A performance analysis of subspace-based methods in the presence of model errors. I. The MUSIC algorithm

DOI10.1109/78.143447zbMath0850.62267OpenAlexW2097670707MaRDI QIDQ4017854

Thomas Kailath, A. Lee Swindlehurst

Publication date: 16 January 1993

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/956103208426f3381784b414e002dffa756722be

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10)

Related Items (14)

Polynomial-exponential decomposition from moments ⋮ Statistical distribution of difference of the maximum-likelihood angle-of-arrival spectra ⋮ Direction of arrival estimation in vector-sensor arrays using higher-order statistics ⋮ Large-sample analysis of MUSIC and min-norm direction estimators in the presence of model errors ⋮ About noneigenvector source localization methods ⋮ Sparse polynomial interpolation: sparse recovery, super-resolution, or Prony? ⋮ Structured low rank decomposition of multivariate Hankel matrices ⋮ Investigation of array robustness to sensor failure ⋮ One- and two-dimensional direction-of-arrival estimation: an overview of search-free techniques ⋮ Direction of arrival estimation of sources with intersecting signature in time-frequency domain using a combination of if estimation and MUSIC algorithm ⋮ Estimation of angle of arrival in a discrete random scattering environment using a Capon beamformer with a compensation matrix ⋮ Direction of arrival estimation using adaptive directional time-frequency distributions ⋮ Effect of spatial smoothing on the performance of subspace methods in the presence of array model errors ⋮ Three stochastic measurement schemes for direction-of-arrival estimation using compressed sensing method

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