CN114859420A - Shallow sea target sorting and underwater target motion situation and depth judgment method - Google Patents
Shallow sea target sorting and underwater target motion situation and depth judgment method Download PDFInfo
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Abstract
The invention discloses a method for sorting targets in shallow sea and judging the motion situation and depth of underwater targets. The method comprises the steps of establishing a shallow sea bottom seismic wave field model, operating the shallow sea bottom seismic wave field model to obtain seismic wave signals induced by simulated underwater target sound sources, adjusting the distance from the simulated underwater target sound sources in the shallow sea bottom seismic wave field model to the sea bottom, and respectively receiving and processing to obtain a plurality of simulated seabed surface wave energy variation curves 1 at different depths and a simulated frequency variation curve 2 at different depths corresponding to seabed surface wave energy peak values; acquiring seismic wave signals induced by an actually measured underwater target sound source in a real marine environment, and receiving and processing to obtain an actually measured seabed surface wave energy low-frequency change curve 3 and an actually measured seabed surface wave energy peak value corresponding frequency change curve 4 along with a certain period of time; and selecting the underwater target as a water surface target or an underwater target according to the judgment standard, and acquiring the depth information of the underwater target.
Description
Technical Field
The invention belongs to the technical field of underwater sound target identification, and particularly relates to a method for sorting targets in shallow sea and judging the motion situation and depth of underwater targets.
Background
The sorting in this document refers to distinguishing whether the target is located on the water surface or underwater, and the depth information in the document is acquired, including the change situation (or depth or shallow, or depth change) of the target depth and the depth value thereof.
The passive target classification and identification by using the acoustic signals (such as radiation noise) emitted by the target is a research hotspot in the field of underwater acoustic signal processing at home and abroad, and is also a difficult problem. Although the research of underwater sound passive target classification and identification has been greatly concerned by academia. The difficulty is that the generation of the underwater sound target radiation noise has a very complex physical mechanism, and people have very limited understanding on the physical process, so that the characteristic of the target radiation noise is difficult to represent by using a statistical model; another difficulty is that most of the concerned key points are long-distance targets, most of the features for classification and identification of the underwater acoustic targets are based on energy, and the long-distance propagation of the underwater acoustic signals causes random changes such as attenuation, distortion, fluctuation and the like of the features based on the energy because the marine environment where the underwater acoustic targets are located is time-varying; the underwater acoustic targets are various in types, maneuvering strategies adopted by different targets are different, and due to the reasons of multi-target interference, limitation of sonar equipment and the like, the characteristics of the targets of the same type are discrete, while the characteristics of the targets of different types present cross, so that the work of searching invariant characteristics capable of distinguishing the targets of different types is extremely difficult.
With the improvement of vibration and noise reduction technology, the difficulty of detecting targets in water in the marine environment is gradually increased. In a shallow sea environment, an underwater acoustic signal below its cut-off frequency will not be able to propagate in a body of water. The very low frequency band signals in the radiated noise generated in water may be difficult to propagate in water, but these energies couple to the seafloor and excite the shallow ocean bottom seismic wavefield, which also contains the target information, as signals for remote detection in water.
Researches show that shallow sea bottom seismic wave fields comprise underwater acoustic signals and ground acoustic signals mainly comprising bottom surface waves, wherein the bottom surface waves propagating along the sea water-bottom surface have the characteristics of very low frequency, slow signal attenuation, long propagation distance and the like, so that the shallow sea bottom seismic wave fields become important points of attention and are important components of seismic wave signals. In addition, studies have shown that the main factors influencing the energy of the seismic wave signal generated by a target in shallow sea water are the distance from the sea bottom of the target (i.e. the sea depth minus the navigation depth of the target) and the seabed sediment, whereas in the same sea area, the largest difference between the water surface and the underwater target is the target depth, and the distance between the underwater sound source and the seabed determines the basic shape of the energy distribution of the seabed surface wave. Therefore, it is necessary to provide a method for sorting targets at different depths in a shallow sea based on the energy change characteristics of the surface wave on the sea bottom.
Disclosure of Invention
The invention aims to solve the defects in the background technology and provides a method for sorting targets in shallow sea and judging the motion situation and depth of underwater targets.
