Underwater acoustic signal noise reduction method based on adaptive singular value decomposition and application

Abstract

The embodiment of the invention discloses a self-adaptive singular value decomposition-based underwater acoustic signal noise reduction method and application. The method comprises the following steps of: 1) acquiring a noise-containing underwater acoustic signal to be analyzed; 2) constructing an m * n-dimensional Hankel matrix according to the underwater acoustic signal, and performing singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix; 3) acquiring the curvature of each singular value in the singular value diagonal matrix, determining a threshold value according to the curvature, and acquiring singular values in the threshold value as effective singular values; (4) effective singular values in the singular value diagonal matrix are reserved, other singular values are set to be zero, and the singular value diagonal matrix is updated; and step 5) reconstructing a noise reduction Hankel matrix by using the updated singular value diagonal matrix, and selecting all elements of the first row of the noise reduction Hankel matrix and m-1 elements of the nth column to the mth column of the second row for reduction to obtain a denoised underwater acoustic signal.

Description

Technical Field

[0001] This application relates to the technical field of signal processing, and specifically relates to an underwater acoustic signal denoising method and application based on adaptive singular value decomposition. Background Art

[0002] Underwater acoustic signal denoising is an important technology, which has extensive applications in the fields of underwater communication, underwater acoustic sensing, ocean exploration, etc. In the underwater environment, due to the conduction characteristics of water and the complexity of the underwater environment, underwater acoustic signals are usually interfered by various noises, such as water flow noise, underwater biological sounds, ship noise, etc. These noises cause great trouble to the transmission, detection, and analysis of underwater acoustic signals. The background of underwater acoustic signal denoising can be traced back to the early 20th century when people began to study underwater acoustic communication technology. Since underwater acoustic signals are easily interfered by noises during underwater transmission, reducing the impact of noises on underwater acoustic signals has become the focus of research. The early underwater acoustic signal denoising methods mainly relied on filters for noise suppression. However, due to the fixed parameters of filters that could not adapt to different underwater acoustic environments and noise characteristics, the denoising effect was limited.

[0003] With the progress of technology, underwater acoustic signal denoising technology has developed rapidly. Wavelet transform is a commonly used signal denoising method. It can transform underwater acoustic and noise signals into the time-frequency domain, and by selecting appropriate wavelet basis functions, the noise can be eliminated or suppressed to extract clear underwater acoustic signals. The key to wavelet transform lies in the selection of the threshold and wavelet basis functions, resulting in the need for empirically setting parameters in wavelet filtering, that is, the self-adjustability is not good. In addition, there will be an endpoint effect in the EMD filtering during the decomposition process. This shortcoming will cause the waveforms of some modal components to be distorted, and in signals with complex frequency components, the EMD filtering effect is often not good. The filtering based on singular value decomposition overcomes the deficiencies of the above several methods, and the key point of singular value decomposition denoising lies in the selection of singular values, that is, selecting the points where the singular values mutate. In the method of selecting singular values, there are methods such as singular value difference spectrum, singular value characteristic mean, singular value median method, etc., and there is also a method of selecting the maximum peak value of the curvature of singular values. However, all of the above-mentioned multiple underwater acoustic signal denoising methods have a problem: that is, the denoising effect on simulated signals is good, but they do not consider the characteristics of strong Gaussian white noise, high-frequency noise, and complex frequency components of underwater acoustic signals. Therefore, the filtering effect is often better in the lower frequency segments, but there is still strong Gaussian white noise in the high-frequency part. Summary of the Invention

[0004] The purpose of this application is to provide an underwater acoustic signal noise reduction method and application based on adaptive singular value decomposition, so as to solve the problems existing in various underwater acoustic signal noise reduction methods in the prior art. Although the noise reduction effect of the simulation signal is good, the strong Gaussian white noise, high-frequency noise, and complex frequency components of the underwater acoustic signal are not considered. Therefore, the filtering effect is better in the lower frequency segment, but there is still strong Gaussian white noise in the high-frequency part.

[0005] To achieve the above purpose, an embodiment of this application provides an underwater acoustic signal noise reduction method based on adaptive singular value decomposition, including the following steps:

[0006] Step 1) Obtain the noisy underwater acoustic signal to be analyzed;

[0007] Step 2) Construct an m×n-dimensional Hankel matrix according to the underwater acoustic signal, perform singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix;

[0008] Step 3) Obtain the curvature of each singular value in the singular value diagonal matrix, determine a threshold according to the curvature, and obtain the singular values within the threshold as effective singular values;

[0009] Step 4) Retain the effective singular values in the singular value diagonal matrix, set the other singular values to zero, and update the singular value diagonal matrix;

[0010] Step 5) Use the updated singular value diagonal matrix to reconstruct a noise-reduced Hankel matrix, and select all elements in the first row and m - 1 elements from the nth column of the second row to the nth column of the mth row of the noise-reduced Hankel matrix for restoration to obtain the noise-reduced underwater acoustic signal.

[0011] Optionally, the specific content of step 2) includes:

[0012] Use the formula: H m×n = U m×m ·D m×n ·V n×n , perform singular value decomposition on the Hankel matrix H m×n to obtain the singular value diagonal matrix D m×n , where the Hankel matrix is constructed based on the underwater acoustic signal x, and U m×m and V n×n respectively represent m×m and n×n-dimensional orthonormal matrices.

[0013] Optionally, in step 2),

[0014] The expressions of the Hankel matrix H m×n and the singular value diagonal matrix D m×n are:

[0015]

[0016]

[0017] wherein, σ = [σ1, σ2, σ3, …, σ p is a sequence of non - zero singular values, and p = min(m, n).

[0018] Optionally,

[0019] In the step 2), the criteria for determining the number of rows m and the number of columns n of the Hankel matrix are:

[0020] m and n satisfy N is the number of samples of the underwater acoustic signal, n = N - m + 1, and 1 < n < N. Additionally, m ≥ 2 and n ≥ 2.

[0021] Optionally, in the step 3),

[0022] The determination of the threshold according to the curvature specifically includes:

[0023] Obtain the curvatures C p corresponding to the singular values σ1, σ2, …, σ i , divide [0, max(C i )] into equal parts with a preset scale, and count the number Q j of curvature values corresponding to each scale range. Starting from Q1, obtain the first Q j that contains only one curvature value and make a judgment;

[0024] The judgment criteria for making the judgment are:

[0025] wherein, Q j-1 , Q j , Q j+1 are the numbers of curvature values contained in the (j - 1) - th, j - th, and (j + 1) - th scale ranges respectively,

[0026] If the above conditions are simultaneously satisfied, then select the lower boundary of the scale range of Q j as the threshold; otherwise, select the singular value corresponding to the only curvature value in Q j as the threshold.

[0027] Optionally, the preset scale is 0.01.

[0028] To achieve the above object, the present application further provides an underwater acoustic signal denoising device based on adaptive singular value decomposition, including:

[0029] An acquisition device for acquiring underwater acoustic signals containing noise and a server communicating with the acquisition device, wherein the server is configured to perform the following operations:

[0030] Obtain an underwater acoustic signal containing noise to be analyzed;

[0031] Construct an m×n-dimensional Hankel matrix based on the underwater acoustic signal, perform singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix;

[0032] Obtain the curvature of each singular value in the singular value diagonal matrix, determine a threshold based on the curvature, and obtain the singular values within the threshold as effective singular values;

[0033] Retain the effective singular values in the singular value diagonal matrix, set the other singular values to zero, and update the singular value diagonal matrix;

[0034] Utilize the updated singular value diagonal matrix to reconstruct a noise-reduced Hankel matrix, select all elements of the first row and m - 1 elements from the second row, column n to the mth row, column n of the noise-reduced Hankel matrix for restoration to obtain a noise-reduced underwater acoustic signal.

[0035] Optionally, it further includes: a terminal communicating with the server, and the server is further configured to visually display the noise-reduced underwater acoustic signal through the terminal.

[0036] Optionally, the acquisition device is any one of the following devices: an underwater acoustic buoy, an underwater acoustic recorder, and an underwater sonar.

