论文标题
基于高斯信念繁殖的分布式静态估计器的收敛性和准确性分析
Convergence and Accuracy Analysis for A Distributed Static State Estimator based on Gaussian Belief Propagation
论文作者
论文摘要
本文提出了提出基于分布式的静态估计问题，并提出了基于信念的估计算法（BP）。我们提供了完整的分析，以融合其的融合和准确性。更确切地说，我们提供的条件是，在这些条件下，建议的分布式估计器可以融合，并提供其准确性的具体特征。我们的结果不仅提供了具有良好性能的新算法，而且还提供了一个有用的分析框架来了解分布式算法的属性。它对静态分布式状态估计器产生更好的理论理解，并可能在将来产生更多的应用程序。
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.
