Chen Yang, Xue Yuanzhi, Wang Rui, Zhang Guangyuan
School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China.
Sensors (Basel). 2023 Oct 16;23(20):8491. doi: 10.3390/s23208491.
Source counting is the key procedure of autonomous detection for underwater unmanned platforms. A source counting method with local-confidence-level-enhanced density clustering using a single acoustic vector sensor (AVS) is proposed in this paper. The short-time Fourier transforms (STFT) of the sound pressure and vibration velocity measured by the AVS are first calculated, and a data set is established with the direction of arrivals (DOAs) estimated from all of the time-frequency points. Then, the density clustering algorithm is used to classify the DOAs in the data set, with which the number of the clusters and the cluster centers are obtained as the source number and the DOA estimations, respectively. In particular, the local confidence level is adopted to weigh the density of each DOA data point to highlight samples with the dominant sources and downplay those without, so that the differences in densities for the cluster centers and sidelobes are increased. Therefore, the performance of the density clustering algorithm is improved, leading to an improved source counting accuracy. Experimental results reveal that the enhanced source counting method achieves a better source counting performance than that of basic density clustering.
声源计数是水下无人平台自主探测的关键步骤。本文提出了一种基于单矢量水听器（AVS）的局部置信度增强密度聚类声源计数方法。首先计算AVS测量的声压和振速的短时傅里叶变换（STFT），并根据所有时频点估计的波达方向（DOA）建立数据集。然后，利用密度聚类算法对数据集中的DOA进行分类，得到的聚类数和聚类中心分别作为声源数和DOA估计值。特别地，采用局部置信度对每个DOA数据点的密度进行加权，突出主要声源的样本，淡化无主要声源的样本，从而增大聚类中心和旁瓣的密度差异。因此，改进了密度聚类算法的性能，提高了声源计数精度。实验结果表明，改进后的声源计数方法比基本密度聚类方法具有更好的声源计数性能。
