2-Dimensional high-quality reconstruction of compressive measurements of phased array weather radar

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 more than 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.