Underwater target depth classification method and device based on small-aperture horizontal array

Abstract

The application discloses a kind of underwater target depth classification method and device based on small aperture horizontal array, belong to underwater target depth classification technical field, wherein, the method includes: obtaining the received signal of each element of horizontal array, to solve distance-frequency sound intensity;Utilize the two-dimensional power spectrum of distance-frequency sound intensity estimated jointly by discrete fourier transform and autoregressive spectrum model;In polar coordinate system, energy integration is carried out along polar axis to two-dimensional power spectrum, and the polar angle energy distribution of two-dimensional power spectrum ridge is obtained;The relationship model of polar angle and waveguide invariant is established, and the waveguide invariant energy distribution is obtained from polar angle energy distribution and based on relationship model;The maximum position of waveguide invariant energy distribution is extracted, the maximum position is used as characteristic quantity, and compared with threshold, to give the decision of deep source target or shallow source target.The method improves the wave number direction resolution of range image, reduces the demand for array aperture, and has good adaptability to small aperture horizontal array.

Description

Technical Field

[0001] This invention relates to the field of underwater target depth classification technology, and in particular to an underwater target depth classification method based on a small-aperture horizontal array. Background Technology

[0002] The task of underwater target depth classification is to analyze the underwater acoustic signals received by sonar to determine whether the target is a deep-source or shallow-source target, providing information support for further underwater acoustic detection and countermeasures. This is a hot and challenging problem in underwater acoustics. Features used for target depth classification mainly include: target depth, normal mode propagation characteristics, and interference structure characteristics. Among these, interference structure characteristics are generated by the spatial coherence of the low-frequency sound field and vary with frequency, horizontal distance, and depth; they are often used as feature quantities in target depth classifiers.

[0003] In shallow-sea negative-gradient waveguides, both reflected and refracted normal modes exist simultaneously, resulting in complex interference structures. Waveguide invariants, used to characterize these structures, are typically modeled as a distribution related to factors such as sound velocity profiles and source depth, reflecting the inherent propagation characteristics of the sound source within the waveguide. Therefore, waveguide invariants can be used to solve underwater target depth classification problems. However, accurately extracting the distribution of waveguide invariants from the acoustic field interference structure usually requires a large aperture horizontal array. Under small aperture conditions, it is difficult to accurately extract the distribution of waveguide invariants, leading to a decrease in target depth classification performance. Therefore, achieving target depth classification under small-aperture horizontal array conditions remains a challenging problem. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose an underwater target depth classification method based on a small-aperture horizontal array. This method can effectively classify deep-source targets and shallow-source targets in shallow sea negative gradient environments, and also has good adaptability to small-aperture horizontal arrays.

[0006] The second objective of this invention is to propose an underwater target depth classification device based on a small-aperture horizontal array.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, one embodiment of the present invention proposes an underwater target depth classification method based on a small-aperture horizontal array, comprising the following steps: Step S1, acquiring the received signals of each element of the horizontal array to solve for the range-frequency acoustic intensity; Step S2, jointly estimating the two-dimensional power spectrum of the range-frequency acoustic intensity using discrete Fourier transform and autoregressive spectral model; Step S3, performing energy integration along the polar axis of the two-dimensional power spectrum in polar coordinates to obtain the polar angle energy distribution of the two-dimensional power spectrum ridge; Step S4, establishing a relationship model between the polar angle and waveguide invariants, obtaining the waveguide invariant energy distribution from the polar angle energy distribution and based on the relationship model; Step S5, extracting the maximum value position of the waveguide invariant energy distribution, using the maximum value position as a feature quantity, comparing it with a threshold, and giving a decision on whether it is a deep-source target or a shallow-source target.

