Distributed Iterative FIR Consensus Filter for Multiagent Systems Over Sensor Networks

Liya Li, Peng Shi, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

For the target-tracking problem, full state of the target may not be available since it may be expensive or impossible to obtain. Thus, the state needs to be reconstructed or estimated only according to measured inputs and outputs. The impossible case that all followers can measure the target directly yields the study of distributed methods, thus reducing the communication and computation resource while resulting in more robustness. This article confronts these problems by addressing a distributed iterative finite impulse response (DIFIR) consensus filter for leader-following systems. A solution to the underlying problem is obtained by involving a distributed measurement model wherein not only the neighbors' estimates are applied but also the directed measurement data are used, and expressed by a computationally efficient iterative algorithm. Applying this DIFIR strategy, it is shown that the leader's estimates by all followers reach H∞ consensus, whose value is the local unbiased estimates of the leader. Then, the result is extended to multiagent systems whose leader has unknown inputs. Incorporating the input estimates, a new DIFIR is proposed. Finally, examples are given to illustrate the consistency and robustness of the developed new design techniques.

Original languageEnglish
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Consensus
  • distributed iterative finite impulse response (DIFIR) filter
  • multiagent systems
  • sensor network

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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