Underwater sound unsteady-state signal judgment method, device, equipment, medium and product

Abstract

The invention discloses an underwater acoustic unsteady-state signal judgment method, device and equipment, a medium and a product, and relates to the field of underwater acoustic detection. According to the method, comprehensive judgment is carried out by considering multiple factors, the influence proportion of different characteristic quantities of the underwater acoustic unsteady-state signal on the existing state is obtained by calculating the membership degree of each judgment result based on a fuzzy comprehensive judgment method, and the result is close to the objective actual level and high in accuracy. According to the method, the problem that the existence state of the underwater acoustic unsteady-state signal is difficult to judge is solved, and the judgment accuracy of the underwater acoustic unsteady-state signal is improved.

Description

Technical Field

[0001] The present application relates to the field of underwater acoustic detection, and particularly to a method, device, equipment, medium and product for judging underwater acoustic non-steady signals. Background Art

[0002] Underwater acoustic signals refer to all acoustic signals propagated through water media, including ship radiated noise, torpedo radiated noise, marine biological noise, offshore oil drilling platform noise, and marine environmental noise in the ocean. Although these signals can all be received by sonars and there is a need for recognition and judgment, the amount of original signal data is large and usually cannot be directly used as signal judgment feature quantities. In order to effectively achieve signal classification and judgment, it is necessary to refine or transform the original signal features to obtain the features that can best reflect the essence of signal classification, form a judgment vector, and achieve the purpose of effectively realizing classification and judgment.

[0003] Common signal judgment and classification methods include time-domain and frequency-domain analysis. The frequency-domain analysis method is applicable to the judgment of periodic signals and requires a long signal period; time-domain analysis generally observes the envelope of a long-period signal. Both require signals with a long period for analysis to obtain results. However, there is a type of noise generated by ships or torpedoes during navigation that is instantaneously non-steady in time, such as the sound of a torpedo entering and leaving the water pipe, the sound of a submarine opening and closing a watertight door, etc. In addition, the acoustic disturbances generated by the launch of shipborne missiles, sudden changes in the operating states of ship machinery and equipment, and sudden changes in working conditions and other sudden signals can all be considered underwater acoustic non-steady signals. The sound of an airborne sonobuoy falling into the water is also a typical underwater acoustic non-steady signal. These underwater acoustic non-steady signals are generally non-periodic signals, with the characteristics of strong time-variation and short time segments, and the duration is generally on the order of milliseconds. It is difficult to obtain signal feature information using frequency-domain analysis, and it is also difficult to obtain judgment results in a short period using time-domain analysis. Therefore, directly using time-domain or frequency-domain analysis methods cannot achieve good results. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, equipment, medium and product for judging underwater acoustic non-steady signals, which can improve the accuracy of judging underwater acoustic non-steady signals.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a method for judging underwater acoustic non-steady signals, including the following steps.

[0007] Construct a comprehensive factor set for judging underwater acoustic non-steady signals; the comprehensive factor set includes multiple factors.

[0008] Construct a comprehensive judgment set for judging underwater acoustic non-steady signals; the comprehensive judgment set includes multiple judgment results.

[0009] Calculate the weight of each factor based on the actual values of each factor and the limits of each factor under each judgment result, and construct a weight matrix.

[0010] Calculate the membership degree of each factor to each judgment result based on the actual values of each factor and the limits of each factor under each judgment result, and construct a membership degree matrix.

[0011] Based on the weight matrix and the membership degree matrix, perform underwater acoustic non-steady signal judgment to determine the judgment result of the non-steady signal in the underwater acoustic signal.

[0012] In a second aspect, the present application provides an underwater acoustic non-steady signal judgment device. The underwater acoustic non-steady signal judgment device applies the above-mentioned underwater acoustic non-steady signal judgment method, and the underwater acoustic non-steady signal judgment device includes the following modules.

