基于概率方法進行水聲定位的主成分分析--外文翻譯

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：
本文給出了水下定位,從無線通信信號的聲學(xué)特征空間的PCA的概率模式識別(利用主元分析法)。應(yīng)該強調(diào)的是,我們的水下定位從現(xiàn)有的無線聲學(xué)通信信號,但不是額外的定位系統(tǒng)。我們的水下定位方案是基于指紋并且包含兩個階段,即:離線(例如,訓(xùn)練)和在線(例如,預(yù)測)階段。一般來說,收到的聲音信號波動在水下環(huán)境中比較嚴重。為了降低復(fù)雜性和噪聲影響,所有接收到的信號都被投射到特征空間PCA的主成分分析中。假定每個投影特征都有高斯概率分布。因此, 可以很容易在PCA空間通過投影特征概率模式識別得到其地理位置的信息。注意,我們的水下定位方案是未受影響的反射訊號。為了說明這樣一種效益,進行了在有界水池接近城墻的地方有反射信號的存在的實驗。實驗結(jié)果表明,本文提出的水下定位方案的效率和準確率。在水下的技術(shù)中，該定位方案是有益的水聲通信網(wǎng)絡(luò)。