An advanced source number estimation (SNE) algorithm based on both fuzzy C-means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one-dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal-to-noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

中文翻译：

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基于数据局部密度和模糊C-均值聚类的源数估计算法

提出了一种基于模糊C均值聚类（FCM）和数据局部密度（DLD）的高级信源数估计（SNE）算法。特征值的DLD是指该特征值的特定邻域内属于数据协方差矩阵的特征值的数量。该局部密度本质上作为FCM的一维样本特征被提取到基于FCM的SNE算法中，并且能够提高基于FCM的SNE算法的正确检测（PCD）的概率，特别是对于低信噪比的情况。 -噪声比（SNR）环境。对比实验结果表明，与基于FCM的SNE算法和其他类似算法相比，