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Multi-snapshot Newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors

机译:多快照牛顿正交匹配追踪技术在线谱估计中的应用

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In this paper, multi-snapshot Newtonized orthogonal matching pursuit (MNOMP) algorithm is proposed to deal with the line spectrum estimation with multiple measurement vectors (MMVs). MNOMP has the low computation complexity and state-of-the-art performance advantage of NOMP, and also includes two key steps: Detecting a new sinusoid on an oversampled discrete Fourier transform (DFT) grid and refining the parameters of already detected sinusoids to avoid the problem of basis mismatch. We provide a stopping criterion based on the overestimating probability of the model order. In addition, the convergence of the proposed algorithm is also proved. Finally, numerical results are conducted to show that the performance of MNOMP benefits from MMVs, and the effectiveness of MNOMP when compared against the state-of-the-art algorithms in terms of frequency estimation accuracy and computation complexity. (C) 2019 Elsevier B.V. All rights reserved.
机译:提出了多快照牛顿化正交匹配追踪(MNOMP)算法来处理具有多个测量向量(MMV)的线谱估计。 MNOMP具有NOMP的低计算复杂度和先进的性能优势,并且还包括两个关键步骤:在过采样的离散傅立叶变换(DFT)网格上检测新的正弦波,并完善已检测到的正弦波的参数以避免基础不匹配的问题。我们基于模型订单的高估概率提供了一个停止准则。另外,还证明了该算法的收敛性。最后,数值结果表明,MNOMP的性能得益于MMV,以及与最新算法相比,MNOMP在频率估计精度和计算复杂度方面的有效性。 (C)2019 Elsevier B.V.保留所有权利。

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