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An Augmented Extended Kalman Filter Algorithm for Complex-Valued Recurrent Neural Networks

su.goh{at}imperial.ac.uk

Danilo P. Mandic

d.mandic{at}imperial.ac.uk Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.

An augmented complex-valued extended Kalman filter (ACEKF) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.