CN113011006B - Target depth estimation method based on cross-correlation function pulse waveform matching - Google Patents

Target depth estimation method based on cross-correlation function pulse waveform matching Download PDF

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CN113011006B
CN113011006B CN202110211679.9A CN202110211679A CN113011006B CN 113011006 B CN113011006 B CN 113011006B CN 202110211679 A CN202110211679 A CN 202110211679A CN 113011006 B CN113011006 B CN 113011006B
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张波
李风华
张燕君
余炎欣
朱飞龙
李文
翟铎
刘若芸
杨习山
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Abstract

The invention discloses a target depth estimation method based on cross-correlation function pulse waveform matching, which comprises the following steps: step 1) extracting a time domain pseudo-Green function from radiation noise data of a target to be detected by using two approximately parallel acoustic arrays to obtain a pulse envelope of the time domain pseudo-Green function; step 2) dividing grids along the depth and distance directions, then according to the hydrological environment of the sea area where the acoustic array is laid, calculating corresponding time domain pseudo-grid functions on each grid point, and obtaining the pulse envelope of each grid point; and 3) matching the pulse envelope extracted in the step 1) with the pulse envelopes on the grid points to obtain a depth estimation result of the underwater target. The method of the invention separates the target depth estimation from the target distance estimation, can obtain the target depth estimation result under the condition of unknown target distance, and overcomes the limitation that the traditional matching field processing method needs to estimate the target distance and the target depth at the same time or estimate the target distance first and then estimate the target depth.

Description

Target depth estimation method based on cross-correlation function pulse waveform matching
Technical Field
The invention belongs to the field of ocean acoustics, and particularly relates to a target depth estimation method based on cross-correlation function pulse waveform matching.
Background
Depth information of the target is a very important target parameter. A reliable target depth estimation result is given according to signals received by the acoustic sensor, and the method is an effective way for solving the depth resolution of the targets in water. The matching field processing method is one of methods for depth estimation of an object. The method fully utilizes the interference characteristic of the sound field, calculates the copy field vector by adopting the sound propagation model, and then carries out matching processing on the copy field and the measurement field, thereby obtaining the matching processing result of the unknown variable. Since 1976 the proposal of the concept of Matching Field (MFP) positioning, hundreds of related papers have been published at home and abroad, and a great deal of offshore experiments are carried out, thus making many progress. However, the marine environment conditions are very complex, the environmental parameters are often time-varying and space-varying, and the matching field processing method faces the difficult problem that the marine environment parameters cannot be accurately measured, so that the copy field calculation is inaccurate, the deviation between the matching processing result and the real result is too large, and even the processing result with the confidence coefficient exceeding the set threshold value cannot be given. The method requires that receiving hydrophones are arranged at a specific depth, the depth needs to be calculated according to sound field analysis frequency and marine environments such as water depth, bottom material, sound velocity profile and the like, and the depth is difficult to accurately calculate in practical application, so that the use of the method is limited. In addition, the method can utilize the dispersion characteristics of the pulse signals to carry out depth estimation on the target sound source, and the method requires that the sound source sends out the pulse signals with short time domain, and the received signals in practical application are continuous radiation noise signals, and various ships rarely radiate the transient pulse signals with short duration and high intensity.
Disclosure of Invention
Aiming at the requirement of depth resolution of an acoustic target, the invention provides a target depth estimation method based on cross-correlation function pulse waveform matching according to the physical characteristics of a horizontal longitudinal cross-correlation function of a shallow sea low-frequency broadband sound field, which is suitable for the depth estimation of a low-frequency broadband passive target in a shallow sea area and provides a technical means for improving the target depth resolution capability of the shallow sea area. According to physical characteristics of a horizontal longitudinal cross-correlation function of a shallow sea low-frequency broadband sound field, two approximately parallel acoustic arrays are used for extracting a time domain pseudo-gray function, and depth estimation results of targets in water are given out by matching the actually extracted pseudo-gray function pulse envelopes with pseudo-gray function pulse envelopes simulated and calculated at different depths.
In order to achieve the above object, the present invention provides a target depth estimation method based on cross-correlation function pulse waveform matching, the method comprising:
step 1) extracting a time domain pseudo-Green function from radiation noise data of a target to be detected by using two approximately parallel acoustic arrays to obtain a pulse envelope of the time domain pseudo-Green function;
step 2) dividing grids along the depth and distance directions, then according to the hydrological environment of the sea area where the acoustic array is laid, calculating corresponding time domain pseudo-grid functions on each grid point, and obtaining the pulse envelope of each grid point;
and 3) matching the pulse envelope extracted in the step 1) with the pulse envelopes on the grid points to obtain a depth estimation result of the underwater target.
As an improvement of the above method, the step 1) specifically includes:
step 1-1) processing multi-beat data of the acoustic array A within a period of time to obtain a broadband beam output result of the acoustic array A; obtaining the azimuth course theta of a certain target relative to the acoustic array A along the time tA(t)；
Step 1-2) processing multi-beat data of the acoustic array B within a period of time to obtain a broadband beam output result of the acoustic array B; obtaining an azimuth history theta of a target relative to the acoustic array B as a function of time tB(t)；
Step 1-3) if | θA(t)-θB(t) alpha is less than or equal to l, and alpha is a threshold value; turning to the step 1-4); otherwise, changing the time period, and turning to the step 1-1),until | θ is satisfied in the broadband beam output resultA(t)-θB(t) the appearance of a target with the value less than or equal to alpha;
step 1-4) preprocessing the output result of the two acoustic arrays in the beam forming of the target position; and performing cross-correlation operation on the preprocessed beam data, accumulating the beam data within a certain time, and calculating an average value of the beam data, namely a pseudo-Green function.