[0037] To achieve the above object, the present application also provides a computer storage medium, on which a computer program is stored, wherein when the computer program is executed by a machine, the steps of the method described above are implemented.

[0038] The embodiments of the present application have the following advantages:

[0039] An underwater acoustic signal denoising method based on adaptive singular value decomposition provided by an embodiment of the present application includes: Step 1) obtaining an underwater acoustic signal containing noise to be analyzed; Step 2) constructing an m×n-dimensional Hankel matrix according to the underwater acoustic signal, performing singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix; Step 3) obtaining the curvature of each singular value in the singular value diagonal matrix, determining a threshold according to the curvature, and obtaining the singular values within the threshold as effective singular values; Step 4) retaining the effective singular values in the singular value diagonal matrix, setting other singular values to zero, and updating the singular value diagonal matrix; Step 5) using the updated singular value diagonal matrix to reconstruct a denoised Hankel matrix, and selecting all elements of the first row and m-1 elements from the nth column of the second row to the nth column of the mth row of the denoised Hankel matrix for restoration to obtain a denoised underwater acoustic signal.

[0040] Through the above method, by adopting the criterion of adaptively selecting a threshold, the demarcation point between the effective signal and the noise signal can be accurately judged, and it has strong universality and self-adaptability. Compared with the denoising effects of other methods, the present application is more stable and effective, solving the problems existing in various underwater acoustic signal denoising methods in the prior art. Although the denoising effect on the simulation signal is good, the strong Gaussian white noise, high-frequency noise, and complex frequency components of the underwater acoustic signal are not considered. Therefore, the filtering effect is often good in the low-frequency segment, but there is still strong Gaussian white noise in the high-frequency part. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, other implementation drawings can be obtained according to the provided drawings without creative efforts.

[0042] Figure 1 The flowchart of an underwater acoustic signal denoising method based on adaptive singular value decomposition provided by an embodiment of the present application:

[0043] Figure 2 The logic block diagram of an underwater acoustic signal denoising method based on adaptive singular value decomposition provided by an embodiment of the present application:

[0044] Figure 3 The spectrogram of the signal before and after denoising the simulation signal processed by the method of the present application, where (a) is the spectrogram of the noisy signal, and (b) is the spectrogram of the signal after denoising by the method of the present application;

[0045] Figure 4In (a), it is the spectrogram processed by the original engineering signal; (b) is the spectrogram processed by wavelet transform for noise reduction; (c) is the spectrogram processed by EMD decomposition for noise reduction; (d) is the spectrogram processed by filtering based on the maximum peak of singular value curvature; (e) is the spectrogram processed by singular value median filtering; (f) is the spectrogram processed by the method of this application. Detailed implementation manners

[0046] The following specific embodiments illustrate the implementation manners of this application. Those skilled in the art can easily understand the other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0047] In addition, the technical features involved in different implementation manners of this application described below can be combined with each other as long as they do not conflict with each other.

[0048] An embodiment of this application provides a method for reducing underwater acoustic signal noise by adaptive singular value decomposition, referring to Figure 1 and Figure 2 , Figure 1 is a flowchart of a method for reducing underwater acoustic signal noise by adaptive singular value decomposition provided in an implementation manner of this application, Figure 2 is a logic block diagram of a method for reducing underwater acoustic signal noise by adaptive singular value decomposition provided in an embodiment of this application. It should be understood that this method may also include additional blocks not shown and / or the shown blocks may be omitted, and the scope of this application is not limited in this regard.

[0049] In step 1), an underwater acoustic signal containing noise to be analyzed is acquired.

[0050] Specifically, the underwater acoustic signal containing noise to be analyzed is denoted as x.

[0051] In step 2), according to the underwater acoustic signal, an m×n-dimensional Hankel matrix is constructed, and the Hankel matrix is subjected to singular value decomposition to obtain a singular value diagonal matrix.

