Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150169
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dc.contributor.authorJiang, Chaoyangen_US
dc.contributor.authorChen, Zhenghuaen_US
dc.contributor.authorSu, Rongen_US
dc.contributor.authorSoh, Yeng Chaien_US
dc.date.accessioned2021-06-03T13:52:06Z-
dc.date.available2021-06-03T13:52:06Z-
dc.date.issued2019-
dc.identifier.citationJiang, C., Chen, Z., Su, R. & Soh, Y. C. (2019). Group greedy method for sensor placement. IEEE Transactions On Signal Processing, 67(9), 2249-2262. https://dx.doi.org/10.1109/TSP.2019.2903017en_US
dc.identifier.issn1053-587Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/150169-
dc.description.abstractThis paper discusses greedy methods for sensor placement in linear inverse problems. We comprehensively review the greedy methods in the sense of optimizing the mean squared error (MSE), the volume of the confidence ellipsoid, and the worst-case error variance. We show that the greedy method of optimizing an MSE related cost function can find a near-optimal solution. We then provide a new fast algorithm to optimize the MSE. In greedy methods, we select the sensing location one by one. In this way, the searching space is greatly reduced but many valid solutions are ignored. To further improve the current greedy methods, we propose a group-greedy strategy, which can be applied to optimize all the three criteria. In each step, we reserve a group of suboptimal sensor configurations which are used to generate the potential sensor configurations of the next step and the best one is used to check the terminal condition. Compared with the current greedy methods, the group-greedy strategy increases the searching space but greatly improve the solution performance. We find the necessary and sufficient conditions that the current greedy methods and the proposed group greedy method can obtain the optimal solution. The illustrative examples show that the group greedy method outperforms the corresponding greedy method. We also provide a practical way to find a proper group size with which the proposed group greedy method can find a solution that has almost the same performance as the optimal solution.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipBuilding and Construction Authority (BCA)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF2015ENC-GBICRD001-057en_US
dc.relationNRF-CRP8-2011-03en_US
dc.relationA1788a0023en_US
dc.relation.ispartofIEEE Transactions on Signal Processingen_US
dc.rights© 2019 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleGroup greedy method for sensor placementen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TSP.2019.2903017-
dc.identifier.scopus2-s2.0-85062649606-
dc.identifier.issue9en_US
dc.identifier.volume67en_US
dc.identifier.spage2249en_US
dc.identifier.epage2262en_US
dc.subject.keywordsGreedy Methoden_US
dc.subject.keywordsGroup Greedy Methoden_US
dc.description.acknowledgementThis work was supported in part by the Building and Construction Authority (BCA) of Singapore through the NRF GBIC Program with the Project NRF2015ENC-GBICRD001-057, in part by the National Research Foundation Singapore through the NRF CRP Program under Grant NRF-CRP8-2011-03, in part by the A*STAR Industrial Internet of Things Research Program under the RIE2020IAF-PP Grant A1788a0023, and in part by Beijing Institute of Technology Re-search Fund Program for Young Scholars.en_US
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