As an improvement of the above method, the step 1-1) specifically comprises:
step 1-1-1) dividing acoustic signals received by each array element of the acoustic array A into multiple beats, wherein the time length of each beat is delta T, two adjacent beats are overlapped by P%, and each beat signal is subjected to Fourier transform to obtain a frequency domain signal; the frequency domain signal of the acoustic array A is sA(f；t)＝[sA1(f；t),sA2(f；t),...,sAM(f；t)]T(ii) a f is the signal frequency, t is the starting time of the current beat data; m is the number of array elements of the acoustic array A; (.)TRepresenting a transpose;
step 1-1-2) taking the geometric center of the acoustic array A as a reference point, calculating a weighting vector w of each frequency point of the acoustic array A in a beam forming scanning direction thetaA(f,θ)＝[wA0(f,θ),wA1(f,θ),...,wAM-1(f,θ)]TWherein
Figure BDA0002952583400000021
Wherein, tauAm(θ) represents the time delay of the m +1 th array element of the acoustic array a relative to the reference point:
Figure BDA0002952583400000031
v(θ)＝-[cosθ,sinθ]T
pAm＝[pxm,pym]T
v (θ) is the unit vector of the incident signal, pAmIs a two-dimensional coordinate of the m +1 array element relative to a reference point；
Step 1-1-3) calculating a beam forming result b of the pointing direction theta of the acoustic array AA(f,θ；t)：
bA(f,θ；t)＝wA H(f,θ)sA(f；t)
Wherein, (.)HRepresents a conjugate transpose; the value range of theta is [ theta ]0-45°，θ0+45°]，θ0Representing the angle of the connecting line of the reference array elements of the acoustic array A and the acoustic array B;
steps 1-1-4) time period [ t ] for Acoustic array A1，t2]Processing the multi-beat data in the step 1-1-1) to the step 1-1-3) to obtain a broadband beam output result H of the acoustic array AA(θ,t)：
Figure BDA0002952583400000032
Wherein the symbol "Σ" denotes summation;
step 1-5) obtaining the azimuth course theta of a certain target relative to the acoustic array A along with the change of time t according to the broadband wave beam output result of the acoustic array AA(t)。
As an improvement of the above method, the step 1-2) specifically includes:
step 1-2-1) dividing the acoustic signals received by each array element of the acoustic array B into multiple beats, wherein the time length of each beat is delta T, two adjacent beats are overlapped by P%, and each beat is subjected to Fourier transform to obtain frequency domain signals; the frequency domain signal of the acoustic array B is sB(f；t)＝[sB1(f；t),sB2(f；t),...,sBN(f；t)]T(ii) a f is the signal frequency, selected in the range of [ fL,fH](ii) a t is the starting time of the current beat data; n is the number of array elements of the acoustic array B;
step 1-2-2) taking the geometric center of the acoustic array B as a reference point, calculating a weighting vector w of each frequency point of the acoustic array B in the beam forming scanning direction thetaB(f,θ)＝[wB0(f,θ),wB1(f,θ),...,wB,N-1(f,θ)]TWherein
Figure BDA0002952583400000033
Wherein tau isBn(θ) represents the time delay of the n +1 th array element of the acoustic array B with respect to the reference point:
Figure BDA0002952583400000041
v(θ)＝-[cosθ,sinθ]T
pBn＝[pxn,pyn]T
v (θ) is the unit vector of the incident signal, pBnThe two-dimensional coordinates of the (n + 1) th array element relative to the reference point;
step 1-2-3) calculating a beam forming result B of the pointing direction theta of the acoustic array BB(f,θ；t)：
bB(f,θ；t)＝wB H(f,θ)sB(f；t)
Wherein, (.)HRepresents a conjugate transpose;
step 1-2-4) time period [ t ] for acoustic array B1，t2]Processing the multi-beat data in the step 1-2-1) to the step 1-2-3), and calculating a broadband beam output result H of the acoustic array BB(θ,t)：
Figure BDA0002952583400000042
Step 1-2-5) obtaining the azimuth course theta of the same target relative to the acoustic array B along with the change of time t according to the broadband wave beam output result of the acoustic array BB(t)。
As a modification of the above method, the steps 1 to 4) include:
step 1-4-1) preprocessing the output result of the beam forming of the two acoustic arrays in the target direction, and preprocessing the beam of the acoustic array A, B in the target directionForm an output
Figure BDA0002952583400000043
Respectively as follows:
Figure BDA0002952583400000044
Figure BDA0002952583400000045
wherein k is wave number, k is 2 pi f/c0(ii) a L is the horizontal spacing of the geometric centers of acoustic array A, B;
Figure BDA0002952583400000046
a horizontal orientation of the selected target relative to the acoustic array B for a starting time within the analysis time period;
IA(f) average intensity spectrum for the chosen target relative to acoustic array a:
Figure BDA0002952583400000047
IB(f) average intensity spectrum for the chosen target relative to acoustic array B:
Figure BDA0002952583400000051
wherein N istThe total number of beats; n is a radical oftThe value range of (1) is [ 600/(delta T (1-P%)), 1800/(delta T (1-P%))](ii) a Δ T is the time length of a single beat of data; p% is the overlapping rate of two adjacent beats;
step 1-4-2) performing cross-correlation operation on the preprocessed beam data, accumulating the beam data within a certain time, calculating the average value of the beam data, and obtaining a frequency domain pseudo-Green function
Figure BDA0002952583400000052
Figure BDA0002952583400000053
(·)*Represents a conjugation;
step 1-4-3) to the frequency domain pseudo-Green function
Figure BDA0002952583400000054
Performing inverse Fourier transform to obtain time domain pseudo-Green function
Figure BDA0002952583400000055
Figure BDA0002952583400000056
Wherein ifft represents inverse Fourier transform, and Re represents a real part;
step 1-4-4) time domain pseudo Green function
Figure BDA0002952583400000057
Is a pulse function whose envelope is v (τ):
Figure BDA0002952583400000058
where | represents the absolute value, and H (·) represents the hilbert transform.
