Underwater acoustic localization by probabilistic fingerprinting in eigenspace

Kun-Chou Lee, Jhih Sian Ou, Lan Ting Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, the underwater acoustic localization is given by probabilistic fingerprinting in eigenspace. The eigenspace of this study means the projection of PCA (principal components analyses). The goal is to predict the receiver location through wireless acoustic communication signals in underwater environments. It should be emphasized that our underwater localization is performed from wireless acoustic communication signals, but not from commercial localization systems. In other words, the hardware can be utilized for both communication and localization simultaneously in our experiments. Our underwater localization scheme is based on the fingerprinting of wireless acoustic communication signals in eigenspace of PCA (principal components analyses). It is based on fingerprinting and contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In the off-line stage, there are some reference locations. At each reference location, acoustic communication signals at different frequencies are collected and sampled at discrete time points to constitute an acoustic-signal map. In the on-line (predicting) stage, acoustic communication signals at the unknown location are collected to constitute a signal vector. The problem becomes to predict the coordinate of the unknown location by comparing the signal vector with existing acoustic-signal maps. To reduce the complexity of acoustic-signal maps and overcome the severe fluctuation of measured data, all received signals are projected onto the eigenspace of PCA. Each component of the feature vector in eigenspace is assumed to be random Gaussian distribution. In addition, the components of the feature vector are assumed to be independent. The final probability that the signal vector occurred at an arbitrary reference location becomes the product of different Gaussian distribution functions. Such a probability is viewed as the weight for such a reference location. The unknown location can be approximated by the weighted summation of different reference locations.

Original languageEnglish
Title of host publicationMTS/IEEE Biloxi - Marine Technology for Our Future
Subtitle of host publicationGlobal and Local Challenges, OCEANS 2009
Publication statusPublished - 2009
EventMTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009 - Biloxi, MS, United States
Duration: 2009 Oct 262009 Oct 29

Other

OtherMTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009
CountryUnited States
CityBiloxi, MS
Period09-10-2609-10-29

Fingerprint

Underwater acoustics
Acoustics
Communication
Gaussian distribution
Distribution functions
Hardware

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering
  • Ocean Engineering

Cite this

Lee, K-C., Ou, J. S., & Wang, L. T. (2009). Underwater acoustic localization by probabilistic fingerprinting in eigenspace. In MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009 [5422226]
Lee, Kun-Chou ; Ou, Jhih Sian ; Wang, Lan Ting. / Underwater acoustic localization by probabilistic fingerprinting in eigenspace. MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009. 2009.
@inproceedings{d8cba2413a8840d89d3b03f99ed9b6c9,
title = "Underwater acoustic localization by probabilistic fingerprinting in eigenspace",
abstract = "In this paper, the underwater acoustic localization is given by probabilistic fingerprinting in eigenspace. The eigenspace of this study means the projection of PCA (principal components analyses). The goal is to predict the receiver location through wireless acoustic communication signals in underwater environments. It should be emphasized that our underwater localization is performed from wireless acoustic communication signals, but not from commercial localization systems. In other words, the hardware can be utilized for both communication and localization simultaneously in our experiments. Our underwater localization scheme is based on the fingerprinting of wireless acoustic communication signals in eigenspace of PCA (principal components analyses). It is based on fingerprinting and contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In the off-line stage, there are some reference locations. At each reference location, acoustic communication signals at different frequencies are collected and sampled at discrete time points to constitute an acoustic-signal map. In the on-line (predicting) stage, acoustic communication signals at the unknown location are collected to constitute a signal vector. The problem becomes to predict the coordinate of the unknown location by comparing the signal vector with existing acoustic-signal maps. To reduce the complexity of acoustic-signal maps and overcome the severe fluctuation of measured data, all received signals are projected onto the eigenspace of PCA. Each component of the feature vector in eigenspace is assumed to be random Gaussian distribution. In addition, the components of the feature vector are assumed to be independent. The final probability that the signal vector occurred at an arbitrary reference location becomes the product of different Gaussian distribution functions. Such a probability is viewed as the weight for such a reference location. The unknown location can be approximated by the weighted summation of different reference locations.",
author = "Kun-Chou Lee and Ou, {Jhih Sian} and Wang, {Lan Ting}",
year = "2009",
language = "English",
isbn = "9781424449606",
booktitle = "MTS/IEEE Biloxi - Marine Technology for Our Future",

}

Lee, K-C, Ou, JS & Wang, LT 2009, Underwater acoustic localization by probabilistic fingerprinting in eigenspace. in MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009., 5422226, MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009, Biloxi, MS, United States, 09-10-26.

