圧縮センシングを用いた気象用レーダの大容量観測データの圧縮

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2015/11/01

タイトル(英語): Large-volume Data Compression using Compressed Sensing for Meteorological Radar

著者名: 嶋村　重治（大阪大学大学院 工学研究科），菊池　博史（大阪大学大学院 工学研究科），松田　崇弘（大阪大学大学院 工学研究科），金　寛（大阪大学大学院 工学研究科），吉川　栄一（宇宙航空研究開発機構），中村　佳敬（神戸市立工業高等専門学校），牛尾　知雄（大阪大学大学院 工学研究科）

著者名(英語): Shigeharu Shimamura (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University), Hiroshi Kikuchi (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University), Takahiro Matsuda (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University), Gwan Kim (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University), Eiichi Yoshikawa (Japan Aerospace eXploration Agency), Yoshitaka Nakamura (Kobe City College of Technology), Tomoo Ushio (Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University)

キーワード: フェーズドアレイレーダ，Ku帯広帯域レーダ，大容量データ，圧縮センシング　　phased array radar，broad band radar，large-volume data，compressed sensing

要約(英語): In Japan, severe weather phenomena such as heavy rains and tornados sometimes cause meteorological disasters. In many cases, these are micro scale phenomena in the sense of spatial and temporal resolutions, which make it difficult to detect them with conventional meteorological radars due to their insufficient spatial and temporal resolutions. Therefore, we have been developing meteorological radars with high resolution and accuracy such as phased array radar (PAR) and Ku-band broadband radar (BBR), and radar network systems consisting of multiple PARs and BBRs to realize further enhancement of the radar performance in terms of efficiency and accuracy. These high-resolution radars, however, definitely produce large-volume data, which is unacceptable in a current backbone information network. In order to solve this problem, in this paper, we tackle the compression of the large-volume radar data by using Compressed sensing (CS), which can realize highly efficient data compression for sparse signals. When using CS, the radar data is compressed by projecting it onto a randomly generated subspace, and the compressed data is reconstructed by solving a simple l1 optimization problem. We apply the CS-based data compression scheme to measured radar reflectivity factor, and evaluate the relation between compression ratio and reconstruction accuracy. For the compression ratio of 0.3, rainfall rate calculated from the reconstructed radar reflectivity factor has a mean error of -0.89 mm/h with more than 30 dBZ precipitation.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.135 No.11 （2015） 特集：電気電子工学関連分野における教育フロンティア

本誌掲載ページ: 704-710 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejfms/135/11/135_704/_article/-char/ja/