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    • 稀疏阵列的鲁棒矩阵填充DOA估计算法

    ZHANG Yunmeng,DONG Mei,CHEN Boxiao

    2023 6th International Conference on Information Communication and Signal Processing (ICICSP)（2023）

    National Key Laboratory of Radar Signal Processing

    Cited 0|Views7
    Abstract
    稀疏阵列布阵灵活，增大阵列孔径的同时还能减少阵元间耦合，但基于稀疏阵列的传统波达方向估计会导致角度模糊混叠，带来估计精度差和稳健性不足的问题。针对以上问题，提出一种适用于稀疏阵列波达方向估计的加权截断奇异值投影（weighted truncated singular value projection, WT-SVP）的鲁棒矩阵填充算法。在填充迭代过程中根据奇异值的大小分配权重，突出大奇异值包含的阵列信息，减少小奇异值中不必要的噪声信息，从而优化传统奇异值投影算法。该算法可以实现稀疏阵列的孔洞信息恢复，对不连续阵元充分利用，同时WT-SVP填充算法实现了稀疏阵列波达方向估计的高精度、高分辨以及在低信噪比、低快拍时的高鲁棒性。
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    Key words
    sparse array,matrix completion,singular value projection,direction of arrival(DOA)estimation
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
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    【要点】：本文提出了一种加权截断奇异值投影（WT-SVP）的稳健矩阵填充算法，用于提高稀疏阵列方向估计（DOA）的准确性和鲁棒性。
    

    【方法】：通过在矩阵填充迭代过程中根据奇异值大小分配权重，突出大奇异值包含的阵列信息并减少小奇异值中的不必要权重，优化了传统的奇异值投影算法。
    

    【实验】：在低信噪比和低快照数条件下，使用该算法对稀疏阵列进行了DOA估计，实验结果表明算法在准确性、分辨率和鲁棒性方面表现优越，具体数据集名称未提及。