偏置补偿的复值双线性滤波
Bias-Compensated Complex-Valued Bilinear Filtering
- Institute of Signal Processing, Johannes Kepler University Linz, Austria（因斯布鲁克大学信号处理研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对输入噪声导致复值双线性滤波器性能下降的问题，提出偏置补偿的C-BWF和C-BLMS滤波器，估计输入噪声统计量并给出收敛分析，仿真验证其优于标准及现有方法。
中文摘要 AI 辅助
复值最优和自适应滤波器广泛应用于各种实际应用中，以识别未知系统。近期，已提出多种复值双线性滤波器，如复值双线性维纳滤波器（C-BWF）和复值双线性最小均方（C-BLMS）滤波器，用于建模和识别系数呈双线性的复值系统。然而，若输入信号受噪声污染，由于额外的噪声引起的偏置，这些复值双线性滤波器的性能会显著下降。为解决此问题，我们提出了新颖的偏置补偿复值双线性滤波器，其估计并考虑输入噪声统计量。具体而言，本工作推导了偏置补偿的C-BWF和偏置补偿的C-BLMS滤波器。我们还对后者进行了收敛性分析。由于实际应用需要输入噪声方差的知识，我们简要介绍了一种从文献中改编的估计该量的方法。最后，仿真结果表明，所提出的滤波器相比其标准（非偏置补偿）对应物及多种最先进方法具有更优性能。
英文摘要
Complex-valued optimal and adaptive filters are widely used to identify unknown systems in a broad range of practical applications. Recently, a number of complex-valued bilinear filters, such as the complex-valued bilinear Wiener filter (C-BWF), and the complex-valued bilinear least mean squares (C-BLMS) filter, have been proposed to model and identify complex-valued systems, which are bilinear with respect to their coefficients. However, if the input signals are contaminated with noise, the performance of these complex-valued bilinear filters degrades significantly due to an additional noise-induced bias. To overcome this issue, we propose novel bias-compensated complex-valued bilinear filters that estimate and account for input-noise statistics. Specifically, a bias-compensated C-BWF and a bias-compensated C-BLMS filter are derived in this work. We further include a convergence analysis for the latter. Since practical applications require knowledge of the input-noise variance, we briefly present an adapted method from literature to estimate this quantity. Finally, simulation results demonstrate the superior performance of the proposed filters compared to their standard (non-bias-compensated) counterparts and several state-of-the-art methods.
