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Deep Learning For RADAR Signal Processing

Wharton, Michael K
http://orcid.org/0000-0002-6796-8732
http://rave.ohiolink.edu/etdc/view?acc_num=osu1619097997226646

Abstract Details

2021, Master of Science, Ohio State University, Electrical and Computer Engineering.
We address the current approaches to radar signal processing, which model radar signals with several assumptions (e.g., sparse or synchronized signals) that limit their performance and use in practical applications. We propose deep learning approaches to radar signal processing which do not make such assumptions. We present well-designed deep networks, detailed training procedures, and numerical results which show our deep networks outperform current approaches. In the first part of this thesis, we consider synthetic aperture radar (SAR) image recovery and classification from sub-Nyquist samples, i.e., compressive SAR. Our approach is to first apply back-projection and then use a deep convolutional neural network (CNN) to de-alias the result. Importantly, our CNN is trained to be agnostic to the subsampling pattern. Relative to the basis pursuit (i.e., sparsity-based) approach to compressive SAR recovery, our CNN-based approach is faster and more accurate, in terms of both image recovery MSE and downstream classification accuracy, on the MSTAR dataset. In the second part of this thesis, we consider the problem of classifying multiple overlapping phase-modulated radar waveforms given raw signal data. To do this, we design a complex-valued residual deep neural network and apply data augmentations during training to make our network robust to time synchronization, pulse width, and SNR. We demonstrate that our optimized network significantly outperforms the current state-of-the-art in terms of classification accuracy, especially in the asynchronous setting.
Philip Schniter (Advisor)
Emre Ertin (Committee Member)
46 p.
Electrical Engineering; Engineering
Deep learning, deep neural networks, radar, signal processing, compressive SAR, automatic target recognition, cognitive radar, classification, multi-label classification

Recommended Citations

Citations

  • Wharton, M. K. (2021). Deep Learning For RADAR Signal Processing [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619097997226646

    APA Style (7th edition)

  • Wharton, Michael. Deep Learning For RADAR Signal Processing. 2021. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1619097997226646.

    MLA Style (8th edition)

  • Wharton, Michael. "Deep Learning For RADAR Signal Processing." Master's thesis, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619097997226646

    Chicago Manual of Style (17th edition)

osu1619097997226646
390
© 2021, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.
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