The technical scheme adopted by the invention is as follows: a method for sorting targets in shallow sea and judging the motion situation and depth of underwater targets comprises the following steps:
step 1: establishing a shallow sea bottom seismic wave field model, operating the shallow sea bottom seismic wave field model to obtain seismic wave signals induced by simulated underwater target sound sources, adjusting the distance from the simulated underwater target sound sources in the shallow sea bottom seismic wave field model to the sea bottom, and respectively receiving and processing to obtain a plurality of simulated seabed surface wave energy variation curves 1 at different depths and a simulated seabed surface wave energy peak value frequency variation curve 2 at different depths; acquiring seismic wave signals induced by an actually measured underwater target sound source in a real marine environment, and receiving and processing to obtain an actually measured seabed surface wave energy low-frequency change curve 3 and an actually measured seabed surface wave energy peak value corresponding frequency change curve 4 along with a certain period of time;
step 2: comparing a measured submarine surface wave energy low-frequency variation curve 3 with a plurality of simulated submarine surface wave energy low-frequency variation curves 1:
if the actually measured curve 3 of the submarine surface wave energy changing along with the low frequency is not a parabola, primarily judging that the target sound source is a target close to the water surface, and if the actually measured curve 3 of the submarine surface wave energy changing along with the low frequency is a parabola, primarily judging that the target sound source is an underwater target;
if the actually measured submarine surface wave energy variation with low frequency curve 3 is overlapped with one of the curves induced by the underwater target sound source in the plurality of simulated submarine surface wave energy variation with low frequency curve 1, or the actually measured submarine surface wave energy variation with low frequency curve 3 is between two of the curves induced by the plurality of simulated submarine surface wave energy variation with the underwater target sound source in the curve 1, the underwater target is selected, otherwise, the underwater target is selected;
if the target sound source is an underwater target, when the slope of a curve 4 of the frequency corresponding to the actually measured energy peak value of the surface wave on the seabed along with the change of a certain period of time is close to or equal to 0, the underwater navigation depth change of the target in the period of time is judged to be small or the target navigates at a constant depth; if the slope of the curve 4 of the variation of the frequency corresponding to the energy peak value of the actually measured submarine surface wave along with a certain period of time is positive, the target is judged to submerge in the period of time, and if the slope of the curve 4 of the variation of the frequency corresponding to the energy peak value of the actually measured submarine surface wave along with a certain period of time is negative, the target is judged to float in the water in the period of time; accordingly, the change information of the underwater target depth in the time period can be given;
comparing a curve 4 of the variation of the frequency corresponding to the energy peak value of the measured submarine surface wave along with a certain period of time with a simulated curve 2 of the variation of the frequency corresponding to the energy peak value of the submarine surface wave along with the depth at different depths:
if the frequency variation curve 4 corresponding to the energy peak value of the measured submarine surface wave along with a certain period of time is close to or coincident with a certain frequency band in the simulated submarine surface wave energy peak value along with a certain depth variation curve 2 at different depths, the target is judged to be in the depth range corresponding to the frequency band;
and taking the sorting result as output.
In the step 1, a shallow sea bottom seismic wave field model is established through a high-order finite difference algorithm, the shallow sea bottom seismic wave field model is operated to obtain simulated seismic wave signals induced by an underwater target sound source, and simulated stress time domain data are obtained through virtual receiving points; and acquiring a seismic wave signal induced by an actually measured underwater target sound source in the real marine environment, and receiving and acquiring actually measured stress time domain data through a seismic wave sensor.
The stress time domain data is obtained by setting an original point as a projection point of a volume source central point vertical to the seabed surface, setting the length of a receiving array and the array element interval to obtain a corresponding number of receiving points, operating a shallow sea seabed seismic wave field model to obtain stress time domain data under each receiving point, wherein the original point of the stress time domain data is the vertical stress intensity received by the receiving point at the time 0, the horizontal axis is time, and the vertical axis is the vertical stress intensity.
