Underwater Acoustic Source Localization and Sounds Classification in Distributed Measurement Networks

Underwater sound signals classification, localization and tracking of sound sources, are challenging tasks due to the multi-path nature of sound propagation, the mutual effects that exist between different sound signals and the large number of non-linear effects that reduces substantially the signal to noise ratio (SNR) of sound signals. In the region under observation, the Sado estuary, dolphins’ sounds and anthropogenic noises are those that are mainly present. Referring to the dolphins’ sounds, they can be classified in different types: narrow-band-frequency-modulated continuous tonal sounds, referred to as whistles, broadband sonar clicks and broadband burst pulse sounds. The system used to acquire the underwater sound signals is based on a set of hydrophones. The hydrophones are usually associated with pre-amplifying blocks followed by data acquisition systems with data logging and advanced signal processing capabilities for sound recognition, underwater sound source localization and motion tracking. For the particular case of dolphin’s sound recognition, dolphin localization and tracking, different practical approaches are reported in the literature that combine time-frequency representation and intelligent signal processing based on neural networks (Au et al., 2000; Wright, 2002; Carter, 1981). This paper presents a distributed virtual system that includes a sound acquisition component expressed by 3 hydrophones array, a sound generation device, expressed by a sound projector, and two acquisition, data logging, data processing and data communication units, expressed by a laptop PC, a personal digital assistant (PDA) and a multifunction acquisition board. A water quality multiparameter measurement unit and two GPS devices are also included in the measurement system. Several filtering blocks were designed and incorporated in the measurement system to improve the SNR ratio of the captured sound signals and a special attention was dedicated to present two techniques, one to locate sound signals’ sources, based on triangulation, and other to identify and classify different signal types by using a wavelet packet based technique.