| *商品について |
|
表紙はついていません（本文のみ中綴じ製本です）。
号単位でも購入できます。
すべてモノクロ印刷です。
Extended Summaryはついていません。
|
 |
| ・会員価格 ¥550 |
| ・一般価格 ¥770 |
|
|
| こちらはBookPark「電気学会 電子図書館（IEEJ Electronic Library）」による文献紹介ページです。 |
|
 |
 |
電気学会会員の方はこちらから一旦ログインのうえ、マイページからお入りください。
会員価格で購入することができます。
|
|
| 非会員の方はログインの必要はありません。このまま お進みください。 |
|
|
| ■論文No. |
|
| ■ページ数 |
7ページ |
■発行日
|
2017/07/01 |
| ■タイトル |
Simple Compression Technique for Phased Array Weather Radar and 2-Dimensional High-Quality Reconstruction |
| ■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) |
| ■価格 |
会員 ¥550 一般 ¥770 |
| ■書籍種類 |
論文誌(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 |
| ■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ページ |
| ■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/
|
| ■キーワード |
Phased array weather radar (PAWR)，compressed sensing，total-variation，convex optimization |
| ■要約(日本語) |
|
| ■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. |
| ■版 型 |
A4 |
|
|
|