[0013] A comprehensive factor set construction module for constructing a comprehensive factor set for underwater acoustic non-steady signal judgment; the comprehensive factor set includes multiple factors.

[0014] A comprehensive judgment set construction module for constructing a comprehensive judgment set for underwater acoustic non-steady signal judgment; the comprehensive judgment set includes multiple judgment results.

[0015] A weight matrix construction module for calculating the weight of each factor based on the actual values of each factor and the limits of each factor under each judgment result, and constructing a weight matrix.

[0016] A membership degree matrix construction module for calculating the membership degree of each factor to each judgment result based on the actual values of each factor and the limits of each factor under each judgment result, and constructing a membership degree matrix.

[0017] A comprehensive judgment module for performing underwater acoustic non-steady signal judgment based on the weight matrix and the membership degree matrix to determine the judgment result of the non-steady signal in the underwater acoustic signal.

[0018] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the steps of the above-mentioned underwater acoustic non-steady signal judgment method.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned underwater acoustic non-steady signal judgment method are implemented.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned underwater acoustic non-steady signal judgment method are implemented.

[0021] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application.

[0022] The present application provides a method, device, equipment, medium and product for judging underwater acoustic non-steady signals. The present application considers from multiple factors and makes a comprehensive judgment. Based on the fuzzy comprehensive judgment method, by calculating the membership degrees of each judgment result, the influence ratio of different characteristic quantities of an underwater acoustic non-steady signal on the existence state is obtained. Such a result is closer to the objective actual level and has a higher accuracy. This method solves the problem that it is difficult to judge the existence state of underwater acoustic non-steady signals and improves the accuracy of judging underwater acoustic non-steady signals. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0024] Figure 1 It is a schematic flow chart of a method for judging underwater acoustic non-steady signals provided by an embodiment of the present application;

[0025] Figure 2 It is a schematic principle diagram of a method for judging underwater acoustic non-steady signals provided by an embodiment of the present application;

[0026] Figure 3 It is a schematic diagram of the time-domain signal in an embodiment of the present application and the values of each factor calculated based on the time-domain signal;

[0027] Figure 4 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed Embodiments

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0029] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0030] Currently, the main focus of underwater target recognition and judgment lies in the processing of continuous noise signals and the extraction of eigenvalues. However, in actual sonar use, determining the existence of a certain underwater acoustic non-stationary signal can determine the existence of a target and even determine the nature of the target. Therefore, the research on underwater acoustic non-stationary signals is of great significance for target recognition.

[0031] In an exemplary embodiment, as Figure 1 and Figure 2 shown, a method for judging underwater acoustic non-stationary signals is provided, including the following steps 101 to 105.

[0032] Step 101, construct a comprehensive factor set for judging underwater acoustic non-stationary signals; the comprehensive factor set includes multiple factors.

[0033] Step 102, construct a comprehensive judgment set for judging underwater acoustic non-stationary signals; the comprehensive judgment set includes multiple judgment results.

[0034] Step 103, calculate the weight of each factor based on the actual value of each factor and the limit value of each factor under each judgment result, and construct a weight matrix.

[0035] Step 104, calculate the membership degree of each factor to each judgment result based on the actual value of each factor and the limit value of each factor under each judgment result, and construct a membership degree matrix.

[0036] Step 105, perform a judgment on underwater acoustic non-stationary signals according to the weight matrix and the membership degree matrix, and determine the judgment result of the non-stationary signals in the underwater acoustic signals.

[0037] Implementing the above steps 101 to 105 can solve the problem that it is difficult to judge the existence state of underwater acoustic non-stationary signals and improve the accuracy of judging underwater acoustic non-stationary signals.

