DEMON spectrum analysis frequency band selection method and device based on holographic Hilbert spectrum analysis

Abstract

The invention discloses a DEMON spectrum analysis frequency band selection method and device based on holographic Hilbert spectrum analysis, and the method comprises the steps: carrying out the time-frequency analysis of a signal through short-time Fourier transform, and intercepting a stable time-domain signal in combination with a time-domain graph; performing holographic Hilbert spectrum analysis on the intercepted signal, and establishing a corresponding relation between a modulation frequency and a carrier frequency by using a high-dimensional representation method through adding additional dimensions in a spectrum result by using a nested empirical mode decomposition and Hilbert-Huang transform method; then, in combination with the holographic Hilbert amplitude modulation spectrum, determining a frequency band range of a band-pass filter in DEMON spectrum analysis; and finally, performing DEMON spectrum analysis on the signal to realize accurate estimation of parameter information such as shaft frequency, blade frequency and blade number of the underwater target propeller. The holographic Hilbert spectrum decomposition technology is utilized to directly obtain the corresponding relation between the modulation frequency and the carrier frequency, and the problem that a band-pass filter is difficult to select in the DEMON spectrum analysis process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater target recognition, and in particular to a method and device for selecting a DEMON spectrum analysis frequency band based on holographic Hilbert spectrum analysis. Background Art

[0002] The radiation noise signal of underwater targets usually contains many characteristic quantities that reflect the nature of the target. The feature extraction method based on the radiation noise of underwater targets is an effective means to achieve accurate detection of underwater targets. Therefore, the key to detecting and identifying underwater targets lies in whether the effective features reflecting the nature of underwater vehicles can be accurately extracted from their radiation noise.

[0003] The radiated noise of underwater targets such as ships and underwater vehicles can be divided into three categories: mechanical noise generated by the vibration of various mechanical devices (such as power machinery) inside the underwater vehicle; propeller noise generated by the rotation of the propeller in the water, including propeller cavitation noise and propeller rotation noise; and hydrodynamic noise generated by the interaction between the hull of the vehicle and the water body when the underwater vehicle is sailing in the water. The low-frequency line spectrum of the shaft frequency, blade frequency and its harmonic frequency generated by the rotation of the propeller often exists in the high-frequency cavitation noise continuous spectrum in the form of modulation. Detection of Envelope Modulation On Noise (DEMON) analysis is a method of obtaining the modulation spectrum characteristics of the low-frequency band by demodulating the received broadband high-frequency signal with modulation characteristics and performing spectral analysis on the envelope signal. Characteristic information such as the target shaft frequency and blade frequency can be obtained through DEMON analysis.

[0004] When the DEMON spectrum analysis method is actually used to classify and track underwater vehicles, the selection of the bandpass filter frequency band range will significantly affect the algorithm performance. The traditional DEMON spectrum analysis method requires the artificial selection of the bandpass filter frequency band range, which is very blind in practical applications. When the selected frequency band range does not have modulation characteristics or the modulation characteristics are not strong, the useful information in the signal may be filtered out, and the characteristic line spectrum reflecting the parameter information such as propeller shaft frequency, blade frequency and number of blades in the obtained DEMON spectrum will become difficult to extract.

[0005] Therefore, how to accurately select the frequency band range of the bandpass filter is an urgent problem that the DEMON spectrum analysis algorithm needs to solve. Summary of the invention

[0006] In order to solve the above technical problems existing in the prior art, the present invention provides a method and device for selecting a DEMON spectrum analysis band based on holographic Hilbert spectrum analysis to solve the problem that the frequency band range of the bandpass filter is difficult to select in the DEMON spectrum analysis method in the prior art.

[0007] In order to achieve the above object, the technical solution of the embodiment of the present invention is:

[0008] A first aspect of the present invention provides a method for selecting a frequency band of a DEMON spectrum analysis based on holographic Hilbert spectrum analysis, the method comprising:

[0009] Performing spectrum analysis on the obtained underwater target radiation noise signal to obtain the main frequency range of the radiation noise signal, and performing downsampling processing on the radiation noise signal to obtain a simplified radiation noise signal;

[0010] Performing time-frequency analysis on the simplified radiation noise signal by short-time Fourier transform, and performing signal interception in combination with a time domain diagram to obtain an intercepted radiation noise signal;

[0011] Based on the empirical mode decomposition in the holographic Hilbert spectrum analysis, the intercepted radiation noise signal is decomposed into a plurality of eigenmode functions, and the Hilbert spectrum analysis is performed on each of the eigenmode functions to obtain an amplitude variation function and a carrier frequency variation function of each of the eigenmode functions;

[0012] Calculating the envelope of each of the eigenmode functions, and performing empirical mode decomposition and Hilbert spectrum analysis on each of the envelopes to obtain an envelope amplitude variation function and a modulation frequency variation function;

[0013] Integrating the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum;

[0014] Based on the holographic Hilbert amplitude modulation spectrum, the corresponding relationship between the modulation frequency and the carrier frequency of the intercepted radiation noise signal is obtained, the frequency band range of the bandpass filter in the DEMON spectrum analysis is determined, and the DEMON spectrum analysis is performed on the intercepted radiation noise signal to obtain a DEMON spectrum;

[0015] Feature extraction is performed on the DEMON spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater target propeller.

