Low-Complexity Fixed-Point Convolutional Neural Networks for Automatic Target Recognition

Hassan Dbouk, Hanfei Geng, Craig M. Vineyard, Naresh R. Shanbhag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to bring the cost of implementing these networks down, we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984 reduction in terms of storage requirements and 71 × reduction in terms of computational complexity compared to state-of-the-art con-volutional neural networks for automatic target recognition (ATR), while maintaining a classification accuracy of > 99% on the MSTAR dataset.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1598-1602
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • automatic target recognition
  • deep learning
  • neural networks
  • quantization
  • synthetic aperture radar

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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