分布式系统状态估计的邻近点法
A Proximal Point Approach for Distributed System State Estimation
摘要：
系统状态估计是多智能体系统结构中的一个关键问题。这取决于对组中每个代理的状态的估计，该组应该仅访问相对测量值w.r.t.一些邻居说。利用标准的最小二乘范例，系统状态估计任务所面临的这项工作，推导出一个分布式的邻近点为基础的迭代计划。这个解决方案需要出现有趣的连接的随机矩阵的结构特性描述系统的动态和收敛行为的最佳估计。这种关系的深入分析，共同进一步讨论的惩罚参数，表征的近点方法。
英文摘要：
System state estimation constitutes a key problem in several applications involving multi-agent system architectures. This rests upon the estimation of the state of each agent in the group, which is supposed to access only relative measurements w.r.t. some neighbors state. Exploiting the standard least-squares paradigm, the system state estimation task is faced in this work by deriving a distributed Proximal Point-based iterative scheme. This solution entails the emergence of interesting connections between the structural properties of the stochastic matrices describing the system dynamics and the convergence behavior toward the optimal estimate. A deep analysis of such relations is provided, jointly with a further discussion on the penalty parameter that characterizes the Proximal Point approach.
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