Cooperative Wireless Sensor Network Positioning via Implicit Convex Feasibility

TL;DR

This paper introduces a distributed convex feasibility approach for wireless sensor network positioning, enabling parallel updates and sharing of node locations, with proven convergence and improved performance in sparse networks.

Contribution

It presents a novel distributed convex feasibility algorithm for sensor positioning that allows parallel processing and sharing, with mathematical convergence proof.

Findings

Enhanced accuracy in sparse networks

Convergence of the proposed algorithm proven mathematically

Outperforms existing projection-based methods

Abstract

We propose a distributed positioning algorithm to estimate the unknown positions of a number of target nodes, given distance measurements between target nodes and between target nodes and a number of reference nodes at known positions. Based on a geometric interpretation, we formulate the positioning problem as an implicit convex feasibility problem in which some of the sets depend on the unknown target positions, and apply a parallel projection onto convex sets approach to estimate the unknown target node positions. The proposed technique is suitable for parallel implementation in which every target node in parallel can update its position and share the estimate of its location with other targets. We mathematically prove convergence of the proposed algorithm. Simulation results reveal enhanced performance for the proposed approach compared to available techniques based on projections, especially for sparse networks.