Research of Axis Mismatches Between Pairs of Sensitive Elements of Underwater Acoustic Velocity Gradient Sensors

Sun, Xinyi; Yang, Desen; Hong, Liang; Shi, Shengguo; Zhou, Hongkun

doi:10.1007/978-981-10-2422-1_10

Cited by 1 publication

(7 citation statements)

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“…Before reducing the mismatch, it is necessary to understand the mismatch through analysis. According to the reported literatures [3,4,23,25], the mismatches of an ADS's sensitive units can be divided into three types: amplitude mismatch, phase mismatch and axial mismatch.…”

Section: Reported Mismatch Analysis and Correction Methodsmentioning

confidence: 99%

“…The high directivity of an ADS depends on the accurate measurement of the secondorder physical quantities of the sound field (second-order sound pressure gradient or acoustic particle velocity gradient) at its acoustic center. At present, the second-order physical quantities of sound field are usually measured using the finite-difference approximation method [4,7,[20][21][22][23][24][25][26][27], so an ADS is composed of zero-order or first-order sensors. These lower-order sensors are the differential units or sensitive units of the ADS.…”

Section: Impact Of Mismatches Between Sensitive Unitsmentioning

confidence: 99%

“…When there is a mismatch between sensitive units, the measurement error of the second-order physical quantity at the acoustic center of the ADS increases [4], the directivity beam pattern at a specific frequency deviates from the ideal shape [23,24], the directivity of the ADS decreases [3], and the working frequency band of the ADS narrows [4,25]. Therefore, in order to ensure the high directivity of the ADS, it is of great significance to reduce the mismatch between sensitive units.…”

Section: Impact Of Mismatches Between Sensitive Unitsmentioning

confidence: 99%

“…Sound pressure type ADSs, composed of APSs, measure the second-order sound pressure gradient after twice finite-difference approximation of sound pressure [ 22 ]. Particle velocity type ADSs, composed of AVSs, measure the acoustic particle velocity gradient after once finite-difference approximation of acoustic particle velocity [ 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Sun et al [ 25 ] studied the axial mismatches between pairs of sensitive units of underwater acoustic velocity gradient sensors combined with one pressure sensor and six biaxial vector sensors. The formulas of measurement errors of particle velocity gradients measured by the particle velocity type ADS were derived while the axial mismatch exists.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Quantitative Analysis Method and Correction Algorithm Based on Directivity Beam Pattern for Mismatches between Sensitive Units of Acoustic Dyadic Sensors

Yang

Zhu

Chen

et al. 2023

Sensors

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Acoustic dyadic sensors (ADSs) are a new type of acoustic sensor with higher directivity than microphones and acoustic vector sensors, which has great application potential in the fields of sound source localization and noise cancellation. However, the high directivity of an ADS is seriously affected by the mismatches between its sensitive units. In this article, (1) a theoretical model of mixed mismatches was established based on the finite-difference approximation model of uniaxial acoustic particle velocity gradient and its ability to reflect the actual mismatches was proven by the comparison of theoretical and experimental directivity beam patterns of an actual ADS based on MEMS thermal particle velocity sensors. (2) Additionally, a quantitative analysis method based on directivity beam pattern was proposed to easily estimate the specific magnitude of the mismatches, which was proven to be useful for the design of ADSs to estimate the magnitudes of different mismatches of an actual ADS. (3) Moreover, a correction algorithm based on the theoretical model of mixed mismatches and quantitative analysis method was successfully demonstrated to correct several groups of simulated and measured beam patterns with mixed mismatches.

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Section: Reported Mismatch Analysis and Correction Methodsmentioning

confidence: 99%