论文标题
高斯设置下的分布式参数估计的替代方法
An alternative approach for distributed parameter estimation under Gaussian settings
论文作者
论文摘要
本文对多代理网络的分布式线性参数估计采用了不同的方法。参数矢量被认为是随机的，具有高斯分布。每个代理处的传感器测量是线性的，并用添加剂白色高斯噪声损坏。在这种情况下，本文介绍了一种新颖的分布式估计算法，该算法通过将（相邻估计的）共识项（相邻估计）纳入创新项中，融合了共识和创新的概念。在本文中引入的分布式参数可观察性的假设下，我们设计了最佳增益矩阵，以使分布式估计值保持一致并实现快速收敛。
This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measurements at each agent are linear and corrupted with additive white Gaussian noise. Under such settings, this paper presents a novel distributed estimation algorithm that fuses the the concepts of consensus and innovations by incorporating the consensus terms (of neighboring estimates) into the innovation terms. Under the assumption of distributed parameter observability, introduced in this paper, we design the optimal gain matrices such that the distributed estimates are consistent and achieves fast convergence.