[0016] The embodiment of the present invention provides a DEMON spectrum analysis frequency band selection device based on holographic Hilbert spectrum analysis, the device comprising:

[0017] A downsampling module is used to perform spectrum analysis on the acquired underwater target radiation noise signal to obtain the main frequency range of the radiation noise signal, and to perform downsampling processing on the radiation noise signal to obtain a simplified radiation noise signal;

[0018] A signal interception module, used to perform time-frequency analysis on the simplified radiation noise signal through short-time Fourier transform, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal;

[0019] A first analysis module is used to decompose the intercepted radiation noise signal into multiple eigenmode functions based on empirical mode decomposition in holographic Hilbert spectrum analysis, and perform Hilbert spectrum analysis on each of the eigenmode functions to obtain an amplitude change function and a carrier frequency change function of each of the eigenmode functions;

[0020] A second analysis module is used to calculate the envelope of each of the eigenmode functions, and perform empirical mode decomposition and Hilbert spectrum analysis on each of the envelopes to obtain an envelope amplitude variation function and a modulation frequency variation function;

[0021] An integration module, used for integrating the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum;

[0022] A third analysis module is used to obtain the corresponding relationship between the modulation frequency and the carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum, determine the frequency band range of the bandpass filter in the DEMON spectrum analysis, and perform DEMON spectrum analysis on the intercepted radiation noise signal to obtain a DEMON spectrum;

[0023] The feature extraction module is used to extract features from the DEMON spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater target propeller.

[0024] In some embodiments, when performing spectrum analysis on the obtained underwater target radiation noise signal, the calculation formula of Fourier transform is:

[0025]

[0026] Where f(t) is the original received signal, ω is the signal frequency, t represents time, and j is the imaginary unit.

[0027] In some embodiments, when the simplified radiation noise signal is subjected to time-frequency analysis by short-time Fourier transform, the calculation formula of the short-time Fourier transform is:

[0028]

[0029] Where x1(t) is the downsampled signal, h(t) is the window function, f is the signal frequency, t represents time, and j is the imaginary unit.

[0030] In some embodiments, the empirical mode decomposition based on holographic Hilbert spectrum analysis decomposes the intercepted radiation noise signal into multiple eigenmode functions, wherein the intercepted radiation noise signal is expressed as:

[0031]

[0032] Among them, c j (t) is the jth intrinsic mode function, and the amplitude a of each IMF component is j (t) and frequency ω j (t) are expressed as functions of time, respectively, and N is the number of eigenmode functions;

[0033] The nested expression of the amplitude change function is:

[0034]

[0035] Where Ω represents the slowly varying envelope frequency.

[0036] In some embodiments, the integration module is further used to use an edge expression to integrate the time variables of the carrier frequency change function, the modulation frequency change function and the envelope amplitude change function to obtain a frequency spectrum diagram with only frequency, and perform multiple iterations until the amplitude change function no longer has a cyclic characteristic in the envelope, thereby obtaining a pure frequency high-dimensional full-information representation of the signal amplitude, that is, the holographic Hilbert amplitude modulation spectrum; wherein the calculation formula of the edge spectrum is:

[0037]

[0038] Where T is the integration time range, ω is the signal frequency, and t represents time.

[0039] In some embodiments, the third analysis module is also used to obtain the correspondence between the modulation frequency and the carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum; based on the correspondence, determine the frequency band range in which the modulation characteristics exist in the intercepted radiation noise signal; in combination with the holographic Hilbert amplitude modulation spectrum, select a suitable frequency band range to perform bandpass filtering on the intercepted radiation noise signal to obtain a filtered radiation noise signal; use the Hilbert change demodulation method to perform envelope demodulation on the filtered radiation noise signal to obtain an envelope signal; and perform low-pass filtering and spectral analysis on the envelope signal to obtain a DEMON spectrum.

[0040] An embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned DEMON spectrum analysis frequency band selection method based on holographic Hilbert spectrum analysis when executing the executable instructions stored in the memory.

[0041] An embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned DEMON spectrum analysis frequency band selection method based on holographic Hilbert spectrum analysis.

[0042] The DEMON spectrum analysis frequency band selection method based on holographic Hilbert spectrum analysis provided by the present invention first performs empirical mode decomposition on the target radiation noise signal, decomposes the signal into multiple intrinsic mode functions IMF, and performs Hilbert spectrum analysis to obtain the function of the amplitude and frequency of each IMF component changing with time, then calculates the envelope of each IMF, and performs empirical mode decomposition and Hilbert spectrum analysis on each envelope again to obtain the function of the amplitude and frequency of the envelope changing with time, then integrates the time variable of each layer of Hilbert spectrum analysis results to obtain a frequency spectrum diagram with only frequency, and finally performs multiple iterations as needed until the amplitude function is within the envelope. The network no longer has cyclic characteristics, and finally a pure frequency high-dimensional full-information representation of the signal amplitude is obtained, namely the holographic Hilbert amplitude modulation spectrum; the holographic Hilbert spectrum decomposition technology uses nested empirical mode decomposition and Hilbert-Huang transform methods to identify the amplitude and frequency modulations that often appear in nonlinear and non-stationary systems. By adding extra dimensions to the spectrum results and using high-dimensional representation methods to establish the correspondence between the modulation frequency and the carrier frequency, it can quantitatively study complex signal modulation mechanisms; the frequency band range of the bandpass filter in the DEMON spectrum analysis can be directly obtained through holographic Hilbert spectrum analysis, avoiding the problem of difficult selection of bandpass filters in the DEMON spectrum analysis process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for selecting a frequency band of a DEMON spectrum analysis based on holographic Hilbert spectrum analysis provided by the present invention;

[0044] Figure 2 Another DEMON spectrum analysis frequency band selection method based on holographic Hilbert spectrum decomposition technology provided by the present invention;

[0045] Figure 3 It is a schematic diagram of the process of holographic Hilbert spectrum analysis provided by the present invention;

[0046] Figure 4 It is a time domain diagram of radiation noise when an AUV performs a task provided by the test example of the present invention;

[0047] Figure 5 It is a time-frequency diagram of radiation noise provided by the test example of the present invention;

[0048] Figure 6 This is a time domain diagram of a stable AUV radiation noise of 30 seconds provided in the test example of the present invention;

[0049] Figure 7 is the holographic Hilbert amplitude modulation spectrum of the intercepted signal provided by the experimental example of the present invention;

[0050] Figure 8 is a corresponding relationship curve between the integral value of the holographic spectrum amplitude modulation frequency and the FM frequency provided by the test example of the present invention;

[0051] Fig. 9 is the DEMON spectrum after the intercepted signal bandpass filtering provided by the test example of the present invention;

[0052] Fig.10 It is a schematic diagram of the composition structure of a DEMON spectrum analysis frequency band selection device based on holographic Hilbert spectrum analysis provided by an embodiment of the present invention;

[0053] Fig.11 It is a schematic diagram of the composition structure of a DEMON spectrum analysis frequency band selection device based on holographic Hilbert spectrum analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0055] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as those commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0056] The embodiment of the present invention provides a DEMON spectrum analysis band selection method based on holographic Hilbert spectrum analysis, see Figure 1 , Figure 1 is a flow chart of a method for selecting a frequency band of a DEMON spectrum analysis based on holographic Hilbert spectrum analysis provided by an embodiment of the present invention, which is combined with Figure 1 The steps shown are explained.

[0057] Step S110, performing spectrum analysis on the obtained underwater target radiation noise signal to obtain the main frequency range of the radiation noise signal, and performing downsampling processing on the radiation noise signal to obtain a simplified radiation noise signal.

[0058] In some embodiments, before performing spectrum analysis on the underwater target radiation noise signal, the obtained underwater target radiation noise signal may be preprocessed, such as denoising, filtering, etc., to reduce noise interference and improve signal quality.

[0059] In some embodiments, a suitable downsampling rate may be selected based on the results of spectrum analysis, and the radiation noise signal may be resampled using the selected downsampling rate to ensure that the main features of the signal are retained while reducing the amount of data, thereby simplifying subsequent operations.

[0060] In some embodiments, when performing spectrum analysis on the obtained underwater target radiation noise signal, the calculation formula of Fourier transform is:

[0061]

[0062] Where f(t) is the original received signal, ω is the signal frequency, t represents time, and j is the imaginary unit.

[0063] Step S120, performing time-frequency analysis on the simplified radiation noise signal by short-time Fourier transform, and performing signal interception in combination with the time domain diagram to obtain an intercepted radiation noise signal.

[0064] In some embodiments, short-time Fourier transform (STFT) is a method of dividing a signal into multiple small segments (or "frames") in time and performing Fourier transform on each frame. In this way, the spectral characteristics of the signal at different time points, that is, the frequency components of the signal changing with time, can be obtained.

[0065] In some embodiments, the simplified radiation noise signal is subjected to time-frequency analysis using a short-time Fourier transform. After obtaining the STFT result of the signal, further signal analysis can be performed in combination with the time domain diagram of the signal (i.e., a waveform diagram of the signal changing over time). By observing the time domain diagram and the STFT result, specific time intervals in the signal can be identified, which may contain features of interest. Afterwards, based on the results of the analysis, the time interval to be intercepted is determined on the time domain diagram, and the signal within this interval is intercepted using appropriate signal processing techniques, thereby obtaining the intercepted radiation noise signal.

[0066] In some embodiments, the intercepted radiation noise signal is a subset of the original signal, which only includes the portion of the original signal within a specific time interval. The intercepted signal may be used for further analysis, processing or comparison with other signals.

[0067] In some embodiments, when the simplified radiation noise signal is subjected to time-frequency analysis by short-time Fourier transform, the calculation formula of the short-time Fourier transform is:

[0068]

[0069] Where x1(t) is the downsampled signal, h(x) is the window function, f is the signal frequency, t represents time, and j is the imaginary unit.

[0070] Step S130, based on the empirical mode decomposition in the holographic Hilbert spectrum analysis, decompose the intercepted radiation noise signal into multiple eigenmode functions, and perform Hilbert spectrum analysis on each of the eigenmode functions to obtain the amplitude change function and the carrier frequency change function of each of the eigenmode functions.

[0071] In the present invention, the purpose of Empirical Mode Decomposition (EDM) is to decompose the complex radiated noise signal into a series of Intrinsic Mode Functions (IMFs) with specific frequency and time-frequency characteristics. Here, each IMF represents a component of different frequency scales in the original signal. Afterwards, each IMF is Hilbert transformed to obtain its amplitude change function and carrier frequency transformation function.

