Kernel recursive generalized mixed norm algorithm |
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Authors: | Wentao Ma Xinyu Qiu Jiandong Duan Yingsong Li Badong Chen |
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Institution: | 1. School of Automation and Information Engineering, Xi''an University of Technology, Xi''an, China;2. College of Information and Communications Engineering, Harbin Engineering University, Harbin, China;3. School of Electronic and Information Engineering, Xi''an Jiaotong University, Xi''an, China |
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Abstract: | This work studies the problem of kernel adaptive filtering (KAF) for nonlinear signal processing under non-Gaussian noise environments. A new KAF algorithm, called kernel recursive generalized mixed norm (KRGMN), is derived by minimizing the generalized mixed norm (GMN) cost instead of the well-known mean square error (MSE). A single error norm such as lp error norm can be used as a cost function in KAF to deal with non-Gaussian noises but it may exhibit slow convergence speed and poor misadjustments in some situations. To improve the convergence performance, the GMN cost is formed as a convex mixture of lp and lq norms to increase the convergence rate and substantially reduce the steady-state errors. The proposed KRGMN algorithm can solve efficiently the problems such as nonlinear channel equalization and system identification in non-Gaussian noises. Simulation results confirm the desirable performance of the new algorithm. |
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Keywords: | Corresponding author |
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