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Correlation and Covariance Matrices of a Complex Random Vector

  • Frédéric Cohen TenoudjiEmail author
Chapter
Part of the Modern Acoustics and Signal Processing book series (MASP)

Abstract

We introduce in this chapter the correlation and covariance matrices of a complex random vector. The Hermitian nature of these matrices allows their diagonalization in the basis of their orthogonal eigenvectors. These concepts are discussed on jointly Gaussian variables. We study the principal component analysis of a vector of observations and the optimum Karhunen-Loève development.

Keywords

Correlation Function Covariance Matrix Probability Density Function Correlation Matrix Random Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Pierre and Marie Curie University, UPMCParisFrance

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