3-dimensional compressive sensing and high-quality recovery for phased array weather radar

Ryosuke Kawami, Akira Hirabayashi, Takashi Ijiri, Shigeharu Shimamura, Hiroshi Kikuchi, Tomoo Ushio

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper proposes an effective three-dimensional compressive sensing method for the phased array weather radar (PAWR), which is capable of three-dimensional observation with spatially and temporally high resolution. Because of the large amount of observation data, which is approximately 1 gigabyte per minute, data compression is an essential technology to conduct a network observation by multiple PAWRs. Even though many conventional studies applied compressive sensing (CS) to weather radar measurements, their reconstruction quality should be further improved. To this end, we define a cost function for a three-dimensional recovery exploiting not only local similarity but also global redundancy of weather radar measurements. Since the cost function is convex, we can derive an efficient algorithm based on the standard convex optimization techniques. Simulation results show that the proposed method achieves normalized errors less than 10% for 25% compression ratio with outperforming conventional two-dimensional methods.

本文言語English
ホスト出版物のタイトル2017 12th International Conference on Sampling Theory and Applications, SampTA 2017
編集者Gholamreza Anbarjafari, Andi Kivinukk, Gert Tamberg
出版社Institute of Electrical and Electronics Engineers Inc.
ページ658-661
ページ数4
ISBN(電子版)9781538615652
DOI
出版ステータスPublished - 2017 9 1
外部発表はい
イベント12th International Conference on Sampling Theory and Applications, SampTA 2017 - Tallinn, Estonia
継続期間: 2017 7 32017 7 7

出版物シリーズ

名前2017 12th International Conference on Sampling Theory and Applications, SampTA 2017

Other

Other12th International Conference on Sampling Theory and Applications, SampTA 2017
国/地域Estonia
CityTallinn
Period17/7/317/7/7

ASJC Scopus subject areas

  • 信号処理
  • 統計学、確率および不確実性
  • 分析
  • 統計学および確率
  • 応用数学

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