Abstract
Diver breathing sounds can be used as a characteristic for the passive detection of divers. This work introduces an approach for detecting the presence of a diver based on diver breathing sounds signals. An underwater channel model for passive diver detection is built to evaluate the impacts of acoustic energy transmission loss and ambient noise interference. The noise components of the observed signals are suppressed by spectral subtraction based on block-based threshold theory and smooth minimal statistic noise tracking theory. Then the envelope spectrum features of the denoised signal are extracted for diver detection. The performance of the proposed detection method is demonstrated through experimental analysis and numerical modeling.
Highlights
A diver is an underwater swimmer who carries a self contained underwater breathing apparatus (SCUBA) system and can stay underwater for a long time
The above impacts are taken into account in the established underwater acoustic channel model for diver detection
Because the diver breathing sounds collected in the experiment have very obvious human breath rate characteristics, we regard them as source signals
Summary
A diver is an underwater swimmer who carries a self contained underwater breathing apparatus (SCUBA) system and can stay underwater for a long time. Because of the presence of water, people ashore find it difficult to find, to search for, and to communicate with divers. When a diver is in danger, the probability of misfortune is high, even with the help of rescuers. There are active and passive sonar system for underwater detection. The active sonar system faces the challenge of reverberation, and the performance requirements of small targets are high. Compared with the active mode, passive sonar has small energy consumption, is cheaper and more hidden, and is being pursued as an alternative [1]
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