Cooperative indoor positioning with factor graph based on FIM for wireless sensor network

In this paper, we propose a novel cooperative positioning algorithm that fuses information from anchor nodes and neighboring agent nodes, which is suitable for internet of things and robot applications. The mathematical formulation of the cooperative localization problem with factor graph based on Fisher Information Matrix (FIM) theory is presented. We examine the information from an agent node to its neighboring nodes with FIM to evaluate the ranging performance. From this, we will develop the Bayesian inference on factor graph and FIM that will be applied for cooperative positioning. Through simulations, we examine the Cramér-Rao lower bound (CRLB)of the proposed algorithm and how estimation performance is affected by the geometric distributions of anchor nodes and neighboring nodes. Finally, we demonstrate the efficacy and accuracy of our algorithm with multiple anchor nodes and agent nodes.