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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone
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A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

机译：基于压缩感测和基于密度的空间聚类的到达方向估计算法及其在MEMS向量水母信号处理中的应用

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Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).
机译：抵达方向（DOA）估计一直是研究人员的热门话题。复杂且变化的环境使得估计在小快照和强烈的噪声环境中的DOA非常具有挑战性。基于压缩感感测的到达方式（CS）是近年来提出的一种新方法。它受到了广泛的关注，因为它可以在小快照下实现到达方向估计。然而，这种方法将在强烈的噪声环境中造成严重失真。为了解决这个问题，本文提出了一种基于CS和基于密度的空间聚类原理的DOA估计算法（DBSCAN）。首先，为了使估计精度更高，本文选择基于基础追踪去噪（BPDN）的信号重建策略。响应于在该策略中选择正则化参数的挑战，提出了功率谱熵来表征信号的噪声强度，以便为选择正则化参数提供合理的建议;然后，本文发现，基于CS原理的DOA估计将在通过分析的小快照条件下的实际角度附近获得更密度估计，因此建议使用DBSCAN方法来处理上述数据以获得最后的Doa估计;最后，计算每个群集的群集中心值，群集的数量是信号源的数量，群集中心值是最终的DOA估计。所提出的方法应用于仿真实验和微电器机械系统（MEMS）矢量流水声湖试验实验，并证明了所提出的方法可以在小快照和低信号条件下获得DOA估计的良好结果 - 不良比率（SNR）。

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