As an improvement of the above method, the step 2) specifically includes:
step 2-1) according to [0,4H/5 ]]Spacing, dividing the acoustic array deployment sea area into Q in depthzEach grid, wherein H is water depth; according to [ R ]min,Rmax]Spacing, dividing the acoustic array arrangement sea area into Q in distanceRA grid, RminMinimum distance, R, required to meet acoustic array far field requirementsmaxMaximum theoretical detection range of the target for the acoustic array;
step 2-2) according to the water depth, the sound velocity profile and the seabed parametersAnd (3) calculating the normal wave information by adopting a sound field calculation method for the marine environment parameters: horizontal wave number k of No. l normal wavel＝μl+iηl，μl、ηlAre each klThe real part and the imaginary part of (c); characteristic function Ψ of the ith normal wavel(z); wherein, l is 1,2, …, I; i is the total number of the normal waves;
step 2-3) calculating a time domain pseudo-Green function of each grid point according to the normal wave information;
for the qth grid point, its time-domain pseudo-green function is:
Figure BDA0002952583400000061
wherein z isq、rqRespectively corresponding to the depth of a sound source and the horizontal distance to the midpoint of a connecting line of the geometric centers of the two acoustic arrays; tau represents time delay, ifft represents inverse Fourier transform, and Re represents a real part;
Figure BDA0002952583400000062
for the frequency domain pseudo-green function for the qth grid point:
Figure BDA0002952583400000063
wherein, B (z)1,z2,zq) As a normalization factor:
Figure BDA0002952583400000064
z1,z2the depth of the two hydrophones is shown, the horizontal distance between the two hydrophones is L, and the horizontal distances between the two hydrophones and the sound source are r respectively1,r2；
Step 2-4) time domain pseudo Green function
Figure BDA0002952583400000065
Is a pulse function having an envelope vr(τ；zq,rq)：
Figure BDA0002952583400000066
Where | represents the absolute value, and H (·) represents the hilbert transform.
As an improvement of the above method, the depth grid number QzHas a value range of [8H/5 lambda, 40H/5 lambda]λ is the acoustic wavelength corresponding to the center frequency within the analysis bandwidth; number of distance grids QRHas a value range of [ (R)max-Rmin)/10,(Rmax-Rmin)]。
As an improvement of the above method, the step 3) specifically includes:
step 3-1) calculating a time domain pseudo-Green function envelope v (tau) of the qth grid point and a time domain pseudo-Green function envelope v (tau) extracted from actual datar(τ；zq,rq) Correlation coefficient W (z)q,rq)：
Figure BDA0002952583400000071
Wherein, V (τ) and Vr(τ；zq,rq) Are for v (tau) and v, respectivelyr(τ；zq,rq) Taking the result after the sliding average in the time delay dimension:
Figure BDA0002952583400000072
Figure BDA0002952583400000073
wherein Δ τ is the sliding average window width; the value range of delta tau is [ 5/(f)H-fL)，20/(fH-fL)]；fL、fHRespectively a lower limit frequency and an upper limit frequency of the analysis frequency band;
step 3-2) for all W (z)q,rq) And the depth of the grid point corresponding to the maximum value is the depth estimation result of the measured target.
As an improvement of the above method, if the target distance is known, it is noted as rsThen, only the local maximum search is performed on the grid points in the range near the target distance; a typical range for the distance search is 0.8rs,1.2rs]。
The invention has the advantages that:
1. the method of the invention separates the target depth estimation from the target distance estimation, can obtain the target depth estimation result under the condition of unknown target distance, and overcomes the limitation that the traditional matching field processing method needs to estimate the target distance and the target depth at the same time or estimate the target distance first and then estimate the target depth;
2. the method of the invention obtains the target depth estimation result by matching the time domain pseudo-Green function pulse envelope, reduces the requirement on the accuracy of marine environment parameters and improves the tolerance.
Drawings
FIG. 1 is a schematic diagram of the relative positions of hydrophones and acoustic sources in accordance with the present invention;
FIG. 2 is a flow chart of a target depth estimation method based on cross-correlation function pulse waveform matching according to the present invention;
FIG. 3 is a seawater average sound velocity profile in accordance with an embodiment of the present invention; the water depth is 200m, the sound velocity of the sea surface is 1544m/s, the sound velocity is gradually reduced along with the depth, and the sound velocity at the depth of 200m is about 1502 m/s; the seafloor acoustic velocity of 1608 m/s; in the figure, the solid line (labeled ssp1 in the figure) is the sound speed profile used in calculating the measurement field, and the dashed line (labeled ssp2 in the figure) is the sound speed profile used in calculating the copy field;
FIG. 4(a) is a time domain pseudo-Green's function envelope of the measured field with an analysis bandwidth of 200Hz to 300Hz in one embodiment of the present invention;
FIG. 4(b) is a two-dimensional image of the time-domain pseudo-Green's function envelope of the corresponding copy field of FIG. 4(a) with time delay and target depth.
FIG. 5(a) is a diagram illustrating the estimation result of the corresponding target depth is 8m and the actual target depth is 10 m;
fig. 5(b) is a diagram illustrating that the estimation result of the corresponding target depth is 101m and the actual target depth is 100 m.
Detailed Description
Aiming at the requirement of depth resolution of an acoustic target, the invention provides a target depth estimation method based on cross-correlation function pulse waveform matching according to the physical characteristics of a horizontal longitudinal cross-correlation function of a shallow sea low-frequency broadband sound field, which is suitable for the depth estimation of a low-frequency broadband passive target in a shallow sea area and provides a technical means for improving the target depth resolution capability of the shallow sea area.
