Receding horizon-based infotaxis with random sampling for source search and estimation in complex environments

Simple item page
dc.contributor.authorPark, Minkyu
dc.contributor.authorLadosz, Pawel
dc.contributor.authorKim, Jongyun
dc.contributor.authorOh, Hyondong
dc.date.accessioned2022-07-26T15:40:30Z
dc.date.available2022-07-26T15:40:30Z
dc.date.issued2022-06-21
dc.description.abstractThis paper proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the receding horizon Infotaxis for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.en_UK
dc.identifier.citationPark M, Ladosz P, Kim J, Oh H. (2023) Receding horizon-based infotaxis with random sampling for source search and estimation in complex environments. IEEE Transactions on Aerospace and Electronic Systems, Volume 59, Issue 1, February 2023, pp. 591-609en_UK
dc.identifier.issn0018-9251
dc.identifier.urihttps://doi.org/10.1109/TAES.2022.3184923
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18231
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAutonomous mobile sensor managementen_UK
dc.subjectbayesian inferenceen_UK
dc.subjectdispersion modelingen_UK
dc.subjectinformation-theoretic searchen_UK
dc.subjectreceding horizon path planningen_UK
dc.subjectsequential monte carlo methoden_UK
dc.titleReceding horizon-based infotaxis with random sampling for source search and estimation in complex environmentsen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Receding_horizon-based_infotaxis-2022.pdf
Size:
3.44 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)