書誌事項
- タイトル別名
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- Large-volume Data Compression using Compressed Sensing for Meteorological Radar
- アッシュク センシング オ モチイタ キショウヨウ レーダ ノ ダイ ヨウリョウ カンソク データ ノ アッシュク
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抄録
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.
収録刊行物
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- 電気学会論文誌. A
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電気学会論文誌. A 135 (11), 704-710, 2015
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679576823808
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- NII論文ID
- 130005106278
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- NII書誌ID
- AN10136312
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- ISSN
- 13475533
- 03854205
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- NDL書誌ID
- 026916025
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可