The technical scheme adopted by the invention is as follows:
according to the theory of normal waves, the depth is zsCan be expressed as a green's function (green's function) of the generated sound field of the point sound source at horizontal distance r, depth z
Figure BDA0002952583400000081
Wherein k ismIs the horizontal wave number, k, of the No. m normal wavem＝μm(f)+jηm(f)，μm、ηmAre each kmThe real part and the imaginary part of (a) are both functions of frequency f; ΨmAnd (z) is a characteristic function of the No. m normal wave. j is an imaginary symbol; e is the base of the natural logarithm, e 2.718 … ….
As shown in FIG. 1, the two dots represent hydrophones with a horizontal separation L and a depth z1、z2And the horizontal distances from the sound source (indicated by five-pointed star) are respectively r1、r2(ii) a A circular point is positioned at the central position of the two hydrophones, and the horizontal distance between the circular point and a sound source is R; the orientation of the source with respect to the line connecting the two hydrophones is θ.
For a sound source with the azimuth theta, a time-domain pseudo-Green function can be obtained by using the two hydrophones shown in FIG. 1
Figure BDA0002952583400000082
Wherein, tau represents time delay, ift represents inverse Fourier transform, and Re represents a real part.
Figure BDA0002952583400000083
Wherein B (z)1,z2,zs) Is a normalization factor
Figure BDA0002952583400000091
The symbol "Σ" is the summation operator.
The time domain pseudo-Green function represented by the formula (2) is a pulse function, and the waveform of the pulse function is related to parameters such as target depth, longitudinal spacing and depth of hydrophones, marine environment and the like. Under the condition of knowing parameters such as longitudinal distance and depth of hydrophones, marine environment and the like, the depth of a measured target can be estimated according to the pulse waveform.
It is noted that the above discussion is for the case of two hydrophones. However, in general, in order to improve the processing gain, two approximately parallel acoustic arrays are usually used to replace the two hydrophones in practical applications, so as to obtain a spatial gain and improve the signal-to-noise ratio.
As shown in fig. 2, the present invention provides a target depth estimation method based on cross-correlation function pulse waveform matching, which includes the following steps:
and S1, processing the actual data, extracting a time domain pseudo-Green function, and obtaining the pulse envelope v (tau) of the time domain pseudo-Green function.
S1-1, respectively forming beams of acoustic data received by two approximately parallel acoustic arrays (acoustic array A, B) arranged in water, wherein the beam forming results are marked as bA(f, theta; t) and bB(f, θ; t). Wherein f is the signal frequency, and is selected to be in the range of [ fL,fH](ii) a t is the start time of the current beat dataEngraving; θ is the pre-beam azimuth.
Preferably, fL、fHHas a typical value range of [2c ]0/H,20c0/H]。c0For reference to the speed of sound, it is common to take the average of the speeds of sound of the bodies of water. H is the water depth.
Preferably, the typical value range of theta is [ theta ]0-45°，θ0+45°]。θ0The angle at which the reference array elements of acoustic array a and acoustic array B are connected is shown.
S1-2, according to the result of the previous step, selecting the surface ship near the positive transverse position of the acoustic array as the sound source of the radiation noise, and the azimuth histories of the surface ship relative to the acoustic array A, B along with the change of time t are respectively marked as thetaA(t)、θB(t)。
If multiple targets are present within the analyzed time period, the targets near the lateral position of the acoustic array should be selected as much as possible.
θA(t)、θB(t) should satisfy | θA(t)-θB(t)|≤2°。
And S1-3, preprocessing the beam forming output result of the two acoustic arrays at the target position. The beamformed outputs of the pre-processed acoustic array A, B at the target azimuth are each
Figure BDA0002952583400000101
Figure BDA0002952583400000102
Wherein k is wave number, k is 2 pi f/c0(ii) a L is the horizontal spacing of the geometric centers of acoustic array A, B;
Figure BDA0002952583400000103
a horizontal orientation of the selected target relative to the acoustic array B for a starting time within the analysis time period;
Figure BDA0002952583400000104
Figure BDA0002952583400000105
Ntthe total number of beats.
Preferably, NtTypical values of (A) are [ 600/(Delta T (1-P%)), 1800/(Delta T (1-P%))]. Δ T is the time length of a single beat of data, in units of s; p% is the overlapping rate of two adjacent beats.
S1-4, performing cross-correlation operation on the preprocessed beam data, accumulating the beam data within a certain time, calculating the average value of the beam data, and obtaining a frequency domain pseudo-Green function
Figure BDA0002952583400000106
Figure BDA0002952583400000107
(·)*Representing conjugation.
S1-5, performing inverse Fourier transform on the frequency domain pseudo Green function to obtain a time domain pseudo Green function
Figure BDA0002952583400000108
Figure BDA0002952583400000109
Wherein, tau represents time delay, ift represents inverse Fourier transform, and Re represents a real part.
Time domain pseudo green function
Figure BDA00029525834000001010
Is a pulse function whose envelope is v (τ):
Figure BDA00029525834000001011
where | represents the absolute value, and H (·) represents the hilbert transform.
S2, according to the hydrological environment of the sea area where the acoustic array is distributed, simulating and calculating a time domain pseudo-Green function to obtain the corresponding time domain pseudo-Green function pulse envelopes v at different depths and distancesr(τ；zq,rq)。
S2-1, dividing the grid in depth and distance. The depth is set at [0,4H/5 ]]In the range divided into QZGrid, H is water depth. Will be at a distance of [ R ]min,Rmax]In the range divided into QRA grid, RminMinimum distance, R, required to meet acoustic array far field requirementsmaxThe maximum theoretical range of action of the acoustic array on the target.
Let Q be QR×QZ，QRIs the number of distance grids, QZIs the depth grid number.
Preferably, QZTypical values of (A) are in the range of [8H/5 lambda, 40H/5 lambda]. H is the water depth, and lambda is the acoustic wavelength corresponding to the center frequency within the analysis bandwidth.
