Field focusing control in multimode plane-layered waveguides

Григорьев, В. А.; Kuz’kin, V. M.

doi:10.1134/1.1922542

Cited by 16 publications

(6 citation statements)

References 2 publications

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“…Here, the phase in the exponential term

\Delta \kappa _{nm}(\omega )r

is called the interference phase. According to the generalized theory of waveguide invariance presented in [9], the mode wave number difference

\Delta \kappa _{nm}(\omega )

can be approximated as:

\begin{equation} \Delta {{\kappa }_{nm}}{\left(\omega \right)}\approx {{\gamma }_{nm}}{{\omega }^{-1/\beta }},\beta &gt;0 ,\end{equation}

where

\gamma _{nm}&gt;0

, is a constant which depends upon mode order and waveguide model. In the range of

[ {{\omega }_{0}},{{\omega }_{0}}+\Delta \omega ,\ldots,{{\omega }_{0}}+(K-1 )\Delta \omega ]

, where K is a positive integer, ω 0 is the initial frequency,

\Delta {\omega }

is the frequency resolution, and

(K-1)\Delta {\omega }

is the bandwidth.…”

Section: Methodsmentioning

confidence: 99%

“…Here, the phase in the exponential term κ nm (ω)r is called the interference phase. According to the generalized theory of waveguide invariance presented in [9], the mode wave number difference κ nm (ω) can be approximated as:…”

Section: Introductionmentioning

confidence: 99%

“…Simulation: In order to verify the feasibility of our presented method above, three numerical simulations are performed using the KRAKEN normal-mode code. It should be explained that different frequency bands will be chosen for different simulations in order to satisfy the assumed conditions of waveguide invariance theory [9]. The schematic diagrams of these simulations are shown in Figure 1a,c,e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide

Gao,

Zhu,

2023

Electronics Letters

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A statistical inversion method of estimating normal mode interference spectral parameters (including waveguide invariance and interference frequency) from one‐dimensional broadband sound intensity spectrum (1D BSIS) is presented. First, the interference phase of 1D BSIS is compensated by a non‐linear factor for each pair of unknown parameters in a prior parameter space. Second, the singular value decomposition is performed for a data matrix, which is constructed by the phase‐compensated BSIS. Then, a misfit function is defined based on both the maximum singular value and the corresponding singular vector of the data matrix. Finally, the posterior probability distributions of unknown parameters are analysed based on statistical inversion theory. This method is suitable for the situation using a single receiver and without any environmental information. Numerical simulations illustrate the validity of the presented method.

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“…Here, the phase in the exponential term

\Delta \kappa _{nm}(\omega )r

is called the interference phase. According to the generalized theory of waveguide invariance presented in [9], the mode wave number difference

\Delta \kappa _{nm}(\omega )

can be approximated as:

\begin{equation} \Delta {{\kappa }_{nm}}{\left(\omega \right)}\approx {{\gamma }_{nm}}{{\omega }^{-1/\beta }},\beta &gt;0 ,\end{equation}

where

\gamma _{nm}&gt;0

, is a constant which depends upon mode order and waveguide model. In the range of

[ {{\omega }_{0}},{{\omega }_{0}}+\Delta \omega ,\ldots,{{\omega }_{0}}+(K-1 )\Delta \omega ]

, where K is a positive integer, ω 0 is the initial frequency,

\Delta {\omega }

is the frequency resolution, and

(K-1)\Delta {\omega }

is the bandwidth.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide

Gao,

Zhu,

2023

Electronics Letters

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“…According to the theory of normal-mode, for a point source excitation with circular frequency w and depth z s in shallow water, the received SIS I T at a depth of z r after propagation over a long distance d can be expressed approximately as in Equation 1 (Grachev, 1993;Gao et al, 2021):…”

Section: Sound Intensity Spectrummentioning

confidence: 99%

SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

Zhu,

Gao,

2024

Front. Mar. Sci.

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In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance.

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“…As is well known, the field distribution inside the waveguide is nonuniform, and the field focusings and defocusings are observed in almost all the existing types of oceanic waveguides [2,19]. For effective illumination of observation objects, as well as for consistent reception of signals scattered by them, the focusing action of the waveguide should be taken into account during the formation of amplitude-phase distributions within the limits of the apertures of the radiating and receiving arrays [22,23,[34][35][36][37][38][39]. In this case, the optimal set of aperture distributions will depend on the location of the inhomogeneity in the observation region.…”

Section: Introductionmentioning

confidence: 99%

Tomographic observation of shallow-water inhomogeneities in the case of probing by a focused high-frequency acoustic field. I. Structure of simulation model

Smirnov

Khil’ko

2009

Radiophys Quantum El

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UDC 534.26We study the possibilities of acoustic tomography observation of small-size bodies in shallow-water layered oceanic waveguides within the framework of geometric acoustics. For increasing the spatial resolution and sensitivity in the considered inverse problem, we used the waveguide-matched focusing of the received and radiated fields into the region of expected location of the observed body. The algorithm of observation in this case consists of search for a global extremum of decision statistics, which is determined by the difference between the measured data and the hypothetic body location. Such hypotheses are formed on the basis of a priori information in the form of the waveguide model and the data on the structure of the observation system. For decreasing the influence of noise associated with the interference of fields in the waveguide, selection of separate tomographic ray projections with the subsequent incoherent accumulation of partial images is used. A tomographic shallow-water observation model based on such principles is analyzed.

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Field focusing control in multimode plane-layered waveguides

Cited by 16 publications

References 2 publications

Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide

Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide

SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

Tomographic observation of shallow-water inhomogeneities in the case of probing by a focused high-frequency acoustic field. I. Structure of simulation model

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