Robust Distributed Consensus-Based Filtering for Uncertain Systems over Sensor Networks

Author(s)

Rocha, Kaio D. T. ; Terra, Marco H.

• Document type: Journal article
• Source: IFAC PAPERSONLINE; v. 53, n. 2, p. 6-pg., 2020-01-01.

Abstract

Distributed consensus-based estimation is one of the main applications of sensor networks. Most approaches are highly dependent on exact model knowledge. This limitation motivated the development of robust distributed filters that deal with model uncertainties. Many of these works, however, are not fully distributed filters or demand high communication and computational efforts. In this paper, we propose a robust distributed consensus-based filter for uncertain discrete-time linear systems. We assume norm-bounded parametric uncertainties in all matrices of both the target system and sensing models. The approach consists of adopting a purely deterministic interpretation of the robust distributed estimation problem, formulated by combining the penalty function method and the robust regularized least-squares estimation problem. The filter is presented in a fully distributed Kalman-like structure that is suitable for online applications, requiring acceptable computational and communication efforts. We evaluate the effectiveness of the proposed filter by comparing its performance with an existing robust distributed filter, as well as with a centralized strategy. Copyright (C) 2020 The Authors. (AU)

• FAPESP's process: 17/16346-4 - Communication network fault tolerant control for coordinated movement of heterogeneous robots
• Grantee: Kaio Douglas Teófilo Rocha
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment
• Grantee: Marco Henrique Terra
• Support Opportunities: Research Projects - Thematic Grants