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SAMV (iterative sparse asymptotic minimum variance) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing. 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 correlated sources in challenging environments (e.g., limited number of snapshots and low signal-to-noise ratio). Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI).

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  • SAMV (ko)
  • SAMV (アルゴリズム) (ja)
  • SAMV (algorithm) (en)
  • 迭代稀疏漸近最小方差算法 (zh)
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  • SAMV (iterative sparse asymptotic minimum variance) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing. 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 correlated sources in challenging environments (e.g., limited number of snapshots and low signal-to-noise ratio). Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI). (en)
  • SAMV (반복 스파 스 점근 최소 분산)는 신호 처리의 스펙트럼 추정 및 도착 방향 (DOA) 추정을 위한 파라미터 무료 슈퍼 해상도 알고리즘이다. 이 이름은 점근 최소 분산 (AMV) 기준의 기초를 강조하기 위해 만들어 낸되었다. 제한된 수의 스냅 샷 낮은 신호대 잡음비 등 어려운 환경에서 여러 높은 상관 소스의 진폭과 주파수의 두 특성을 복구하는 강력한 도구다. 합성 개구 레이다 영상과 다양한 소스 지역화. (ko)
  • SAMV（反復スパース漸近最小分散）は、信号処理におけるスペクトル推定および到着方向 (DOA) 推定のためのパラメータフリーの超解像アルゴリズムである。この名前は、漸近最小分散 (AMV) 基準の基礎を強調するために造語された。限られた数のスナップショット、低い信号対雑音比など、厳しい環境下で複数の高相関ソースの振幅と周波数の両方の特性を回復する強力なツールである。合成アパーチャレーダーイメージングとさまざまなソースローカリゼーション。 (ja)
  • 迭代稀疏漸近最小方差算法是用於信號處理中的譜估計和到達方向（DOA）估計的無參數超分辨率算法。 這個名稱是為了強調漸近最小方差（AMV）標準的創造基礎。 它是在惡劣環境下恢復多個高相關源的幅度和頻率特性的有力工具，例如有限數量的快照，低信噪比。 它可以用於合成孔徑雷達。 (zh)
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  • SAMV (iterative sparse asymptotic minimum variance) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing. 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 correlated sources in challenging environments (e.g., limited number of snapshots and low signal-to-noise ratio). Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI). (en)
  • SAMV (반복 스파 스 점근 최소 분산)는 신호 처리의 스펙트럼 추정 및 도착 방향 (DOA) 추정을 위한 파라미터 무료 슈퍼 해상도 알고리즘이다. 이 이름은 점근 최소 분산 (AMV) 기준의 기초를 강조하기 위해 만들어 낸되었다. 제한된 수의 스냅 샷 낮은 신호대 잡음비 등 어려운 환경에서 여러 높은 상관 소스의 진폭과 주파수의 두 특성을 복구하는 강력한 도구다. 합성 개구 레이다 영상과 다양한 소스 지역화. (ko)
  • SAMV（反復スパース漸近最小分散）は、信号処理におけるスペクトル推定および到着方向 (DOA) 推定のためのパラメータフリーの超解像アルゴリズムである。この名前は、漸近最小分散 (AMV) 基準の基礎を強調するために造語された。限られた数のスナップショット、低い信号対雑音比など、厳しい環境下で複数の高相関ソースの振幅と周波数の両方の特性を回復する強力なツールである。合成アパーチャレーダーイメージングとさまざまなソースローカリゼーション。 (ja)
  • 迭代稀疏漸近最小方差算法是用於信號處理中的譜估計和到達方向（DOA）估計的無參數超分辨率算法。 這個名稱是為了強調漸近最小方差（AMV）標準的創造基礎。 它是在惡劣環境下恢復多個高相關源的幅度和頻率特性的有力工具，例如有限數量的快照，低信噪比。 它可以用於合成孔徑雷達。 (zh)
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