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

Abstract

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.

Journal paper accepted on IEEE Transactions on Signal Processing

References in corpus (1)

• Statistical mechanics of complex networks

Cited by in corpus (10)

• Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks
• Decentralized Quasi-Newton Methods
• DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
• Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
• Efficient Rewirings for Enhancing Synchronizability of Dynamical Networks
• Distributed Consensus over Wireless Sensor Networks Affected by Multipath Fading
• Networked Signal and Information Processing
• Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Nonreciprocal Channels
• Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
• A Bayesian framework for distributed estimation of arrival rates in asynchronous networks