[0038] In another exemplary embodiment, the comprehensive factor set u constructed in step 101 of the present application is u = {u1, u2,..., u I}. Among them, u i(i = 1, 2, …, I) represents the i-th factor, that is, the i-th factor affecting the judgment object. The power spectral density represents the power distribution of the signal in the frequency domain and can obtain the energy characteristic information of the signal in the frequency domain; the short-time average amplitude is a characterization of the energy size of a frame of underwater acoustic signal and can obtain the energy characteristic information of the signal in the time domain; the short-time zero-crossing rate is the number of times the signal crosses the zero value in a frame of signal and can obtain the frequency change characteristic information of the signal in the time domain; the short-time energy is a function that measures the amplitude change of the underwater acoustic signal and can obtain the energy characteristic information of the signal in the time domain. Therefore, exemplarily, in the embodiments of the present application, the comprehensive factor set u = {u1, u2, u3, u4} is determined, where u1, u2, u3, and u4 are four factors, respectively representing the power spectral density, the short-time average amplitude, the short-time zero-crossing rate, and the short-time energy. In the application process, the calculation methods of the actual values of each factor are as follows.

[0039] 1. Power spectral density: The power spectral density represents the power distribution of the signal in the frequency domain.

[0040]

[0041] P n (m) = (|X n (m)| 2 ) / df;

[0042] Among them, P n (m) represents the power spectral density of the m-th component of the Fourier transform of the n-th frame of underwater acoustic signal, X n (m) represents the m-th component of the Fourier transform of the n-th frame of underwater acoustic signal, M represents the order of the Fourier transform of the n-th frame of underwater acoustic signal, df represents the frequency resolution; x n (k) is the amplitude of the discrete signal at the k-th sampling point in the time domain of the underwater acoustic signal, and K is the number of sampling points of the n-th frame of underwater acoustic signal.

[0043] 2. Short-time average amplitude.

[0044]

[0045] Among them, M n is the short-time average amplitude of the n-th frame of underwater acoustic signal. The short-time average amplitude is also a characterization of the energy size of a frame of underwater acoustic signal. The difference between the short-time average amplitude and the short-time energy is that when the underwater acoustic signal changes greatly, there will be no large difference due to the square calculation.

[0046] 3. Short-time zero-crossing rate: The short-time zero-crossing rate represents the number of times a signal crosses the zero value in a frame of signal. High-frequency signals have a higher short-time zero-crossing rate, while low-frequency signals have a lower short-time zero-crossing rate. During the signal judgment process, the change in the difference of the short-time zero-crossing rate can better reflect the change of the signal. Therefore, the difference of the short-time zero-crossing rate is used to diversity the signal state.

[0047]

[0048] Among them, Z n is the short-time zero-crossing rate of the nth frame of underwater acoustic signal, x n (k - 1) is the amplitude of the discrete signal at the (k - 1)th sampling point in the time domain of the nth frame of underwater acoustic signal, and sgn[·] is the sign function, that is:

[0049]

[0050] Among them, x is any input variable of the sign function.

[0051] 4. Short-time energy: Short-time energy is a function that measures the amplitude change of underwater acoustic signals. Short-time energy is very sensitive to high levels. When there are large changes in underwater acoustic signals, the short-time energy will increase by an order of magnitude. Therefore, the order of magnitude is used to classify the signal state.

[0052]

[0053] Among them, E n is the short-time energy of the nth frame of underwater acoustic signal.

[0054] In another exemplary embodiment, the comprehensive judgment set v constructed in step 102 of the present application is: v = {v1, v2,..., v J}; among them, v j (j = 1, 2,..., J) represents the jth judgment result, that is, the jth judgment result of the judgment object, and can be represented by different levels, comments or numbers according to actual needs. The comprehensive judgment set is a set composed of various results that can be obtained for a judgment event or object. In the embodiment of the present application, the existence state of underwater acoustic non-steady signals is divided into three levels, denoted as v = {v1, v2, v3}, and v1, v2, and v3 are three judgment results, respectively representing that the signal is absent, weak, and strong, and the limit values of the corresponding factors are determined according to the measurement environment and the influence of various factors on the results.

