基于概率方法进行水声定位的主成分分析--外文翻译.rar
基于概率方法进行水声定位的主成分分析--外文翻译,a b s t r a c tin this paper, the underwater localization is given from wireless acoustic communication signals by probabilisticpattern ...
该文档为压缩文件,包含的文件列表如下:
内容介绍
原文档由会员 叼着吸管的猪 发布
基于概率方法进行水声定位的主成分分析--外文翻译
a b s t r a c t
In this paper, the underwater localization is given from wireless acoustic communication signals by probabilistic
pattern recognition in eigenspace of PCA (principal components analyses). It should be emphasized
that our underwater localization is from existing wireless acoustic communication signals, but not
from additional localization systems. Our underwater localization scheme is based on fingerprinting and
contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In general, the
received acoustic signals fluctuate seriously in underwater environments. To reduce the complexity
and noise effects, all received signals are projected onto the eigenspace of PCA. Each projected feature
is assumed to have Gaussian probabilistic distributions. Therefore, the location information can be easily
obtained by probabilistic pattern recognition of projected features in PCA space. Note that our underwater
localization scheme is not affected by reflected signals. To illustrate such a benefit, experiments were
conducted in a bounded water pool where reflected signals exist near the walls. Experimental results
show that the proposed underwater localization scheme is efficient and accurate. The proposed localization
scheme is useful for underwater acoustic communication networks, and then in underwater
technologies.
摘要：
本文给出了水下定位,从无线通信信号的声学特征空间的PCA的概率模式识别(利用主元分析法)。应该强调的是,我们的水下定位从现有的无线声学通信信号,但不是额外的定位系统。我们的水下定位方案是基于指纹并且包含两个阶段,即:离线(例如,训练)和在线(例如,预测)阶段。一般来说,收到的声音信号波动在水下环境中比较严重。为了降低复杂性和噪声影响,所有接收到的信号都被投射到特征空间PCA的主成分分析中。假定每个投影特征都有高斯概率分布。因此, 可以很容易在PCA空间通过投影特征概率模式识别得到其地理位置的信息。注意,我们的水下定位方案是未受影响的反射讯号。为了说明这样一种效益,进行了在有界水池接近城墙的地方有反射信号的存在的实验。实验结果表明,本文提出的水下定位方案的效率和准确率。在水下的技术中，该定位方案是有益的水声通信网络。
a b s t r a c t
In this paper, the underwater localization is given from wireless acoustic communication signals by probabilistic
pattern recognition in eigenspace of PCA (principal components analyses). It should be emphasized
that our underwater localization is from existing wireless acoustic communication signals, but not
from additional localization systems. Our underwater localization scheme is based on fingerprinting and
contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In general, the
received acoustic signals fluctuate seriously in underwater environments. To reduce the complexity
and noise effects, all received signals are projected onto the eigenspace of PCA. Each projected feature
is assumed to have Gaussian probabilistic distributions. Therefore, the location information can be easily
obtained by probabilistic pattern recognition of projected features in PCA space. Note that our underwater
localization scheme is not affected by reflected signals. To illustrate such a benefit, experiments were
conducted in a bounded water pool where reflected signals exist near the walls. Experimental results
show that the proposed underwater localization scheme is efficient and accurate. The proposed localization
scheme is useful for underwater acoustic communication networks, and then in underwater
technologies.
摘要：
本文给出了水下定位,从无线通信信号的声学特征空间的PCA的概率模式识别(利用主元分析法)。应该强调的是,我们的水下定位从现有的无线声学通信信号,但不是额外的定位系统。我们的水下定位方案是基于指纹并且包含两个阶段,即:离线(例如,训练)和在线(例如,预测)阶段。一般来说,收到的声音信号波动在水下环境中比较严重。为了降低复杂性和噪声影响,所有接收到的信号都被投射到特征空间PCA的主成分分析中。假定每个投影特征都有高斯概率分布。因此, 可以很容易在PCA空间通过投影特征概率模式识别得到其地理位置的信息。注意,我们的水下定位方案是未受影响的反射讯号。为了说明这样一种效益,进行了在有界水池接近城墙的地方有反射信号的存在的实验。实验结果表明,本文提出的水下定位方案的效率和准确率。在水下的技术中，该定位方案是有益的水声通信网络。