Preferably, QRIs typically in the range [ (R)max-Rmin)/10,(Rmax-Rmin)]。Rmin、RmaxThe unit of (c) is km.
S2-2, calculating the normal wave information by adopting a sound field calculation program (such as Kraken or Krakenc) according to the water depth, the sound velocity profile, the sea bottom parameters and other marine environment parameters: horizontal wave number k of No. m normal wavem＝μm+jηm，μm、ηmAre each kmThe real part and the imaginary part of (c); characteristic function Ψ of mth normal wavem(z). Wherein M is 1,2, …, M. M is the total number of the normal waves.
And S2-3, calculating the frequency domain pseudo Green function of each grid point according to the normal wave information.
For the qth grid point, the time-domain pseudo-Green function is
Figure BDA0002952583400000111
Wherein, tau represents time delay, ift represents inverse Fourier transform, and Re represents a real part.
Figure BDA0002952583400000112
Figure BDA0002952583400000113
Wherein, B (z)1,z2,zs) Is a normalization factor; the symbol "Σ" is a summation operator; z is a radical ofq、rqRespectively corresponding to the depth of a sound source and the horizontal distance to the midpoint of a connecting line of the geometric centers of the two acoustic arrays;
Figure BDA0002952583400000114
is the target bearing obtained in step S1.
Time domain pseudo green function
Figure BDA0002952583400000115
Is a pulse function having an envelope vr(τ；zq,rq)：
Figure BDA0002952583400000116
Wherein argmax (·) represents the time delay corresponding to the maximum value, | · | represents the absolute value, and H (·) represents hilbert transform.
S3, extracting the time domain pseudo-Green function envelope v (tau) and the time domain pseudo-Green function envelope v extracted from the actual datar(τ；zq,rq) And performing matching processing to obtain the target depth.
S3-1, calculating the time domain pseudo-Green function envelope v (tau) of each grid point and the time domain pseudo-Green function envelope v extracted from the actual datar(τ；zq,rq) The correlation coefficient of (2).
For the q grid point, the corresponding correlation coefficient is
Figure BDA0002952583400000121
Wherein, V (τ) and Vr(τ；zq,rq) Are for v (tau) and v, respectivelyr(τ；zq,rq) Taking the result of the running average over the delay dimension,
Figure BDA0002952583400000122
Figure BDA0002952583400000123
where Δ τ is the sliding average window width.
Preferably, Δ τ is typically in the range of [ 5/(f)H-fL),20/(fH-fL)]。fL、fHRespectively, the lower limit frequency and the upper limit frequency of the analysis frequency band.
S3-2, for all W (z)q,rq) And Q is 1,2,3 and … Q, a local maximum value is taken, and the depth of the corresponding grid point is the depth estimation result of the measured object.
Since the above algorithm is very insensitive to the target distance, the distance between the local maximum and the grid point may be greatly different from the target real distance, and therefore, the local maximum cannot be used as the estimation result of the target distance.
If the target distance is known, it is noted as rsThen, only the local maximum search may be performed for the grid points within the range near the target distance. A typical range for the distance search is 0.8rs,1.2rs]。
The invention provides a target depth estimation method based on cross-correlation function pulse waveform matching according to physical characteristics of a horizontal and longitudinal cross-correlation function of a shallow sea low-frequency broadband sound field, which is suitable for low-frequency broadband passive target depth estimation in a shallow sea area and provides a technical means for improving the target depth resolution capability in the shallow sea area.
The technical solution of the present invention will now be explained by means of the accompanying drawings and specific embodiments.
The water depth of the shallow sea area is 200m, the average sound velocity profile of the sea water is shown in figure 3, the sound velocity of the sea surface is 1544m/s, the sound velocity gradually decreases along with the depth, and the sound velocity at the depth of 200m is about 1502 m/s. The seafloor acoustic velocity is 1608 m/s. The solid line (labeled ssp1 in the figure) is the sound speed profile used in calculating the measurement field and the dashed line (labeled ssp2 in the figure) is the sound speed profile used in calculating the copy field.
Two acoustic arrays (acoustic array A, B) are arranged on the sea floor surface, the acoustic arrays A, B are linear arrays and are parallel to each other, the number of array elements is 11, and the spacing between the array elements is 5 m. The geometric centers of the acoustic arrays A, B are all the 6 th array elements, the length of the connecting line of the geometric centers is 700m, the connecting line is perpendicular to the two acoustic arrays, and the target orientation is 0 degrees.
The target distance is uniformly increased from R to 30km to R to 33km within 10 minutes, the target azimuth is theta to 0 DEG, and the target depth is zs10m or zsThe sound field received by the acoustic array A, B is calculated as the measurement field according to the sound velocity profile 1 in fig. 3, 100 m. The average signal-to-noise ratio of the elements of acoustic array A, B is set to-5 dB. Then, the beam outputs of the acoustic array A, B at the target azimuth are obtained and normalized respectively to obtain
Figure BDA0002952583400000131
And
Figure BDA0002952583400000132
performing cross-correlation operation in the frequency domain, and averaging in a certain time period to obtain a frequency domain pseudo-Green function
Figure BDA0002952583400000133
Then, inverse Fourier transform is carried out on the frequency domain pseudo Green function to obtain a time domain pseudo Green function
Figure BDA0002952583400000134
And its envelope v (τ). In this example, NtTaking 100, wherein the corresponding time period is 10 minutes; the frequency analysis range is 200Hz to 300 Hz. FIG. 4(a) shows sound source depths zs＝10m、zsThe horizontal axis represents time delay, unit ms, and the vertical axis represents normalized intensity, which is a time domain pseudo-green function envelope of 100 m.