[0055] In another exemplary embodiment, the weight matrix constructed in step 103 of the present application is: A = (a1, a2,..., a I ), where a i (i = 1, 2,..., I) satisfies a i ≥0, ai Represents the normalized weight of the \(i\)-th factor \(u\) i , which can reflect the influence degree of the factor on the judgment result.

[0056] The determination of the weight is to perform weighting according to the influence degree of each factor on the result. The greater the influence, the greater the weighting. The calculation formula of the weight is as follows:

[0057]

[0058] In the formula, is the weight of the \(i\)-th factor \(u\) i , is the average value of the limits of the \(i\)-th factor \(u\) i under each judgment result, \(T\) ij is the limit of the \(i\)-th factor \(u\) i under the \(j\)-th judgment result, and \(J\) is the number of judgment results; is the actual value of the \(i\)-th factor \(u\) i .

[0059] For fuzzy judgment, the single-item weights are normalized again:

[0060]

[0061] Among them, \(a\) i is the normalized weight of the \(i\)-th factor \(u\) i , is the weight of the \(i'\)-th factor \(u\) i' , and \(I\) is the number of factors in the comprehensive factor set.

[0062] Exemplarily, assume that the limits of each level of the power spectral density \(u1\) are: -70, -60, and -50 respectively. The arithmetic mean of the limits of each level of the power spectral density \(u1\) is:

[0063]

[0064] Similarly, the arithmetic means of the limits of each level of the short-time average amplitude \(u2\), short-time zero-crossing rate \(u3\), and short-time energy \(u4\) are obtained

[0065] When the actual value of the power spectral density is -53, the weight of the power spectral density \(u1\) is:

[0066]

[0067] Exemplarily, when the measured signal mean value is an integer of -49, the weight of the power spectral density \(u1\) is approximately 0.81, and the weight of the short-time average amplitude \(u2\) is approximately 0.48, and the weight of the short-time zero-crossing rate \(u3\) is approximately 2.3, the weight of the short-time energy u4 is approximately 1.6, corresponding to

[0068] Normalize each weight to obtain the normalized weights as a1 = 0.16, a2 = 0.09, a3 = 0.44, a4 = 0.31 respectively. a1, a2, a3, and a4 are the normalized weights of the power spectral density u1, short-time average amplitude u2, short-time zero-crossing rate u3, and short-time energy u4 respectively.

[0069] In the above example, the influence weights of each factor on the result are different, and the weight matrix constructed is A = (0.16, 0.09, 0.44, 0.31).

[0070] Each value in the above embodiments is an exemplary representation, aiming to illustrate the calculation method of the weight matrix of the present application. In actual applications, it can be adaptively modified according to needs and test results.

[0071] In another exemplary embodiment, the construction method of the membership matrix in step 104 above is as follows:

[0072] First, start the judgment from a factor u i and record the judgment result as R i . R i is called the fuzzy judgment set of the i-th factor, and R i = (r i1 , r i2 , …, r iJ ). Among them, r ij (j = 1, 2, …, J) is called the membership degree, and j = 1, 2, …, J represents J possible judgment results. The membership degree represents the influence of the i-th factor on the judgment result.

[0073] The membership degree is obtained by the median method and is represented by the membership function:

[0074]

[0075] Among them, r ij , r i1 and r iJ represent the membership degrees of the i-th factor u i to the j-th, first, and J-th judgment results respectively, and T i(j-1) , T ij , T i(j+1) , T i1 , T i2 , T i(J-1) and T iJThe limits of the i-th factor u under the (j-1)-th, j-th, (j+1)-th, 1st, 2nd, (J-1)-th, and J-th judgment results respectively i are the actual value of the i-th factor u i , and J is the number of judgment results.

[0076] Each single-factor fuzzy judgment set is inferred from the single-factor fuzzy judgment sets:

[0077]

[0078] where R1, R2, and R I are the fuzzy judgment sets of the 1st, 2nd, and I-th factors respectively.

