基于高斯置信传播的分布式静态估计器的收敛性和精度分析
Convergence and Accuracy Analysis for A Distributed Static State Estimator based on Gaussian Belief Propagation
摘要：
针对分布式静态估计问题，提出了一种基于置信传播（BP）的估计算法。我们对它的收敛性和精度进行了全面的分析，给出了保证它收敛的条件，并给出了它的精度的具体刻画。我们的结果不仅给出了一个新的算法具有良好的性能，但也提供了一个有用的分析框架，学习分布式算法的性能。它产生更好的静态分布状态估计器的理论理解，并可能产生更多的应用在未来。
英文摘要：
This paper focuses on the distributed static estimation problem and a Belief Propagation (BP) based estimation algorithm is proposed. We provide a complete analysis for convergence and accuracy of it. More precisely, we offer conditions under which the proposed distributed estimator is guaranteed to converge and we give concrete characterizations of its accuracy. Our results not only give a new algorithm with good performance but also provide a useful analysis framework to learn the properties of a distributed algorithm. It yields better theoretical understanding of the static distributed state estimator and may generate more applications in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
