2-Dimensional high-quality reconstruction of compressive measurements of phased array weather radar

Ryosuke Kawami, Akira Hirabayashi, Nobuyuki Tanaka, Motoi Shibata, Takashi Ijiri, Shigeharu Shimamura, Hiroshi Kikuchi, Gwan Kim, Tomoo Ushio

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper proposes a compressive sensing method for the phased array weather radar (PAWR), which is capable of three-dimensional observation with high spatial resolution in 30 seconds. Because of the large amount of observation data, which is more than 1 gigabyte per minute, data compression is an essential technology to operate PAWR in the real world. 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 new cost function that expresses prior knowledge about weather radar measurements, i.e., local similarities. Since the cost function is convex, we can derive an efficient algorithm based on the so-called convex optimization techniques, in particular simultaneous direction method of multipliers (SDMM). Simulation results show that the proposed method outperforms the conventional methods for real observation data with improvement of 4% in the normalized error.

本文言語English
ホスト出版物のタイトル2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9789881476821
DOI
出版ステータスPublished - 2017 1 17
外部発表はい
イベント2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
継続期間: 2016 12 132016 12 16

Other

Other2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
CountryKorea, Republic of
CityJeju
Period16/12/1316/12/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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