Real-Time, Deep Synthetic Aperture Sonar (SAS) Autofocus

Gerg, Isaac D.; Monga, Vishal

doi:10.1109/igarss47720.2021.9554141

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Our simulated results use 7 images from the SASSED dataset [68] to serve as a ground truth scene of point scatterers. Each image in the SASSED dataset is resized to a 400 × 400 image where each pixel represents a point scatterers in the scene.…”

Section: Simulation Resultsmentioning

confidence: 99%

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Reed¹,

Blanford²,

Brown³

et al. 2022

Preprint

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Circular Synthetic aperture sonars (CSAS) capture multiple observations of a scene to reconstruct high-resolution images. We can characterize resolution by modeling CSAS imaging as the convolution between a scene's underlying point scattering distribution and a system-dependent point spread function (PSF). The PSF is a function of the transmitted waveform's bandwidth and determines a fixed degree of blurring on reconstructed imagery. In theory, deconvolution overcomes bandwidth limitations by reversing the PSF-induced blur and recovering the scene's scattering distribution. However, deconvolution is an illposed inverse problem and sensitive to noise. We propose a selfsupervised pipeline (does not require training data) that leverages an implicit neural representation (INR) for deconvolving CSAS images. We highlight the performance of our SAS INR pipeline, which we call SINR, by implementing and comparing to existing deconvolution methods. Additionally, prior SAS deconvolution methods assume a spatially-invariant PSF, which we demonstrate yields subpar performance in practice. We provide theory and methods to account for a spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to achieve superior deconvolution performance on simulated and real acoustic SAS data. We provide code 1 to encourage reproducibility of research.

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Section: Simulation Resultsmentioning

confidence: 99%

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Reed¹,

Blanford²,

Brown³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, in SAR, SAE-Net 6 employs a denoising autoencoder to combine image formation with autofocus in a self-supervised paradigm and the AFnet/PAFnet models 7 directly learn to correct polynomial phase error. Most relevant to SAS, Gerg and Monga developed a highly effective deep learning model for performing SAS autofocus on low size, weight, and power (SWaP) devices 8 .…”

Section: Introductionmentioning

confidence: 99%

Dafi: A Deep Learning-Based Autofocus Improvement Metric for Sas

DALE,

EMIGH,

PRATER Jr.

2023

Synthetic Aperture Sonar and Synthetic Aperture Radar 2023

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“…In the context of radar autofocus problems, deep denoisers have been proposed in the literature as part algorithm unfolding where the network learns to correct the phase of the iterates of the reconstructed image [28]. Other works considered the autofocus problem synthentic aperture sonar imaging as a postprocessing step where a deep denoiser was employed to correct the phase of the reconstructed image and reduce the computational time [29].…”

Section: Introductionmentioning

confidence: 99%

Distributed Radar Autofocus Imaging Using Deep Priors

Mansour

Lohit

Boufounos

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Antenna position ambiguity is a common problem that affects radar imaging systems that are mounted on mobile platforms. Existing approaches that aim to recover a sharp radar image despite this ambiguity aim to estimate the shift in the antenna position by modeling the radar scene as a sparse image with a small number of targets using explicit analytical models for the statistical distribution of the targets in a radar image. The radar imaging problem is then solved by alternating between estimating the radar image, followed by estimating the shift in the antenna positions, until convergence is reached. While such approaches have shown tremendous success, they still struggle to recover the true target positions and may arrive at incorrect local optima when the measurement noise level is high. In this work, we develop a data-driven learning-based strategy for modeling the image of the radar scene instead of relying on explicit analytical models. We adopt a residual Unet architecture of a neural network to act as a denoising operator which takes a backprojected radar image as input and outputs a true target image. While deep denoisers may generally result in unstable iterative algorithms, we introduce a simple filtering step that suppresses noise belonging to the null space of the radar operator from the iterates to stabilize the iterative procedure. We evaluate the effectiveness of our solution using simulated numerical experiments and demonstrate its superiority over the analytic signal prior.

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Real-Time, Deep Synthetic Aperture Sonar (SAS) Autofocus

Cited by 5 publications

References 15 publications

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Dafi: A Deep Learning-Based Autofocus Improvement Metric for Sas

Distributed Radar Autofocus Imaging Using Deep Priors

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