Displacement structure: theory and applications
SIAM Review
Capon estimation of covariance sequences
Circuits, Systems, and Signal Processing
Matched-filter bank interpretation of some spectral estimators
Signal Processing
Fast Implementation of Two-Dimensional APES and CAPON Spectral Estimators
Multidimensional Systems and Signal Processing
Efficient algorithms for adaptive capon and APES spectral estimation
IEEE Transactions on Signal Processing
An adaptive filtering approach to spectral estimation and SARimaging
IEEE Transactions on Signal Processing
Computationally efficient two-dimensional Capon spectrum analysis
IEEE Transactions on Signal Processing
Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
A Fast Algorithm for APES and Capon Spectral Estimation
IEEE Transactions on Signal Processing
Estimating and Time-Updating the 2-D Coherence Spectrum
IEEE Transactions on Signal Processing
IAA Spectral Estimation: Fast Implementation Using the Gohberg–Semencul Factorization
IEEE Transactions on Signal Processing
Efficient Implementation of Iterative Adaptive Approach Spectral Estimation Techniques
IEEE Transactions on Signal Processing
Superfast Approximative Implementation of the IAA Spectral Estimate
IEEE Transactions on Signal Processing
SAR imaging via efficient implementations of sparse ML approaches
Signal Processing
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The magnitude squared coherence (MSC) spectrum is an often used frequency-dependent measure for the linear dependency between two stationary processes, and the recent literature contain several contributions on how to form high-resolution data-dependent and adaptive MSC estimators, and on the efficient implementation of such estimators. In this work, we further this development with the presentation of computationally efficient implementations of the recent iterative adaptive approach (IAA) estimator, present a novel sparse learning via iterative minimization (SLIM) algorithm, discuss extensions to two-dimensional data sets, examining both the case of complete data sets and when some of the observations are missing. The algorithms further the recent development of exploiting the estimators' inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these formulations to the coherence estimation using IAA and SLIM formulations. The performance of the proposed algorithms and implementations are illustrated both with theoretical complexity measures and with numerical simulations.