In this paper, the indoor wireless localization problem is addressed both from the theoretical and application standpoints. The main result of this paper is on the theoretical side: the topological definition of regular and irregular nodes is introduced, and formal results are presented to support regularity as a desirable network property for the attainment of precise node localization. In force of this definition, a mixed convex/nonconvex optimization approach has been derived for the solution of the positioning problem. The two procedures, suitably combined, allow the achievement of better convergence toward the best positioning of a multitude of blind wireless nodes. A completely decentralized algorithm is presented, which proceeds locally on each node based on the sole knowledge of the distances measured from, and of the estimated positions of the connected nodes only. Its repeated asynchronous application on each node guarantees the convergence of the algorithm to the positioning of the whole network, even in the presence of a limited number of peripheral reference points. Indeed, no global information is required for the proper functioning of the algorithm. Simulations of relevant case studies have been performed to qualify the proposed scheme in realistic conditions, and the results are presented.
A Mixed Convex/Nonconvex Distributed Localization Approach for the Deployment of Indoor Positioning Services
GERETTI, Luca;
2008-01-01
Abstract
In this paper, the indoor wireless localization problem is addressed both from the theoretical and application standpoints. The main result of this paper is on the theoretical side: the topological definition of regular and irregular nodes is introduced, and formal results are presented to support regularity as a desirable network property for the attainment of precise node localization. In force of this definition, a mixed convex/nonconvex optimization approach has been derived for the solution of the positioning problem. The two procedures, suitably combined, allow the achievement of better convergence toward the best positioning of a multitude of blind wireless nodes. A completely decentralized algorithm is presented, which proceeds locally on each node based on the sole knowledge of the distances measured from, and of the estimated positions of the connected nodes only. Its repeated asynchronous application on each node guarantees the convergence of the algorithm to the positioning of the whole network, even in the presence of a limited number of peripheral reference points. Indeed, no global information is required for the proper functioning of the algorithm. Simulations of relevant case studies have been performed to qualify the proposed scheme in realistic conditions, and the results are presented.
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