Distributed State Estimation for Discrete-time LTI Systems: the Design Trilemma and a Novel Framework

TL;DR

This paper addresses the complex trade-offs in distributed state estimation for discrete-time systems, proposing a new iterative framework that balances observability, communication, and network connectivity to improve estimation performance.

Contribution

It introduces a novel framework that systematically balances the interdependent factors in discrete-time distributed observers using semidefinite programming.

Findings

The framework effectively manages the trade-offs among key factors.

Simulation results demonstrate improved estimation accuracy.

The approach reduces restrictive assumptions of prior methods.

Abstract

With the advancement of IoT technologies and the rapid expansion of cyber-physical systems, there is increasing interest in distributed state estimation, where multiple sensors collaboratively monitor large-scale dynamic systems. Compared with its continuous-time counterpart, a discrete-time distributed observer faces greater challenges, as it cannot exploit high-gain mechanisms or instantaneous communication. Existing approaches depend on three tightly coupled factors: (i) system observability, (ii) communication frequency and dimension of the exchanged information, and (iii) network connectivity. However, the interdependence among these factors remains underexplored. This paper identifies a fundamental trilemma among these factors and introduces a general design framework that balances them through an iterative semidefinite programming approach. As such, the proposed method mitigates the restrictive assumptions present in existing works. The effectiveness and generality of the proposed approach are demonstrated through a simulation example.