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A Variable Forgetting Factor Proportionate Recursive KRSL Algorithm for Underwater Sparse Channel Estimation
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DOI：10.3390/jmse14100916.png)
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Abstract
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Accurate estimation of sparse underwater acoustic channels is challenging because of multipath delay spread, correlated inputs, and impulsive non-Gaussian noise. Existing KRSL-based algorithms still suffer from limited convergence speed and tracking capability in time-varying sparse scenarios. This paper proposes a VFF-PRKRSL algorithm, which jointly introduces an error-driven variable forgetting factor and a proportionate gain matrix into the recursive KRSL framework to achieve adaptive historical-information weighting and enhanced updating of dominant taps. Simulation results show that for Bellhop-generated underwater acoustic channels with sparsity levels of 0.125 and 0.4688, the proposed algorithm achieves NMSD values of −38.4763 dB and −37.9417 dB at the 2000th iteration, improving upon PRKRSL by approximately 5.31 dB and 5.29 dB, respectively.Under Cauchy noise, it reaches an NMSD of −46.3042 dB, about 5.95 dB better than PRKRSL. Ablation results indicate that the variable forgetting factor is the main source of the performance gain and is complementary to the proportionate update mechanism. These results demonstrate that VFF-PRKRSL outperforms existing methods in convergence speed, steady-state accuracy, and robustness against impulsive noise.
Keywords:
sparse underwater acoustic channel estimation
kernel risk-sensitive loss
recursive adaptive filtering
variable forgetting factor
proportionate updating
impulsive noise
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