Active PAR

P3520

Recommended Practice for Leveraging Machine Learning in Synthetic Aperture Imaging and Sensing

The recommended practice leverages Machine Learning (ML) in synthetic aperture applications, including radar, sonar, radiometry, magnetic resonance imaging, automotive radar, remote sensing, ultrasound, and other imaging modes. The recommended practice addresses the choice of Machine Learning architectures for different imaging scenarios and the appropriate training regime. The recommended practice provides techniques for object classification and segmentation in synthetic aperture images. The document explores the best data format for ML algorithms in different synthetic aperture applications. Examples of machine learning architectures described by the recommended practice include traditional approaches such as deep learning, autoencoders, reinforcement learning, transformer models, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and new generative approaches. Further different data regimes are considered, including subsampled and sparse approaches, as well as fully sampled data acquisitions.

Sponsor Committee
SPS/SASC - Synthetic Aperture Standards Committee
Status
Active PAR
PAR Approval
2024-09-26

Working Group Details

Society
IEEE Signal Processing Society
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Sponsor Committee
SPS/SASC - Synthetic Aperture Standards Committee
Working Group
Machine Learning for Synthetic Apertures - Recommended Practice for Leveraging Machine Learning in Synthetic Apertures
IEEE Program Manager
Dalisa Gonzalez
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Working Group Chair
Corina Nafornita

Other Activities From This Working Group

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