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 language | English |
|---|---|
| Title of host publication | 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 658-661 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538615652 |
| DOIs | |
| Publication status | Published - 2017 Sep 1 |
| Externally published | Yes |
| Event | 12th International Conference on Sampling Theory and Applications, SampTA 2017 - Tallinn, Estonia Duration: 2017 Jul 3 → 2017 Jul 7 |
Other
| Other | 12th International Conference on Sampling Theory and Applications, SampTA 2017 |
|---|---|
| Country | Estonia |
| City | Tallinn |
| Period | 17/7/3 → 17/7/7 |
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ASJC Scopus subject areas
- Signal Processing
- Statistics, Probability and Uncertainty
- Analysis
- Statistics and Probability
- Applied Mathematics
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - 3-dimensional compressive sensing and high-quality recovery for phased array weather radar
AU - Kawami, Ryosuke
AU - Hirabayashi, Akira
AU - Ijiri, Takashi
AU - Shimamura, Shigeharu
AU - Kikuchi, Hiroshi
AU - Ushio, Tomoo
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85031696994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031696994&partnerID=8YFLogxK
U2 - 10.1109/SAMPTA.2017.8024428
DO - 10.1109/SAMPTA.2017.8024428
M3 - Conference contribution
SP - 658
EP - 661
BT - 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -