Institute of Acoustics, Chinese Academy of Science
대리인 / 주소
Bushnell, Esq., Robert E.
인용정보
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0인용 특허 :
6
초록▼
The present invention discloses a method and a system for measuring flow layer velocities using correlation velocity measuring sonar. The present invention provides a new theoretical expression for fluid medium sonar array temporal and spatial correlation function, the velocities of each flow layer
The present invention discloses a method and a system for measuring flow layer velocities using correlation velocity measuring sonar. The present invention provides a new theoretical expression for fluid medium sonar array temporal and spatial correlation function, the velocities of each flow layer are derived by fitting experimental data and a theoretical function, or fitting absolute value operated and localized experimental data and a theoretical function. The fluid medium sonar array temporal and spatial correlation function of the present invention is succinctly expressed by Kummer function, and well coincided with the experiments. This function is applicable not only to far field region, i.e. planar wave region, but also Fraunhofer region, i.e. spherical wave region. The present invention has the merits of high measurement accuracy, small calculation load, good robustness and fast convergence.
대표청구항▼
The invention claimed is: 1. A method for measuring flow layer velocities using correlation velocity measuring sonar, the method comprising steps of: (1) selecting a transmit code for acoustic pulses; (2) according to the transmit code, transmitting the acoustic pulses into a fluid medium, and rece
The invention claimed is: 1. A method for measuring flow layer velocities using correlation velocity measuring sonar, the method comprising steps of: (1) selecting a transmit code for acoustic pulses; (2) according to the transmit code, transmitting the acoustic pulses into a fluid medium, and receiving echo signals backscattered by flow layers in the fluid medium; (3) demodulating and filtering the echo signals of the flow layers, and calculating a data temporal and spatial correlation function matrix of the flow layers according to the demodulated and filtered echo signals; (4) extracting a data matrix for fitting from the data temporal and spatial correlation function matrix derived from the step (3), the data matrix for fitting being a localized data temporal and spatial correlation function matrix of the flow layers derived by the steps of: (a) performing an absolute value operation on the data temporal and spatial correlation function matrix of the flow layers to obtain a data temporal and spatial correlation function absolute value matrix of the flow layers, elements of said data temporal and spatial correlation function absolute value matrix having a maximum value of EMax; and (b) setting a threshold value X of 0<X≦1, deriving the localized temporal and spatial correlation function absolute matrix by setting those elements in the absolute value matrix with a numerical value less than XEMax to zero, and by retaining those elements in the absolute value matrix with a numerical value equal to or larger than XEMax; (5) setting a search range of an unknown parameter ensemble ={ Vx, Vy, σvx, σvy, γ}, wherein Vx and Vy are average values of relative velocities of the flow layers in x, y directions, respectively, σvx and σvy are standard deviations of the velocities in x, y directions, respectively, and γ is a width factor; (6) using a computer to fit the data matrix for fitting derived from the step (4) into a theoretical fluid medium sonar array function in the search range of the unknown parameter ensemble to obtain fitting results, the theoretical fluid medium sonar array function being: φ ( τ , ϑ , d ) = C { exp ( γβ θ ) - ζ 2 2 [ θ e 2 2 π 1 F 1 ( 2 ; 1 ; β θ ) - cos 2 ( α 3 - α 2 ) B 2 2 θ e 4 8 π 2 F 1 1 ( 3 ; 3 ; β θ ) ] } wherein, C is a constant, τ is a time delay parameter, d is a distance between receive elements of the sonar array, 1F1(.) is a Kummer function, β 0 = - β 2 2 θ e / 4 π , β 2 = ω 0 c ( ( τ V _ x + d x ) 2 + ( τ V _ y + d y ) 2 ) 1 / 2 , ζ 2 = ω 0 τ c ( σ vx 2 + σ wy 2 ) 1 / 2 , α 2 = tg - 1 τ V _ y + d y τ V _ x + d x , α 3 = tg - 1 σ vy σ vx , θ e 2 = 1 2 θ b 2 θ c 2 θ b 2 + θ c 2 , ω0 is a central frequency of the transmitted acoustic pulses, c is the velocity of sound, dx and dy are components of d in x direction and y direction, respectively, and θb and θc are beam widths of the transmitted acoustic pulses and the received echo signal, respectively; and (7) Obtaining absolute velocities of each of the flow layers by cooperating the velocity of a vessel relative to the seabed with the average values of the relative velocities { Vx, Vy} derived according to the fitting results obtained in the step (6). 2. The method for measuring flow layer velocities using correlation velocity measuring sonar as claimed in claim 1, characterized in that the steps (1)˜(7) are repeated for the next velocity measurement of flow layer. 3. The method for measuring flow layer velocities using correlation velocity measuring sonar as claimed in claim 2, characterized in that a value selected from among a previous measured relative velocity and an average value of multiple previous measured relative velocities is used as the initial value of the search range of the unknown parameter ensemble . 4. The method for measuring flow layer velocities using correlation velocity measuring sonar as claimed in claim 1, characterized in that the autocorrelation of the transmit code in the step (1) has a peak value at a non-zero time delay. 