Split-Spectrum Based Distributed State Estimation for Linear Systems

TL;DR

This paper introduces a spectrum separation-based distributed state estimator for linear systems, capable of handling switching networks and ensuring exponential convergence, with robustness to network topology changes.

Contribution

It proposes a novel distributed estimator leveraging spectrum separation, adaptable to switching networks, and provides convergence guarantees with robustness to network variations.

Findings

Estimator guarantees exponential convergence of state errors.

Robustness to abrupt network topology changes.

Effective for both continuous and discrete-time systems.

Abstract

This paper studies a distributed state estimation problem for both continuous- and discrete-time linear systems. A simply structured distributed estimator (comprising interconnected local estimators) is first described for estimating the state of a continuous and multi-channel linear system whose sensed outputs are distributed across a fixed multi-agent network. The estimator is then extended to non-stationary networks whose graphs switch according to a switching signal. The estimator is guaranteed to solve the problem, provided a network-widely shared high gain condition achieving a form of spectrum separation is satisfied. As an alternative to sharing a common gain across the network, a fully distributed version of the estimator is also studied in which each agent adaptively adjusts a local gain, though the practicality of this approach is subject to a robustness issue common to adaptive control. A discrete-time version of the distributed state estimation problem is also studied, and a corresponding estimator based again on spectrum separation, but not high gain, is proposed for time-varying networks. For each scenario, it is explained how to construct the estimator so that the state estimation errors in the local estimators all converge to zero exponentially fast at a fixed but arbitrarily chosen rate, provided the network's graph is strongly connected for all time. The proposed estimators are inherently resilient to abrupt changes in the number of agents and communication links in the inter-agent communication graph upon which the algorithms depend, provided the network is redundantly strongly connected and redundantly jointly observable.