抄録
Phased array weather radar (PAWR) is capable of spatially and temporally high resolution observation. This means that a PAWR generates a huge amount of observation data, say 500 megabytes in every 30 seconds. To transfer this big data in a public internet line, this paper proposes a fast 3D compressive sensing method for PAWR. The proposed method reconstructs the original data, from compressed data, as the minimizer of a convex function which evaluates the local similarity in the spatial domain and the sparsity in the frequency domain. By combining blockwise optimization with Nesterov's acceleration, we obtain an approximate solution of the above convex optimization problem at high speed. Numerical simulations show that the proposed method outperforms conventional reconstruction methods.
本文言語 | English |
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ホスト出版物のタイトル | Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ページ | 44-49 |
ページ数 | 6 |
巻 | 2018-February |
ISBN(電子版) | 9781538615423 |
DOI | |
出版ステータス | Published - 2018 2 5 |
イベント | 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia 継続期間: 2017 12 12 → 2017 12 15 |
Other
Other | 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 |
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Country | Malaysia |
City | Kuala Lumpur |
Period | 17/12/12 → 17/12/15 |
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Information Systems
- Signal Processing