CN112987004A - Water surface and underwater target classification method based on horizontal array in shallow sea environment - Google Patents
Water surface and underwater target classification method based on horizontal array in shallow sea environment Download PDFInfo
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Abstract
The invention belongs to the technical field of underwater acoustic engineering, ocean engineering and the like, and particularly relates to a method for classifying water and underwater targets based on a horizontal array in a shallow sea environment. The beneficial results of the invention are: the method utilizes the horizontal array fixed on the seabed to perform different beam forming processing on the received target radiated acoustic signals to complete the water surface and underwater resolution of the target, integrates the time-frequency interference structure characteristics of a shallow sea sound field into the target identification process, has simple processing flow, realizes the rapid classification of the moving sound source, and has concise target discrimination process and intuitive result.
Description
Technical Field
The invention belongs to the technical field of underwater acoustic engineering, ocean engineering and the like, and particularly relates to a method for classifying water surface and underwater targets based on a horizontal array in a shallow sea environment.
Background
In the field of underwater sound, the resolution of underwater targets on water surface is always an urgent problem to be solved. The traditional method has the defects of high requirements on acoustic environment parameters, complex arrangement of acoustic information acquisition equipment and the like due to the utilization of ocean channel characteristics. In recent years, advanced computer technologies such as machine learning are introduced into underwater target information processing, and typical characteristics of classified targets can be well trained when target samples are sufficient. In practice, however, the accuracy of the target classification result estimated by this type of method is generally not high due to the small number of data samples of the underwater target.
In a shallow sea environment, the sound propagation characteristics of a target can be characterized by using a normal wave model, so that sound sources with different depths radiate sound fields composed of different modes. In fact, since the shallow sea environment is usually a typical negative-jump environment (such as summer environment of east and yellow seas in our country), the surface target sound field received by the sea bottom land matrix in the shallow sea environment mainly consists of SRBR mode (sea surface sea bottom reflection mode), and the underwater target sound field mainly consists of one special mode of nsrb mode (non-sea surface sea bottom reflection mode), namely, RBR mode (refraction-sea bottom reflection mode). When describing the SRBR mode and the nSRBR mode with a waveguide invariant, it can be seen that the SRBR mode waveguide invariant is about 1, while the waveguide invariant of the nSRBR mode is negative or greater than 1. On the other hand, the waveguide invariants with different values have obvious difference corresponding to the slope direction of the interference fringes displayed in the distance-frequency spectrogram.
In addition, compared with a vertical array adopted by a traditional method, the horizontal array is easier to arrange in engineering, and the array form is more fixed after arrangement, so that the array beam forming result is more accurate, and the time-frequency spectrogram of the target radiation noise can be more conveniently obtained.
Disclosure of Invention
Based on the analysis, if the modal differences of the water surface and underwater targets are effectively utilized based on the horizontal array, the classification of the water surface and underwater targets can be more conveniently, intuitively and accurately realized compared with the traditional method. In order to expand the resolution capability of the shore-based horizontal array water surface and underwater targets, the invention provides a water surface and underwater target classification method based on a horizontal array in a shallow sea environment. The method mainly utilizes the angle difference of interference fringes in a distance-frequency spectrogram of a target sound field mode received by a horizontal array when a water surface target sound source and an underwater target sound source radiate signals in a shallow sea environment to realize the rapid classification and judgment of the water surface underwater target.
The invention adopts the technical scheme that the horizontal array is distributed on the seabed shallow than 100m, the number N of array elements is an odd number larger than 30, the length of the horizontal array is larger than 200m, the sound velocity profile of the water body in the sea area is a negative jump layer, and the method comprises the following steps:
step 1: and actually measuring and obtaining the maximum value of the sound velocity profile of the water body in the sea area and the sound velocity value of the sea bottom in the target radiated sound signal time period.
Step 2: and actually measuring and obtaining a sound pressure vector of a target radiation noise time sequence signal received by each array element of the N-element horizontal array.
And step 3: and obtaining a time-frequency spectrogram of radiation noise of the tracked moving target by using a conventional beam forming method (in detail, see the literature: Du voter et al, Sonar array signal processing technology, electronic industry Press, 2018: 52-53.).
And 4, step 4: and obtaining a time-frequency spectrogram of the radiation noise of the tracked moving target by using a frequency self-adaptive optimal weight array processing method (see the literature: Xuzhou et al, frequency self-adaptive optimal weight array interference fringe processing technology, acoustical science report, 2017, 42 (3): 257 and 266.).
And 5: and (4) respectively carrying out Radon transformation on the tracked moving target radiation noise time-frequency spectrograms obtained in the step (3) and the step (4) to obtain two distance-angle graphs after Radon transformation.