[0010] The underwater target depth classification method based on a small-aperture horizontal array in this invention constructs a target depth classifier based on a small-aperture horizontal array in shallow sea negative gradient environments. This classifier uses the location of the maximum value of the waveguide invariant energy distribution as a feature quantity to classify target depth, and obtains the two-dimensional power spectrum of the acoustic intensity of the received signal of each array element by joint estimation of discrete Fourier transform and autoregressive model. This two-dimensional power spectrum has a high resolution in the wavenumber direction of the range image, thus accurately extracting the energy distribution of the waveguide invariant. In addition, it can also achieve effective classification of deep-source targets and shallow-source targets in shallow sea negative gradient environments. The implementation method is simple, easy to operate, and has good adaptability to small-aperture horizontal arrays.

[0011] In addition, the underwater target depth classification method based on a small-aperture horizontal array according to the above embodiments of the present invention may also have the following additional technical features:

[0012] Furthermore, in one embodiment of the present invention, step S1 specifically includes:

[0013] Step S101: In a horizontally layered marine environment, acquire the received signals of each element of the horizontal array located at a distant target. :

[0014]

[0015] in, For target radiated noise, The channel impulse response at each array element. For received noise;

[0016] Step S102, based on the received signals of each element of the horizontal array. Solve for the distance-frequency sound intensity for:

[0017]

[0018] in, To receive signals The Fourier transform result, for . conjugate.

[0019] Furthermore, in one embodiment of the present invention, step S2 specifically includes:

[0020] Step S201: Perform a Discrete Fourier Transform (DFT) on the distance-frequency sound intensity along the frequency axis.

[0021] Step S202: Estimate the distance-frequency sound intensity after discrete Fourier transform (DFT) in the distance axis direction using the AR model. The power spectrum is solved by the LD algorithm. AR model One parameter;

[0022] Step S203, using the AR model Calculate the distance-frequency sound intensity after discrete Fourier transform along the distance axis. The AR power spectrum is used to obtain the two-dimensional power spectrum. .

[0023] Furthermore, in one embodiment of the present invention, step S3 specifically includes:

[0024] Step S301, let , The two-dimensional power spectrum Transforming to polar coordinates yields the two-dimensional power spectrum in polar coordinates. ;

[0025] Step S302, for the two-dimensional power spectrum Along polar axis By performing energy integration, the polar energy distribution of the two-dimensional power spectrum ridge is obtained.

[0026] Furthermore, in one embodiment of the present invention, the polar angle energy distribution in step S302... The solution formula is:

[0027]

[0028] in, It is a two-dimensional power spectrum. It is the polar axis.

[0029] Furthermore, in one embodiment of the present invention, step S4 specifically includes:

[0030] Step S401: Establish the relationship model between the polar angle and waveguide invariants:

[0031]

[0032] in, For waveguide invariants, Polar angle, For distance, The center frequency;

[0033] Step S402: Based on the relationship model between the polar angle and waveguide invariants, and according to the polar angle energy distribution, calculate the corresponding waveguide invariant energy distribution.

[0034] Furthermore, in one embodiment of the present invention, the specific process of step S5 is as follows:

[0035] Step S501: Extract the location of the maximum value of the waveguide invariant energy distribution. And use it as a characteristic quantity;

[0036] Step S502, compare the feature quantity with the threshold. The specific process of comparison is as follows:

[0037]

[0038]

[0039] Among them, threshold Determined by environmental parameters and the deployment depth of the horizontal array, set to 1, and then compared using characteristic quantities... With threshold The relative size is used to determine whether it satisfies Assuming Suppose, if If the assumption is true, the target is considered a shallow source; otherwise, the target is considered a deep source.

[0040] To achieve the above objectives, a second aspect of the present invention proposes an underwater target depth classification device based on a small-aperture horizontal array, comprising: a first solution module for acquiring the received signals of each element of the horizontal array to solve for the range-frequency acoustic intensity; a joint estimation module for jointly estimating the two-dimensional power spectrum of the range-frequency acoustic intensity using discrete Fourier transform and autoregressive spectral model; an energy integration module for performing energy integration along the polar axis of the two-dimensional power spectrum in polar coordinates to obtain the polar angle energy distribution of the two-dimensional power spectrum ridge; a second solution module for establishing a relationship model between the polar angle and waveguide invariants, and solving for the waveguide invariant energy distribution based on the polar angle energy distribution and the relationship model; and a comparison and decision module for extracting the maximum value position of the waveguide invariant energy distribution, using the maximum value position as a feature quantity, comparing it with a threshold, and giving a decision on whether the target is deep or shallow.

