2011 16th International Solid-State Sensors, Actuators and Microsystems Conference 2011
DOI: 10.1109/transducers.2011.5969568
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Engineering application of MEMS vector hydrophone and self-adapting root-MUSIC algorithm

Abstract: The MEMS technology is a new attempt to design hydrophone. In this paper, the fundamental principle of a novel biomimetic MEMS vector hydrophone based on the meso-piezoresistive effect is introduced, and a selfadapting root-MUSIC algorithm for vector hydrophone array is proposed. The simulation experiment results show the better performance with low Signal-to-Noise (SNR) in direction of arrival (DOA) estimation, and it is also applied in lake trials of MEMS vector hydrophone array. The experiment results show … Show more

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Cited by 1 publication
(4 citation statements)
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“…A novel biomimetic MEMS vector hydrophone in Figure 1 has been developed for acoustic detection application [3][4][5][6], which is based on the meso-piezoresistive effect and the acoustics theory of cylinder. It exploits the thin film of quantum as well as the sensitive unit, which adopts cilia bionic structure.…”
Section: Introductionmentioning
confidence: 99%
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“…A novel biomimetic MEMS vector hydrophone in Figure 1 has been developed for acoustic detection application [3][4][5][6], which is based on the meso-piezoresistive effect and the acoustics theory of cylinder. It exploits the thin film of quantum as well as the sensitive unit, which adopts cilia bionic structure.…”
Section: Introductionmentioning
confidence: 99%
“…By taking advantage of the extra information, acoustic vector sensor array is able to improve the direction-ofarrival (DOA) estimation performance without increasing array aperture size. Nehorai and Paldi have developed the measurement model of the acoustic vector sensor array for dealing with narrowband sources [1]; many methods such as MUSIC algorithm and root-MUSIC algorithm have been proposed for applying acoustic vector sensor array to DOA estimation problems [6][7][8][9].…”
Section: Introductionmentioning
confidence: 99%
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