Step 6: and (5) adding the pixel points in the two Radon-transformed distance-angle graphs obtained in the step (5) along the distance dimension respectively to obtain two groups of angle estimation vectors, and reading out the angle corresponding to the maximum value of the vectors as two interference fringe estimation angle values.
And 7: comparing the angle difference of the two interference fringe estimation angle values in the step 6, and judging that the target is a water surface target when the angle difference is less than 5 degrees; and when the angle difference is greater than 5 degrees, judging the underwater target.
The beneficial results of the invention are:
the method utilizes the horizontal array fixed on the seabed to perform different beam forming processing on the received target radiated acoustic signals to complete the water surface and underwater resolution of the target, integrates the time-frequency interference structure characteristics of a shallow sea sound field into the target identification process, has simple processing flow, realizes the rapid classification of the moving sound source, and has concise target discrimination process and intuitive result.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a diagram of a Radon transform;
FIG. 3 is a sound velocity profile of a sea area and a distance-frequency spectrum of a sound field received by a single hydrophone. Wherein fig. 3(a) is a sound velocity profile, the abscissa represents the sound velocity in units of "m/s", and the ordinate represents the depth in units of "m"; fig. 3(b) and 3(c) are the distance-frequency spectra of the sound field received by the single hydrophone of sound source with the depth of 10m and 60m respectively, the horizontal coordinate is distance and is in the unit of "m", and the vertical coordinate is frequency and is in the unit of "Hz".
Fig. 4 is a result of a conventional beamforming process for a sound source having a depth of 10 m. Where fig. 4(a) is a conventional beamforming distance-frequency spectrum, with distance in "m" on the abscissa and frequency in "Hz" on the ordinate; FIG. 4(b) is the Radon transform results, with distance in "m" along the abscissa and angle in "°" along the ordinate; fig. 4(c) shows the angle result estimated from the interference fringes, with the abscissa being angle in degrees and the ordinate being amplitude in 1.
Fig. 5 shows the result of the frequency-adaptive optimal-weight beamforming process for a sound source with a depth of 10 m. Wherein fig. 5(a) is a frequency adaptive beamforming distance-frequency spectrum, the abscissa represents distance in units of "m", and the ordinate represents frequency in units of "Hz"; FIG. 5(b) is the Radon transform results, with distance in "m" along the abscissa and angle in "°" along the ordinate; fig. 5(c) shows the angle result estimated from the interference fringes, with the abscissa being angle in degrees and the ordinate being amplitude in 1.
Fig. 6 is a result of a conventional beamforming process for a sound source having a depth of 60 m. Where fig. 6(a) is a conventional beamforming distance-frequency spectrum with distance in "m" on the abscissa and frequency in "Hz" on the ordinate; FIG. 6(b) is the Radon transform results, with distance in "m" along the abscissa and angle in "°" along the ordinate; fig. 6(c) shows the angle result estimated from the interference fringes, with the abscissa being angle in degrees and the ordinate being amplitude in 1.
Fig. 7 shows the result of the frequency-adaptive optimal-weight beamforming process for a sound source with a depth of 60 m. Wherein fig. 7(a) is a frequency adaptive beamforming distance-frequency spectrum, the abscissa represents distance in units of "m", and the ordinate represents frequency in units of "Hz"; FIG. 7(b) is the Radon transform results, with distance in "m" along the abscissa and angle in "°" along the ordinate; fig. 7(c) is an angle result estimated from the interference fringes, and the abscissa is an angle in units of "°", and the ordinate is an amplitude in units of "1".
Detailed Description
FIG. 1 is a flow chart of an implementation of the present invention. The specific implementation mode comprises the following steps:
step 1: and actually measuring and obtaining the maximum value of the sound velocity profile of the water body in the sea area and the sound velocity value of the sea bottom in the target radiated sound signal time period.
The maximum value of the sound velocity profile of the water body in the sea area is the maximum value of sound velocity at each depth of the water body in the sea area, and the seabed sound velocity value is the seabed sediment layer sound velocity value.
Step 2: and actually measuring and obtaining a sound pressure vector of a target radiation noise time sequence signal received by each array element of the N-element horizontal array.
And step 3: and obtaining a time-frequency spectrogram of radiation noise of the tracked moving target by using a conventional beam forming method (in detail, see the literature: Du voter et al, Sonar array signal processing technology, electronic industry Press, 2018: 52-53.).