[0079] Then, taking the membership degrees of each single-factor judgment set as rows, a membership degree matrix is formed:

[0080]

[0081] Exemplarily, in actual operation, the background noise of the working environment is first collected as the judgment standard. In this case, the background noise of the working environment and the values of each factor calculated based on this background noise are taken as shown Figure 3 . According to the time-domain signal, the signal power spectral density, short-time average amplitude, short-time zero-crossing rate, and short-time energy are calculated to be -49.8576, 0.0017534, 4611.0267, and 25.7596 respectively. Taking the short-time average amplitude as an example for membership degree calculation. The short-time average amplitude is rounded to 0.0018. According to the limits shown in Table 1, the three membership degrees of the short-time average amplitude are obtained as:

[0082] r 21 = 0;

[0083] r 22 = (0.0018 - 0.001) / (0.01 - 0.001) = 0.08;

[0084] r 23 = (0.01 - 0.0018) / (0.01 - 0.001) = 0.91.

[0085] According to the same method, according to the limits shown in Table 1, the membership degrees of the signal power spectral density, short-time zero-crossing rate, and short-time energy are obtained, and the membership degree matrix R is obtained:

[0086]

[0087] Table 1 Limit List

[0088] Judgment factor No signal Weak signal Strong signal Power spectral density -70 -60 -50 Short-time average amplitude 0.0001 0.001 0.01 Short-time zero-crossing rate 1000 2000 3000 Short-time energy 1 3 5

[0089] In another exemplary embodiment, the method for judging underwater acoustic non-steady signals is as follows.

[0090] Combining the weights of various factors for the judgment event or object, comprehensively considering the influence of all factors on the judgment result, a fuzzy vector is obtained, expressed as:

[0091]

[0092] where B is the fuzzy vector, A is the weight matrix, R is the membership degree matrix, a1, a2, a I are the normalized weights of the first, second, and Ith factors respectively, b1, b2, b J are the first, second, and Jth comprehensive judgment results respectively, r ij is the membership degree of the ith factor u i to the jth judgment result, i = 1, 2,..., I, where I is the number of factors in the comprehensive factor set, j = 1, 2,..., J, and J is the number of judgment results; (j = 1, 2,..., J), b j is the jth comprehensive judgment result.

[0093] The ith row of R represents the degree to which the ith factor affects the judgment event or object to take each judgment result; the jth column represents the degree to which all factors affect the judgment object to take the jth judgment result. Therefore, multiplying each column of factors by the corresponding weight value a i (i = 1, 2,..., I) can more reasonably reflect the comprehensive judgment result of all factors.

[0094] Exemplarily, after determining the membership degree matrix R and the weight matrix A, the fuzzy vector on u is transformed into the fuzzy vector V on v through fuzzy transformation.

[0095] In this example, using the formula B = A·R, B = (0, 0.0072, 0.99) is obtained. Further, according to the maximum membership degree principle, the judgment result is that the signal is strong.

[0096] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application.

[0097] The present application analyzes the characteristic quantities of underwater acoustic non-steady signals, and according to the signal characteristics reflected by the characteristic quantities, a comprehensive judgment model is established through the corresponding fuzzy membership function. Using the fuzzy judgment algorithm to perform fuzzy judgment on the existence state of underwater acoustic non-steady signals, it can be seen from the examples that the expected purpose and good effects are achieved.

[0098] In this application, the signal power spectral density, short-time average amplitude of the signal, short-time zero-crossing rate, and short-time energy are selected as the comprehensive judgment results. The amplitude means of the signal power spectral density, short-time average amplitude of the signal, short-time zero-crossing rate, and short-time energy of the background noise in the same working environment are used as the comprehensive judgment criteria. By establishing the membership functions of each judgment result, the membership degrees of each judgment result are calculated, so as to judge the state of the signal by using the weight values and the judgment result membership degree matrix. This method can judge the existence state of underwater acoustic non-stationary signals.

