Simple compression technique for phased array weather radar and 2-dimensional high-quality reconstruction

Ryosuke Kawami, Akira Hirabayashi, Nobuyuki Tanaka, Takashi Ijiri, Shigeharu Shimamura, Hiroshi Kikuchi, Gwan Kim, Tomoo Ushio

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper proposes a compressive sensing method for the phased array weather radar (PAWR), which is capable of three-dimensional observation with high spatial resolution in 30 seconds. Because of the large amount of observation data, which is approximately 1 gigabyte per minute, data compression is an essential technology to operate PAWR in the real world. 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 new cost function that expresses prior knowledge about weather radar measurements, i.e., local similarities. Since the cost function is convex, we can derive an efficient algorithm based on the so-called convex optimization techniques, in particular simultaneous direction method of multipliers (SDMM). Simulation results show that the proposed method outperforms the conventional methods for real observation data with improvement of 4%in the normalized error.

Original languageEnglish
Pages (from-to)864-870
Number of pages7
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number7
Publication statusPublished - 2017
Externally publishedYes


  • Compressed sensing
  • Convex optimization
  • Phased array weather radar (PAWR)
  • Total-variation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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