Cooperative Robust Estimation with Local Performance Guarantees

TL;DR

This paper introduces a novel distributed filtering approach for sensor networks that accounts for uncertainties in inter-node communication, achieving robust consensus and local performance guarantees.

Contribution

It proposes a new cooperative filtering method that models inter-node communication as uncertain signals, enabling local performance assessment and tuning within sensor networks.

Findings

Achieves suboptimal $H_ Infty$ consensus performance.

Provides local performance guarantees for each estimator.

Offers a framework for tuning estimation performance in sensor networks.

Abstract

The paper considers the problem of cooperative estimation for a linear uncertain plant observed by a network of communicating sensors. We take a novel approach by treating the filtering problem from the view point of local sensors while the network interconnections are accounted for via an uncertain signals modelling of estimation performance of other nodes. That is, the information communicated between the nodes is treated as the true plant information subject to perturbations, and each node is endowed with certain believes about these perturbations during the filter design. The proposed distributed filter achieves a suboptimal $H_\infty$ consensus performance. Furthermore, local performance of each estimator is also assessed given additional constraints on the performance of the other nodes. These conditions are shown to be useful in tuning the desired estimation performance of the sensor network.