Three-dimensional data compression and fast high-quality reconstruction for phased array weather radar

被引:3
作者
Kawami R. [1 ]
Kitahara D. [1 ]
Hirabayashi A. [1 ]
Yoshikawa E. [2 ]
Kikuchi H. [3 ]
Ushio T. [4 ]
机构
[1] Graduate School of Information Science and Engineering, Ritsumeikan University, 1-1-1, Noji-higashi, Kusatsu
[2] Aeronautical Technology Directorate, Japan Aerospace Exploration Agency, 6-13-1, Osawa, Mitaka, Tokyo
[3] Center for Space Science and Radio Engineering, University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo
[4] Division of Electrical, Electronic and Information Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka
基金
日本学术振兴会;
关键词
Compressed sensing; Convex optimization; Data compression; Nesterov's acceleration; Phased array weather radar;
D O I
10.1541/ieejeiss.140.40
中图分类号
学科分类号
摘要
This paper proposes a fast high-quality three-dimensional (3D) compressed sensing for a phased array weather radar (PAWR), which is capable of spatially and temporally high-resolution observation of the atmosphere. Because of the high-resolution, the PAWR generates huge observation data of approximately 500 megabytes every thirty seconds. To transfer this huge data in a public internet line for real time weather forecast, an efficient data compression technology is required. The proposed method compresses the PAWR data by randomly transferring several measurements only in the troposphere, and then reconstructs the missing measurements for each small 3D tensor data by minimizing a cost function based on a prior knowledge on weather phenomena. The minimizer of the cost function can be quickly computed by using a convex optimization algorithm with Nesterov's acceleration technique. Numerical simulations using real PAWR data show the effectiveness of the proposed method compared to conventional two-dimensional methods. © 2020 The Institute of Electrical Engineers of Japan.
引用
收藏
页码:40 / 48
页数:8
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