The effect of communication topology on scalar field estimation by large networks with partially accessible measurements

Ragesh K. Ramachandran, Spring Berman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper studies the problem of reconstructing a two-dimensional scalar field using measurements from a subset of a network with local communication between nodes. We consider the communication network of the nodes to form either a chain or a grid topology. We formulate the reconstruction problem as an optimization problem that is constrained by first-order linear dynamics on a large interconnected system. To solve this problem, we employ an optimization-based scheme that uses a gradient-based method with an analytical computation of the gradient. The main contribution of the paper is a derivation of bounds on the trace of the observability Gramian of the system, which can be used to quantify and compare the field estimation capabilities of chain and grid networks. A comparison based on a performance measure related to the ℋ2 norm of the system is also used to study the robustness of the network topologies. Our results are validated in simulation using both Gaussian scalar fields and actual ocean salinity data.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3886-3893
Number of pages8
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Other

Other2017 American Control Conference, ACC 2017
CountryUnited States
CitySeattle
Period5/24/175/26/17

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Keywords

  • field estimation
  • Networked robotic systems
  • sensor networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Ramachandran, R. K., & Berman, S. (2017). The effect of communication topology on scalar field estimation by large networks with partially accessible measurements. In 2017 American Control Conference, ACC 2017 (pp. 3886-3893). [7963550] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2017.7963550