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Changes in the Orientation and Frequency Dependence of Target Strength due to Morphological Differences in the Fish Swim Bladder
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 Title & Authors
Changes in the Orientation and Frequency Dependence of Target Strength due to Morphological Differences in the Fish Swim Bladder
Lee, Dae-Jae;
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 Abstract
Controlled broadband acoustic scattering laboratory experiments were conducted using a linear chirp signal (95-220 kHz), and x-ray images of live and model fish with an artificial swim bladder were analyzed to investigate the changes in orientation and frequency dependence of target strength (TS) due to morphological differences in fish swim bladders. The broadband echoes from live and model fish were measured over an orientation angle range of in the dorsal plane and in approximately increments. The location of nulls in the simulated echo response of the SINC [sinc function] model was overlaid on the TS map, showing the orientation and frequency dependence of fish TS, and they matched very well. It was possible to infer the equivalent fish scattering size (or swim bladder) using the null spacing in the experimentally obtained broadband TS map. Good agreement was observed for inferring the equivalent scattering size between the SINC model and the broadband echoes measured for the three fish species (black scraper Thamnaconus modestus; goldeye rockfish Sebastes thompsoni; and whitesaddled reef fish Chromis notatus). Some results of this inference are discussed.
 Keywords
Broadband acoustic scattering;Orientation and frequency dependence;SINC model;Equivalent scattering size;Artificial swimbladder;
 Language
Korean
 Cited by
1.
Time-Frequency Feature Extraction of Broadband Echo Signals from Individual Live Fish for Species Identification, Korean Journal of Fisheries and Aquatic Sciences, 2016, 49, 2, 214  crossref(new windwow)
2.
Acoustical backscattering characteristic depending on the changes in the body of sandfish (Arctoscopus japonicus), Journal of the Korean society of Fisheries Technology, 2016, 52, 1, 36  crossref(new windwow)
3.
Acoustic Identification of Six Fish Species using an Artificial Neural Network, Korean Journal of Fisheries and Aquatic Sciences, 2016, 49, 2, 224  crossref(new windwow)
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