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.

翻译：本文对多试剂网络的分布线性参数估计采取了不同的方法。 参数矢量被认为具有随机性, 分布在高斯分布上。 每种物剂的传感器测量线性, 并被添加白高斯噪音腐蚀。 在这样的背景下, 本文展示了一种新的分布式估计算法, 通过将共识条款( 相邻估计)纳入创新条款, 融合了共识和创新概念。 根据本文提出的分布参数可观察性假设, 我们设计了最佳收益矩阵, 使分布式估计一致, 并实现快速趋同 。