Consensus-Based Distributed Source Term Estimation with Particle Filter and Gaussian Mixture Model

Yang Liu, Matthew Coombes, Cunjia Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Source term estimation (STE) techniques provide an effective way of understanding the key parameters of an atmospheric release in different scenarios. Following the Bayesian inference framework, this paper investigates the distributed STE problem over a sensor network based on a consensus-based particle filtering scheme. Among different consensus strategies, the posterior-based consensus method is selected, so that all the sensor nodes can reach the same belief of the source term. To effectively approximate the local posterior density functions (PDFs) and share them over the sensor network, the Gaussian mixture model (GMM) is constructed at each node by resorting to the expectation-maximization method, and the parameters of the GMMs are exchanged between the sensor nodes. The consensus between the GMMs from different nodes is realised in the sense of Kullback-Leibler average (KLA). To provide a numerical solution to this process, an importance sampling method with a novel importance density function is proposed to draw particles at each node with respect to the GMMs from the neighboring nodes. Finally, the effectiveness of the proposed distributed STE solution is demonstrated with an experimental dataset.

Original languageEnglish
Title of host publicationROBOT 2022
Subtitle of host publicationFifth Iberian Robotics Conference
EditorsDanilo Tardioli, Vicente Matellán, Guillermo Heredia, Manuel F. Silva, Lino Marques
PublisherSpringer, Cham
Number of pages12
ISBN (Electronic)9783031210624
ISBN (Print)9783031210617
Publication statusPublished - 19 Nov 2022
Externally publishedYes
Event5th Iberian Robotics Conference - Zaragoza, Spain
Duration: 23 Nov 202225 Nov 2022
Conference number: 5

Publication series

NameLecture Notes in Networks and Systems
PublisherSpringer Cham
Volume590 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference5th Iberian Robotics Conference
Abbreviated titleROBOT 2022
Internet address

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