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Logarithmic-sum function constrained set-membership FxNLMS algorithm for active noise control
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DOI：10.1016/j.dsp.2026.105905.png)
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In the field of active noise control (ANC), the traditional filtered-x normalized least mean square (FxNLMS) algorithm does not utilize the sparsity of the adaptive filter's weight vector, resulting in poor noise reduction performance. Additionally, when the reverberation time is long, the FxNLMS algorithm suffers from excessive computational load. To address the above two shortcomings of the FxNLMS algorithm, this paper proposes a logarithmic-sum function constrained set-membership FxNLMS (LSF-SM-FxNLMS) algorithm, which introduces a constraint and a logarithmic-sum function penalty to the cost function of the FxNLMS algorithm to reduce the computational load and utilize the sparsity of the adaptive filter's weight vector. A hardware-in-the-loop test bench was constructed to measure the actual primary and secondary paths. In this paper, the proposed algorithm is described and derived in detail, and its performance is analyzed through computer simulations based on the actual primary and secondary paths. Simulation results show that the proposed algorithm outperforms the traditional algorithms in terms of the noise reduction.
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论文数:
771
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