[0041] The underwater target depth classification device based on a small-aperture horizontal array in this invention constructs a target depth classifier based on a small-aperture horizontal array in shallow sea negative gradient environments. This classifier uses the maximum position of the waveguide invariant energy distribution as a feature quantity to classify target depth, and obtains the two-dimensional power spectrum of the acoustic intensity of the received signal of each array element by joint estimation of discrete Fourier transform and autoregressive model. This two-dimensional power spectrum has a high resolution in the wavenumber direction of the range image, thus accurately extracting the energy distribution of the waveguide invariant. In addition, it can also achieve effective classification of deep-source targets and shallow-source targets in shallow sea negative gradient environments. The implementation method is simple, easy to operate, and has good adaptability to small-aperture horizontal arrays.

[0042] A third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the underwater target depth classification method based on a small aperture horizontal array as described in the above embodiments.

[0043] In another aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the underwater target depth classification method based on a small aperture horizontal array as described in the above embodiments.

[0044] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0045] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0046] Figure 1This is a flowchart of an embodiment of the underwater target depth classification method based on a small-aperture horizontal array according to the present invention;

[0047] Figure 2 This is an execution block diagram of a target depth classification method based on a small aperture horizontal array according to an embodiment of the present invention;

[0048] Figure 3 (a) is the sound velocity profile of the negative gradient waveguide, and (b) is the sound field layout diagram.

[0049] Figure 4 The range-frequency sound intensity values ​​are for shallow and deep source targets, respectively, where (a) represents a shallow source and (b) represents a deep source.

[0050] Figure 5 Two-dimensional power spectra of shallow and deep source targets are shown, respectively, where (a) represents a shallow source and (b) represents a deep source.

[0051] Figure 6 The two-dimensional power spectra of shallow and deep source targets are respectively in polar coordinate system, where (a) is shallow source and (b) is deep source;

[0052] Figure 7 This is a schematic diagram of the extraction result of polar angle energy distribution according to an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram of the extraction result of waveguide invariant energy distribution according to an embodiment of the present invention;

[0054] Figure 9 This is a schematic diagram illustrating the impact of signal-to-noise ratio on underwater target depth classification performance according to an embodiment of the present invention;

[0055] Figure 10 This is a schematic diagram of the underwater target depth classification device based on a small-aperture horizontal array according to an embodiment of the present invention. Detailed Implementation

[0056] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0057] The following describes, with reference to the accompanying drawings, an underwater target depth classification method and apparatus based on a small-aperture horizontal array according to an embodiment of the present invention. First, the underwater target depth classification method based on a small-aperture horizontal array according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0058] Figure 1This is a flowchart of an underwater target depth classification method based on a small-aperture horizontal array according to an embodiment of the present invention.

[0059] like Figure 1 As shown, the underwater target depth classification method based on a small-aperture horizontal array includes the following steps:

[0060] In step S1, the received signals of each element of the horizontal array are obtained in order to solve for the distance-frequency sound intensity.

[0061] Furthermore, in one embodiment of the present invention, step S1 specifically includes:

[0062] Step S101: In a horizontally layered marine environment, acquire the received signals of each element of the horizontal array located at a distant target. :

[0063]

[0064] in, For target radiated noise, The channel impulse response at each array element. For received noise;

[0065] Step S102, based on the received signals of each element of the horizontal array Solve for distance-frequency sound intensity for:

[0066]

[0067] in, To receive signals The Fourier transform result, for . conjugate.

