# Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments

arXiv: https://arxiv.org/abs/2210.05833 · HTML: https://mlsanity.com/paper/2210.05833/

## Key facts

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- First posted on arXiv 11 Oct 2022.

## Abstract (arXiv)

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.