The process of obtaining the target radiation noise time-frequency spectrogram after beam forming by using the conventional beam forming method comprises the following steps:
the horizontal array beam response function can be expressed as:
wherein
For beam response, ω is the angular frequencyRate, thetaT,θS,rtRespectively, the azimuth of the target, and the distance between the target and the reference array element of the horizontal array (which is set as the first array element in this embodiment without loss of generality), which varies with time t as the target moves, v (k)t) For each array element, a vector is steered, W ═ W1,w2,…wN)TWeighting the weight vector for each array element, T denotes the vector transposition, PHAnd (3) transposing a complex conjugate of the sound pressure of the target radiation noise time sequence signal received by each array element. When the pilot bearing coincides with the target bearing,
i.e. the result when the output beam of the target is at frequency f at time t, where f is ω/(2 pi).
When the array analysis is processed by conventional beam forming, the weight vector of each array element is w1＝w2＝…＝wN1. In the process of beam processing, firstly, the target radiation noise at a certain time t is obtained, and the target azimuth is calculated to be consistent with the guide azimuth, namely thetaT＝θSTime of flight
And f is the frequency, and finally, traversing all the time t and the frequency f to obtain a target radiation noise time-frequency spectrogram after beam forming.
And 4, step 4: and obtaining a time-frequency spectrogram of the radiation noise of the tracked moving target by using a frequency self-adaptive optimal weight array processing method (see the literature: Xuzhou et al, frequency self-adaptive optimal weight array interference fringe processing technology, acoustical science report, 2017, 42 (3): 257 and 266.).
The weight vector calculation process in the frequency self-adaptive optimal weight array processing method is as follows:
the calculation process of the frequency self-adaptive optimal weight array processing method is basically consistent with that in the step 3, only the weighting weights of the array elements are different, namely, each element in the weight vector W is not equal to 1, but depends on the frequency value and the array information. Considering the symmetry of the weights and the symmetry of the array element positions,only the first half of the array element weight vector (the second half of the array element weight vector is symmetrical to the first half) needs to be designed to be W(1)＝(w1,w2,…w(N+1)/2)TThen, the calculation formula of the weight vector of partial array elements is as follows:
wherein the number of each vector element of c, A and a is (N +1)/2, and the specific calculation formula of each variable is as follows:
a＝(2 2 … 2 1)T，
l is 2 pi/d, d is array element spacing;
cmax、cseafloorrespectively is a maximum sound velocity value of the seawater body and a seabed sound velocity value; di＝(i-(N+1)/2)d,i＝1,2,…,(N-1)/2；
In the time-frequency spectrograms obtained in the step 3 and the step 4, the frequency band range is 300-500Hz, and a frequency point is taken every 1 Hz; the total length of the time dimension is more than 5 minutes, and every 1 second is a time point.
Through the step, the filtering of the sound field mode can be realized, and the SRBR mode in the sound field is output.
And 5: and (4) respectively carrying out Radon transformation on the tracked moving target radiation noise time-frequency spectrograms obtained in the step (3) and the step (4) to obtain two distance-angle graphs after Radon transformation.
The acquisition of the interference fringe angle in the time-frequency spectrogram is realized by Radon transformation. Transforming a line (line P in FIG. 2) on the graph to be processed into a distanceA point p on the angle diagram, the integral value of the line being converted into the amplitude of the point, the horizontal axis being the angle and the vertical axis being the distance in fig. 2 after conversion. See fig. 2, angle
(the value is 0-180 degrees) is the deflection angle of a straight line P relative to the y axis, rho is the distance between the line P and the distance in the transformed distance-angle diagram, the distance between the middle point (namely, a reference point) of the diagram and the straight line is generally taken, obviously, a plurality of stripes with the same slope in the diagram can present a plurality of points on different distance positions at the same angle in the transformed diagram after Radon transformation, and the distance-angle diagram after Radon transformation is obtained.
Step 6: and (5) adding the pixel points in the two Radon-transformed distance-angle graphs obtained in the step (5) along the distance dimension respectively to obtain two groups of angle estimation vectors, and reading out the angle corresponding to the maximum value of the vectors as two interference fringe estimation angle values.
And 7: comparing the angle difference of the two interference fringe estimation angle values in the step 6, and judging that the target is a water surface target when the angle difference is less than 5 degrees; and when the angle difference is greater than 5 degrees, judging the underwater target.
Application example:
setting the sea area environment as a typical yellow sea summer sound velocity profile as a negative jump layer environment, 0-20m as an acoustic layer on a jump layer, 20-75m as a negative jump layer, 75-100m as a weak negative gradient under the jump layer, and 100-150m as a deposition layer. The sound velocity profile is shown in fig. 3 (a). The maximum value of the sound velocity of the sea area and the value of the sound velocity of the sea bottom are 1539.6m/s and 1572m/s respectively. The sound sources are respectively positioned at 10m and 60m and arranged in the end-fire direction of the submarine horizontal array, the starting time is 5km away from the array element nearest to the array element, the distance and the frequency spectrogram of the sound field received by the single element in the frequency range of 200-300Hz are shown in fig. 3(b) and fig. 3 (c).
