Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport

Filip Elvander, Isabel Haasler, Andreas Jakobsson, Johan Karlsson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

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 solu- tion 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.
Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2019 IEEE International Conference on
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages4415-4419
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - 2019
EventIEEE International Conference on Acoustics, Speech, and Signal Processing 2019 - Brighton, United Kingdom
Duration: 2019 May 132019 May 17
Conference number: 44

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing 2019
Abbreviated titleICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period2019/05/132019/05/17

Subject classification (UKÄ)

  • Signal Processing
  • Probability Theory and Statistics

Free keywords

  • optimal mass transport
  • entropy regularization
  • target localization
  • non-coherent processing

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