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Table of Contents
1. Introduction: A brief history, the importance of the problem, non-circular signals, applications
2. Model and useful statistics: Mixing model, assumptions about mixing matrix and sources, statistics matrices and non-circular statistics matrices, Maximum Likelihood framework, Identifiability conditions, CRLB.
3. Normalization: Whitening, the case with additive noise, the case of over-determined systems, advantages/drawbacks
4. Direct estimation; Algebraic tools, exact diagonalization of two matrices, identifiability issues, some illustrations by computer simulations
5. Unitary recursive estimation: Unitary joint diagonalization of a set of Hermitian matrices, unitary joint diagonalization of a set of complex symmetric matrices, illustrations by computer simulations
6. General recursjve estimation: General Joint diagonalization of a set of Hermitian matrices, General joint diagonalization of a set of complex symmetric matrices, some illustrations by computer simulations
7. Adaptive complex estimation: ML and maximization of negentropy