Underwater acoustic localization by probabilistic fingerprinting in eigenspace. / Lee, Kun-Chou; Ou, Jhih Sian; Wang, Lan Ting.

MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009. 2009. 5422226.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Underwater acoustic localization by probabilistic fingerprinting in eigenspace

AU - Lee, Kun-Chou

AU - Ou, Jhih Sian

AU - Wang, Lan Ting

PY - 2009

Y1 - 2009

N2 - In this paper, the underwater acoustic localization is given by probabilistic fingerprinting in eigenspace. The eigenspace of this study means the projection of PCA (principal components analyses). The goal is to predict the receiver location through wireless acoustic communication signals in underwater environments. It should be emphasized that our underwater localization is performed from wireless acoustic communication signals, but not from commercial localization systems. In other words, the hardware can be utilized for both communication and localization simultaneously in our experiments. Our underwater localization scheme is based on the fingerprinting of wireless acoustic communication signals in eigenspace of PCA (principal components analyses). It is based on fingerprinting and contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In the off-line stage, there are some reference locations. At each reference location, acoustic communication signals at different frequencies are collected and sampled at discrete time points to constitute an acoustic-signal map. In the on-line (predicting) stage, acoustic communication signals at the unknown location are collected to constitute a signal vector. The problem becomes to predict the coordinate of the unknown location by comparing the signal vector with existing acoustic-signal maps. To reduce the complexity of acoustic-signal maps and overcome the severe fluctuation of measured data, all received signals are projected onto the eigenspace of PCA. Each component of the feature vector in eigenspace is assumed to be random Gaussian distribution. In addition, the components of the feature vector are assumed to be independent. The final probability that the signal vector occurred at an arbitrary reference location becomes the product of different Gaussian distribution functions. Such a probability is viewed as the weight for such a reference location. The unknown location can be approximated by the weighted summation of different reference locations.

AB - In this paper, the underwater acoustic localization is given by probabilistic fingerprinting in eigenspace. The eigenspace of this study means the projection of PCA (principal components analyses). The goal is to predict the receiver location through wireless acoustic communication signals in underwater environments. It should be emphasized that our underwater localization is performed from wireless acoustic communication signals, but not from commercial localization systems. In other words, the hardware can be utilized for both communication and localization simultaneously in our experiments. Our underwater localization scheme is based on the fingerprinting of wireless acoustic communication signals in eigenspace of PCA (principal components analyses). It is based on fingerprinting and contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In the off-line stage, there are some reference locations. At each reference location, acoustic communication signals at different frequencies are collected and sampled at discrete time points to constitute an acoustic-signal map. In the on-line (predicting) stage, acoustic communication signals at the unknown location are collected to constitute a signal vector. The problem becomes to predict the coordinate of the unknown location by comparing the signal vector with existing acoustic-signal maps. To reduce the complexity of acoustic-signal maps and overcome the severe fluctuation of measured data, all received signals are projected onto the eigenspace of PCA. Each component of the feature vector in eigenspace is assumed to be random Gaussian distribution. In addition, the components of the feature vector are assumed to be independent. The final probability that the signal vector occurred at an arbitrary reference location becomes the product of different Gaussian distribution functions. Such a probability is viewed as the weight for such a reference location. The unknown location can be approximated by the weighted summation of different reference locations.

UR - http://www.scopus.com/inward/record.url?scp=77951616777&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951616777&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781424449606

BT - MTS/IEEE Biloxi - Marine Technology for Our Future

ER -

Lee K-C, Ou JS, Wang LT. Underwater acoustic localization by probabilistic fingerprinting in eigenspace. In MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009. 2009. 5422226