Underwater acoustic localisation by GMM fingerprinting with noise reduction

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)


In this paper, the underwater acoustic localisation is given by Gaussian mixture model (GMM) fingerprinting with noise reduction. Underwater acoustic measurement always contains a lot of noises and fluctuates seriously. The fluctuating measurement will make the localisation unreliable. To overcome this disadvantage, this paper gives two contributions. First, the singular value decomposition (SVD) technique is utilised to reduce the noise of underwater acoustic measurement. Second, the processed acoustic signal is statistically modelled by the GMM, which is a linear combination of multiple Gaussian functions, for fingerprinting and localisation. By using the SVD technique, one can suppress the noise-related subspace and reconstruct clean signals from the signal subspace only. Experiments are conducted in a large indoor tank to examine the boundary reflection effect. Note that our underwater localisation scheme is based on fingerprinting, which does not require any range or angle information. It can tolerate reflected, multi-path and random-noise components.

頁(從 - 到)1-9
期刊International Journal of Sensor Networks
出版狀態Published - 2019

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電腦科學應用
  • 電腦網路與通信
  • 電氣與電子工程


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