[0068] In step S2, the two-dimensional power spectrum of distance-frequency acoustic intensity is estimated by combining discrete Fourier transform and autoregressive spectral model.

[0069] Furthermore, in one embodiment of the present invention, step S2 specifically includes:

[0070] Step S201: Perform a Discrete Fourier Transform (DFT) on the distance-frequency sound intensity along the frequency axis. The expression is:

[0071]

[0072] in, for The number of points along the frequency axis, The image wavenumber along the frequency axis;

[0073] Step S202: Estimate the distance-frequency sound intensity after discrete Fourier transform (DFT) in the distance axis direction using the AR model. The power spectrum is solved by the LD algorithm. AR model Parameters for:

[0074]

[0075] in, One-third of the number of elements in the horizontal array. The excitation white noise energy of the model, These are the parameters of the model;

[0076] Step S203, using AR model Calculate the distance-frequency sound intensity after discrete Fourier transform along the distance axis. The AR power spectrum was obtained, and the two-dimensional power spectrum was obtained. Specifically:

[0077]

[0078] in, It is a two-dimensional power spectrum. The wavenumber of the image along the distance axis. The image wavenumber along the frequency axis. The order of the AR model. The excitation white noise energy of the model, These are the parameters of the model.

[0079] In step S3, the energy of the two-dimensional power spectrum is integrated along the polar axis in polar coordinates to obtain the polar angle energy distribution of the two-dimensional power spectrum ridge.

[0080] Furthermore, in one embodiment of the present invention, step S3 specifically includes:

[0081] Step S301, let , Two-dimensional power spectrum Transforming to polar coordinates yields the two-dimensional power spectrum in polar coordinates. ;

[0082] Step S302, analyze the two-dimensional power spectrum. Along polar axis By performing energy integration, the polar energy distribution of the two-dimensional power spectrum ridge is obtained, expressed as:

[0083]

[0084] in, It is a two-dimensional power spectrum. It is the polar axis.

[0085] In step S4, a relationship model between the polar angle and waveguide invariants is established. Based on the polar angle energy distribution and the relationship model, the waveguide invariant energy distribution is obtained.

[0086] Furthermore, in one embodiment of the present invention, step S4 specifically includes:

[0087] Step S401: Establish the relationship model between the polar angle and waveguide invariants, expressed as:

[0088]

[0089] in, For waveguide invariants, Polar angle, For distance, The center frequency;

[0090] Step S402: Based on the relationship model between polar angle and waveguide invariants, calculate the corresponding waveguide invariant energy distribution according to the polar angle energy distribution.

[0091] In step S5, the maximum value location of the waveguide invariant energy distribution is extracted, the maximum value location is used as a feature quantity, and compared with a threshold to give a decision on whether it is a deep source target or a shallow source target.

[0092] Furthermore, in one embodiment of the present invention, the specific process of step S5 is as follows:

[0093] Step S501: Extract the location of the maximum value of the waveguide invariant energy distribution. And use it as a characteristic quantity;

[0094] Step S502, combine the feature quantity with the threshold The specific process of comparison is as follows:

[0095]

[0096]

[0097] Among them, threshold Determined by environmental parameters and the deployment depth of the horizontal array, set to 1, and then compared using characteristic quantities... With threshold The relative size is used to determine whether it satisfies Assuming Suppose, if If the assumption is true, the target is considered a shallow source; otherwise, the target is considered a deep source.

[0098] The following two specific embodiments further illustrate the underwater target depth classification method based on a small-aperture horizontal array proposed in this invention.

[0099] Specific Implementation Example 1: According to Figures 2 to 8 As shown, the following simulation experiment was designed to verify the effectiveness of the present invention.

