Underwater ship noise pattern detection and identification

Abstract

The anthropogenic underwater noise event could be identified by spectral analysis and acoustic pattern pre-classification of previously measured noise sources. This paper discusses real-time applicable methods for separation of purely natural noise recordings when ships are absent from the data polluted by the ship noise. The natural noise periods are used for the statistical modeling of underwater channel environment and relative levels of marine ambient noise (Wenz curves). The anthropogenic noise periods allow us to identify the noise sources. Moreover, most of the ship identification methods require spectrum component processing and pre-determined ship classification, which could be based on this automatic separation results. The correlational and multiple criteria detection methods compared with cyclostationary feature detection and widely used energy detection. False alarm and misclassification minimization could be achieved by multiple sensor data fusion: weather and Automatic Identification System (AIS). The measured data were collected near the seafloor with an autonomous long-term passive acoustic recorder and were combined with ship passage information from the AIS. Three years of measurements include multiple individual ship observations with different speeds and directions. Analysis of the experimental results emphasizes the importance of pre-classification, especially in multiple target cases.The anthropogenic underwater noise event could be identified by spectral analysis and acoustic pattern pre-classification of previously measured noise sources. This paper discusses real-time applicable methods for separation of purely natural noise recordings when ships are absent from the data polluted by the ship noise. The natural noise periods are used for the statistical modeling of underwater channel environment and relative levels of marine ambient noise (Wenz curves). The anthropogenic noise periods allow us to identify the noise sources. Moreover, most of the ship identification methods require spectrum component processing and pre-determined ship classification, which could be based on this automatic separation results. The correlational and multiple criteria detection methods compared with cyclostationary feature detection and widely used energy detection. False alarm and misclassification minimization could be achieved by multiple sensor data fusion: weather and Automatic Identification System...