The W-disjoint Orthogonality of Underwater Acoustic Signals and Underdetermined Source Counting for Acoustic Vector Sensor

Chen, Yang; Wang, Jinxia; Yun, Yu; Zhang, Xiao; Yang, Biao; Zhu, Yanping

doi:10.1109/icsidp47821.2019.9172991

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…Therefore, the performance of the density clustering algorithm is enhanced. An analysis of lake trial data is conducted by comparing the proposed local-confidence-level-enhanced density clustering method with the methods in [10] and [18]. The results confirm the availability of the proposed local-confidence-level-enhanced density clustering method in improving source counting performance.…”

Section: Introductionmentioning

confidence: 60%

“…In an AVS sea trial experiment based on the Argo buoy platform in [17], it was also found that the DOA histogram could distinguish two wideband targets, the test ship and the engineering ship, whose adjacent interval was about 80 degrees. In reference [18,19], the windowed-disjoint orthogonality (WDO) of the target signal was further introduced to explain the mechanism of the wideband multi-target resolution ability of the DOA histogram.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, an underdetermined method based on the density clustering algorithm was formed to solve the source counting problem for single AVSs in [18]. The density clustering algorithm was used to classify the DOAs of all the time-frequency points, where the cluster centers and the number of the clusters were obtained as the DOA estimations and target number, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

Chen,

Xue,

Wang

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

Chen,

Xue,

Wang

et al. 2023

Sensors

Self Cite

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show abstract

“…The WDO of underwater acoustic signals will decrease as the number of sources increases, at which point the ability of a single AVS to distinguish multiple sources will decrease [ 10 ]. If the number of sources can be reduced, the accuracy of underdetermined source counting will be improved.…”

Section: Avs Multi-source Detection Algorithm Based On Multimodal Fusionmentioning

confidence: 99%

“…The W-disjoint orthogonality (WDO) is a degree that represents the ability of a DOA histogram based on AVS to distinguish multiple sources [ 10 ]. In a single time-frequency point, if only the energy of one source is dominant, whereas that of the other sources is secondary, the time-frequency point is called the dominant time-frequency point of the source.…”

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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Estimating the Number of Speakers by Novel Zig-Zag Nested Microphone Array Based on Wavelet Packet and Adaptive GCC Method

Firoozabadi

Irarrázaval

Adasme

et al. 2022

2022 8th International Conference on Signal Processing and Communication (ICSC)

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The W-disjoint Orthogonality of Underwater Acoustic Signals and Underdetermined Source Counting for Acoustic Vector Sensor

Cited by 3 publications

References 7 publications

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering

Acoustic Vector Sensor Multi-Source Detection Based on Multimodal Fusion

Estimating the Number of Speakers by Novel Zig-Zag Nested Microphone Array Based on Wavelet Packet and Adaptive GCC Method

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