SAMV (algorithm)

<p>SAMV (iterative sparse asymptotic minimum variance) is a parameter-free <a href="/facts/Super-resolution_imaging/npt0E08g">superresolution</a> algorithm for the linear <a href="/facts/Inverse_problem/Fe01pQ9E">inverse problem</a> in <a href="/facts/Spectral_density_estimation/JefWFBsx">spectral estimation</a>, <a href="/facts/Direction_of_arrival/xVJ5jv5a">direction-of-arrival</a> (DOA) estimation and <a href="/facts/Tomographic_reconstruction/rk84hL6s">tomographic reconstruction</a> with applications in <a href="/facts/Signal_processing/Npaja6zb">signal processing</a>, <a href="/facts/Medical_imaging/pL6JyJEp">medical imaging</a> and <a href="/facts/Remote_sensing/pj2crmGs">remote sensing</a>. The name was coined in 2013 to emphasize its basis on the asymptotically minimum variance (AMV) criterion. It is a powerful tool for the recovery of both the amplitude and frequency characteristics of multiple highly <a href="/facts/Correlation_coefficient/j8Swz1R9">correlated</a> sources in challenging environments (e.g., limited number of snapshots and low <a href="/facts/Signal-to-noise_ratio/qohClhyG">signal-to-noise ratio</a>). Applications include <a href="/facts/Synthetic-aperture_radar/MJbDGvTh">synthetic-aperture radar</a>, <a href="/facts/CT_scan/NusPJ2S8">computed tomography scan</a>, and <a href="/facts/Magnetic_resonance_imaging/Vh9yG9qq">magnetic resonance imaging (MRI)</a>.
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SAMV (algorithm) open-in-new

SAMV (algorithm)