Maximum likelihood filters in spectral estimation problems
Signal Processing
Digital spectral analysis: with applications
Digital spectral analysis: with applications
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Matched-filter bank interpretation of some spectral estimators
Signal Processing
Minimum Variance Distortionless Response (MVDR) Modeling of Voiced Speech
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Advances in Network and Acoustic Echo Cancellation
Advances in Network and Acoustic Echo Cancellation
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
Acoustic vector-sensor beamforming and Capon direction estimation
IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
An iterative algorithm for the computation of the MVDR filter
IEEE Transactions on Signal Processing
System modeling and signal processing for a switch antenna array radar
IEEE Transactions on Signal Processing
Performance analysis of forward-backward matched-filterbankspectral estimators
IEEE Transactions on Signal Processing
Rank-deficient robust Capon filter bank approach to complex spectral estimation
IEEE Transactions on Signal Processing - Part I
A recursive least squares implementation for LCMP beamforming underquadratic constraint
IEEE Transactions on Signal Processing
A Capon's time-octave representation application in room acoustics
IEEE Transactions on Signal Processing
Efficient algorithms for adaptive capon and APES spectral estimation
IEEE Transactions on Signal Processing
Multi-dimensional Capon spectral estimation using discrete Zhang neural networks
Multidimensional Systems and Signal Processing
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The Capon algorithm, which was originally proposed for wavenumber estimation in array signal processing, has become a powerful tool for spectral analysis. Over several decades, a significant amount of research attention has been devoted to the estimation of the Capon spectrum. Most of the developed algorithms thus far, however, rely on the direct computation of the inverse of the input correlation (or covariance) matrix, which can be computationally very expensive particularly when the dimension of the matrix is large. This paper deals with fast and efficient algorithms in computing the Capon spectrum. Inspired from the recursive idea established in adaptive signal processing theory, we first derive a recursive Capon algorithm. This new algorithm does not require an explicit matrix inversion, and hence it is more efficient to implement than the direct-inverse approach. We then develop a fast version of the recursive algorithm based on techniques used in fast recursive least-squares adaptive algorithms. This new fast algorithm can further reduce the complexity of the recursive Capon algorithm by an order of magnitude. Although our focus is on the Capon spectral estimation, the ideas shown in this paper can also be generalized and applied to other applications. To illustrate this, we will show how to apply the recursive idea to the estimation of the magnitude squared coherence function, which plays an important role for problems like time-delay estimation, signal-to-noise ratio estimation, and doubletalk detection in echo cancellation.