The depth is divided into 80 grids within the range of [2.5m,200m ], and the step size is 2.5 m. The distance is divided into 41 grids within the range of [10km,50km ], and the step length is 1 km. According to the sea area environment parameters shown in the sound velocity profile 2 in fig. 3, the Kraken program is adopted to calculate the normal wave information, and the time domain pseudo-green function and the envelope of the time domain pseudo-green function of each grid point are calculated according to the normal wave information. For the q grid point, the time domain pseudo Green function envelope is
Figure BDA0002952583400000135
Wherein the content of the first and second substances,
Figure BDA0002952583400000136
is a pseudo-gray function of the time domain,
Figure BDA0002952583400000137
wherein, tau represents time delay, ift represents inverse Fourier transform, and Re represents a real part.
Figure BDA0002952583400000138
Figure BDA0002952583400000139
Wherein, B (z)1,z2,zs) Is a normalization factor; z is a radical ofq、rqSound source depth and to two acoustic array geometries corresponding to the q-th mesh point, respectivelyHorizontal distance of the midpoint of the cardiac line.
In this embodiment, acoustic array A, B depth z1＝z2200m, 700m horizontal spacing L of the geometric centers of the acoustic array A, B, target orientation
Figure BDA0002952583400000141
When r isqTaking 30km, a two-dimensional image of the time domain pseudo-Green's function envelope changing with time delay and depth obtained by simulation calculation is shown in FIG. 4(b), wherein the horizontal axis is time delay and the unit ms, and the vertical axis is depth and the unit m.
Then, for v (τ) and vr(τ；zq,rq) Performing correlation operation according to the following formula to obtain a correlation coefficient of each grid point
Figure BDA0002952583400000142
Wherein, V (τ) and Vr(τ；zq,rq) Are for v (tau) and v, respectivelyr(τ；zq,rq) Taking the result of the running average over the delay dimension,
Figure BDA0002952583400000143
Figure BDA0002952583400000144
where Δ τ is the sliding average window width, and in this embodiment Δ τ is 100 ms.
When the depth of the sound source is zsWhen 10m, W (z)q,rq) The horizontal axis of the two-dimensional image (2) is horizontal distance in km and the vertical axis is depth in m, as shown in fig. 5 (a). When the depth of the sound source is zsWhen the total length is 100m, W (z)q,rq) The horizontal axis of the two-dimensional image (2) is horizontal distance in km and the vertical axis is depth in m, as shown in fig. 5 (a).
The estimation result of the target depth corresponding to fig. 5(a) is 8m, and the actual target depth is 10 m; the target depth estimation result of fig. 5(b) is 101m, and the actual target depth is 100 m. The above results verify the validity of the algorithm presented in the present invention.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (8)

1. A method of target depth estimation based on cross-correlation function pulse shape matching, the method comprising:
step 1) extracting a time domain pseudo-Green function from radiation noise data of a target to be detected by using two approximately parallel acoustic arrays to obtain a pulse envelope of the time domain pseudo-Green function;
step 2) dividing grids along the depth and distance directions, then according to the hydrological environment of the sea area where the acoustic array is laid, calculating corresponding time domain pseudo-grid functions on each grid point, and obtaining the pulse envelope of each grid point;
step 3) matching the pulse envelope extracted in the step 1) with the pulse envelopes on the grid points to obtain a depth estimation result of the underwater target;
the step 1) specifically comprises the following steps:
step 1-1) processing multi-beat data of the acoustic array A within a period of time to obtain a broadband beam output result of the acoustic array A; obtaining the azimuth course theta of a certain target relative to the acoustic array A along the time tA(t)；
Step 1-2) processing multi-beat data of the acoustic array B within a period of time to obtain a broadband beam output result of the acoustic array B; obtaining an azimuth history theta of a target relative to the acoustic array B as a function of time tB(t)；
Step 1-3) if | θA(t)-θB(t) alpha is less than or equal to l, and alpha is a threshold value; turning to the step 1-4); otherwise change the timeSegment, go to step 1-1) until there is a satisfying | theta in the wideband beam output resultA(t)-θB(t) the appearance of a target with the value less than or equal to alpha;
step 1-4) preprocessing the output result of the two acoustic arrays in the beam forming of the target position; and performing cross-correlation operation on the preprocessed beam data, accumulating the beam data within a certain time, and calculating an average value of the beam data, namely a pseudo-Green function.
