A Generative deep learning approach for shape recognition of arbitrary objects from phaseless acoustic scattering data

TL;DR

This paper introduces a deep generative neural network approach for recognizing arbitrary object shapes from phaseless acoustic scattering data, leveraging unsupervised learning and latent space modeling to solve the inverse scattering problem.

Contribution

It presents a novel deep learning framework combining adversarial autoencoders and variational inference for shape recognition from acoustic data, bypassing complex analytical methods.

Findings

Accurately recognizes arbitrary shapes from acoustic scattering data.

Effectively handles the ill-posed inverse scattering problem.

Demonstrates potential for underwater object identification.

Abstract

We propose and demonstrate a generative deep learning approach for the shape recognition of an arbitrary object from its acoustic scattering properties. The strategy exploits deep neural networks to learn the mapping between the latent space of a two-dimensional acoustic object and the far-field scattering amplitudes. A neural network is designed as an Adversarial autoencoder and trained via unsupervised learning to determine the latent space of the acoustic object. Important structural features of the object are embedded in lower-dimensional latent space which supports the modeling of a shape generator and accelerates the learning in the inverse design process.The proposed inverse design uses the variational inference approach with encoder and decoder-like architecture where the decoder is composed of two pretrained neural networks, the generator and the forward model. The data-driven framework finds an accurate solution to the ill-posed inverse scattering problem, where non-unique solution space is overcome by the multifrequency phaseless far-field patterns. This inverse method is a powerful design tool that does not require complex analytical calculation and opens up new avenues for practical realization, automatic recognition of arbitrary shaped submarines or large fish, and other underwater applications.