[0052] Specifically, an m×n-dimensional Hankel matrix is constructed according to the criterion that the product of the number of rows and the number of columns is as large as possible, and singular value decomposition is performed: H m×n =U m×m ·D m×n ·V n×n , to obtain the singular value diagonal matrix D m×n , where U m×m and V n×nrepresent the m×m and n×n dimensional orthonormal matrices respectively; in addition, H m×n and D m×n The expressions are as follows:

[0053]

[0054]

[0055] In some embodiments, the criteria for determining the optimal number of rows m and columns n of the Hankel matrix are: N is the number of samples of the underwater acoustic signal, and m and n satisfy n = N - m + 1, and 1 < n < N. In addition, m ≥ 2 and n ≥ 2.

[0056] At step 3), obtain the curvature of each singular value in the singular value diagonal matrix, determine the threshold according to the curvature, and obtain the singular values within the threshold as the effective singular values.

[0057] Specifically, in the singular value diagonal matrix D m×n the non-zero singular value sequence σ = [σ1, σ2, σ3,..., σ p can be obtained, p = min(m, n), calculate the curvature of each singular value, obtain the threshold according to the curvature judgment criterion, and obtain the order i of the effective singular value.

[0058] In some embodiments, the optimal method for selecting the threshold includes the following steps:

[0059] Obtain the curvatures C p corresponding to the singular values σ1, σ2,..., σ i , divide [0, max(C i )] into equal parts with a preset scale (e.g., 0.01), count the number Q j of corresponding curvature values in each scale range, starting from Q1, and obtain the first Q j that contains only one curvature value for judgment;

[0060] The judgment criterion is:

[0061]

[0062] If the above conditions are satisfied simultaneously, select the lower boundary of the scale range of Q j as the threshold, otherwise select the singular value corresponding to the only curvature value in Q j as the threshold;

[0063] In the above formula, Q j-1 , Q j , Q j+1 are the numbers of curvature values contained in the (j - 1)-th, j-th, and (j + 1)-th scale ranges respectively.

[0064] At step 4), retain the valid singular values in the singular value diagonal matrix, set the other singular values to zero, and update the singular value diagonal matrix.

[0065] Specifically, retain the valid singular values and set the other singular values to zero: σ k = 0, where k = i + 1, i + 2,..., p, i is the order of the singular value, to obtain a new diagonal matrix D'.

[0066] At step 5), use the updated singular value diagonal matrix to reconstruct the denoised Hankel matrix, and select all elements in the first row of the denoised Hankel matrix and m - 1 elements from the nth column of the second row to the nth column of the mth row for restoration to obtain the denoised underwater acoustic signal.

[0067] Specifically, use the new diagonal matrix D' to reconstruct the Hankel matrix to obtain the denoised Hankel matrix H' = U m×m ·D'·V n×n ; According to the reconstructed denoised Hankel matrix H', obtain the denoised underwater acoustic signal.

[0068] See Figure 3 , this embodiment conducts simulation verification with specific examples:

[0069] 1. Determine the simulation signal construction matrix

[0070] Take the signal x1 as an example to conduct a signal denoising simulation experiment based on singular value decomposition. The expression of x1 is: x1 = (sin(20πt)) 2 + 0.5(sin(80πt)) 3 , the number of samples is 1000, and add zero - mean, Gaussian white noise with a standard deviation of 1 to x1 to obtain the signal x. The number of rows and columns m = 500, n = 501 of the Hankel matrix constructed according to the maximum row - column product criterion;

[0071] 2. Determine the selected threshold

[0072] Perform singular value decomposition on the Hankel matrix and calculate the curvature of each singular value. According to the judgment criterion, select the singular value i = 5 corresponding to the unique curvature value as the threshold, so only the first 5 singular values are selected;

[0073] 3. Reconstruct the Hankel matrix and restore the signal

[0074] Retain the valid singular values and set the other singular values to zero: σ k = 0, where k = i + 1, i + 2,..., p, to obtain a new diagonal matrix D', and then reconstruct the Hankel matrix H' = U m×m ·D'·Vn×n , select all elements in the first row of H' and m - 1 elements from the nth column to the mth row in the second row for restoration, which is the denoised signal x'.

[0075] Figure 3 The results of this simulation are shown. (a) is the signal x, and (b) is the denoised signal x'. It can be seen that this application has a good denoising effect. From Figure 4 it can be seen that by comparing the selection methods of the common singular value decomposition order, the denoising effect of this application is significantly better than other methods, and it clearly describes the dividing line between Gaussian white noise and the useful signal. Moreover, compared with other methods, it will not affect the actual denoising effect due to the mutation of singular values, highlighting its advantages in underwater acoustic signal denoising.

