Direction finding using a biaxial particle-velocity sensor

Song, Yang; Wong, Kainam Thomas; Li, Yiu Lung

doi:10.1016/j.jsv.2014.11.027

Cited by 21 publications

(6 citation statements)

References 16 publications

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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%

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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Section: Introductionmentioning

confidence: 99%

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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“…On the other hand, all the variants discussed in Refs. [25][26][27][28][29][30] can be considered as a specific case of the U-AVS array, with limited spatial locations. Those variants can be realized by considering either a pressure or a particle velocity transducer present at each spatial location.…”

Section: Introductionmentioning

confidence: 99%

Uniaxial Acoustic Vector Sensors for direction-of-arrival estimation

Ramamohan

Comesaña

Leus

2018

Journal of Sound and Vibration

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In this paper, a specific reduced-channel Acoustic Vector Sensor (AVS) is proposed comprising one omni-directional microphone and only one particle velocity transducer, such that it can have an arbitrary orientation. Such a reduced transducer configuration is referred to as a Uniaxial AVS (U-AVS). The DOA performance of an array of U-AVSs is analyzed through its beampattern and compared to conventional configurations. It is shown that the U-AVS array beampattern results in an asymptotically biased estimate of the source location and it can be varied by choosing the orientation angles of the particle velocity transducers. Analytical expressions for the asymptotic bias of classical beamforming are proposed and verified both numerically as well as experimentally for Uniform Linear Arrays (ULAs). Furthermore, the Cramér-Rao Bound (CRB) and Mean Square Error (MSE) expressions are derived for a U-AVS array under a single source scenario and they are numerically evaluated for ULA. The implications of changing the orientations of the U-AVSs in the array on the MSE are discussed as well.

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“…Acoustic vector hydrophones (AVHs) employ a colocated sensor structure consisting of two or three orthogonally oriented velocity sensors and a pressure sensor [1][2][3]. The manifold structure suggests that AVHs have the following advantages over traditional pressure sensors: (1) they measure acoustic pressure as well as particle velocity at the sensor position and thus produce extra information for localization, and (2) the manifold is independent of the frequency of the source signal, which makes AVHs suitable for wideband source signals [4].…”

Section: Introductionmentioning

confidence: 99%

Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

2018

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Azimuth angle estimation using a single vector hydrophone is a well-known problem in underwater acoustics. In the presence of multiple sources, a conventional complex acoustic intensity estimator (CAIE) cannot distinguish the azimuth angle of each source. In this paper, we propose a steering acoustic intensity estimator (SAIE) for azimuth angle estimation in the presence of interference. The azimuth angle of the interference is known in advance from the global positioning system (GPS) and compass data. By constructing the steering acoustic energy fluxes in the x and y channels of the acoustic vector hydrophone, the azimuth angle of interest can be obtained when the steering azimuth angle is directed toward the interference. Simulation results show that the SAIE outperforms the CAIE and is insensitive to the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR). A sea trial is presented that verifies the validity of the proposed method.

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Direction finding using a biaxial particle-velocity sensor

Cited by 21 publications

References 16 publications

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

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

Uniaxial Acoustic Vector Sensors for direction-of-arrival estimation

Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

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