DOA Estimation via Acoustic Sensor Array With Non-Orthogonal Elements

Wang, Weidong; Zhang, Qunfei; Zhu, Wei-Ping; Shi, Wentao; Tan, Weijie

doi:10.1109/jsen.2020.2993723

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Considering the actual situation that the orthogonality of the two sound velocity channels cannot be guaranteed during the manufacturing process, an iterative DOA estimation method is proposed to solve the angle mismatch problem. 17 The DOA performance of the Uniaxial AVS array was analyzed and a classical beamforming method for Uniaxial AVSs was proposed. 18 The receive data model incorporating a directional bias parameter was constructed, and an alternating iterative adaptive algorithm for DOA estimation was proposed.…”

Section: Introductionmentioning

confidence: 99%

Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

Liang

Shi

et al. 2022

Noise & Vibration Worldwide

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In order to estimate the direction of arrival (DOA) of acoustic vector sensor, the phased fractional low-order moment (PFLOM) is used to suppress impulse noise. First, based on PFLOM, an off-gird sparse based two-dimensional (2D) DOA estimation model for acoustic vector sensor array is proposed. Then, a three-step alternating iterative Orthogonal Matching Pursuit (OMP) algorithm is introduced to realize the off-grid 2D DOA estimation, and the self-pairing of azimuth and elevation is achieved. Finally, the effectiveness and accuracy of the proposed algorithm under impulse noise and small snapshots are verified by simulation.

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Section: Introductionmentioning

confidence: 99%

Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

Liang

Shi

et al. 2022

Noise & Vibration Worldwide

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“…That is, the two component-sensor spatial collocations intrinsically decouple the time–frequency dimensions from the azimuth–elevation spatial dimensions of the data. The use of the biaxial velocity sensor for direction finding can be found in [ 11 , 12 , 13 ]. Moreover, in beamforming applications, its directivity was studied in [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…The directions of arrival estimation performances of perturbed orthogonal vector sensors have been studied in [ 13 , 18 , 19 ]. In [ 13 ], an algorithm was developed for direction finding using an imperfect (non-orthogonal) biaxial velocity sensor. This study did not derive a theoretical bound of the performance of such an imperfect biaxial vector sensor.…”

Section: Introductionmentioning

confidence: 99%

“…In general, these studies modeled the skew angle as an unknown deterministic parameter. This assumption, as done by [ 13 , 19 ], is less practical since skew angles vary across sensors (making prior knowledge of the skew angle distribution an important feature to consider). This becomes more useful for an array of multiple biaxial velocity sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The maximum a posterior estimator was used to verify the derived bounds for the polar and azimuth. This analysis differs from [ 13 ], where aside from not deriving the Cramér-Rao bound, the authors assumed the skew angle to be deterministic and unknown (see also [ 38 ]), not considering the effect of the uncertainty in the measurement on the direction-finding performance of the skewed biaxial sensor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Lower Bound on the Estimation Variance of Direction-of-Arrival and Skew Angle of a Biaxial Velocity Sensor Suffering from Stochastic Loss of Perpendicularity

Nnonyelu

Jiang

Lundgren

2022

Sensors

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The biaxial velocity sensor comprises two nominally perpendicular particle velocity sensors and a collocated pressure sensor. Due to real-world imperfections in manufacturing or setup errors, the two axes may suffer from perpendicularity losses. To analytically study how skewness affects its direction-finding performance, the hybrid Cramér-Rao bound (HCRB) of the directions-of-arrival for the polar angle, azimuth angle and the skew angle of a biaxial velocity sensor that suffers from stochastic loss of perpendicularity were derived in closed form. The skew angle was modeled as a zero-mean Gaussian random variable of a known variance, which was assumed to be very small, to capture the uncertainty in the orthogonality of the biaxial velocity sensor. The analysis shows that for the polar and azimuth angle, the loss of perpendicularity introduces the variation of the HCRB along the azimuth angle axis, which is independent of the skew angle, but on its variance. The dynamic range of this variation increases as the variance of the skew angle increases. For the estimation of the skew angle, the HCRB of the skew angle is bounded upwards by the variance of the skew angle and varies with the azimuth angle. The hybrid maximum likelihood- maximum a posterior (hybrid ML/MAP) estimator was used to verify the derived bounds.

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Direction finding method via acoustic vector sensor array with fluctuating misorientation

Wang

Liu

et al. 2023

Applied Acoustics

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DOA Estimation via Acoustic Sensor Array With Non-Orthogonal Elements

Cited by 5 publications

References 29 publications

Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

A Lower Bound on the Estimation Variance of Direction-of-Arrival and Skew Angle of a Biaxial Velocity Sensor Suffering from Stochastic Loss of Perpendicularity

Direction finding method via acoustic vector sensor array with fluctuating misorientation

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