Distributed bounded-error state estimation

Abstract

This paper presents a distributed bounded-error state estimation algorithm suited, e.g., to measurement processing by a network of sensors. Contrary to centralized estimation, where all data are collected to a central processing unit, here, each data is processed locally by the sensor, the results are broadcasted to the network and taken into account by the other sensors. A first analysis of the conditions under which distributed and centralized estimators provide the same results is presented. An application to the tracking of a moving source using a network of sensors measuring the strength of the signal emitted by the source is considered * . * This work has been partly supported by NEWCOM++

Guaranteed estimation and distributed control of vehicle formations

“…However, some interesting solutions for this problem can be found. The information received from the neighbourhood can be incorporated in the estimated set by intersection, see García et al (2017); Keiffer (2009), or by means of linear combinations of the sets, as in Combastel and Zolghadri (2018); Orihuela et al (2017). Previous papers described the sets using zonotopes.…”

“…When it comes to the extension of the aforementioned formulation to a distributed paradigm, a revision of the state of the art reveals that the literature is rather scarce. Using a zonotope-based description, the authors in García, Rubio, Orihuela, Millán, and Ortega (2017); Keiffer (2009) were able to present distributed estimation algorithms that make use of intersections between sets. Another idea was used in Orihuela, Millán, Roshany-Yamchi, and Garcia (2018); Orihuela, Roshany-Yamchi, Garcia, and Millán (2017), where the authors presented a Kalman-inspired set-membership observer extending the idea in Combastel (2015) to a distributed framework with network-based problems.…”