Jiao Yuzhong, Cheung Rex Y P, Chow Winnie W Y, Mok Mark P C
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:4803-6. doi: 10.1109/EMBC.2013.6610622.
Least mean square (LMS) adaptive filter has been used to extract life signals from serious ambient noises and interferences in biomedical applications. However, a LMS adaptive filter with a fixed step size always suffers from slow convergence rate or large signal distortion due to the diversity of the application environments. An ideal adaptive filtering system should be able to adapt different environments and obtain the useful signals with low distortion. Adaptive filter with gradient adaptive step size is therefore more desirable in order to meet the demands of adaptation and convergence rate, which adjusts the step-size parameter automatically by using gradient descent technique. In this paper, a novel gradient adaptive step size LMS adaptive filter is presented. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately, thus achieves good adaptation and performance. Though it uses two LMS adaptive filters, it has a low computational complexity. An active noise cancellation (ANC) system with two applications for extracting heartbeat and lung sound signals from noises is used to simulate the performance of the proposed algorithm.
最小均方（LMS）自适应滤波器已被用于在生物医学应用中从严重的环境噪声和干扰中提取生命信号。然而，由于应用环境的多样性，具有固定步长的LMS自适应滤波器总是存在收敛速度慢或信号失真大的问题。理想的自适应滤波系统应该能够适应不同的环境并以低失真获取有用信号。因此，为了满足自适应和收敛速度的要求，具有梯度自适应步长的自适应滤波器更受青睐，它通过使用梯度下降技术自动调整步长参数。本文提出了一种新颖的梯度自适应步长LMS自适应滤波器。该算法利用两个自适应滤波器准确估计梯度，从而实现良好的自适应能力和性能。虽然它使用了两个LMS自适应滤波器，但其计算复杂度较低。一个具有从噪声中提取心跳和肺音信号这两种应用的有源噪声消除（ANC）系统被用于模拟所提算法的性能。
