A Novel Method to Select Minimum Neighbors in Cooperative Localization Network

Abstract

Cooperative localization is an emerging paradigm that circumvents the needs for high-power, high-density anchor deployment, and offers additional positioning accuracy by exchanging information between adjacent agents. Considering that positioning accuracy only need to meet the application requirements, too high positioning accuracy means that too much redundant information is exchanged. This paper presents a neighbors selection algorithm that tries to meet the positioning accuracy required by an application , while the system consumption is optimized. More specifically, this proposed algorithm tries to minimize the number of neighbors involved in cooperation on the basis of the equivalent Fisher information matrix. Before putting forward the algorithm, we first introduce the notion of equivalent Fisher information and characterize localization accuracy called the squared position error bound. Then we find that not only the Fisher information of neighbor nodes determines the positioning accuracy of the agent to be located, but also the direction of Fisher information affected the positioning accuracy. Based on this, an algorithm for selecting the minimum cooperative subset is proposed. Simulation results show that the proposed algorithm can obviously improving the utilization of energy compared to other commonly used methods by exchanging information with a few nodes, while achieving the specified positioning accuracy.