[Oral Presentation]DOA estimation using sparse Bayesian learning for colocated MIMO radar with dynamic waveforms - THE ELEVENTH IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (IEEE SAM2020)

DOA estimation using sparse Bayesian learning for colocated MIMO radar with dynamic waveforms

ID：64 Submission ID：162 View Protection：ATTENDEE Updated Time：2020-08-05 10:17:00 Hits：536 Oral Presentation

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

Duration：20min

Session：[S] Special Session » [SS02] Sparse 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