Abstract
Recent years have witnessed a growing academic interest in the utilization of correntropy to improve the robustness of adaptive filtering, which generally employs Gaussian function as the kernel. However, it is an inappropriate choice for asymmetrically distributed noise. The asymmetric correntropy proposed recently adopts an asymmetric Gaussian function as the kernel, and the maximum asymmetric correntropy criterion (MACC) shows great superiority in presence of asymmetric noise. Inspired by it, we aim at proposing a more suitable cost function and its corresponding algorithm for more effective adaptive filtering in the case where the complex-valued system is disturbed by some noises with asymmetric distributions. In this paper, combined with complex correntropy, we define a new variant, called asymmetric complex correntropy, which employs an asymmetric complex Gaussian function as the kernel. Then, we propose a novel optimization criterion, called maximum asymmetric complex correntropy criterion (MACCC). Besides that, we further develop a stochastic gradient-based MACCC algorithm for complex-domain filtering. The steady-state performance analysis derives the bound of step size and the theoretical results of excess mean square error. Simulations are provided to verify the correctness of theoretical value and the superiority of MACCC algorithm.
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Acknowledgements
This work was supported by Chongqing Municipal Training Program of Innovation and Entrepreneurship for Undergraduates (Grant: 202110635039).
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The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Yin, H., Mei, J., Dong, F. et al. Asymmetric Complex Correntropy for Robust Adaptive Filtering. Circuits Syst Signal Process 41, 4692–4706 (2022). https://doi.org/10.1007/s00034-022-02004-8
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DOI: https://doi.org/10.1007/s00034-022-02004-8