On the DOA Estimation Performance of Optimum Arrays Based on Deep Learning
ID:32 View Protection:ATTENDEE Updated Time:2020-08-05 10:16:59 Hits:1172 Oral Presentation

Start Time:2020-06-09 15:00(Asia/Shanghai)

Duration:20min

Session:R Regular Session » R08Multi-Channel Imaging

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Abstract
In this paper, we investigate the optimality of deep learning-based optimal sparse arrays in comparison to well known conventional sparse linear arrays. Recently, a deep learning-based approach was proposed for antenna selection purposes as a measure towards reducing high hardware and computational cost in radar systems. Through numerical examples, we demonstrated that the proposed approach yields sparse arrays whose performance and configurations are comparably closer to conventional sparse arrays.
Keywords
antenna selection; sparse arrays; direction-of-arrival estimation; deep learning
Speaker
Steven Wandale
Yokohama National University, Japan

Submission Author
Steven Wandale Yokohama National University, Japan
Koichi Ichige Yokohama National University, Japan
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Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

Sponsored By
IEEE Signal Processing Society
Organized By
Zhejiang University
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