Complex-Valued Nonlinear Adaptive Filters With Applications in α-Stable Environments
Journal article, Peer reviewed
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Original versionIEEE Signal Processing Letters. 2019, 26 (9), 1315-1319. 10.1109/LSP.2019.2929874
A nonlinear adaptive filtering framework for processing complex-valued signals is derived. The introduced adaptive filter extends the fractional-order framework of the authors for dealing with real-valued signals to the complex domain via the augmented statistical approach to complex-valued signal processing. This results in a versatile class of adaptive filtering techniques, which allows the classical Gaussian assumption to be extended to the generalized context of α-stables. For rigor, the performance of the introduced adaptive filtering framework is analyzed, its convergence criteria is established, and its application in tracking signals of chaotic systems is demonstrated using simulations.