[0100] Simulation conditions: The sound velocity distribution in the negative gradient waveguide is as follows Figure 3 As shown in (a), the density of the seabed is assumed to be 1.6. The seabed absorption coefficient is 0.2. Assume the distance between the target sound source and the first element of the horizontal array is 4km, the array aperture is 50m, the horizontal array deployment depth is 90m, the shallow source target depth is 10m (above the tier), and the deep source target depth is 50m (below the tier). Figure 3 As shown in (b), the observation frequency band is 300~340Hz.

[0101] The simulation experiment includes the following steps:

[0102] Step 1: Obtain the received signal from the horizontal array, and calculate the distance-frequency acoustic intensity of the received signal from each array element. The result is as follows: Figure 4 (a) and Figure 4 As shown in (b);

[0103] Step 2: Estimate the two-dimensional power spectrum of the distance-frequency acoustic intensity from Step 1 using a combination of discrete Fourier transform and autoregressive spectral model, such as... Figure 5 (a) and Figure 5 As shown in (b). By Figure 5 (a) and Figure 5 (b) It can be seen that the two-dimensional power spectrum in this invention still has high resolution even under small array aperture conditions.

[0104] Step 3: Integrate the energy along the polar axis of the two-dimensional power spectrum from Step 2 in polar coordinates to obtain the polar angle energy distribution of the ridge of the two-dimensional power spectrum. The two-dimensional power spectrum in polar coordinates is shown below. Figure 6 (a) and Figure 6 As shown in (b). From the figure Figure 6 (a) and Figure 6 (b) It can be seen that the energy distribution of the two-dimensional power spectrum in the polar coordinate system of this invention is relatively concentrated. The polar angle energy distribution is as follows: Figure 7 As shown.

[0105] Step 4: Establish a relationship model between the polar angle and waveguide invariants. Based on the energy distribution of the polar angle in Step 3, and using this relationship model, obtain the energy distribution of the waveguide invariants, such as... Figure 8 As shown. By Figure 8It can be seen that the present invention can accurately extract the waveguide invariant energy distribution of deep-source and shallow-source targets with significant differences, and the peak position of the latter distribution is further to the left.

[0106] Step 5: Extract the maximum value locations of the waveguide invariant energy distribution from Step 4, as well as the maximum value locations of deep-source and shallow-source targets. The values ​​are 1.1 and 0.9 respectively, which are used as feature quantities and compared with the threshold. The comparison can correctly determine whether an underwater target belongs to a deep-source or shallow-source location, thus verifying the feasibility of the invention. Specific Implementation Example 2:

[0108] Forty target depths were randomly generated at uniform distributions within the depth ranges of both shallow and deep targets, increasing the SNR from 6dB to 12dB. Other simulation conditions remained the same as in Specific Implementation Example 1. Drawing on concepts from binary detection, the detection probability Pd represents the probability of classifying a deep target as a true deep target, i.e., the probability of correctly classifying a deep target, while the false alarm probability Pf represents the probability of classifying a shallow target as a true deep target, i.e., the probability of incorrectly classifying a shallow target.

[0109] Figure 7 The ROC curve of this invention is given. (From...) Figure 7 It can be seen that when the SNR is high ( When the false alarm probability is 0.2, the classifier provides a detection probability of over 0.8. This verifies the effectiveness of the invention.

[0110] In summary, the underwater target depth classification method based on a small-aperture horizontal array proposed in this embodiment of the invention constructs a target depth classifier based on a small-aperture horizontal array in shallow sea negative gradient environments. This classifier uses the maximum position of the waveguide invariant energy distribution as a feature quantity to classify target depth, and obtains the two-dimensional power spectrum of the acoustic intensity of the received signal of each array element by joint estimation of discrete Fourier transform and autoregressive model. This two-dimensional power spectrum has high resolution in the wavenumber direction of the range image, thus accurately extracting the energy distribution of the waveguide invariant. In addition, it can also achieve effective classification of deep-source targets and shallow-source targets in shallow sea negative gradient environments. The implementation method is simple, easy to operate, and has good adaptability to small-aperture horizontal arrays.

