2 research outputs found
DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles
How can teams of artificial agents localize and position themselves in
GPS-denied environments? How can each agent determine its position from
pairwise ranges, own velocity, and limited interaction with neighbors? This
paper addresses this problem from an optimization point of view: we directly
optimize the nonconvex maximum-likelihood estimator in the presence of range
measurements contaminated with Gaussian noise, and we obtain a provably
convergent, accurate and distributed positioning algorithm that outperforms the
extended Kalman filter, a standard centralized solution for this problem
Distributed Localization of Tree-structured Scattered Sensor Networks
Many of the distributed localization algorithms are based on relaxed
optimization formulations of the localization problem. These algorithms
commonly rely on first-order optimization methods, and hence may require many
iterations or communications among computational agents. Furthermore, some of
these distributed algorithms put a considerable computational demand on the
agents. In this paper, we show that for tree-structured scattered sensor
networks, which are networks that their inter-sensor range measurement graphs
have few edges (few range measurements among sensors) and can be represented
using a tree, it is possible to devise an efficient distributed localization
algorithm that solely relies on second-order methods. Particularly, we apply a
state-of-the-art primal-dual interior-point method to a semidefinite relaxation
of the maximum-likelihood formulation of the localization problem. We then show
how it is possible to exploit the tree-structure in the network and use
message-passing or dynamic programming over trees, to distribute computations
among different computational agents. The resulting algorithm requires far
fewer iterations and communications among agents to converge to an accurate
estimate. Moreover, the number of required communications among agents, seems
to be less sensitive and more robust to the number of sensors in the network,
the number of available measurements and the quality of the measurements. This
is in stark contrast to distributed algorithms that rely on first-order
methods. We illustrate the performance of our algorithm using experiments based
on simulated and real data.Comment: 14 pages and 11 Figure