Underwater acoustic localisation by GMM fingerprinting with noise reduction Online publication date: Mon, 12-Aug-2019
by Kun-Chou Lee
International Journal of Sensor Networks (IJSNET), Vol. 31, No. 1, 2019
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook