Underwater acoustic localization by principal components analyses based probabilistic approach

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.