Simple Compression Technique for Phased Array Weather Radar and 2-Dimensional High-Quality Reconstruction

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2017/07/01

タイトル(英語): Simple Compression Technique for Phased Array Weather Radar and 2-Dimensional High-Quality Reconstruction

著者名: Ryosuke Kawami (College of Information Science and Engineering, Ritsumeikan University), Akira Hirabayashi (College of Information Science and Engineering, Ritsumeikan University), Nobuyuki Tanaka (College of Information Science and Engineering, Ritsumeik

著者名(英語): Ryosuke Kawami (College of Information Science and Engineering, Ritsumeikan University), Akira Hirabayashi (College of Information Science and Engineering, Ritsumeikan University), Nobuyuki Tanaka (College of Information Science and Engineering, Ritsumeikan University), Takashi Ijiri (College of Information Science and Engineering, Ritsumeikan University), Shigeharu Shimamura (Graduate School of Engineering, Osaka University), Hiroshi Kikuchi (Department of Aerospace Engineering, Tokyo Metropolitan University), Gwan Kim (Graduate School of Engineering, Osaka University), Tomoo Ushio (Department of Aerospace Engineering, Tokyo Metropolitan University)

キーワード: Phased array weather radar (PAWR)，compressed sensing，total-variation，convex optimization

要約(英語): 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 approximately 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.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.7 （2017） 特集：平成28年電子・情報・システム部門大会

本誌掲載ページ: 864-870 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/137/7/137_864/_article/-char/ja/