2. The method for estimating the target depth based on the cross-correlation function pulse shape matching according to claim 1, wherein the step 1-1) specifically comprises:
step 1-1-1) dividing acoustic signals received by each array element of the acoustic array A into multiple beats, wherein the time length of each beat is delta T, two adjacent beats are overlapped by P%, and each beat signal is subjected to Fourier transform to obtain a frequency domain signal; the frequency domain signal of the acoustic array A is sA(f；t)＝[sA1(f；t),sA2(f；t),...,sAM(f；t)]T(ii) a f is the signal frequency, t is the starting time of the current beat data; m is the number of array elements of the acoustic array A; (.)TRepresenting a transpose;
step 1-1-2) taking the geometric center of the acoustic array A as a reference point, calculating a weighting vector w of each frequency point of the acoustic array A in a beam forming scanning direction thetaA(f,θ)＝[wA0(f,θ),wA1(f,θ),...,wAM-1(f,θ)]TWherein
Figure FDA0003220165120000011
Wherein, tauAm(θ) represents the time delay of the m +1 th array element of the acoustic array a relative to the reference point:
Figure FDA0003220165120000021
v(θ)＝-[cosθ,sinθ]T
pAm＝[pxm,pym]T
v (θ) is the unit vector of the incident signal, pAmIs a two-dimensional coordinate of the m +1 th array element relative to a reference point, c0Is a reference sound velocity;
step 1-1-3) calculating a beam forming result b of the pointing direction theta of the acoustic array AA(f,θ；t)：
bA(f,θ；t)＝wA H(f,θ)sA(f；t)
Wherein, (.)HRepresents a conjugate transpose; the value range of theta is [ theta ]0-45°，θ0+45°]，θ0Representing the angle of the connecting line of the reference array elements of the acoustic array A and the acoustic array B;
steps 1-1-4) time period [ t ] for Acoustic array A1，t2]Processing the multi-beat data in the step 1-1-1) to the step 1-1-3) to obtain a broadband beam output result H of the acoustic array AA(θ,t)：
Figure FDA0003220165120000022
Wherein the symbol "Σ" denotes summation;
step 1-5) obtaining the azimuth course theta of a certain target relative to the acoustic array A along with the change of time t according to the broadband wave beam output result of the acoustic array AA(t)。
3. The method for estimating the target depth based on the cross-correlation function pulse waveform matching according to claim 2, wherein the step 1-2) specifically comprises:
step 1-2-1) dividing the acoustic signals received by each array element of the acoustic array B into multiple beats, wherein the time length of each beat is delta T, two adjacent beats are overlapped by P%, and each beat is subjected to Fourier transform to obtain frequency domain signals; the frequency domain signal of the acoustic array B is sB(f；t)＝[sB1(f；t),sB2(f；t),...,sBN(f；t)]T(ii) a f is the signal frequency, selected in the range of [ fL,fH](ii) a t is the starting time of the current beat data; n is the number of array elements of the acoustic array B;
step 1-2-2) taking the geometric center of the acoustic array B as a reference point, calculating a weighting vector w of each frequency point of the acoustic array B in the beam forming scanning direction thetaB(f,θ)＝[wB0(f,θ),wB1(f,θ),...,wB,N-1(f,θ)]TWherein
Figure FDA0003220165120000031
Wherein tau isBn(θ) represents the time delay of the n +1 th array element of the acoustic array B with respect to the reference point:
Figure FDA0003220165120000032
v(θ)＝-[cosθ,sinθ]T
pBn＝[pxn,pyn]T
v (θ) is the unit vector of the incident signal, pBnThe two-dimensional coordinates of the (n + 1) th array element relative to the reference point;
step 1-2-3) calculating a beam forming result B of the pointing direction theta of the acoustic array BB(f,θ；t)：
bB(f,θ；t)＝wB H(f,θ)sB(f；t)
Wherein, (.)HRepresents a conjugate transpose;
step 1-2-4) time period [ t ] for acoustic array B1，t2]Processing the multi-beat data in the step 1-2-1) to the step 1-2-3), and calculating a broadband beam output result H of the acoustic array BB(θ,t)：
Figure FDA0003220165120000033
Step 1-2-5) outputting the result according to the broadband wave beam of the acoustic array BObtaining an azimuth history theta of the same target with respect to the acoustic array B as a function of time tB(t)。
4. The method for estimating the target depth based on the cross-correlation function pulse shape matching according to claim 3, wherein the steps 1-4) comprise:
step 1-4-1) preprocessing the beam forming output results of the two acoustic arrays in the target direction, and outputting the preprocessed beam forming output of the acoustic array A, B in the target direction
Figure FDA0003220165120000034
Respectively as follows:
Figure FDA0003220165120000035
Figure FDA0003220165120000036
wherein k is wave number, k is 2 pi f/c0(ii) a L is the horizontal spacing of the geometric centers of acoustic array A, B;
Figure FDA0003220165120000037
a horizontal orientation of the selected target relative to the acoustic array B for a starting time within the analysis time period;
IA(f) average intensity spectrum for the chosen target relative to acoustic array a:
Figure FDA0003220165120000041
IB(f) average intensity spectrum for the chosen target relative to acoustic array B:
Figure FDA0003220165120000042
wherein N istThe total number of beats; n is a radical oftThe value range of (1) is [ 600/(delta T (1-P%)), 1800/(delta T (1-P%))](ii) a Δ T is the time length of a single beat of data; p% is the overlapping rate of two adjacent beats;
step 1-4-2) performing cross-correlation operation on the preprocessed beam data, accumulating the beam data within a certain time, calculating the average value of the beam data, and obtaining a frequency domain pseudo-Green function
Figure FDA0003220165120000043
Figure FDA0003220165120000044
(·)*Represents a conjugation;
step 1-4-3) to the frequency domain pseudo-Green function
Figure FDA0003220165120000045
Performing inverse Fourier transform to obtain time domain pseudo-Green function
Figure FDA0003220165120000046
Figure FDA0003220165120000047
Wherein ifft represents inverse Fourier transform, and Re represents a real part;
step 1-4-4) time domain pseudo Green function
Figure FDA0003220165120000048
Is a pulse function whose envelope is v (τ):
Figure FDA0003220165120000049
where | represents the absolute value, and H (·) represents the hilbert transform.
5. The method for estimating the target depth based on the cross-correlation function pulse waveform matching according to claim 4, wherein the step 2) specifically comprises:
step 2-1) according to [0,4H/5 ]]Spacing, dividing the acoustic array deployment sea area into Q in depthzEach grid, wherein H is water depth; according to [ R ]min,Rmax]Spacing, dividing the acoustic array arrangement sea area into Q in distanceRA grid, RminMinimum distance, R, required to meet acoustic array far field requirementsmaxMaximum theoretical detection range of the target for the acoustic array;
step 2-2) calculating the normal wave information by adopting a sound field calculation method according to the marine environment parameters such as water depth, sound velocity profile and seabed parameters: horizontal wave number k of No. l normal wavel＝μl+iηl，μl、ηlAre each klThe real part and the imaginary part of (c); characteristic function Ψ of the ith normal wavel(z); wherein, l is 1,2, …, I; i is the total number of the normal waves;
step 2-3) calculating a time domain pseudo-Green function of each grid point according to the normal wave information;
for the qth grid point, its time-domain pseudo-green function is:
Figure FDA0003220165120000051
wherein z isq、rqRespectively corresponding to the depth of a sound source and the horizontal distance to the midpoint of a connecting line of the geometric centers of the two acoustic arrays; tau represents time delay, ifft represents inverse Fourier transform, and Re represents a real part;
Figure FDA0003220165120000052
for the frequency domain pseudo-green function for the qth grid point:
Figure FDA0003220165120000053
wherein, B (z)1,z2,zq) As a normalization factor:
Figure FDA0003220165120000054
z1,z2the depth of the two hydrophones is shown, the horizontal distance between the two hydrophones is L, and the horizontal distances between the two hydrophones and the sound source are r respectively1,r2；
Step 2-4) time domain pseudo Green function
Figure FDA0003220165120000055
Is a pulse function having an envelope vr(τ；zq,rq)：
Figure FDA0003220165120000056
Where | represents the absolute value, and H (·) represents the hilbert transform.