[0072] In the present invention, the amplitude of the analytical signal is the envelope of the original signal, which indicates the instantaneous amplitude change of the signal. The phase of the analytical signal contains the instantaneous frequency information of the signal. By differentiating or derivatizing the phase, the carrier frequency change function of the signal can be obtained.

[0073] In some embodiments, the intercepted radiation noise signal can be expressed as:

[0074]

[0075] Among them, c j (t) is the jth intrinsic mode function, and the amplitude a of each IMF component is j (t) and frequency ω j (t) are expressed as functions of time, respectively, and N is the number of eigenmode functions;

[0076] The nested expression of the amplitude change function is:

[0077]

[0078] Where Ω represents the slowly varying envelope frequency.

[0079] Step S140, calculating the envelope of each of the eigenmode functions, and performing empirical mode decomposition and Hilbert spectrum analysis on each of the envelopes to obtain an envelope amplitude variation function and a modulation frequency variation function.

[0080] In some embodiments, the envelope refers to the outer contour around the IMF waveform, describing the instantaneous amplitude change of the IMF. Calculating the envelope involves finding the local maximum and local minimum of the IMF, generating an upper envelope and a lower envelope by interpolation or other methods, and then calculating the average of the two envelopes (in some cases, the upper envelope or the lower envelope is directly used as the envelope).

[0081] In the present invention, since the envelope itself is also a signal, it can also be decomposed into multiple IMFs by EMD again, which can be called envelope IMFs. Afterwards, each envelope IMF is subjected to Hilbert transform to obtain its analytical representation. The instantaneous amplitude (i.e., envelope amplitude variation function) and instantaneous phase of the envelope IMF can be calculated by the analytical representation. The rate of change of the instantaneous phase corresponds to the instantaneous frequency, but here we focus more on the modulation frequency, i.e., the frequency variation of the envelope IMF, which reflects the modulation characteristics of the envelope itself.

[0082] In some embodiments, the envelope amplitude variation function describes the variation of the instantaneous amplitude of the IMF envelope over time, and the modulation frequency variation function describes the variation of the instantaneous frequency (or modulation frequency) of the envelope IMF over time.

[0083] It should be noted that the present invention does not specifically limit the envelope calculation method, which depends on the specific characteristics of the signal and the analysis requirements. Here, the envelope calculation method includes but is not limited to the above-mentioned method based on local extreme points, and the envelope can also be calculated by other methods, such as the Hilbert transform method.

[0084] Step S150, integrating the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum.

[0085] In some embodiments, the purpose of integrating the time variables of the carrier frequency change function, the modulation frequency change function and the envelope amplitude change function is to calculate the impact of these functions on the time accumulation within a specific time period, generating a holographic Hilbert amplitude modulation spectrum that can fully reflect the signal characteristics.

[0086] In some embodiments, the above step S150 may be determined in the following manner:

[0087] The edge expression is used to integrate the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a frequency spectrum diagram with only frequency, and multiple iterations are performed until the amplitude variation function no longer has a cyclic characteristic in the envelope, and a pure frequency high-dimensional full-information representation of the signal amplitude is obtained, that is, the holographic Hilbert amplitude modulation spectrum; wherein the calculation formula of the edge spectrum is:

[0088]

[0089] Where T is the integration time range, ω is the signal frequency, and t represents time.

[0090] Step S160, based on the holographic Hilbert amplitude modulation spectrum, obtain the correspondence between the modulation frequency and the carrier frequency of the intercepted radiation noise signal, determine the frequency band range of the bandpass filter in the DEMON spectrum analysis, perform DEMON spectrum analysis on the intercepted radiation noise signal, and obtain a DEMON spectrum.

[0091] In some embodiments, the correspondence between the modulation frequency and the carrier frequency can be determined based on the holographic Hilbert amplitude modulation spectrum; and based on the correspondence, the frequency band range of the bandpass filter in the DEMON spectrum analysis can be determined; then, based on the frequency band range, the intercepted radiation noise signal is subjected to DEMON spectrum analysis to obtain the DEMON spectrum.

[0092] In some embodiments, the above step S160 may be determined in the following manner:

[0093] Based on the holographic Hilbert amplitude modulation spectrum, the corresponding relationship between the modulation frequency and the carrier frequency of the intercepted radiation noise signal is obtained; based on the corresponding relationship, the frequency band range in which the modulation characteristics exist in the intercepted radiation noise signal is determined; in combination with the holographic Hilbert amplitude modulation spectrum, a suitable frequency band range is selected to perform bandpass filtering on the intercepted radiation noise signal to obtain a filtered radiation noise signal; using the Hilbert change demodulation method, envelope demodulation is performed on the filtered radiation noise signal to obtain an envelope signal; low-pass filtering and spectrum analysis are performed on the envelope signal to obtain a DEMON spectrum.

[0094] Step S170, extracting features from the DEMON spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater target propeller.

[0095] In some embodiments, shaft frequency refers to the frequency of one rotation of the propeller, that is, the frequency of rotation of the propeller shaft. In the DEMON spectrum, shaft frequency reflects the speed of propeller rotation.

[0096] In some embodiments, blade frequency refers to the frequency at which a blade of a propeller passes a fixed point (such as a sonar sensor) during rotation. Since a propeller has multiple blades, blade frequency is usually an integer multiple of shaft frequency. In the DEMON spectrum, blade frequency is represented as a series of harmonic components with shaft frequency as the fundamental frequency, and the intervals of these harmonics are equal to the shaft frequency.

