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Convex regularized recursive kernel risk-sensitive loss adaptive filtering algorithm and its performance analysis
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DOI：10.1016/j.sigpro.2024.109568.png)
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Abstract
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In the context of channel estimation amid non -Gaussian impulse noise, traditional non -kernel -space methods face challenges of divergence, while many kernel -space methods fail to fully exploit the a priori information embedded in the channel. To address this, we introduce a robust sparse recursive adaptive filtering algorithm named convex regularized recursive kernel risk -sensitive loss (CR-RKRSL) in this paper. By combining the KRSL with a convex function constraint term, our proposed algorithm maximizes the utilization of channel a priori information. Furthermore, we delve into the theoretical aspects of the proposed algorithm, presenting expressions for the convergence and steady-state error. Through extensive simulation results, we demonstrate that CR-RKRSL outperforms the APSA, LHCAF, PRMCC, CR-RMC, RZAMCC algorithms. In comparison to existing algorithms, CR-RKRSL exhibits superior robustness and faster convergence, particularly in scenarios involving highly sparse systems.
Keywords:
Kernel risk-sensitive loss (KRSL)
Convex regularized
Normalized mean squared deviation (NMSD)
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