Flow-induced noise on pressure gradient hydrophones

Lauchle, Gerald C.; McEachern, James F.; Jones, A. R.; McConnell, James A.

doi:10.1063/1.50332

Cited by 9 publications

(4 citation statements)

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“…In addition, the varying trend of the flow noise spectrum is similar to the long-term statistics of ambient noise level in the northeast South China Sea [42], but the flow noise level is significantly lower than the ambient noise level. Furthermore, the speed of acoustic Sea-Wing underwater gliders is usually set to 0.25 m/s in actual sea trials, which will induce a lower flow noise than a speed of 0.5 m/s [43]. Therefore the flow noise of acoustic Sea-Wing underwater gliders can be ignored when analyzing acoustic records recorded during sea trials.…”

Section: B Cfd-based Flow Noise Analysismentioning

confidence: 99%

Self-Noise Spectrum Analysis and Joint Noise Filtering for the Sea-Wing Underwater Glider Based on Experimental Data

et al. 2020

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Using underwater gliders to measure underwater acoustic signals is a new measurement method emerging with the development of platform technology. Although underwater gliders are relatively quiet due to the absence of propellers, vehicle noise generated during motion is still inevitable, which will affect the recorded acoustic data. In this paper, we analyze the self-noise characteristics of underwater gliders based on simulated data by CFD technology for the hydrodynamic flow noise and experimental data acquired in an anechoic-water-tank experiment for the mechanical noise. The mechanical noise covers noises generated by buoyancy regulating (increasing and reducing), pitch regulating, rudder regulating and CTD pump working. According to the analysis results, the flow noise and CTD pump working noise could be ignored for the experimental data processing of sea trials. An experiment was conducted with an acoustic Sea-Wing underwater glider in the South China Sea from July 31 to September 4, 2018. Two kinds of noisy data were recorded, including target signals and ambient noise. All the target signals could be recognized after convolution filtering, except during the buoyancy regulating periods due to the high noise spectrum level. For the recorded ambient noise, in addition to the buoyancy regulating noise, the rudder and pitch regulating noises affected the recorded data. Then based on the acquired knowledge, a joint convolution filtering and thresholding method is proposed to remove the rudder and pitch noises from recorded noisy data. Kernels extracted from data acquired in the anechoic-water-tank experiment are used in the convolution filtering to localize each regulating action and energy thresholding is adopted to determine the duration of each regulation. All the rudder and pitch noises are removed from the recorded noisy data. INDEX TERMS Underwater glider, noise filtering, self-noise characteristics, hydrodynamic flow noise, mechanical noise, experimental data processing.

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Section: B Cfd-based Flow Noise Analysismentioning

confidence: 99%

Self-Noise Spectrum Analysis and Joint Noise Filtering for the Sea-Wing Underwater Glider Based on Experimental Data

et al. 2020

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“…When there is water flow past the sensors, which is the case if the vessel is moving in hull-mounted sonar, the different sensor types are also differently affected. For example, in one experiment, it was shown that if the flow was fast enough to become turbulent on the surface of the sensor packaging, then velocity sensors were more adversely affected than pressure sensors [25]. However, [26] showed that encasing the sensors in elastomer, which was not done in [25], can significantly reduce flow noise.…”

Section: ) Statistical Assumptionsmentioning

confidence: 99%

“…For example, a vector-sensor uniform linear array (ULA) on a rigid surface is able to determine both azimuth and elevation. Comparison of (25) and (24) shows that the entry of corresponding to azimuth is larger relative to the corresponding entry of than is the elevation entry of relative to the elevation entry of . Thus, in this scenario, adding the velocity sensors improves the ability to estimate azimuth more than it does elevation.…”

Section: A Rigid Boundarymentioning

confidence: 99%

Acoustic vector-sensor processing in the presence of a reflecting boundary

Hawkes

Nehorai

2000

IEEE Trans. Signal Process.

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We consider the passive direction-of-arrival (DOA) estimation problem using arrays of acoustic vector sensors located in a fluid at or near a reflecting boundary. We formulate a general measurement model applicable to any planar surface, derive an expression for the Cramér-Rao bound (CRB) on the azimuth and elevation of a single source, and obtain a bound on the mean-square angular error (MSAE). We then examine two applications of great practical interest: hull-mounted and seabed arrays. For the former, we use three models for the hull: an ideal rigid surface for high frequency, an ideal pressure-release surface for low frequency, and a more complex, realistic layered model. For the seabed scenario, we model the ocean floor as an absorptive liquid layer. For each application, we use the CRB, MSAE bound, and beampatterns to quantify the advantages of using velocity and/or vector sensors instead of pressure sensors. For the hull-mounted application, we show that normal component velocity sensors overcome the well-known, low-frequency problem of small pressure signals without the need for an undesirable "stand-off" distance. For the seabed scenario, we also derive a fast wideband estimator of the source location using a single vector sensor.

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“…Studies on the effect of flow noise already exist in the literature such as those by Lauchle et al [197], [198]. In [198] Lauchle et al present a model for flow-induced self-noise on moored and drifting velocity sensors that arise due to oceanic currents such as those caused due to wave motion and tides. In [197] the mechanism of flowinduced self noise on velocity sensors has been studied.…”

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confidence: 99%

Improved techniques for detection and localization of underwater acoustic sources using acoustic vector sensors

Vishnu¹

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approximation to g m (ϕ) V′′(ϕ) : N-dimensional approximate signal subspace Q(.) : right-tail probability function of the standard normal distribution 2 D  : chi square distribution with D degrees of freedom   2 ' D  : non-central chi square distribution with D degrees of freedom, non-centrality parameter λ F D1,D2 : F-distribution with (D1, D2) degrees of freedom F′ D1, D2 (λ): noncentral F-distribution with (D1, D2) degrees of freedom, noncentrality parameter λ xx Gaussian noise. We explore how detection performance in non-Gaussian noise can be enhanced by using an SSR preprocessor and show that this provides a simple and near-optimal solution to detection in a wide range of non-Gaussian noise pdfs. Source localization algorithms in an ocean environment are severely affected by low SNR and perform poorly because they do not exploit the known fact that the environmental noise is non-Gaussian in nature. We present a method to improve the performance of azimuth estimation algorithms by combining the SSR phenomenon with conventional azimuth estimation methods. This offers better performance with relatively no increase in complexity. We also develop a MUSIC-based approach for three-dimensional localization of a single source in the near-field using a single AVS. This method yields closed-form expressions for the location estimates thus allowing 3D source localization with low computational complexity.

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Flow-induced noise on pressure gradient hydrophones

Cited by 9 publications

References 1 publication

Self-Noise Spectrum Analysis and Joint Noise Filtering for the Sea-Wing Underwater Glider Based on Experimental Data

Self-Noise Spectrum Analysis and Joint Noise Filtering for the Sea-Wing Underwater Glider Based on Experimental Data

Acoustic vector-sensor processing in the presence of a reflecting boundary

Improved techniques for detection and localization of underwater acoustic sources using acoustic vector sensors

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