[0097] In some embodiments, the number of blades refers to the number of blades on the propeller. Since the blade frequency is an integer multiple of the shaft frequency, the number of blades can be determined by analyzing the blade frequency harmonics in the DEMON spectrum. Specifically, the interval between the blade frequency harmonics (i.e., the shaft frequency) is observed, and the ratio of the blade frequency to the shaft frequency is calculated, and this ratio is the number of blades.

[0098] In the present invention, the shaft frequency component and blade frequency harmonics of the DEMON spectrum are identified by extracting features from the DEMON spectrum, and then the shaft frequency, blade frequency and number of blades of the underwater target propeller are estimated.

[0099] The DEMON spectrum analysis frequency band selection method based on holographic Hilbert spectrum analysis provided by the present invention first performs empirical mode decomposition on the target radiation noise signal, decomposes the signal into multiple intrinsic mode functions IMF, and performs Hilbert spectrum analysis to obtain the function of the amplitude and frequency of each IMF component changing with time, then calculates the envelope of each IMF, and performs empirical mode decomposition and Hilbert spectrum analysis on each envelope again to obtain the function of the amplitude and frequency of the envelope changing with time, then integrates the time variable of each layer of Hilbert spectrum analysis results to obtain a frequency spectrum diagram with only frequency, and finally performs multiple iterations as needed until the amplitude function is within the envelope. The network no longer has cyclic characteristics, and finally a pure frequency high-dimensional full-information representation of the signal amplitude is obtained, namely the holographic Hilbert amplitude modulation spectrum; the holographic Hilbert spectrum decomposition technology uses nested empirical mode decomposition and Hilbert-Huang transform methods to identify the amplitude and frequency modulations that often appear in nonlinear and non-stationary systems. By adding extra dimensions to the spectrum results and using high-dimensional representation methods to establish the correspondence between the modulation frequency and the carrier frequency, it can quantitatively study complex signal modulation mechanisms; the frequency band range of the bandpass filter in the DEMON spectrum analysis can be directly obtained through holographic Hilbert spectrum analysis, avoiding the problem of difficult selection of bandpass filters in the DEMON spectrum analysis process.

[0100] The following describes an exemplary application of an embodiment of the present invention in a practical application scenario.

[0101] The present invention provides another DEMON spectrum analysis band selection method based on holographic Hilbert spectrum decomposition technology. The specific process is as follows: Figure 2 As shown, the implementation steps are as follows:

[0102] S1, perform spectrum analysis on the collected underwater target radiation noise signal, determine the main frequency range of the signal, and downsample the signal to simplify the operation.

[0103] Among them, the calculation formula of Fourier transform is:

[0104]

[0105] S2, use short-time Fourier transform to perform time-frequency analysis on the signal, and combine the time domain diagram to intercept the stable time domain signal.

[0106] Among them, the short-time Fourier transform calculation formula is:

[0107]

[0108] S3, perform holographic Hilbert spectrum analysis on the signal intercepted in step S2, decompose the signal into several intrinsic mode functions based on empirical mode decomposition, and perform Hilbert spectrum analysis to obtain the function of the amplitude and frequency of each IMF component changing with time, calculate the envelope of each IMF, perform empirical mode decomposition and Hilbert spectrum analysis on each envelope again, and repeat the above process to clearly describe the addition or multiplication process in the signal.

[0109] Here, empirical mode decomposition adaptively decomposes any complex signal into several intrinsic mode functions according to the characteristics of the signal itself. Each IMF component decomposed represents a simple oscillation mode and contains the local characteristic information of the original signal.

[0110] The first step is to find all local extreme values, obtain the local maximum and local minimum of the signal, and fit the upper envelope and lower envelope. All data should be covered within the upper and lower envelopes. The mean of the upper and lower envelopes is defined as m1, and the difference between the data x(t) and m1 is the first component h1, that is,

[0111] h1=x(t)-m1 (3);

[0112] Step 2: Determine whether h1 is an intrinsic mode function. If not, continue to repeat the extraction process. In the subsequent extraction process, h1 is treated as a signal. Obtain the local maximum and local minimum of h1 and fit the upper and lower envelopes. Define the mean of the upper and lower envelopes as m 11 , h1 and m 11 The difference between them is the second component h 11 ,Right now

[0113] h 11 =h1-m 11 (4);

[0114] According to this method, after repeated extraction process, repeated k times, until the characteristics of the eigenmode function are met, h is obtained. 1k ,h 1k It is called the first IMF.

[0115] h 1k =h 1(k-1) -m 1k (5);

[0116] Assign the first IMF to c1, that is,

[0117] c1=h 1k (6);

[0118] Step 3: Separate c1 from the original signal, that is

[0119] r1=x(t)-c1 (7);

[0120] Treat r1 as a new signal and repeat the above steps. This process can be used to obtain r i Repeat to get c1, c2, ..., c n When ingredient c n or the remainder r n is small enough, or when the residual term r n When it becomes a monotonic function, the decomposition process ends.

[0121] Through empirical mode decomposition, the signal is decomposed into n empirical modes and a residual term. The original signal x(t) can be expressed as

[0122]

[0123] Among them, c j (t) is the jth eigenmode function, r n is the trend term of the original signal or a constant value.

