Digital spectral analysis: with applications
Digital spectral analysis: with applications
Automatica (Journal of IFAC)
Adaptive system identification and signal processing algorithms
Proceedings of the COST #229 international workshop on Adaptive methods and emergent techniques for signal processing and communications
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
A fast algorithm for QR-1 factorization of Toeplitz matrices
Signal Processing
Adaptive modified covariance algorithms for spectral analysis
Signal Processing
Fast Implementation of Two-Dimensional APES and CAPON Spectral Estimators
Multidimensional Systems and Signal Processing
Recursive and fast recursive capon spectral estimators
EURASIP Journal on Applied Signal Processing
An adaptive filtering approach to spectral estimation and SARimaging
IEEE Transactions on Signal Processing
Underdetermined-order recursive least-squares adaptive filtering: the concept and algorithms
IEEE Transactions on Signal Processing
On the numerical stability and accuracy of the conventionalrecursive least squares algorithm
IEEE Transactions on Signal Processing
Time-Frequency ARMA Models and Parameter Estimators for Underspread Nonstationary Random Processes
IEEE Transactions on Signal Processing
A robust recursive least squares algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A Fast Algorithm for APES and Capon Spectral Estimation
IEEE Transactions on Signal Processing
Multi-dimensional Capon spectral estimation using discrete Zhang neural networks
Multidimensional Systems and Signal Processing
Hi-index | 35.68 |
In this paper fast algorithms for adaptive Capon and amplitude and phase estimation (APES) methods for spectral analysis of time varying signals, are derived. Fast, stable, nonrecursive formulae are derived, based on time shifting properties of the pertinent variables. As a consequence, efficient frequency domain recursive least squares (RLS) based, as well as fast RLS based algorithms for the adaptive estimation of the power spectra are developed.Stability issues of the frequency domain estimators are considered,and stabilization procedures are proposed. The computational complexity of the proposed algorithms is lower than relevant existing methods. The performance of the proposed algorithms is demonstrated through extensive simulations.