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 language | English |
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Title of host publication | Acoustics, Speech and Signal Processing (ICASSP), 2019 IEEE International Conference on |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Pages | 4415-4419 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4799-8131-1 |
DOIs | |
Publication status | Published - 2019 |
Event | IEEE International Conference on Acoustics, Speech, and Signal Processing 2019 - Brighton, United Kingdom Duration: 2019 May 13 → 2019 May 17 Conference number: 44 |
Conference
Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing 2019 |
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Abbreviated title | ICASSP 2019 |
Country/Territory | United Kingdom |
City | Brighton |
Period | 2019/05/13 → 2019/05/17 |
Subject classification (UKÄ)
- Signal Processing
- Probability Theory and Statistics
Keywords
- optimal mass transport
- entropy regularization
- target localization
- non-coherent processing