Information-Criterion-Based Agent Selection for Cooperative Localization in Static Networks

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

Abstract

In this paper we propose a Bayesian agent network planning algorithm for information-criterion-based measurement selection for cooperative localization in static networks with anchors. This allows to increase the accuracy of the agent positioning while keeping the number of measurements between agents to a minimum. The proposed algorithm is based on minimizing the conditional differential entropy (CDE) of all agent states to determine the optimal set of measurements between agents. Such combinatorial optimization problems have factorial runtime and quickly become infeasible, even for a rather small number of agents. Therefore, we propose a Bayesian agent network planning algorithm that performs a local optimization for each state. Experimental results demonstrate a performance improvement compared to a random measurement selection strategy, significantly reducing the position RMSE at a smaller number of measurements between agents.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
Place of PublicationDublin, Ireland
Pages1-7
Number of pages7
ISBN (Electronic)9781728174402
DOIs
Publication statusPublished - Jun 2020
Event2020 IEEE International Conference on Communications: ICC Workshops 2020 - Convention Centre Dublin, Virtuell, Ireland
Duration: 7 Jun 202011 Jun 2020

Conference

Conference2020 IEEE International Conference on Communications
Abbreviated titleIEEE ICC 2020
CountryIreland
CityVirtuell
Period7/06/2011/06/20

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Control and Optimization
  • Signal Processing
  • Computer Networks and Communications

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