[0099] Based on the same inventive concept, an embodiment of this application also provides an underwater acoustic non-stationary signal judgment device for implementing the above-mentioned underwater acoustic non-stationary signal judgment method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the underwater acoustic non-stationary signal judgment device provided below can refer to the limitations on the underwater acoustic non-stationary signal judgment method in the above text, and will not be repeated here.

[0100] In an exemplary embodiment, an underwater acoustic non-stationary signal judgment device is provided, which includes the following modules.

[0101] A comprehensive factor set construction module for constructing a comprehensive factor set for underwater acoustic non-stationary signal judgment; the comprehensive factor set includes multiple factors.

[0102] A comprehensive judgment set construction module for constructing a comprehensive judgment set for underwater acoustic non-stationary signal judgment; the comprehensive judgment set includes multiple judgment results.

[0103] A weight matrix construction module for calculating the weight of each factor based on the actual values of each factor and the limits of each factor under each judgment result, and constructing a weight matrix.

[0104] A membership degree matrix construction module for calculating the membership degree of each factor to each judgment result based on the actual values of each factor and the limits of each factor under each judgment result, and constructing a membership degree matrix.

[0105] A comprehensive judgment module for performing underwater acoustic non-stationary signal judgment according to the weight matrix and the membership degree matrix, and determining the judgment result of the non-stationary signal in the underwater acoustic signal.

[0106] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for judging unsteady underwater acoustic signals.

[0107] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps in the above method embodiments are implemented.

[0109] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0110] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0112] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0113] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0114] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0115] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for determining underwater acoustic non-steady-state signals, characterized in that: The method for determining an underwater acoustic non-steady-state signal comprises: Constructing a comprehensive factor set for judging underwater acoustic non-steady-state signals; the comprehensive factor set includes multiple factors; Constructing a comprehensive judgment set for underwater acoustic non-steady-state signal judgment; the comprehensive judgment set includes multiple judgment results; Based on the actual value of each factor and the limit value of each factor under each judgment result, the weight of each factor is calculated and a weight matrix is ​​constructed; Based on the actual value of each factor and the limit value of each factor under each judgment result, the membership of each factor to each judgment result is calculated, and a membership matrix is ​​constructed; The underwater acoustic non-steady-state signal is judged according to the weight matrix and the membership matrix to determine the judgment result of the non-steady-state signal in the underwater acoustic signal.

2. The method for determining underwater acoustic non-steady-state signals according to claim 1, characterized in that: Multiple factors include: power spectral density, short-time average amplitude, short-time zero-crossing rate and short-time energy.

3. The underwater acoustic non-steady-state signal determination method according to claim 2, characterized in that: The actual value of the power spectral density is calculated as: Among them, P n (m) represents the power spectral density of the mth order component of the Fourier transform of the nth frame underwater acoustic signal, X n (m) represents the m-th order component of the Fourier transform of the n-th frame of the water acoustic signal, M represents the order of the Fourier transform of the n-th frame of the water acoustic signal, and df represents the frequency resolution; The actual value of the short-time average amplitude is calculated as: Among them, M n is the short-time average amplitude of the nth frame underwater acoustic signal, x n (k) is the amplitude of the discrete signal of the kth sampling point in the time domain of the nth frame of the underwater acoustic signal, and K is the number of sampling points of the nth frame of the underwater acoustic signal; The actual value of the short-time zero-crossing rate is calculated as: Among them, Z n is the short-time zero-crossing rate of the n-th frame underwater acoustic signal, sgn[·] is the sign function, x n (k-1) is the amplitude of the discrete signal of the k-1th sampling point in the time domain of the nth frame of underwater acoustic signal; The actual value of the short-time energy is calculated as: Among them, E n is the short-time energy of the nth frame of underwater acoustic signal.

