Causal Wiener filter banks for periodically correlated time series
Signal Processing
A numerical solution for multichannel detection
IEEE Transactions on Communications
On testing for impropriety of complex-valued Gaussian vectors
IEEE Transactions on Signal Processing
The quaternion LMS algorithm for adaptive filtering of hypercomplex processes
IEEE Transactions on Signal Processing
Adaptive IIR filtering of noncircular complex signals
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Widely linear estimation algorithms for second-order stationary signals
IEEE Transactions on Signal Processing
A statistical analysis of morse wavelet coherence
IEEE Transactions on Signal Processing
Properness and widely linear processing of quaternion random vectors
IEEE Transactions on Information Theory
Complex blind source extraction from noisy mixtures using second-order statistics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Estimation of ambiguity functions with limited spread
IEEE Transactions on Signal Processing
On entropy rate for the complex domain and its application to i.i.d. sampling
IEEE Transactions on Signal Processing
Order Selection of the Linear Mixing Model for Complex-Valued FMRI Data
Journal of Signal Processing Systems
Widely linear prediction for transfer function models based on the infinite past
Computational Statistics & Data Analysis
A class of quaternion valued affine projection algorithms
Signal Processing
Testing blind separability of complex Gaussian mixtures
Signal Processing
| Hi-index | 35.83 |
The second-order statistical properties of complex signals are usually characterized by the covariance function. However, this is not sufficient for a complete second-order description, and it is necessary to introduce another moment called the relation function. Its properties, and especially the conditions that it must satisfy, are analyzed both for stationary and nonstationary signals. This leads to a new perspective concerning the concept of complex white noise as well as the modeling of any signal as the output of a linear system driven by a white noise. Finally, this is applied to complex autoregressive signals, and it is shown that the classical prediction problem must be reformulated when the relation function is taken into consideration