[0076] An underwater acoustic signal denoising device based on adaptive singular value decomposition provided by an embodiment of this application. The device includes:

[0077] An acquisition device for acquiring underwater acoustic signals containing noise and a server communicating with the acquisition device, where the server is configured to perform the following operations:

[0078] Obtain the underwater acoustic signal containing noise to be analyzed;

[0079] Construct an m×n-dimensional Hankel matrix according to the underwater acoustic signal, perform singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix;

[0080] Obtain the curvature of each singular value in the singular value diagonal matrix, determine a threshold according to the curvature, and obtain the singular values within the threshold as effective singular values;

[0081] Retain the effective singular values in the singular value diagonal matrix, set other singular values to zero, and update the singular value diagonal matrix;

[0082] Use the updated singular value diagonal matrix to reconstruct a denoised Hankel matrix, select all elements in the first row of the denoised Hankel matrix and m - 1 elements from the nth column to the mth row in the second row for restoration, and obtain the denoised underwater acoustic signal.

[0083] In some embodiments, it further includes: a terminal communicating with the server, and the server is further configured to visually display the denoised underwater acoustic signal through the terminal.

[0084] In some embodiments, the device for collecting underwater acoustic signals containing noise may include: (1) Hydrophone: A hydrophone is a sensor device for receiving underwater acoustic signals (essentially a type of sensor), which usually operates in a floating manner underwater. It can measure the acoustic vibrations in water and convert them into electrical signals for processing and analysis.

[0085] (2) Underwater Acoustic Recorder: An underwater acoustic recorder is a device specifically used for long-term or short-term recording of underwater sounds. It usually has a large-capacity memory and highly sensitive underwater acoustic sensors, and can be used to capture and store the sounds in the underwater environment.

[0086] (3) Sonar: Sonar is a device for actively detecting underwater targets. It emits acoustic wave pulses and receives echo signals. Sonar is widely used in fields such as ocean exploration, underwater navigation, and fish school detection.

[0087] For the specific implementation method, refer to the foregoing method embodiments and will not be elaborated here.

[0088] This application can be a method, device, system, and / or computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of this application.

[0089] A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the above. The computer-readable storage medium used here is not construed as an instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated through a waveguide or other transmission media (e.g., optical pulses through an optical fiber cable), or electrical signals transmitted through wires.

[0090] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0091] The computer program instructions for performing the operations of this application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of this application.

[0092] Aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0093] These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions, when executed by the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that causes a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises a manufacture including instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0094] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0095] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion of an instruction, which comprises one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart, and combinations of blocks in the block diagrams and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions.

[0096] Note that, unless otherwise directly stated, all features disclosed in this specification (including any appended claims, abstract, and drawings) may be replaced by alternative features that serve the same, equivalent, or similar purposes. Therefore, unless otherwise explicitly stated, each disclosed feature is merely an example of a set of equivalent or similar features. When used, "furthermore", "preferably", "moreover", and "even more preferably" are simple introductions for elaborating another embodiment based on the foregoing embodiments. The content following the "furthermore", "preferably", "moreover", or "even more preferably" in combination with the foregoing embodiments constitutes a complete composition of another embodiment. A further embodiment can be formed by arbitrarily combining several "furthermore", "preferably", "moreover", or "even more preferably" settings following the same embodiment.

[0097] Although the present application has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it based on the present application, which is obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present application fall within the scope of protection required by the present application.

Claims

1. An underwater acoustic signal noise reduction method based on adaptive singular value decomposition, characterized in that, It includes the following steps: Step 1) Obtain the underwater acoustic signal with noise to be analyzed; Step 2) According to the underwater acoustic signal, construct an m×n-dimensional Hankel matrix, perform singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix; Step 3) Obtain the curvature of each singular value in the singular value diagonal matrix, determine a threshold according to the curvature, and obtain the singular values within the threshold as effective singular values; Step 4) Retain the effective singular values in the singular value diagonal matrix, set the other singular values to zero, and update the singular value diagonal matrix; Step 5) Use the updated singular value diagonal matrix to reconstruct a denoised Hankel matrix, select all elements of the first row of the denoised Hankel matrix and m - 1 elements from the nth column of the second row to the nth column of the mth row for restoration to obtain the denoised underwater acoustic signal.