[0124] Through empirical mode decomposition, the original data is decomposed into n intrinsic mode functions, and Hilbert spectrum analysis of each IMF can be obtained.

[0125]

[0126] Among them, the amplitude and frequency of each IMF component are expressed as a function of time.

[0127] The envelope of each IMF is calculated, and each envelope is subjected to empirical mode decomposition and Hilbert spectrum analysis again to obtain the function of the amplitude and frequency of the envelope changing with time. The above process is repeated to clearly describe the addition or multiplication process in the signal.

[0128] Mathematically, the data analysis process can be summarized as:

[0129]

[0130] For the second and higher layers, the nested expression of the amplitude function can be expressed as:

[0131]

[0132] In equations (10) and (11), ω represents the fast-changing instantaneous frequency of the carrier, and Ω represents the slowly changing envelope frequency. For each additional layer of decomposition, additional dimensions Ω1, Ω2, ... must be added to accommodate the amplitude modulation frequency.

[0133] S4, in each step of step S3, the time variable is integrated to obtain a frequency spectrum diagram with only frequency. This process can be iterated multiple times as needed until the amplitude function no longer has a cyclic characteristic in the envelope, and finally a pure frequency high-dimensional full-information representation of the signal amplitude is obtained, namely, the holographic Hilbert amplitude modulation spectrum.

[0134] Integrate the time variable of each layer of Hilbert spectrum analysis results in step S3 to obtain a frequency spectrum diagram with only frequency, which is an edge expression. The calculation formula of the edge spectrum is:

[0135]

[0136] S5, combining the holographic Hilbert amplitude modulation spectrum calculated in step S4, obtaining the corresponding relationship between the signal modulation frequency and the carrier frequency, based on which the frequency band range of the bandpass filter in the DEMON spectrum analysis is determined, and the DEMON spectrum analysis is performed on the signal.

[0137] In this embodiment, combined with the analysis results of the holographic Hilbert amplitude modulation spectrum, a suitable frequency band range is selected to perform bandpass filtering on the target radiation noise signal; the Hilbert transform demodulation method is used to perform envelope demodulation on the filtered radiation noise signal to obtain an envelope signal; the envelope signal is low-pass filtered and spectrum analyzed to obtain a DEMON spectrum.

[0138] Combined with the above content, Figure 3 A schematic diagram of the process of holographic Hilbert spectrum analysis provided by the present invention is shown in FIG. Figure 3 shown.

[0139] S6, extract features from the DEMON spectrum to achieve accurate estimation of the shaft frequency, blade frequency, number of blades and other parameter information of the underwater target propeller.

[0140] In addition, the present invention also provides a test example, the specific content of the test example is as follows.

[0141] Select the radiation noise data of a certain AUV when performing a certain task, perform downsampling processing, and draw a time domain graph, such as Figure 4 As shown in the figure, the sampling frequency after downsampling is 20480Hz. The short-time Fourier transform is used to perform time-frequency analysis on the signal. The time-frequency diagram is shown in the figure below. Figure 5 Combined with the time domain diagram, a stable time domain signal is captured. The signal duration is 30s. The time domain diagram is as follows Figure 6 shown.

[0142] It is known that the propeller of the aircraft has three blades. According to the propeller PWM frequency, it can be calculated that the shaft frequency of the propeller within the intercepted time range is about 8.24 Hz.

[0143] The intercepted signal is subjected to holographic Hilbert spectrum analysis. Based on empirical mode decomposition, the signal is decomposed into several intrinsic mode functions (IMFs), and Hilbert spectrum analysis is performed to obtain the function of the amplitude and frequency of each IMF component changing with time. The envelope of each IMF is calculated, and each envelope is subjected to empirical mode decomposition and Hilbert spectrum analysis again. The above process is repeated to clearly describe the addition or multiplication process in the signal; the time variable of the Hilbert spectrum obtained in each step is integrated to obtain a frequency spectrum diagram with only frequency. The process can be iterated multiple times as needed until the amplitude function no longer has a cyclic characteristic in the envelope, and finally a pure frequency high-dimensional full-information representation of the signal amplitude is obtained, that is, a holographic Hilbert amplitude modulation spectrum, such as Figure 7 shown.

[0144] Combined with the holographic Hilbert amplitude modulation spectrum, the corresponding relationship between the signal modulation frequency and the carrier frequency is obtained. Integrate the amplitude modulation frequency, such as Figure 8 The results show that the modulation is mainly concentrated in the frequency band of 400-800Hz. Based on this, the frequency band range of the bandpass filter in the DEMON spectrum analysis is determined.

[0145] The Hilbert transform demodulation method is used to perform envelope demodulation on the filtered radiation noise signal to obtain the envelope signal, and the envelope signal is low-pass filtered and spectrum analyzed to obtain the DEMON spectrum, such as Fig. 9 There are line spectra at 8.3Hz, 16.6Hz, 24.9Hz, etc. in the DEMON spectrum, which are harmonic relationships, and the fundamental frequency is 8.3Hz.

[0146] Based on the above analysis, it is estimated that the propeller is a three-blade propeller with a shaft frequency of 8.3 Hz, which is approximately equal to the actual value.

