发表机构
The College of Computer Science and Technology, Inner Mongolia Minzu University（内蒙古民族大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RLS算法用于声学回声消除时计算复杂度高的问题，提出正则化块对角RLS算法，通过块对角近似与Tikhonov正则化实现降复杂度与稳定性，实验验证其收敛性与鲁棒性。
AI 中文摘要
尽管递归最小二乘（RLS）算法因收敛速度快而广泛应用于声学回声消除（AEC）等自适应滤波场景，但其计算复杂度高，严重限制了它在长滤波器中的实际部署。本文提出一种正则化块对角RLS（RBD-RLS）算法以应对这些挑战。通过将逆协方差矩阵近似为块对角结构，RBD-RLS将更新过程简化为子块的独立并行计算，有效降低了计算复杂度。此外，对每个子块应用Tikhonov正则化以提升数值稳定性。一系列实验结果表明，RBD-RLS在保持良好收敛性的同时大幅降低了计算复杂度，且在实际场景中仍表现出较强的鲁棒性。
英文摘要
While the recursive least square (RLS) algorithm is widely used in adaptive filtering applications like acoustic echo cancellation (AEC) due to its fast convergence rate, its high computational complexity severely limit its practical deployment for long filters. In this paper, a regularized block-diagonal RLS (RBD-RLS) algorithm is proposed to address these challenges. By approximating the inverse covariance matrix as a block-diagonal structure, RBD-RLS simplifies the update process into independent parallel computations of sub-blocks, effectively reducing the computational complexity. Additionally, Tikhonov regularization is applied to each sub-blocks for enhance numerical stability. A series of experimental results demonstrate that RBD-RLS maintains good convergence while significantly reducing computational complexity. Moreover, it still exhibits relative robustness in real-world scenarios.
