Dynamic rainfall monitoring using microwave links

Venkat Roy, Shahzad Gishkori, Geert Leus

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
18 Downloads (Pure)


In this work, we propose a sparsity-exploiting dynamic rainfall monitoring methodology using rain-induced attenuation measurements from microwave links. To estimate rainfall field intensity dynamically from a limited number of non-linear measurements, we exploit physical properties of the rainfall such as spatial sparsity and non-negativity along with the dynamics of rainfall intensity. We develop a dynamic state estimation algorithm, where the aforementioned spatial properties are utilized as prior information. To exploit spatial sparsity, we use a basis function to tailor the sparse representation of the rainfall intensity. The basis is selected based on some criteria for sparse reconstruction such as orthonormality and mutual coherence. The tuning parameter that controls the sparsity in the spatial rainfall distribution is dynamically updated at every correction step. The developed methodology is applied to dynamically monitor the rainfall field intensity in an area with a specified spatial resolution using less number of simulated non-linear measurements than pixels. The proposed methodology can be generalized for any dynamic field reconstruction, where the limited number of non-linear measurements are field intensities integrated over a linear path.
Original languageEnglish
Article number77
Number of pages17
JournalEurasip Journal on Advances in Signal Processing (online)
Publication statusPublished - 4 Jul 2016


  • Rainfall monitoring
  • Sparsity
  • Field estimation


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