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Decentralized Maximum Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems

机译:非线性耦合动力系统组成的传感器网络的分散最大似然估计

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摘要

In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximum-likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results.
机译:在本文中,我们提出了一种分散式传感器网络方案,该方案能够通过非线性耦合动力系统的自同步来达到全局最优的最大似然(ML)估计。网络的每个节点都由一个传感器和一个用局部测量值初始化的一阶动力学系统组成。邻近节点彼此交互以交换其状态值,并且最终估计值与每个动力学系统的状态导数相关联。我们推导了耦合机制的条件,该条件保证了如果网络观察到一个普遍现象,则每个节点都收敛到全局最优ML估计。我们证明,如果耦合强度超过给定阈值,则同步状态在全局渐近稳定。通过对单个参数耦合强度的作用,我们展示了在非线性耦合的情况下,网络行为如何从全局共识系统转换为空间聚类系统。最后,我们展示了网络拓扑对网络可扩展性的影响,并通过仿真结果验证了我们的理论发现。

著录项

  • 作者

    BARBAROSSA S; G. SCUTARI;

  • 作者单位
  • 年度 2007
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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