Extracting stress time domain data to obtain a (t, x) matrix according to the simulated and actually measured stress time domain data, wherein t is a time variable, and x is a space variable; performing FK transformation on the (t, x) matrix to obtain an (f, k) matrix, wherein the horizontal axis is wave number, and the vertical axis is frequency; filtering the (f, k) matrix to filter out the underwater acoustic signals with the same frequency to obtain the (f) matrix containing only the surface waves of the sea bottom 1 ,k 1 ) Matrix, pair (f) 1 ,k 1 ) Performing two-dimensional Fourier inverse transformation on the matrix to obtain surface waves (t) 1 ,x 1 ) Data; for surface wave (t) 1 ,x 1 ) Carrying out short-time Fourier transform on the data to obtain a time-frequency diagram; and performing power spectrum analysis on the time-frequency diagram to obtain a curve of the energy of the surface wave on the seabed along with the low frequency, wherein the horizontal axis is frequency, and the vertical axis is energy.
And carrying out power spectrum analysis on a time-frequency graph simulated by a target sound source at a certain depth to obtain the frequency corresponding to the energy peak value of the surface acoustic wave at the seabed, simulating the frequency corresponding to the energy peak value of the surface acoustic wave induced by the target sound source at different depths, and carrying out curve fitting on frequency points at different depths to obtain a simulated curve 2 of the change of the frequency corresponding to the energy peak value of the surface acoustic wave at different depths along with the depth, wherein the horizontal axis is the depth, and the vertical axis is the frequency.
Through framing the surface wave spots in the (f, k) matrix, performing two-dimensional Fourier inversion to obtain (t) containing only the surface wave of the sea bottom 1 ,x 1 ) Data, where in t 1 Is a time variable, x 1 Is a spatial variable.
In the step 1, the energy amplitudes of the surface acoustic waves corresponding to the frequencies of the simulated target sound sources at different depths are extracted one by one, discrete points at the same depth of the target sound source are fitted to obtain a plurality of curves 1 of the simulated surface acoustic waves at different depths along with low-frequency variation, the frequencies corresponding to the energy peaks of the surface acoustic waves induced by the simulated target sound sources at different depths are extracted one by one, and the discrete points of the frequencies corresponding to the energy peaks of the surface acoustic waves at different depths are fitted to obtain a curve 2 of the simulated surface acoustic waves at different depths along with the variation of the depth; extracting the energy amplitude of the surface acoustic wave corresponding to the actually measured target sound source frequency to obtain a curve 3 of the actually measured surface acoustic wave energy changing with the low frequency, wherein the horizontal axis is the frequency, the vertical axis is the energy, the unit is decibel, extracting the frequency corresponding to the energy peak value of the surface acoustic wave induced by the actually measured target sound source to obtain a curve 4 of the frequency corresponding to the energy peak value of the actually measured surface acoustic wave changing with a certain period of time, wherein the horizontal axis is the time, and the vertical axis is the frequency.
The method realizes the sorting of the underwater targets on the shallow sea surface and obtains the depth information of the underwater targets based on the power spectrum characteristic difference of the submarine surface wave signals induced by the water surface and underwater targets sailing at different depths.
Drawings
FIG. 1 is a schematic diagram of target sound source frequency versus wavenumber;
FIG. 2 is a schematic representation of the wavenumber of a surface ocean bottom wave as a function of frequency;
FIG. 3 is a graph illustrating the variation of the energy of a plurality of surface acoustic waves with low frequency at different depths;
FIG. 4 is a diagram of seismic signals received by a seismic sensor from a target vessel in accordance with embodiment 1 of the present invention;
FIG. 5 is a time-frequency diagram according to embodiment 1 of the present invention;
FIG. 6 is a graph 3 illustrating the measured variation of the surface wave energy with low frequency of the seafloor according to embodiment 1 of the present invention;
FIG. 7 is a schematic diagram of a curve 1 of energy of a plurality of simulated surface ocean waves as a function of low frequency simulated in accordance with embodiment 1 of the present invention;
FIG. 8 is a schematic view of a test site in example 2 of the present invention;
FIG. 9 is a schematic illustration of shallow ocean bottom seismic wavefields according to example 2 of the present invention;
fig. 10 is a schematic diagram of filtering out an underwater acoustic signal according to embodiment 2 of the present invention;
FIG. 11 is a time-frequency diagram in embodiment 2 of the present invention;
FIG. 12 is a graph 1 illustrating the energy variation with low frequency of a plurality of simulated ocean bottom surface waves simulated in accordance with example 2 of the present invention;
fig. 13 is a schematic view of a frequency variation curve 4 with times corresponding to an energy peak of a surface acoustic wave measured on the seabed in embodiment 2 of the present invention;
fig. 14 is a schematic view of a curve 2 of the variation of frequency with depth corresponding to the energy peak of the surface acoustic wave at different depths simulated in embodiment 2 of the present invention.
