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
AN - SCOPUS:85031696994
T3 - 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017
SP - 658
EP - 661
BT - 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017
A2 - Anbarjafari, Gholamreza
A2 - Kivinukk, Andi
A2 - Tamberg, Gert
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Sampling Theory and Applications, SampTA 2017
Y2 - 3 July 2017 through 7 July 2017
ER -