Decentralized Cooperative Online Estimation With Random Observation Matrices, Communication Graphs and Time Delays

TL;DR

This paper studies the convergence of decentralized online estimation algorithms in networks with random, time-varying observation matrices, communication delays, and dynamic graphs, providing conditions for accurate parameter estimation.

Contribution

It introduces a novel convergence analysis framework for decentralized estimation with stochastic delays and time-varying network topologies, extending existing theories to more uncertain environments.

Findings

Estimates converge to the true parameter under certain stochastic excitation conditions.

Proper algorithm gains ensure convergence despite random delays and network changes.

The analysis applies to both delay-free and delayed communication scenarios.

Abstract

We analyze convergence of decentralized cooperative online estimation algorithms by a network of multiple nodes via information exchanging in an uncertain environment. Each node has a linear observation of an unknown parameter with randomly time-varying observation matrices. The underlying communication network is modeled by a sequence of random digraphs and is subjected to nonuniform random time-varying delays in channels. Each node runs an online estimation algorithm consisting of a consensus term taking a weighted sum of its own estimate and neighbours' delayed estimates, and an innovation term processing its own new measurement at each time step. By stochastic time-varying system, martingale convergence theories and the binomial expansion of random matrix products, we transform the convergence analysis of the algorithm into that of the mathematical expectation of random matrix products. Firstly, for the delay-free case, we show that the algorithm gains can be designed properly such that all nodes' estimates converge to the true parameter in mean square and almost surely if the observation matrices and communication graphs satisfy the stochastic spatiotemporal persistence of excitation condition. Secondly, for the case with time delays, we introduce delay matrices to model the random time-varying communication delays between nodes. It is shown that under the stochastic spatio-temporal persistence of excitation condition, for any given boundeddelays, proper algorithm gains can be designed to guarantee mean square convergence for the case with conditionally balanced digraphs.