6. The method of claim 5, wherein the depth grid number Q is the number of depth gridszHas a value range of [8H/5 lambda, 40H/5 lambda]λ is the acoustic wavelength corresponding to the center frequency within the analysis bandwidth; number of distance grids QRHas a value range of [ (R)max-Rmin)/10,(Rmax-Rmin)]。
7. The method for estimating the target depth based on the cross-correlation function pulse shape matching according to claim 6, wherein the step 3) specifically comprises:
step 3-1) calculationTime domain pseudo-Green function envelope v (tau) of the q-th grid point and time domain pseudo-Green function envelope v (tau) extracted from actual datar(τ；zq,rq) Correlation coefficient W (z)q,rq)：
Figure FDA0003220165120000061
Wherein, V (τ) and Vr(τ；zq,rq) Are for v (tau) and v, respectivelyr(τ；zq,rq) Taking the result after the sliding average in the time delay dimension:
Figure FDA0003220165120000062
Figure FDA0003220165120000063
wherein Δ τ is the sliding average window width; the value range of delta tau is [ 5/(f)H-fL)，20/(fH-fL)]；fL、fHRespectively a lower limit frequency and an upper limit frequency of the analysis frequency band;
step 3-2) for all W (z)q,rq) And the depth of the grid point corresponding to the maximum value is the depth estimation result of the measured target.
8. The method of claim 7, wherein if the target distance is known, it is denoted as rsThen, only the local maximum search is performed on the grid points in the range near the target distance; a typical range for the distance search is 0.8rs,1.2rs]。
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107783135A (en) * 2016-08-25 2018-03-09 中国科学院声学研究所 A kind of three-element vector battle array passive ranging method
CN108020314A (en) * 2016-11-01 2018-05-11 北京大学 Scale Fiber-Optic Hydrophone Array system and acceleration transducer array system and measuring method
CN109733574A (en) * 2019-01-25 2019-05-10 哈尔滨工程大学 A kind of self-tolerant acoustic information detection system based on underwater glider
US10429505B2 (en) * 2017-07-03 2019-10-01 R2Sonic, Llc Multi-perspective ensonification system and method
CN110703203A (en) * 2019-10-22 2020-01-17 哈尔滨工程大学 Underwater pulsed sound positioning system based on multi-acoustic wave glider
CN110764055A (en) * 2019-10-25 2020-02-07 哈尔滨工程大学 Virtual plane array underwater moving target radiation noise vector measurement system and measurement method
CN110914718A (en) * 2017-05-22 2020-03-24 沙特阿拉伯石油公司 Computing amplitude-independent gradients of seismic velocity inversion in the frequency domain
CN111323746A (en) * 2020-03-19 2020-06-23 哈尔滨工程大学 Double-circular-array azimuth-equivalent delay inequality passive positioning method
CN112285720A (en) * 2020-09-25 2021-01-29 中国人民解放军海军工程大学 Method and device for acquiring azimuth trace of flexible towed linear array sonar noise target

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10349199B2 (en) * 2017-04-28 2019-07-09 Bose Corporation Acoustic array systems
CN111025272B (en) * 2019-12-19 2023-08-01 哈尔滨工程大学 Planar acoustic array ultra-wide coverage beam transmitting method with tunnel effect inhibition capability

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107783135A (en) * 2016-08-25 2018-03-09 中国科学院声学研究所 A kind of three-element vector battle array passive ranging method
CN108020314A (en) * 2016-11-01 2018-05-11 北京大学 Scale Fiber-Optic Hydrophone Array system and acceleration transducer array system and measuring method
CN110914718A (en) * 2017-05-22 2020-03-24 沙特阿拉伯石油公司 Computing amplitude-independent gradients of seismic velocity inversion in the frequency domain
US10429505B2 (en) * 2017-07-03 2019-10-01 R2Sonic, Llc Multi-perspective ensonification system and method
CN109733574A (en) * 2019-01-25 2019-05-10 哈尔滨工程大学 A kind of self-tolerant acoustic information detection system based on underwater glider
CN110703203A (en) * 2019-10-22 2020-01-17 哈尔滨工程大学 Underwater pulsed sound positioning system based on multi-acoustic wave glider
CN110764055A (en) * 2019-10-25 2020-02-07 哈尔滨工程大学 Virtual plane array underwater moving target radiation noise vector measurement system and measurement method
CN111323746A (en) * 2020-03-19 2020-06-23 哈尔滨工程大学 Double-circular-array azimuth-equivalent delay inequality passive positioning method
CN112285720A (en) * 2020-09-25 2021-01-29 中国人民解放军海军工程大学 Method and device for acquiring azimuth trace of flexible towed linear array sonar noise target

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于路径选择的深海水下运动目标被动深度估计;刘炎堃 等;《应用声学》;20200930;第39卷(第5期);第647-655页 *

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