Digital spectral analysis: with applications
Digital spectral analysis: with applications
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
On the convergence of the minimum variance spectral estimator in nonstationary noise
IEEE Transactions on Information Theory
Efficient algorithms for adaptive capon and APES spectral estimation
IEEE Transactions on Signal Processing
Fourier spectral factor model for prediction of multidimensional signals
Signal Processing
| Hi-index | 0.08 |
The minimum variance (MV) spectral estimator is a robust high-resolution frequency-domain analysis tool for short data records. The traditional formulation of the minimum variance spectral estimation (MVSE) depends on the inverse of a Toeplitz autocorrelation matrix, for which a fast computational algorithm exists that exploits this structure. This paper extends the MVSE approach to two data-only formulations linked to the covariance and modified covariance cases of least-squares linear prediction (LP), which require inversion of near-to-Toeplitz data product matrices. We show here that the near-to-Toeplitz matrix inverses in the two new fast algorithms have special representations as sums of products of triangular Toeplitz matrices composed of the LP parameters of the least-squares-based formulations. Fast algorithm solutions of the LP parameters have been published by one of the authors. From these, we develop fast solutions of two least-squares-based minimum variance spectral estimators (LS-based MVSEs). These new MVSEs provide additional resolution improvement over the traditional autocorrelation-based MVSE.