Detailed Description
The invention will be further described in detail with reference to the following drawings and specific examples, which are not intended to limit the invention, but are for clear understanding.
FIG. 1 shows an example of an (f, k) matrix according to the present invention, and FIG. 2 shows an example of an (f, k) matrix according to the present invention 1 ,k 1 ) The matrix example, as shown in fig. 3, is an example of the present invention, namely, the depths of the target sound sources are set to 10m, 20m, 30m, 40m, and 50m, and a plurality of curves 1 of simulated ocean bottom surface wave energy changing with low frequency at different depths are obtained through receiving and processing, respectively.
Example 1:
the test sea area is a certain water area of the east sea, the depth of the sea area is 15m, and the bottom surface layer substrate is mud. The two sides of the port are provided with breakwaters, the length of the breakwaters is about 2km, the breakwaters are formed by piling cement piers, and the breakwaters are about 5m higher than the sea surface.
In order to ensure that the received surface waves of the sea bottom are received, the seismic wave sensor is distributed on the sea bottom at the inner side of the breakwater at a distance of about 1km, the target ship is positioned at the anchoring ground (namely above the water surface) of the ship at the outer side of the breakwater, the linear distance between the target ship and the seismic wave sensor is about 7.5km, the target ship is a cargo ship with the water displacement of 10 ten thousand tons, the target ship sails in an idle load mode, and in the test process, a small number of small fishing boats drive through the target ship from the vicinity and no other large ships exist. Receiving seismic wave signals of the target ship through the seismic wave sensor,as shown in fig. 4, acquiring actually measured stress time domain data, and extracting the stress time domain data to obtain a (t, x) matrix, where t is a time variable and x is a space variable; performing FK transformation on the (t, x) matrix to obtain an (f, k) matrix, wherein the horizontal axis is wave number, and the vertical axis is frequency; filtering the (f, k) matrix to filter out the underwater acoustic signals with the same frequency to obtain the (f) matrix containing only the surface waves of the sea bottom 1 ,k 1 ) Matrix, pair (f) 1 ,k 1 ) Performing two-dimensional Fourier inverse transformation on the matrix to obtain surface waves (t) 1 ,x 1 ) Data, where t 1 Is a time variable, x 1 Is a space variable; for surface wave (t) 1 ,x 1 ) The data is subjected to short-time Fourier transform to obtain a time-frequency diagram, and due to the blocking effect of the breakwater on the underwater acoustic signals, the received submarine seismic wave signals can be determined to be only submarine surface waves, so that corresponding filtering processing is not needed. In the initial period, the target ship is in a static state, other ships do not exist on the nearby sea surface, only tiny background noise on the seabed is displayed through signals, and the 300s target ship is started in situ as shown in fig. 5; weak signals are displayed in a frequency band of 5-15Hz on a time-frequency diagram, a target ship starts sailing to a port at about 1350s, the signal intensity is enhanced instantly, the spectrum distribution is stably concentrated in a frequency band of 5-30Hz, an obvious peak value appears near 18Hz, the target ship approaches the port at 2300s, the speed starts to be reduced, the power spectrum of the target ship is clearly displayed to be concentrated in low frequency by the time-frequency diagram, the energy of 5-30Hz is extracted at 1350s, and a curve 3 of actually measured submarine surface wave energy along with the low frequency is obtained, as shown in fig. 6.
Establishing a shallow sea seabed seismic wave field model, setting a sedimentary layer as argillaceous rock with the thickness of 5m, a substrate as basalt and the seawater depth as 15m according to a test sea area environment, simulating the same real marine environment, setting the depth of a target sound source as 1.5, 5.5, 9.5 and 13.5m from the sea surface, changing the sound source frequency to be 5-30Hz, receiving through a virtual receiving point to obtain simulated stress time domain data, and extracting the stress time domain data according to the simulated stress time domain data to obtain a (t, x) matrix, wherein t is a time variable and x is a space variable; performing FK transformation on the (t, x) matrix to obtain an (f, k) matrix, wherein the horizontal axis is wave number, and the vertical axis is frequency; filtering the (f, k) matrix to filter out the underwater acoustic signals with the same frequencyNumber, obtaining a surface wave containing only the ocean bottom (f) 1 ,k 1 ) Matrix, pair (f) 1 ,k 1 ) Performing two-dimensional Fourier inverse transformation on the matrix to obtain surface waves (t) 1 ,x 1 ) Data, where t 1 Is a time variable, x 1 Is a space variable; for surface wave (t) 1 ,x 1 ) Carrying out short-time Fourier transform on the data to obtain a time-frequency diagram; and performing power spectrum analysis on the time-frequency diagram to obtain a curve of the energy of the surface waves of the seabed changing along with the low frequency, wherein the horizontal axis is frequency, the vertical axis is energy, the energy distribution of the surface waves is calculated by adjusting the distances from a simulated target sound source in the shallow sea bottom seismic wave field model to the sea surface to be 1.5, 5.5, 9.5 and 13.5m respectively, and a plurality of curves 1 of the energy of the surface waves of the seabed changing along with the low frequency simulated at different depths are obtained, as shown in fig. 7.
Comparing a measured submarine surface wave energy low-frequency variation curve 3 with a plurality of simulated submarine surface wave energy low-frequency variation curves 1 (wherein the measured submarine surface wave energy low-frequency variation curve 3 is the same as the plurality of simulated submarine surface wave energy low-frequency variation curves 1 in coordinate axis, the horizontal axis is frequency, the vertical axis is energy, and the unit is decibel): the actually measured submarine surface wave energy variation with low frequency curve 3 is not a parabola and does not fall in a plurality of curve ranges in a plurality of simulated submarine surface wave energy variation with low frequency curves 1, so that the selected target is a water surface target and is consistent with the actual situation; and taking the sorting result as output.
Example 2:
the air gun is used as a target sound source which is actually measured under a real marine environment, the different depths of the underwater target sound source are simulated by adjusting the laying depth of the air gun, and the surface wave energy values under the same frequency are compared due to the fact that the actual measurement is a pulse signal.
The test is carried out in a certain sea area of the east China sea, the bedrock of the test sea area is hard rock, and the upper layer is covered with a sludge layer with the thickness of about 6 m. The water depth fluctuation is small, and a piece of sea area with gentle sea bottom and water depth of about 25m is selected for testing by inquiring the sea chart and measuring the water depth of the sea area.
An S-HF-HZY type air gun is used for exciting in water to generate signals, the air gun is as shown in an oval frame in figure 8, the internal pressure of the air gun is 8Mpa, and in order to monitor the signals excited by the air gun in water, a wavelet detector is arranged 1m above the depth of the air gun and is as shown in a circle frame in figure 8 and used for receiving underwater acoustic signals generated by the excitation of the air gun.
In the test, high-pressure equipment is adopted to pressurize the air gun, then the air gun is placed in water to a specified depth, the air gun is excited to generate seismic wave signals on the seabed, seismic wave sensors are arranged at intervals of 50m in the range of 1350-1600m from the air gun, and the layout of the test field is shown in fig. 8.
When the depth of the air gun is measured actually to be 9m, the seismic wave sensor receives and obtains measured stress time domain data, wherein the measured stress time domain data comprise a ground sound signal and a water sound signal, and the ground sound signal and the water sound signal have difference in frequency and propagation speed, as shown in fig. 9; acquiring actually measured stress time domain data according to the actually measured stress time domain data, and extracting the stress time domain data to obtain a (t, x) matrix, wherein t is a time variable and x is a space variable; performing FK transformation on the (t, x) matrix to obtain an (f, k) matrix, wherein the horizontal axis is wave number, and the vertical axis is frequency; filtering the (f, k) matrix, filtering the underwater sound signals with the same frequency according to the difference of the wave velocity of the ground sound signals and the wave velocity of the underwater sound signals, and obtaining the (f) matrix containing only the seabed surface waves 1 ,k 1 ) Matrix, pair (f) 1 ,k 1 ) Performing two-dimensional Fourier inverse transformation on the matrix to obtain surface waves (t) 1 ,x 1 ) Data, i.e. the measured signal shown in FIG. 10, where t 1 Is a time variable, x 1 Is a space variable; for surface wave (t) 1 ,x 1 ) The data is subjected to short-time fourier transform to obtain a time-frequency diagram, that is, the time-frequency analysis is performed on the actually measured signal at the depth to obtain the time-frequency diagram, as shown in fig. 11. The energy at the center frequency of 30Hz was extracted to obtain an energy value of 82 dB. As shown in fig. 11, it can be concluded that the frequency corresponding to the energy peak of the air gun-induced surface wave on the sea bottom at this depth is 30 Hz.
And adjusting the actually measured air gun depth to be 14m, 18m and 22m, repeating the processing process, and extracting frequencies corresponding to the energy peak value of the seabed surface wave induced by the air gun under different depths, wherein the frequencies are respectively 30.5Hz, 31Hz and 31.3 Hz. Since the air gun is a pulse sound source, and the results of the advanced processing of different sound sources are discontinuous in time, the obtained curve 4 of the variation of the frequency corresponding to the energy peak value of the actually measured surface acoustic wave with a certain period of time is the curve 4 of the variation of the frequency corresponding to the energy peak value of the surface acoustic wave induced by the air gun with the number of times, and the original horizontal axis is time and is adjusted to the number of times, as shown in fig. 13.
Establishing a shallow sea seabed seismic wave field model, setting a sedimentary layer as argillaceous rock with the thickness of 5m, a substrate as basalt and the sea depth as 25m according to a test sea area environment, simulating the same real sea environment, setting the depth of a target sound source as 2.5, 9.5, 16.5 and 22.5m from the sea surface, changing the sound source frequency to be 20-50Hz, receiving through a virtual receiving point to obtain simulated stress time domain data, and extracting the stress time domain data according to the simulated stress time domain data to obtain a (t, x) matrix, wherein t is a time variable and x is a space variable; performing FK transformation on the (t, x) matrix to obtain an (f, k) matrix, wherein the horizontal axis is wave number, and the vertical axis is frequency; filtering the (f, k) matrix to filter out the underwater acoustic signals with the same frequency to obtain the (f) matrix containing only the surface waves of the sea bottom 1 ,k 1 ) Matrix, pair (f) 1 ,k 1 ) Performing two-dimensional Fourier inverse transformation on the matrix to obtain surface waves (t) 1 ,x 1 ) Data, where t 1 Is a time variable, x 1 Is a space variable; for surface wave (t) 1 ,x 1 ) Carrying out short-time Fourier transform on the data to obtain a time-frequency diagram; performing power spectrum analysis on the time-frequency diagram to obtain a curve of the energy of the surface waves of the sea bottom changing with low frequency, wherein the horizontal axis is frequency, the vertical axis is energy, the energy distribution of the surface waves is calculated by adjusting the distances from a simulated target sound source in the shallow sea bottom seismic wave field model to the sea surface to be 2.5, 9.5, 16.5 and 22.5m respectively, so as to obtain a plurality of simulated curves 1 of the energy of the surface waves of the sea bottom changing with low frequency, as shown in fig. 12. When the sound source frequency is 30Hz, the frequency corresponding to the energy peak of the surface wave of the seabed induced by the target sound source at different depths is calculated, and a simulated curve 2 of the change of the frequency corresponding to the energy peak of the surface wave of the seabed at different depths along with the depth is obtained, as shown in fig. 14.
Comparing a measured submarine surface wave energy low-frequency variation curve 3 (energy value in the embodiment) with a plurality of simulated submarine surface wave energy low-frequency variation curves 1 (wherein the measured submarine surface wave energy low-frequency variation curve 3 is the same as the plurality of simulated submarine surface wave energy low-frequency variation curves 1 in coordinate axis, the horizontal axis is frequency, the vertical axis is energy, and the unit is decibel): comparing the surface wave energy value of 82dB with a plurality of simulated seabed surface wave energy variation curves 1 (shown in figure 12) of a plurality of simulated seabed surface waves, wherein the energy spectrum at 30Hz is concentrated in 78-93dB, and the energy of the surface wave energy value of 82dB is in the range, so that the object is sorted to be an underwater object, which is consistent with the actual situation; and taking the sorting result as output. Wherein, when the sound source depth is 2.5-9.5m, the energy spectrum range is 78-83dB, which covers the energy of the measured signal, therefore, the target sound source depth can be roughly judged to be between 2.5-9.5m, which is consistent with the test result.