4. The method for determining underwater acoustic non-steady-state signals according to claim 1, characterized in that: Based on the actual value of each factor and the limit value of each factor under each judgment result, the weight of each factor is calculated and a weight matrix is ​​constructed, including: Based on the actual value of each factor and the limit value of each factor under each judgment result, the weight of each factor is calculated using the following formula; in, is the i-th factor u i The weight of is the i-th factor u under each judgment result i The average value of the limit, T ij is the i-th factor u under the j-th judgment result i The limit of , J is the number of judgment results; is the i-th factor u i The actual value of The weight of each factor is normalized using the following formula to obtain the normalized weight of each factor; Among them, a i is the i-th factor u i The normalized weight of is the i'th factor u i' The weight of , I is the number of factors in the comprehensive factor set; According to the normalized weights of each factor, the weight matrix is ​​constructed as: A = (a1, a2, ..., a I ); Among them, A is the weight matrix, a1, a2, a I are the normalized weights of the 1st, 2nd and Ith factors respectively.

5. The underwater acoustic non-steady-state signal determination method according to claim 1, characterized in that: The formula for calculating the degree of membership of each factor to each judgment result is: Among them, r ij 、r i1 and r iJ Respectively represent the i-th factor u i The membership of the jth, first and Jth judgment results, T i(j-1) , T ij , T i(j+1) , T i1 , T i2 , T i(J-1) and T iJ The i-th factor u under the j-1th, jth, j+1th, 1st, 2nd, J-1th and Jth judgment results respectively i The limit value of is the i-th factor u i The actual value of , J is the number of judgment results.

6. The underwater acoustic non-steady-state signal determination method according to claim 1, characterized in that: According to the weight matrix and the membership matrix, the underwater acoustic non-steady-state signal is judged to determine the judgment result of the non-steady-state signal in the underwater acoustic signal, specifically including: According to the weight matrix and the membership matrix, the fuzzy vector is calculated using the following formula: Among them, B is the fuzzy vector, A is the weight matrix, R is the membership matrix, a1, a2, a I are the normalized weights of the first, second, and I factors, b1, b2, and b J are the first, second, and Jth comprehensive judgment results, r ij is the i-th factor u i The membership degree of the jth judgment result, i = 1, 2, ..., I, I is the number of factors in the comprehensive factor set, j = 1, 2, ..., J, J is the number of judgment results; Based on the fuzzy vector, the underwater acoustic non-steady-state signal is judged to determine the judgment result of the non-steady-state signal in the underwater acoustic signal.

7. A device for determining underwater acoustic non-steady-state signals, characterized in that: The underwater acoustic non-steady-state signal judgment device applies the underwater acoustic non-steady-state signal judgment method described in any one of claims 1 to 6, and the underwater acoustic non-steady-state signal judgment device includes: A comprehensive factor set construction module is used to construct a comprehensive factor set for judging underwater acoustic non-steady-state signals; the comprehensive factor set includes multiple factors; A comprehensive judgment set construction module is used to construct a comprehensive judgment set for underwater acoustic non-steady-state signal judgment; the comprehensive judgment set includes multiple judgment results; The weight matrix construction module is used to calculate the weight of each factor and construct the weight matrix based on the actual value of each factor and the limit value of each factor under each judgment result; A membership matrix construction module is used to calculate the membership of each factor to each judgment result based on the actual value of each factor and the limit value of each factor under each judgment result, and to construct a membership matrix; The comprehensive judgment module is used to judge the underwater acoustic non-steady-state signal according to the weight matrix and the membership matrix, and determine the judgment result of the non-steady-state signal in the underwater acoustic signal.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the underwater acoustic non-steady-state signal judgment method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the underwater acoustic non-steady-state signal judgment method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the underwater acoustic non-steady-state signal judgment method described in any one of claims 1 to 6 are implemented.

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