Second-order analysis of improper complex random vectors and processes

By Peter J. Schreier, Louis L. Scharf. IEEE Transactions on Signal Processing, 2003

402 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bff0957dbbe5c9b4619db12a80348588e2272a37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Danilo P. Mandic
2. Clive Cheong Took
3. T. Adalı
4. Louis L. Scharf
5. D. P. Palomar
6. Yi Jiang
7. Noureddine El Karoui

In Computer Vision and Pattern Recognition

1. Danilo P. Mandic
2. Clive Cheong Took
3. T. Adalı
4. D. P. Palomar

In Signal Processing

1. Danilo P. Mandic
2. Clive Cheong Took
3. T. Adalı
4. Louis L. Scharf
5. D. P. Palomar
6. Peter J. Schreier
7. Vince D. Calhoun
8. Yuhui Du

Citations in the corpus

• The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes (2009, methodology): “have exploited the bivariate model of wind [8], [10] in this context; this was achieved by using the so-called augmented statistics [11].”
• MIMO Transceiver Design via Majorization Theory (2007, background): “Fortunately, many of the commonly employed constellations, such as the family of QAM constellations, are proper [134], which allows the use of a nice complex notation (although some other constellations, such as BPSK and GMSK, are improper and a complex notation is not adequate anymore).”
• Complex-Valued Signal Processing: The Proper Way to Deal With Impropriety (2011, background): “The augmented covariance matrix of is [101], [114], [129] (12) The NW block of the augmented covariance matrix is the usual (Hermitian and positive semi-definite) covariance matrix (13) and the NE block is the complementary covariance matrix (14) which uses a regular transpose rather than a…”