@article{oai:jaxa.repo.nii.ac.jp:00022738,
 author = {嶋村, 重治 and 菊池, 博史 and 松田, 崇弘 and 金, 寛 and 吉川, 栄一 and 中村, 佳敬 and 牛尾, 知雄 and Shimamura, Shigeharu and Kikuchi, Hiroshi and Matsuda, Takahiro and Kim, Gwan and Yoshikawa, Eiichi and Nakamura, Yoshitaka and Ushio, Tomoo},
 issue = {11},
 journal = {電気学会論文誌A （基礎・材料・共通部門誌）, IEEJ Transactions on Fundamentals and Materials},
 month = {Nov},
 note = {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 ℓ(sub 1) 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., 資料番号: PA1620053000},
 pages = {704--710},
 title = {Large-volume Data Compression using Compressed Sensing for Meteorological Radar},
 volume = {135},
 year = {2015}
}