Density Peak Clustering Algorithm and Optimization Based on Measurements of Unlikeness Properties in Position Sensor Environment

Yao, Zhe; Gao, Kun

doi:10.1109/jsen.2021.3058390

Cited by 1 publication

(1 citation statement)

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“…When the number of the sources increases, the degree of WDO in underwater acoustic signals becomes weaker, which will affect the difficulty of distinguishing the Gaussian component of the fitted sound source from the Gaussian component of the fitted background noise. By constructing the local density and minimum distance to find cluster centers, density peak clustering (DPC) can not only deal with noise or high-dimensional problems, but can also automatically find cluster centers without setting the number of the sources in advance [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Vector Sensor Multi-Source Detection Based on Multimodal Fusion

Chen

Zhang

Wang

et al. 2023

Sensors

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The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.

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Section: Introductionmentioning

confidence: 99%