Robust FIR System Identification for Super-Gaussian Noise Based on Hyperbolic Secant Distribution
Robust FIR System Identification for Super-Gaussian Noise Based on Hyperbolic Secant Distribution
复制标题
DOI：
10.1109/ispacs.2018.8923152
复制
发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Hiroki Tanji;T. Murakami;H. Kamata
中科院分区：
文献类型：
--
作者：
Hiroki Tanji;T. Murakami;H. Kamata
本文提出了一种基于双曲正割（sech）分布的有限脉冲响应辨识算法。在我们的算法中，我们假设噪声信号遵循超高斯分布。然后，为了估计FIR系数，我们推导了基于最大-最小（MM）算法的优化算法。在我们的算法中，尺度参数和FIR系数也被估计。仿真结果表明，该方法对超高斯噪声环境下的FIR系统识别是有效的。
In this paper, we present a finite impulse response (FIR) system identification algorithm based on the hyperbolic secant (sech) distribution. In our algorithm, we assume that the noise signal follows a super-Gaussian distribution. Then, to estimate the FIR coefficients, we derive the optimization algorithm based on the majorization-minimization (MM) algorithm. In our algorithm, the scale parameter is also estimated as well as the FIR coefficients. Our simulations demonstrate the proposed method is effective in the FIR system identification in super-Gaussian noise environments.