[0147] In summary, the present invention proposes a method for selecting frequency bands for DEMON spectrum analysis based on holographic Hilbert spectrum decomposition technology. By using holographic Hilbert spectrum decomposition technology, the corresponding relationship between modulation frequency and carrier frequency is directly obtained, which solves the problem of difficulty in selecting bandpass filters in the traditional DEMON spectrum analysis process.

[0148] Fig.10 FIG. 1 is a schematic diagram of the composition structure of a DEMON spectrum analysis frequency band selection device based on holographic Hilbert spectrum analysis provided by an embodiment of the present invention. Fig.10 As shown, the DEMON spectrum analysis frequency band selection device 1000 based on holographic Hilbert spectrum analysis includes: a downsampling module 1001, which is used to perform spectrum analysis on the obtained underwater target radiation noise signal to obtain the main frequency range of the radiation noise signal, and downsample the radiation noise signal to obtain a simplified radiation noise signal; a signal interception module 1002, which is used to perform time-frequency analysis on the simplified radiation noise signal through short-time Fourier transform, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal; a first analysis module 1003, which is used to decompose the intercepted radiation noise signal into multiple eigenmode functions based on the empirical mode decomposition in the holographic Hilbert spectrum analysis, and perform Hilbert spectrum analysis on each of the eigenmode functions to obtain the amplitude change function and the carrier frequency change function of each of the eigenmode functions; a second analysis module 1004, which is used to perform time-frequency analysis on the simplified radiation noise signal through short-time Fourier transform, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal; a first analysis module 1005, which is used to decompose the intercepted radiation noise signal into multiple eigenmode functions based on the empirical mode decomposition in the holographic Hilbert spectrum analysis, and perform Hilbert spectrum analysis on each of the eigenmode functions to obtain the amplitude change function and the carrier frequency change function of each of the eigenmode functions; a second analysis module 1006, which is used to perform time-frequency analysis on the simplified radiation noise signal through short-time Fourier transform, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal. 004, used to calculate the envelope of each of the eigenmode functions, and perform empirical mode decomposition and Hilbert spectrum analysis on each of the envelopes to obtain an envelope amplitude variation function and a modulation frequency variation function; an integration module 1005, used to integrate the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum; a third analysis module 1006, used to obtain the corresponding relationship between the modulation frequency and the carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum, determine the frequency band range of the bandpass filter in the DEMON spectrum analysis, perform DEMON spectrum analysis on the intercepted radiation noise signal to obtain a DEMON spectrum; a feature extraction module 1007, used to perform feature extraction on the DEMON spectrum to obtain the estimation results of the shaft frequency, blade frequency and number of blades of the underwater target propeller.

[0149] In some embodiments, when performing spectrum analysis on the obtained underwater target radiation noise signal, the calculation formula of Fourier transform is:

[0150]

[0151] Where f(t) is the original received signal, ω is the signal frequency, t represents time, and j is the imaginary unit.

[0152] In some embodiments, when the simplified radiation noise signal is subjected to time-frequency analysis by short-time Fourier transform, the calculation formula of the short-time Fourier transform is:

[0153]

[0154] Where x1(t) is the downsampled signal, h(t) is the window function, f is the signal frequency, t represents time, and j is the imaginary unit.

[0155] In some embodiments, the empirical mode decomposition based on holographic Hilbert spectrum analysis decomposes the intercepted radiation noise signal into multiple eigenmode functions, wherein the intercepted radiation noise signal is expressed as:

[0156]

[0157] Among them, c j (t) is the jth intrinsic mode function, and the amplitude a of each IMF component is j (t) and frequency ω j (t) are expressed as functions of time, respectively, and N is the number of eigenmode functions;

[0158] The nested expression of the amplitude change function is:

[0159]

[0160] Where Ω represents the slowly varying envelope frequency.

[0161] In some embodiments, the integration module is further used to use an edge expression to integrate the time variables of the carrier frequency change function, the modulation frequency change function and the envelope amplitude change function to obtain a frequency spectrum diagram with only frequency, and perform multiple iterations until the amplitude change function no longer has a cyclic characteristic in the envelope, thereby obtaining a pure frequency high-dimensional full-information representation of the signal amplitude, that is, the holographic Hilbert amplitude modulation spectrum; wherein the calculation formula of the edge spectrum is:

[0162]

[0163] Where T is the integration time range, ω is the signal frequency, and t represents time.

[0164] In some embodiments, the third analysis module is also used to obtain the correspondence between the modulation frequency and the carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum; based on the correspondence, determine the frequency band range in which the modulation characteristics exist in the intercepted radiation noise signal; in combination with the holographic Hilbert amplitude modulation spectrum, select a suitable frequency band range to perform bandpass filtering on the intercepted radiation noise signal to obtain a filtered radiation noise signal; use the Hilbert change demodulation method to perform envelope demodulation on the filtered radiation noise signal to obtain an envelope signal; and perform low-pass filtering and spectral analysis on the envelope signal to obtain a DEMON spectrum.

[0165] In some embodiments, it should be noted that the description of the device of the embodiment of the present invention is similar to the description of the method embodiment described above, and has similar beneficial effects as the same method embodiment, so it will not be repeated. For technical details not disclosed in the embodiment of the device, please refer to the description of the method embodiment of the present invention for understanding.