[0111] Next, with reference to the accompanying drawings, an underwater target depth classification device based on a small-aperture horizontal array according to an embodiment of the present invention is described.

[0112] Figure 10 This is a schematic diagram of the underwater target depth classification device based on a small-aperture horizontal array according to an embodiment of the present invention.

[0113] like Figure 10As shown, the device 10 includes: a first solution module 100, a joint estimation module 200, an energy integration module 300, a second solution module 400, and a comparison and decision module 500.

[0114] The system comprises the following modules: First, the solution module 100 acquires the received signals of each element of the horizontal array to determine the range-frequency acoustic intensity. Joint estimation module 200 uses discrete Fourier transform and an autoregressive spectral model to jointly estimate the two-dimensional power spectrum of the range-frequency acoustic intensity. Energy integration module 300 performs energy integration along the polar axis of the two-dimensional power spectrum in polar coordinates to obtain the polar angle energy distribution of the two-dimensional power spectrum ridge. Second, the solution module 400 establishes a relationship model between the polar angle and waveguide invariants, and solves for the waveguide invariant energy distribution based on the polar angle energy distribution and the relationship model. Comparison and decision module 500 extracts the maximum value position of the waveguide invariant energy distribution, uses the maximum value position as a feature, compares it with a threshold, and determines whether the target is deep or shallow.

[0115] It should be noted that the foregoing explanation of the underwater target depth classification method based on a small aperture horizontal array also applies to the device of this embodiment, and will not be repeated here.

[0116] In summary, the underwater target depth classification device based on a small-aperture horizontal array proposed in the embodiments of the present invention constructs a target depth classifier based on a small-aperture horizontal array in shallow sea negative gradient environments. This classifier classifies target depth using the maximum position of the waveguide invariant energy distribution as a feature quantity, and obtains the two-dimensional power spectrum of the acoustic intensity of the received signal of each array element by joint estimation of discrete Fourier transform and autoregressive model. This two-dimensional power spectrum has high resolution in the wavenumber direction of the range image, thus accurately extracting the energy distribution of the waveguide invariant. In addition, it can also achieve effective classification of deep-source targets and shallow-source targets in shallow sea negative gradient environments. The implementation method is simple, easy to operate, and has good adaptability to small-aperture horizontal arrays.

[0117] To implement the above embodiments, the present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the underwater target depth classification method based on a small aperture horizontal array as described in the foregoing embodiments.

[0118] To implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the underwater target depth classification method based on a small aperture horizontal array as described in the foregoing embodiments.

[0119] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0122] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0123] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0124] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0125] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0126] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for underwater target depth classification based on a small-aperture horizontal array, characterized in that, Includes the following steps: Step S1: Obtain the received signals of each element of the horizontal array to solve for the distance-frequency sound intensity; Step S2: Estimate the two-dimensional power spectrum of the distance-frequency sound intensity using a combination of discrete Fourier transform and autoregressive spectral model; Step S3: Integrate the energy along the polar axis of the two-dimensional power spectrum in polar coordinates to obtain the polar angle energy distribution of the two-dimensional power spectrum ridge. Step S3 specifically includes: Step S301, set the image wavenumber in the distance axis direction. Image wavenumber along the frequency axis The two-dimensional power spectrum Transforming to polar coordinates yields the two-dimensional power spectrum in polar coordinates. ,in As the polar axis, Polar angle; Step S302, for the two-dimensional power spectrum Along polar axis By performing energy integration, the polar angle energy distribution of the two-dimensional power spectrum ridge is obtained; the formula for solving the polar angle energy distribution is as follows: ; Step S4: Establish a relationship model between the polar angle and waveguide invariants, and obtain the waveguide invariant energy distribution based on the polar angle energy distribution and the relationship model. Step S4 specifically includes: Step S401: Establish the relationship model between the polar angle and waveguide invariants: in, For waveguide invariants, Polar angle, For distance, The center frequency; Step S402: Based on the relationship model between the polar angle and waveguide invariants, and according to the polar angle energy distribution, calculate the corresponding waveguide invariant energy distribution; Step S5: Extract the maximum value position of the waveguide invariant energy distribution, use the maximum value position as a feature quantity, compare it with a threshold, and give a decision on whether it is a deep source target or a shallow source target.

