Improving the Performance of a Vector Sensor Line Array by Deconvolution

Sun, Dong; Ma, Chunguang; Yang, T. C.; Mei, Jidan; Shi, Wenpei

doi:10.1109/joe.2019.2912586

Cited by 26 publications

(8 citation statements)

References 23 publications

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“…The deconvolved CBF (dCv) outputs are summed over frequency for each segment and then displayed as a function of time and angle. We used the Rechardson-Lucy algorithm with 200 iterations [28]. The dCv beam energy versus angle and time is shown in Fig.…”

Section: Experimental Data Analysismentioning

confidence: 99%

Bistatic Localization of Objects in Very Shallow Water

Zhang

Yang

2019

IEEE Access

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Bistatic or multi-static systems use spatially distributed sources and/or receivers to ensonify scattering objects over a distance to obtain the acoustic images of individual targets for classification purposes. The targets need to be localized first and separated from the clutter. To reach targets at a distance, low frequency sound is often used; targets can in principle be classified based on the frequency and azimuthal angle response of the echo return, referred to as acoustic color. Practical application of this technology is limited by the array size and the ability to localize the target and extract the scattering echo. Deconvolution of conventional beamforming (CBF) has been shown to achieve a narrow beam width and low side lobe levels equivalent to CBF of an array of much larger aperture. This method is applied to active sonar in this paper. Analysis of simulated and tank experimental data demonstrated a better separation of targets and a higher target-to-reverberation ratio than that using CBF.

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Section: Experimental Data Analysismentioning

confidence: 99%

Bistatic Localization of Objects in Very Shallow Water

Zhang

Yang

2019

IEEE Access

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“…Acoustic vector sensors can simultaneously measure the scalar acoustic field and the vector gradient field (first order as vector velocity field, or second order as acceleration field), which can contain more information about the acoustic field suitable for passive geoacoustic inversion (Sun et al, 2020). Geoacoustic parameters inversion using acoustic vector sensors has been a hot topic in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Inversion of shallow seabed structure and geoacoustic parameters with waveguide characteristic impedance based on Bayesian approach

Zhu

Xue²,

Ren³

et al. 2023

Front. Mar. Sci.

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Underwater acoustic technology is essential for ocean observation, exploration and exploitation, and its development is based on an accurate predication of underwater acoustic wave propagation. In shallow sea environments, the geoacoustic parameters, such as the seabed structure, the sound speeds, the densities, and the sound speed attenuations in seabed layers, would significantly affect the acoustic wave propagation characteristics. To obtain more accurate inversion results for these parameters, this study presents an inversion method using the waveguide characteristic impedance based on the Bayesian approach. In the inversion, the vertical waveguide characteristic impedance, which is the ratio of the pressure over the vertical particle velocity, is set as the matching object. The nonlinear Bayesian theory is used to invert the above geoacoustic parameters and analysis the uncertainty of the inversion results. The numerical studies and the sea experiment processing haven shown the validity of this inversion method. The numerical studies also proved that the vertical waveguide characteristic impedance is more sensitive to the geoacoustic parameters than that of single acoustic pressure or single vertical particle velocity, and the error of simulation inversion is within 3%. The sea experiment processing showed that the seabed layered structure and geoacoustic parameters can be accurately determined by this method. The root mean square between the vertical waveguide characteristic impedance and the measured impedance is 0.38dB, and the inversion results accurately represent the seabed characteristics in the experimental sea area.

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“…By regarding the CBF spatial spectrum as the beam pattern convolution with the target spatial distribution, the Richardson–Lucy (R-L) [ 27 ] algorithm can be used to restore a high-resolution spatial spectrum. In recent years, DCBF has been widely used for various arrays [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Deconvolution is also used as postprocessing for other beamforming methods, such as Chebyshev weighting beamforming [ 39 ] and frequency-difference beamforming [ 40 ], to optimize their spatial spectrum and to improve resolution.…”

Section: Introductionmentioning

confidence: 99%

Deconvolved Fractional Fourier Domain Beamforming for Linear Frequency Modulation Signals

Liu

Tao

Sun

et al. 2023

Sensors

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To estimate the direction of arrival (DOA) of a linear frequency modulation (LFM) signal in a low signal-to-noise ratio (SNR) hydroacoustic environment by a small aperture array, a novel deconvolved beamforming method based on fractional Fourier domain delay-and-sum beamforming (FrFB) was proposed. Fractional Fourier transform (FrFT) was used to convert the received signal into the fractional Fourier domain, and delay-and-sum beamforming was subsequently performed. Noise resistance was acquired by focusing the energy of the LFM signal distributed in the time–frequency domain. Then, according to the convolution structure of the FrFB complex output, the influence of the fractional Fourier domain complex beam pattern was removed by deconvolution, and the target spatial distribution was restored. Therefore, an improved spatial resolution of DOA estimation was obtained without increasing the array aperture. The simulation and experimental results show that, with a small aperture array at low SNR, the proposed method possesses higher spatial resolution than FrFB and frequency-domain deconvolved conventional beamforming.

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Improving the Performance of a Vector Sensor Line Array by Deconvolution

Cited by 26 publications

References 23 publications

Bistatic Localization of Objects in Very Shallow Water

Bistatic Localization of Objects in Very Shallow Water

Inversion of shallow seabed structure and geoacoustic parameters with waveguide characteristic impedance based on Bayesian approach

Deconvolved Fractional Fourier Domain Beamforming for Linear Frequency Modulation Signals

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