论文标题
基于相对测量的分布式估计：基于图的收敛分析
On the Distributed Estimation from Relative Measurements: a Graph-Based Convergence Analysis
论文作者
论文摘要
对于多代理系统状态估计，基于嘈杂的测量结果构成了与几种应用程序方案有关的问题。采用标准最小二乘方法，在这项工作中，我们既可以得出（集中的）分析解决方案和两个分布式的迭代方案，从而使共识算法的收敛行为与最佳估算的收敛行为之间建立联系，以及描述网络系统动力学的随机矩阵的理论。这项研究一方面突出了定义代理节点附近的拓扑链接的作用，而另一方面则可以通过简单的参数调整来优化收敛速率。通过数值模拟考虑了不同的网络拓扑，对理论发现进行了验证。
For a multi-agent system state estimation resting upon noisy measurements constitutes a problem related to several application scenarios. Adopting the standard least-squares approach, in this work we derive both the (centralized) analytic solution to this issue and two distributed iterative schemes, which allow to establish a connection between the convergence behavior of consensus algorithm toward the optimal estimate and the theory of the stochastic matrices that describe the network system dynamics. This study on the one hand highlights the role of the topological links that define the neighborhood of agent nodes, while on the other allows to optimize the convergence rate by easy parameter tuning. The theoretical findings are validated considering different network topologies by means of numerical simulations.