2. The underwater acoustic signal noise reduction method based on adaptive singular value decomposition according to claim 1, characterized in that, The specific content of Step 2) includes: Using the formula: H m×n = U m×m ·D m×n ·V n×n , perform singular value decomposition on the Hankel matrix H m×n to obtain the singular value diagonal matrix D m×n , where the Hankel matrix is constructed based on the underwater acoustic signal x, and U m×m and V n×n represent m×m and n×n dimensional orthonormal matrices respectively.

3. The underwater acoustic signal noise reduction method based on adaptive singular value decomposition according to claim 2, characterized in that, In Step 2), The Hankel matrix H m×n and the singular value diagonal matrix D m×n are expressed as follows: where, σ = [σ1, σ2, σ3, …, σ p is a sequence of non-zero singular values, and p = min(m, n).

4. The underwater acoustic signal noise reduction method based on adaptive singular value decomposition according to claim 2, characterized in that, In Step 2), the criteria for determining the number of rows m and the number of columns n of the Hankel matrix are: m and n satisfy N is the number of samples of the underwater acoustic signal, n = N - m + 1, and 1 < n < N. Additionally, m ≥ 2 and n ≥ 2.

5. The underwater acoustic signal noise reduction method based on adaptive singular value decomposition according to claim 3, characterized in that, In Step 3), the specific method of determining the threshold according to the curvature includes: Obtain each singular value σ1, σ2, …, σ p The corresponding curvature C i , divide [0, max(C i )] into equal parts with a preset scale, and count the number Q of corresponding curvature values within each scale range j , starting from Q1, obtain the first Q that contains only one curvature value j Make a judgment; The judgment criteria for making a judgment are: Among them, Q j-1 , Q j , Q j+1 are the numbers of curvature values contained in the (j - 1)-th, j-th, and (j + 1)-th scale ranges respectively, If the above conditions are satisfied simultaneously, then select Q j The lower boundary of the scale range of j is used as the threshold, otherwise select the singular value corresponding to the only curvature value in Q as the threshold.

6. The underwater acoustic signal noise reduction method based on adaptive singular value decomposition according to claim 5, characterized in that, In Step 3), The preset scale is 0.

01.

7. An underwater acoustic signal noise reduction device based on adaptive singular value decomposition, characterized in that, It includes: An acquisition device for acquiring an underwater acoustic signal with noise and a server communicating with the acquisition device, where the server is configured to perform the following operations: Obtain the underwater acoustic signal with noise to be analyzed; According to the underwater acoustic signal, construct an m×n-dimensional Hankel matrix, perform singular value decomposition on the Hankel matrix to obtain a singular value diagonal matrix; Obtain the curvature of each singular value in the singular value diagonal matrix, determine a threshold according to the curvature, and obtain the singular values within the threshold as effective singular values; Retain the effective singular values in the singular value diagonal matrix, set the other singular values to zero, and update the singular value diagonal matrix; Use the updated singular value diagonal matrix to reconstruct a denoised Hankel matrix, select all elements of the first row of the denoised Hankel matrix and m - 1 elements from the nth column of the second row to the nth column of the mth row for restoration to obtain the denoised underwater acoustic signal.

8. The underwater acoustic signal denoising device based on adaptive singular value decomposition according to claim 7, characterized in that, It further includes: A terminal communicating with the server, and the server is further configured to visually display the denoised underwater acoustic signal through the terminal.

9. The underwater acoustic signal denoising device based on adaptive singular value decomposition according to claim 7, characterized in that, The acquisition device is any one of the following devices: an underwater acoustic buoy, an underwater acoustic recorder, and an underwater acoustic sonar.

10. A computer storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by a machine, it implements the steps of the method described in any one of claims 1 to 6.

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