Alternative representations and object classification of circular synthetic aperture in-air acoustic data

Park, J. Daniel; Blanford, Thomas E.; Brown, Daniel C.; Plotnick, Daniel S.

doi:10.1121/1.5147416

Cited by 3 publications

(2 citation statements)

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“…Importantly, the relevant sound physics between air and water are directly analogous for the purposes of this work. AirSAS data has been used extensively in prior literature for proof-of-concept demonstrations Cowen et al 2021;Goehle et al 2022;Park et al 2020;Reed et al 2022].…”

Section: Airsasmentioning

confidence: 99%

Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

et al. 2023

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Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.

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Section: Airsasmentioning

confidence: 99%

Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

et al. 2023

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“…Under standard SAS operating conditions, the two reconstruction approaches are equivalent. Recent work also explores the applicability of SAS image representations to machine learning algorithms [13]. In this paper, we utilize a time-domain delay-and-sum beamformer for all our experiments and describe its function Fig.…”

Section: Sas Image Reconstructionmentioning

confidence: 99%

Implicit Neural Representations for Deconvolving SAS Images

Reed¹,

Blanford²,

Brown³

et al. 2021

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Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restores the original distribution of scatterers and yields sharper reconstructions. However, deconvolution is an ill-posed operation that is highly sensitive to noise. In this work, we leverage implicit neural representations (INRs), shown to be strong priors for the natural image space, to deconvolve SAS images. Importantly, our method does not require training data, as we perform our deconvolution through an analysis-bysynthesis optimization in a self-supervised fashion. We validate our method on simulated SAS data created with a point scattering model and real data captured with an in-air circular SAS. This work is an important first step towards applying neural networks for SAS image deconvolution.

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