Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints

TL;DR

This paper proposes a resource-efficient distributed nonlinear fusion estimation framework using event-triggered strategies and compensation methods, validated through vehicle localization experiments.

Contribution

It introduces a unified approach combining event-triggered and dimensionality reduction strategies with compensation techniques for nonlinear networked systems under resource constraints.

Findings

Effective reduction in communication load.

Bounded estimation errors demonstrated.

Improved localization accuracy.

Abstract

This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics. When considering the limited resource problems of two kinds of communication channels (i.e., sensor-to-remote estimator channel and smart sensor-to-fusion center channel), an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified networked framework to lighten the communication burden. Then, two kinds of compensation strategies in terms of a unified model are designed to restructure the untransmitted information, and the local/fusion estimators are proposed based on the compensation information. Furthermore, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then different robust recursive optimization problems are constructed to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also proposed such that the square errors of the designed nonlinear estimators are bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.