Distributed System Identification for Linear Stochastic Systems with Binary Sensors

TL;DR

This paper proposes a stochastic approximation algorithm for distributed identification of linear stochastic systems using only binary sensor outputs over time-varying networks, with proven consensus and convergence.

Contribution

It introduces a novel distributed identification method leveraging binary sensor data, addressing challenges in networked stochastic systems with limited local information.

Findings

Algorithm achieves consensus among agents.

Theoretical convergence guarantees are established.

Simulation results validate the approach.

Abstract

The problem of distributed identification of linear stochastic system with unknown coefficients over time-varying networks is considered. For estimating the unknown coefficients, each agent in the network can only access the input and the binary-valued output of the local system. Compared with the existing works on distributed optimization and estimation, the binary-valued local output observation considered in the paper makes the problem challenging. By assuming that the agent in the network can communicate with its adjacent neighbours, a stochastic approximation based distributed identification algorithm is proposed, and the consensus and convergence of the estimates are established. Finally, a numerical example is given showing that the simulation results are consistent with the theoretical analysis.