2014/06/13 by Kumar Vijay Mishra, Mishra, Kumar Vijay, Anton Kruger +3 · 1 citation
Earth and Planetary Sciences · Environmental Science · #FOS: Computer and information sciences #Information Theory (cs.IT) #Meteorological Phenomena and Simulations #Precipitation Measurement and Analysis #Soil Moisture and Remote Sensing
paper · pdf · doi:10.48550/arxiv.1406.3582
openalex publication_date 2014/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information. Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets. The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains. We propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes. We use Iowa X-band Polarimetric (XPOL) radar data to test and illustrate our algorithms.