Event-Triggered State Estimation with Multiple Noisy Sensor Nodes

TL;DR

This paper presents a novel state estimation framework for nonlinear systems with multiple noisy sensors transmitting asynchronously and at irregular times, ensuring stability and asymptotic accuracy.

Contribution

It introduces a flexible event-triggered transmission scheme for multiple sensors, guaranteeing no Zeno behavior and achieving input-to-state stability.

Findings

Estimation error remains bounded under measurement noise.

Asymptotic state reconstruction in noise-free conditions.

Framework effectively handles asynchronous sensor transmissions.

Abstract

General nonlinear continuous-time systems are considered for which its state is estimated via a packet-based communication network. We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit at discrete (non-equidistant) points in time. Moreover, each node can transmit asynchronously. For this setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in either time-triggered or event-triggered fashions. In the latter case, we guarantee the absence of Zeno behavior by construction. It is shown that, under the provided design conditions, an input-to-state stability property is obtained for the estimation error with respect to the measurement noise and process disturbances and that the state is thus reconstructed asymptotically in the absence of noise. A numerical case study shows the strengths of the developed framework.