3-dimensional compressive sensing and high-quality recovery for phased array weather radar

Ryosuke Kawami, Akira Hirabayashi, Takashi Ijiri, Shigeharu Shimamura, Hiroshi Kikuchi, Tomoo Ushio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper proposes an effective three-dimensional compressive sensing method for the phased array weather radar (PAWR), which is capable of three-dimensional observation with spatially and temporally high resolution. Because of the large amount of observation data, which is approximately 1 gigabyte per minute, data compression is an essential technology to conduct a network observation by multiple PAWRs. 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 cost function for a three-dimensional recovery exploiting not only local similarity but also global redundancy of weather radar measurements. Since the cost function is convex, we can derive an efficient algorithm based on the standard convex optimization techniques. Simulation results show that the proposed method achieves normalized errors less than 10% for 25% compression ratio with outperforming conventional two-dimensional methods.

Original languageEnglish
Title of host publication2017 12th International Conference on Sampling Theory and Applications, SampTA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages658-661
Number of pages4
ISBN (Electronic)9781538615652
DOIs
Publication statusPublished - 2017 Sep 1
Externally publishedYes
Event12th International Conference on Sampling Theory and Applications, SampTA 2017 - Tallinn, Estonia
Duration: 2017 Jul 32017 Jul 7

Other

Other12th International Conference on Sampling Theory and Applications, SampTA 2017
CountryEstonia
CityTallinn
Period17/7/317/7/7

Fingerprint

Meteorological radar
Radar measurement
Recovery
Cost functions
Convex optimization
Data compression
Redundancy
Weather
Cost function
Compression

ASJC Scopus subject areas

  • Signal Processing
  • Statistics, Probability and Uncertainty
  • Analysis
  • Statistics and Probability
  • Applied Mathematics

Cite this

Kawami, R., Hirabayashi, A., Ijiri, T., Shimamura, S., Kikuchi, H., & Ushio, T. (2017). 3-dimensional compressive sensing and high-quality recovery for phased array weather radar. In 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 (pp. 658-661). [8024428] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAMPTA.2017.8024428

3-dimensional compressive sensing and high-quality recovery for phased array weather radar. / Kawami, Ryosuke; Hirabayashi, Akira; Ijiri, Takashi; Shimamura, Shigeharu; Kikuchi, Hiroshi; Ushio, Tomoo.

2017 12th International Conference on Sampling Theory and Applications, SampTA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 658-661 8024428.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kawami, R, Hirabayashi, A, Ijiri, T, Shimamura, S, Kikuchi, H & Ushio, T 2017, 3-dimensional compressive sensing and high-quality recovery for phased array weather radar. in 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017., 8024428, Institute of Electrical and Electronics Engineers Inc., pp. 658-661, 12th International Conference on Sampling Theory and Applications, SampTA 2017, Tallinn, Estonia, 17/7/3. https://doi.org/10.1109/SAMPTA.2017.8024428
Kawami R, Hirabayashi A, Ijiri T, Shimamura S, Kikuchi H, Ushio T. 3-dimensional compressive sensing and high-quality recovery for phased array weather radar. In 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 658-661. 8024428 https://doi.org/10.1109/SAMPTA.2017.8024428
Kawami, Ryosuke ; Hirabayashi, Akira ; Ijiri, Takashi ; Shimamura, Shigeharu ; Kikuchi, Hiroshi ; Ushio, Tomoo. / 3-dimensional compressive sensing and high-quality recovery for phased array weather radar. 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 658-661
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