DOA estimation using sparse Bayesian learning for colocated MIMO radar with dynamic waveforms
ID:64 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:00 Hits:560 Oral Presentation

Start Time:2020-06-08 15:00(Asia/Shanghai)

Duration:20min

Session:S Special Session » SS02Sparse And Low-Rank Signal Processing For Array Processing And Wireless Communications

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Abstract
In this paper, we proposed a direction of arrival (DOA) estimation method based on sparse Bayesian learning (SBL) and a dynamic transmitted waveform design method for colocated multiple-input multiple-output (MIMO) radar. First, the SBL DOA estimation method is introduced into the MIMO radar with arbitrary transmitted waveforms. Our theoretical derivation shows that the estimation error of the SBL method is related to the transmitted waveforms. Then, we minimize the estimation error to obtain an updated transmitted waveforms, which will be transmitted in the next pulse repetition period. Numerical simulations show that compared with traditional orthogonal waveforms, the optimized waveforms could achieve a lower Cram\'{e}r-Rao bound (CRB) and smaller DOA estimation error using the SBL method.
Keywords
MIMO radar; DOA estimation; sparse Bayesian learning (SBL); waveform design
Speaker
Bingfan Liu
Xidian University, China

Submission Author
Bingfan Liu Xidian University, China
Baixiao Chen Xidian University, China
Minglei Yang Xidian University, China
Hui Xu Xidian University, China
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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

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  • Dec 31 2020

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