Iterative Refinement for Cooperative Localization with Maximum Likelihood Estimation

摘要

Iterative localization is designed to more free nodes when the number of anchor is few. When all localizable nodes are localized in the primitive iterative localization, the reciprocal refinement localization is proposed to refine and improve the node positions. To improve the localization accuracy, the position error of pseudo anchor is transformed to the equivalent range error, the optimal weight strategies are employed to maximum likelihood estimation. The simulations show that proposed the refined positions can achieve the CRLBs of node positions and the performances of iterative refinement are much better than the results without refinement.