Compressive Sensing-Based 3-D Rain Field Tomographic Reconstruction Using Simulated Satellite Signals

Weiwei Jiang, Yafeng Zhan, Xi Shen, Defeng David Huang, Jianhua Lu

Research output: Contribution to journalArticlepeer-review

Abstract

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
Early online date12 Mar 2021
DOIs
Publication statusPublished - 2022

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