As shown in fig. 13, since the slope of the frequency variation with number curve 4 (time is changed to number on the horizontal axis) corresponding to the actually measured energy peak of the surface acoustic wave on the sea bottom is positive, the target can be preliminarily determined to be a dive situation; comparing a frequency variation with time curve 4 (time is changed into times on a horizontal axis) corresponding to the actually measured energy peak value of the surface acoustic wave with time curve 2 corresponding to the energy peak value of the surface acoustic wave at different depths, wherein the frequency variation with time curve 4 corresponding to the actually measured energy peak value of the surface acoustic wave is the same as the frequency variation with time curve 2 corresponding to the energy peak value of the surface acoustic wave at different depths, and the vertical axis is the frequency): namely, the frequency 30-42.3Hz corresponding to the energy peak value of the measured submarine surface wave is compared with the frequency-depth change curve 2 (shown in figure 14) corresponding to the energy peak value of the submarine surface wave at different depths, the depth of the sound source corresponding to the frequency 30-42.3Hz is within the range of 9.5-22.5m, and the situation that the target depth is submerged from 9.5m to 22.5m and is close to the depth change of the test sound source can be judged.
Those not described in detail in this specification are within the skill of the art.
Claims (7)
1. A method for sorting targets in shallow sea and judging the motion situation and depth of underwater targets is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a shallow sea bottom seismic wave field model, operating the shallow sea bottom seismic wave field model to obtain seismic wave signals induced by simulated underwater target sound sources, adjusting the distance from the simulated underwater target sound sources in the shallow sea bottom seismic wave field model to the sea bottom, and respectively receiving and processing to obtain a plurality of simulated seabed surface wave energy variation curves 1 at different depths and a simulated seabed surface wave energy peak value frequency variation curve 2 at different depths; acquiring seismic wave signals induced by an actually measured underwater target sound source in a real marine environment, and receiving and processing to obtain an actually measured seabed surface wave energy low-frequency change curve 3 and an actually measured seabed surface wave energy peak value corresponding frequency change curve 4 along with a certain period of time;
step 2: comparing a measured submarine surface wave energy low-frequency variation curve 3 with a plurality of simulated submarine surface wave energy low-frequency variation curves 1:
if the actually measured curve 3 of the submarine surface wave energy changing along with the low frequency is not a parabola, primarily judging that the target sound source is a target close to the water surface, and if the actually measured curve 3 of the submarine surface wave energy changing along with the low frequency is a parabola, primarily judging that the target sound source is an underwater target;
if the actually measured submarine surface wave energy variation with low frequency curve 3 is overlapped with one of the curves induced by the underwater target sound source in the plurality of simulated submarine surface wave energy variation with low frequency curve 1, or the actually measured submarine surface wave energy variation with low frequency curve 3 is between two of the curves induced by the plurality of simulated submarine surface wave energy variation with the underwater target sound source in the curve 1, the underwater target is selected, otherwise, the underwater target is selected;
if the target sound source is an underwater target, when the slope of a curve 4 of the frequency corresponding to the actually measured energy peak value of the surface wave on the seabed along with the change of a certain period of time is close to or equal to 0, the underwater navigation depth change of the target in the period of time is judged to be small or the target navigates at a constant depth; if the slope of the curve 4 of the variation of the frequency corresponding to the energy peak value of the actually measured submarine surface wave along with a certain period of time is positive, the target is judged to submerge in the period of time, and if the slope of the curve 4 of the variation of the frequency corresponding to the energy peak value of the actually measured submarine surface wave along with a certain period of time is negative, the target is judged to float in the water in the period of time; accordingly, the change information of the underwater target depth in the time period can be given;
comparing a curve 4 of the variation of the frequency corresponding to the energy peak value of the measured submarine surface wave with a simulated curve 2 of the variation of the frequency corresponding to the energy peak value of the submarine surface wave with the depth at different depths:
if the curve 4 that the frequency corresponding to the energy peak value of the measured submarine surface wave changes along with a certain period of time is close to or coincided with a certain frequency band in the curve 2 that the frequency corresponding to the energy peak value of the submarine surface wave changes along with the depth at different depths of the simulation, the target is judged to be in the depth range corresponding to the frequency band;
and taking the sorting result as output.
