10,864 research outputs found
Compressed Sensing Applied to Weather Radar
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.Comment: 4 pages, 5 figrue
Analysis of Sparse MIMO Radar
We consider a multiple-input-multiple-output radar system and derive a
theoretical framework for the recoverability of targets in the azimuth-range
domain and the azimuth-range-Doppler domain via sparse approximation
algorithms. Using tools developed in the area of compressive sensing, we prove
bounds on the number of detectable targets and the achievable resolution in the
presence of additive noise. Our theoretical findings are validated by numerical
simulations
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