Fig. 4 shows the processing result of the conventional beam forming method used for the submarine horizontal array when the sound source is 10 meters deep, and the fringe angle obtained by processing the interference fringes of the sound field structure processed by the beams by using Radon transform is 80 degrees. Fig. 5 shows the processing result of the frequency-adaptive optimal weight beam forming method used for the submarine horizontal array when the sound source is 10 meters deep, and the fringe angle is 79.5 degrees obtained by processing the interference fringes of the sound field structure processed by the beams through Radon transformation. Obviously, the angle difference of the stripes obtained after the two wave beam forming methods are processed is less than 5 degrees, and the target is judged to be the water surface target.
Fig. 6 and 7 respectively show the processing results of the submarine horizontal array forming the azimuth by using the conventional beam forming method and the frequency-adaptive optimal weight beam when the sound source is 60 meters deep. The estimated fringe angle in the conventional beamforming output range-frequency spectrogram is 100 degrees; and the interference fringe angle in the frequency-adaptive optimal weight beam forming output distance-frequency spectrogram is estimated to be 79 degrees. Obviously, the angle difference of the stripes obtained after the two wave beam forming methods are processed is more than 5 degrees, and the target is judged to be an underwater target.
Claims (3)
1. A method for classifying water surface and underwater targets based on a horizontal array in a shallow sea environment is characterized in that the method aims at that the horizontal array is arranged on the sea bottom which is shallow to a depth of 100m, the number N of array elements is an odd number which is more than 30, and the length of the horizontal array is more than 200m, and comprises the following steps:
step 1: actually measuring and obtaining the maximum value of the sea area water body sound velocity profile and the seabed sound velocity value in the target radiated sound signal time period;
step 2: actually measuring and obtaining a target radiation noise time sequence signal sound pressure vector received by each array element of the N-element horizontal array;
and step 3: obtaining a time-frequency spectrogram of the radiation noise of the tracked moving target by using a conventional beam forming method;
and 4, step 4: obtaining a time-frequency spectrogram of the radiation noise of the tracked moving target by using a frequency self-adaptive optimal weight array processing method;
and 5: respectively carrying out Radon transformation on the tracked moving target radiation noise time-frequency spectrograms obtained in the step 3 and the step 4 to obtain two distance-angle graphs after Radon transformation;
step 6: adding the pixel points in the two Radon-transformed distance-angle graphs obtained in the step 5 along the distance dimension respectively to obtain two groups of angle estimation vectors, and reading out the angle corresponding to the maximum value of the vectors as two interference fringe estimation angle values;
and 7: comparing the angle difference of the two interference fringe estimation angle values in the step 6, and judging that the target is a water surface target when the angle difference is less than 5 degrees; and when the angle difference is greater than 5 degrees, judging the underwater target.
2. The method for classifying the water surface and underwater targets based on the horizontal array in the shallow sea environment according to claim 1, wherein the method comprises the following steps: the maximum value of the sound velocity profile of the water body in the sea area is the maximum value of sound velocity at each depth of the water body in the sea area, and the seabed sound velocity value is the seabed sediment layer sound velocity value.
3. The method for classifying the water surface and underwater targets based on the horizontal array in the shallow sea environment according to claim 1, wherein the method comprises the following steps: the process of obtaining the target radiation noise time-frequency spectrogram after beam forming by using the conventional beam forming method comprises the following steps:
the horizontal array beam response function can be expressed as:
wherein
For beam response, ω is the angular frequency, θT,θS,rtRespectively, the azimuth of the guide, the azimuth of the target and the distance between the target and the reference array element of the horizontal array, which varies with time t when the target moves, v (k)t) For each array element, a vector is steered, W ═ W1,w2,…wN)TWeighting the weight vector for each array element, T denotes the vector transposition, PHComplex conjugate transpose vector of target radiation noise time sequence signal sound pressure received by each array element; when the pilot bearing coincides with the target bearing,
is thatThe result of the output beam of the target at time t at frequency f, where f ═ ω/(2 π);
when the array analysis is processed by conventional beam forming, the weight vector of each array element is w1＝w2＝…＝wN1 is ═ 1; in the process of beam processing, firstly, the target radiation noise at a certain time t is obtained, and the target azimuth is calculated to be consistent with the guide azimuth, namely thetaT＝θSTime of flight
And f is the frequency, and finally, traversing all the time t and the frequency f to obtain a target radiation noise time-frequency spectrogram after beam forming.
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