DOA estimation using sparse Bayesian learning for colocated MIMO radar with dynamic waveforms
编号:64 访问权限:仅限参会人 更新:2020-08-05 10:17:00 浏览:609次 口头报告

报告开始:2020年06月08日 15:00(Asia/Shanghai)

报告时间:20min

所在会场:[S] Special Session [SS02] Sparse And Low-Rank Signal Processing For Array Processing And Wireless Communications

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摘要
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.
关键词
MIMO radar; DOA estimation; sparse Bayesian learning (SBL); waveform design
报告人
Bingfan Liu
Xidian University, China

稿件作者
Bingfan Liu Xidian University, China
Baixiao Chen Xidian University, China
Minglei Yang Xidian University, China
Hui Xu Xidian University, China
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重要日期
  • 会议日期

    06月08日

    2020

    06月11日

    2020

  • 01月12日 2020

    初稿截稿日期

  • 04月15日 2020

    提前注册日期

  • 12月31日 2020

    注册截止日期

主办单位
IEEE Signal Processing Society
承办单位
Zhejiang University
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