Underwater acoustic localization by probabilistic fingerprinting in eigenspace

Kun Chou Lee, Jhih Sian Ou, Lan Ting Wang

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題MTS/IEEE Biloxi - Marine Technology for Our Future
主出版物子標題Global and Local Challenges, OCEANS 2009
出版狀態Published - 2009
事件MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009 - Biloxi, MS, United States
持續時間: 2009 10月 262009 10月 29

出版系列

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

Other

OtherMTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009
國家/地區United States
城市Biloxi, MS
期間09-10-2609-10-29

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 資訊系統
  • 電氣與電子工程
  • 海洋工程

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