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NON-COHERENT SENSOR FUSION VIA ENTROPY REGULARIZED OPTIMAL MASS TRANSPORT
Lund Univ, Div Math Stat, Lund, Sweden..
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
Lund Univ, Div Math Stat, Lund, Sweden..ORCID iD: 0000-0002-2156-6973
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0001-5158-9255
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 4415-4419Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of the spatial spectrum using a convex barycenter formulation. We introduce an entropy regularization term to the convex objective, which allows for low-complexity iterations of the solution algorithm and thus makes the proposed method applicable also to higher-dimensional problems. We illustrate the proposed method's inherent robustness to misalignment and miscalibration of the sensor arrays using numerical examples of localization in two dimensions.

Place, publisher, year, edition, pages
IEEE , 2019. p. 4415-4419
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords [en]
Optimal mass transport, Entropy regularization, Target localization, Sensor fusion, Non-coherent processing
National Category
Other Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-261060DOI: 10.1109/ICASSP.2019.8682186ISI: 000482554004130Scopus ID: 2-s2.0-85069002151ISBN: 978-1-4799-8131-1 (print)OAI: oai:DiVA.org:kth-261060DiVA, id: diva2:1356648
Conference
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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Haasler, IsabelKarlsson, Johan

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