5. The method for measuring flow layer velocities using correlation velocity measuring sonar as claimed in claim 1, characterized in that the threshold value X is 0.7 <X≦1. 6. The method for measuring flow layer velocities using correlation velocity measuring sonar as claimed in claim 1, characterized in that, a fitting algorithm set forth by step (6) uses a sequential quadratic programming method based on a maximum likelihood principle. 7. The method for measuring flow layer velocities using correlation velocity measuring sonar as claimed in claim 1, characterized in that, a fitting algorithm set forth by step (6) uses a sequential quadratic programming method based on a nonlinear least square principle. 8. A correlation velocity measuring sonar system including a sonar array and an electronic subsystem, the electronic subsystem having a computer, characterized in that the computer comprises: an initialization module for initializing software and hardware; a signal coding module for selecting transmit codes for acoustic pulses; a transmit/receive module for transmitting acoustic pulses into a fluid medium, and receiving echo signals backscattered by flow layers in the fluid medium; a demodulation and filter module for demodulating and filtering the echo signals of the flow layers received by the transmit/receive module; a matrix calculation module for calculating a data temporal and spatial correlation function matrix of the flow layers according to the demodulated and filtered echo signals of the flow layers; a matrix extraction module for extracting a data matrix for fitting from the data temporal and spatial correlation function matrix of the flow layers derived by the matrix calculating module; a parameter module for storing a search range of an unknown parameter ensemble ={ Vx, Vy, σvx, σvy, γ}, wherein Vx and Vy are average values of relative velocities of the flow layers in x, y directions respectively, σvx and σvy are standard deviations of the velocities in x, y directions respectively, and γ is a width factor; a fit module on a computer for fitting the data matrix derived by the matrix extraction module into a theoretical fluid medium sonar array function in the search range of the unknown parameter ensemble to obtain fitting results, the theoretical fluid medium sonar array function being: φ ( τ , ϑ , d ) = C { exp ( γβ θ ) - ζ 2 2 [ θ e 2 2 π 1 F 1 ( 2 ; 1 ; β θ ) - cos 2 ( α 3 - α 2 ) B 2 2 θ e 4 8 π 2 F 1 1 ( 3 ; 3 ; β θ ) ] } wherein, C is a constant, τ is a time delay parameter, d is the distance between receive elements of the sonar array, 1F1(*) is a Kummer function, β θ = - β 2 2 θ e / 4 π , β 2 = ω 0 c ( ( τ V _ x + d x ) 2 + ( τ V _ y + d y ) 2 ) 1 / 2 , ξ 2 = ω 0 τ c ( σ vx 2 + θ vy 2 ) 1 / 2 , α 2 = tg - 1 τ V _ y + d y τ V x + d x , α 3 = tg - 1 σ vy σ yx , θ e 2 = 1 2 θ b 2 θ c 2 θ b 2 + θ c 2 ; ω0 is the central frequency of the transmitted acoustic pulses, c is the velocity of sound, dx and dy are components of d in x direction and y direction respectively, and θb and θc are transmit beam width and receive beam width respectively; and a velocity storage module for storing average values { Vx, Vy} of the relative velocities derived according to the fitting results obtained by the fit module, the data matrix for fitting extracted by the matrix extraction module being a localized data temporal and spatial correlation function absolute value matrix of the flow layers, and the matrix extraction module comprises: an absolute value calculation unit for performing an absolute value operation on the data temporal and spatial correlation function matrix to attain a data temporal and spatial correlation function absolute value matrix of the flow layers; and a localization unit for selecting a maximum value EMax in the data temporal and spatial correlation function absolute value matrix of the flow layers, and for to obtaining the localized temporal and spatial correlation function absolute matrix of the flow layers by setting a threshold value X of 0<X≦1, by setting those elements in the absolute value matrix with a numerical value less than XEMax to zero, and by retaining those elements in the absolute value matrix with a numerical value equal to or larger than XEMax. 9. The correlation velocity measuring sonar system as claimed in claim 8, characterized in that the transmit code generated by the signal coding module has a correlation peak value at a non-zero time delay. 10. The correlation velocity measuring sonar system as claimed in claim 8, characterized in that the fit module is a calculation module using a sequential quadratic programming method based on a maximum likelihood principle for the fitting operation. 11. The correlation velocity measuring sonar system as claimed in claim 8, characterized in that the fit module is a calculation module using a sequential quadratic programming method based on a nonlinear least square principle for the fitting operation. 12. The correlation velocity measuring sonar system as claimed in claim 8, characterized in that an initial value of the search range of the unknown parameter ensemble stored in the parameter module is a previous measured relative velocity or an average value of multiple previous measured relative velocities.
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이 특허에 인용된 특허 (6)
Deines Kent L. (Poway CA) Maier Steve J. (San Diego CA), Bottom tracking system.
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