Network Localization
This paper studies a coordinate alignment problem for cooperative mobile
sensor network localization with range-based measurements. The network consists
of target nodes, each of which has only access position information in a local
fixed coordinate frame, and anchor nodes with GPS position information. To
localize target nodes, we aim to align their coordinate frames, which leads to
a non-convex optimization problem over a rotation group . Then,
we reformulate it as an optimization problem with a convex objective function
over spherical surfaces. We explicitly design both iterative and recursive
algorithms for localizing a target node with an anchor node, and extend to the
case with multiple target nodes. Finally, the advantages of our algorithms
against the literature are validated via simulations