论文标题
基于神经网络和可训练的分数矩量的k分布的参数估计
Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments
论文作者
论文摘要
同源K（HK）分布已被广泛用于描述在各个研究领域（例如超声成像或光学）中产生的散射现象。在这项工作中，我们建议一种基于机器学习的方法来估计HK分布参数。我们开发的神经网络可以基于使用分数阶矩计算出的信噪比，偏度和峰度来估算HK分布参数。与以前的方法相比，我们将矩的顺序视为可训练的变量，可以使用后传播算法将其与网络权重进行优化。基于HK分布生成的样品对网络进行培训。获得的结果表明，所提出的方法可用于准确估计HK分布参数。
Homodyned K (HK) distribution has been widely used to describe the scattering phenomena arising in various research fields, such as ultrasound imaging or optics. In this work, we propose a machine learning based approach to the estimation of the HK distribution parameters. We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments. Compared to the previous approaches, we consider the orders of the moments as trainable variables that can be optimized along with the network weights using the back-propagation algorithm. Networks are trained based on samples generated from the HK distribution. Obtained results demonstrate that the proposed method can be used to accurately estimate the HK distribution parameters.