2. The method for sorting targets in shallow sea and judging motion situation and depth of underwater targets according to claim 1, characterized in that: in the step 1, a shallow sea bottom seismic wave field model is established through a high-order finite difference algorithm, the shallow sea bottom seismic wave field model is operated to obtain simulated seismic wave signals induced by an underwater target sound source, and simulated stress time domain data are obtained through virtual receiving points; and acquiring a seismic wave signal induced by an actually measured underwater target sound source in the real marine environment, and receiving and acquiring actually measured stress time domain data through a seismic wave sensor.
3. The method for sorting targets in shallow sea and judging motion situation and depth of underwater targets according to claim 2, characterized in that: extracting stress time domain data to obtain a (t, x) matrix according to the simulated and actually measured stress time domain data, wherein t is a time variable, and x is a space variable; performing FK transformation on the (t, x) matrix to obtain an (f, k) matrix, wherein the horizontal axis is wave number, and the vertical axis is frequency; filtering the (f, k) matrix to filter out the underwater acoustic signals with the same frequency to obtain the (f) matrix containing only the surface waves of the sea bottom 1 ,k 1 ) Matrix, pair (f) 1 ,k 1 ) Performing two-dimensional Fourier inverse transformation on the matrix to obtain surface waves (t) 1 ,x 1 ) Number ofAccordingly; for surface wave (t) 1 ,x 1 ) Carrying out short-time Fourier transform on the data to obtain a time-frequency diagram; and performing power spectrum analysis on the time-frequency diagram to obtain a curve of the energy of the surface wave on the seabed along with the low frequency, wherein the horizontal axis is frequency, and the vertical axis is energy.
4. The method for sorting targets in shallow sea and judging motion situation and depth of underwater targets according to claim 3, characterized in that: and carrying out power spectrum analysis on a time-frequency graph simulated by a target sound source at a certain depth to obtain the frequency corresponding to the energy peak value of the surface acoustic wave at the seabed, simulating the frequency corresponding to the energy peak value of the surface acoustic wave induced by the target sound source at different depths, and carrying out curve fitting on frequency points at different depths to obtain a simulated curve 2 of the change of the frequency corresponding to the energy peak value of the surface acoustic wave at different depths along with the depth, wherein the horizontal axis is the depth, and the vertical axis is the frequency.
5. The method for sorting targets in shallow sea and judging motion situation and depth of underwater targets according to claim 2, characterized in that: the stress time domain data is obtained by setting an original point as a projection point of a volume source central point vertical to the seabed surface, setting the length of a receiving array and the array element interval to obtain a corresponding number of receiving points, operating a shallow sea seabed seismic wave field model to obtain stress time domain data under each receiving point, wherein the original point of the stress time domain data is the vertical stress intensity received by the receiving point at the time 0, the horizontal axis is time, and the vertical axis is the vertical stress intensity.
6. The method for sorting targets in shallow sea and judging motion situation and depth of underwater targets according to claim 3, characterized in that: by framing the surface wave spots in the (f, k) matrix, performing two-dimensional inverse Fourier transform to obtain (t) containing only the surface waves of the seabed 1 ,x 1 ) Data, where in t 1 Is a time variable, x 1 Is a spatial variable.
7. The method for sorting targets in shallow sea and judging motion situation and depth of underwater targets according to claim 1, characterized in that: in the step 1, the energy amplitudes of the surface acoustic waves corresponding to the frequencies of the simulated target sound sources at different depths are extracted one by one, discrete points at the same depth of the target sound source are fitted to obtain a plurality of curves 1 of the simulated surface acoustic waves at different depths along with low-frequency variation, the frequencies corresponding to the energy peaks of the surface acoustic waves induced by the simulated target sound sources at different depths are extracted one by one, and the discrete points of the frequencies corresponding to the energy peaks of the surface acoustic waves at different depths are fitted to obtain a curve 2 of the simulated surface acoustic waves at different depths along with the variation of the depth; extracting the energy amplitude of the surface acoustic wave corresponding to the actually measured target sound source frequency to obtain a curve 3 of the actually measured surface acoustic wave energy changing with the low frequency, wherein the horizontal axis is the frequency, the vertical axis is the energy, the unit is decibel, extracting the frequency corresponding to the energy peak value of the surface acoustic wave induced by the actually measured target sound source to obtain a curve 4 of the frequency corresponding to the energy peak value of the actually measured surface acoustic wave changing with a certain period of time, wherein the horizontal axis is the time, and the vertical axis is the frequency.
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