2. The underwater target depth classification method based on a small-aperture horizontal array according to claim 1, characterized in that, Step S1 specifically includes: Step S101: In a horizontally layered marine environment, acquire the received signals of each element of the horizontal array located at a distant target. : in, For target radiated noise, The channel impulse response at each array element. For received noise; Step S102, based on the received signals of each element of the horizontal array. Solve for the distance-frequency sound intensity for: in, To receive signals The Fourier transform result, for . conjugate.

3. The underwater target depth classification method based on a small-aperture horizontal array according to claim 1, characterized in that, Step S2 specifically includes: Step S201: Perform a Discrete Fourier Transform (DFT) on the distance-frequency sound intensity along the frequency axis. Step S202: Estimate the distance-frequency sound intensity after discrete Fourier transform (DFT) in the distance axis direction using the AR model. The power spectrum is solved by the LD algorithm. AR model One parameter; Step S203, using the AR model Calculate the distance-frequency sound intensity after discrete Fourier transform along the distance axis. The AR power spectrum is used to obtain the two-dimensional power spectrum. .

4. The underwater target depth classification method based on a small-aperture horizontal array according to claim 1, characterized in that, The specific process of step S5 is as follows: Step S501: Extract the location of the maximum value of the waveguide invariant energy distribution. and the position of the maximum value As a characteristic quantity; Step S502, compare the feature quantity with the threshold. The specific process of comparison is as follows: Among them, threshold Determined by environmental parameters and the deployment depth of the horizontal array, set to 1, and then compared using characteristic quantities. With threshold The relative size determines the position of the maximum value. satisfy Assuming Suppose, if If the assumption is true, the target is considered a shallow source; otherwise, the target is considered a deep source.

5. An underwater target depth classification device based on a small-aperture horizontal array, characterized in that, include: The first solution module is used to obtain the received signals of each element of the horizontal array in order to solve for the distance-frequency sound intensity. The joint estimation module is used to jointly estimate the two-dimensional power spectrum of the distance-frequency acoustic intensity using discrete Fourier transform and autoregressive spectral model; The energy integration module is used to perform energy integration along the polar axis of the two-dimensional power spectrum in polar coordinates to obtain the polar angle energy distribution of the two-dimensional power spectrum ridge. The energy integration module specifically includes: Submodule 1 sets the image wavenumber along the distance axis. Image wavenumber along the frequency axis The two-dimensional power spectrum Transforming to polar coordinates yields the two-dimensional power spectrum in polar coordinates. ,in As the polar axis, Polar angle; Submodule two, for the two-dimensional power spectrum Along polar axis By performing energy integration, the polar angle energy distribution of the two-dimensional power spectrum ridge is obtained; the formula for solving the polar angle energy distribution is as follows: ; The second solution module is used to establish a relationship model between the polar angle and waveguide invariants, and to obtain the waveguide invariant energy distribution from the polar angle energy distribution and the relationship model. The second solution module specifically includes: Sub-module 3: Establishing the relationship model between the polar angle and waveguide invariants: in, For waveguide invariants, Polar angle, For distance, The center frequency; Submodule 4: Based on the relationship model between the polar angle and waveguide invariants, and according to the polar angle energy distribution, calculate the corresponding waveguide invariant energy distribution; The comparison and decision module is used to extract the maximum value position of the waveguide invariant energy distribution, use the maximum value position as a feature quantity, compare it with a threshold, and give a decision on whether it is a deep source target or a shallow source target.

6. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the underwater target depth classification method based on a small aperture horizontal array as described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the underwater target depth classification method based on a small aperture horizontal array as described in any one of claims 1-4.

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