[0166] It should be noted that, in the embodiment of the present invention, if the above-mentioned DEMON spectrum analysis band selection method based on holographic Hilbert spectrum analysis is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), magnetic disk or optical disk and other media that can store program codes. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0167] Correspondingly, an embodiment of the present invention provides a DEMON spectrum analysis band selection device based on holographic Hilbert spectrum analysis, Fig.11 FIG. 1 is a schematic diagram of the composition structure of a DEMON spectrum analysis frequency band selection device based on holographic Hilbert spectrum analysis provided by an embodiment of the present invention, such as Fig.11 As shown, the DEMON spectrum analysis band selection device 1100 based on holographic Hilbert spectrum analysis includes at least: a processor 1101 and a computer-readable storage medium 1102 configured to store executable instructions, wherein the processor 1101 generally controls the overall operation of the DEMON spectrum analysis band selection device based on holographic Hilbert spectrum analysis. The computer-readable storage medium 1102 is configured to store instructions and applications executable by the processor 1101, and can also cache data to be processed or processed by each module in the processor 1101 and the DEMON spectrum analysis band selection device 1100 based on holographic Hilbert spectrum analysis, which can be implemented through flash memory (FLASH) or random access memory (RAM, RandomAccess Memory).

[0168] An embodiment of the present invention provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will be caused to execute the method provided by the embodiment of the present invention, for example, Figure 1 The method shown.

[0169] In some embodiments, the storage medium can be a computer-readable storage medium, for example, a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disk read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above memories.

[0170] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0171] As an example, executable instructions may, but need not necessarily, correspond to a file in a file system, may be stored as part of a file storing other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0172] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement and improvement made within the spirit and scope of the present invention are included in the protection scope of the present invention.

[0173] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0174] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0175] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for selecting frequency bands of DEMON spectrum analysis based on holographic Hilbert spectrum analysis, characterized in that: The method comprises: Performing spectrum analysis on the obtained underwater target radiation noise signal to obtain the main frequency range of the radiation noise signal, and performing downsampling processing on the radiation noise signal to obtain a simplified radiation noise signal; Performing time-frequency analysis on the simplified radiation noise signal by short-time Fourier transform, and performing signal interception in combination with a time domain diagram to obtain an intercepted radiation noise signal; Based on the empirical mode decomposition in the holographic Hilbert spectrum analysis, the intercepted radiation noise signal is decomposed into a plurality of eigenmode functions, and the Hilbert spectrum analysis is performed on each of the eigenmode functions to obtain an amplitude variation function and a carrier frequency variation function of each of the eigenmode functions; Calculating the envelope of each of the eigenmode functions, and performing empirical mode decomposition and Hilbert spectrum analysis on each of the envelopes to obtain an envelope amplitude variation function and a modulation frequency variation function; Integrating the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum; Based on the holographic Hilbert amplitude modulation spectrum, the corresponding relationship between the modulation frequency and the carrier frequency of the intercepted radiation noise signal is obtained, the frequency band range of the bandpass filter in the DEMON spectrum analysis is determined, and the DEMON spectrum analysis is performed on the intercepted radiation noise signal to obtain a DEMON spectrum; Feature extraction is performed on the DEMON spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater target propeller.

2. The method according to claim 1, characterized in that When performing spectrum analysis on the obtained underwater target radiation noise signal, the calculation formula of Fourier transform is: Where f(t) is the original received signal, ω is the signal frequency, t represents time, and j is the imaginary unit.

3. The method according to claim 1, characterized in that: When the simplified radiation noise signal is subjected to time-frequency analysis by short-time Fourier transform, the calculation formula of the short-time Fourier transform is: Where x1(t) is the downsampled signal, h(t) is the window function, f is the signal frequency, t represents time, and j is the imaginary unit.

4. The method according to claim 1, characterized in that: The empirical mode decomposition based on holographic Hilbert spectrum analysis decomposes the intercepted radiation noise signal into multiple eigenmode functions, wherein the intercepted radiation noise signal is expressed as: Among them, c j (t) is the jth intrinsic mode function, and the amplitude a of each IMF component is j (t) and frequency ω j (t) are expressed as functions of time, respectively, and N is the number of eigenmode functions; The nested expression of the amplitude change function is: Where Ω represents the slowly varying envelope frequency.

5. The method according to claim 1, characterized in that The step of integrating the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum comprises: The edge expression is used to integrate the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a frequency spectrum diagram with only frequency, and multiple iterations are performed until the amplitude variation function no longer has a cyclic characteristic in the envelope, and a pure frequency high-dimensional full-information representation of the signal amplitude is obtained, that is, the holographic Hilbert amplitude modulation spectrum; wherein the calculation formula of the edge spectrum is: Where T is the integration time range, ω is the signal frequency, and t represents time.

6. The method according to claim 1, characterized in that The method of obtaining a corresponding relationship between a modulation frequency and a carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum, determining a frequency band range of a bandpass filter in a DEMON spectrum analysis, and performing a DEMON spectrum analysis on the intercepted radiation noise signal comprises: Obtaining a corresponding relationship between a modulation frequency and a carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum; Based on the corresponding relationship, determining a frequency band range in which the modulation characteristic exists in the intercepted radiation noise signal; In combination with the holographic Hilbert amplitude modulation spectrum, a suitable frequency band range is selected to perform bandpass filtering on the intercepted radiation noise signal to obtain a filtered radiation noise signal; Using a Hilbert transform demodulation method, envelope demodulation is performed on the filtered radiation noise signal to obtain an envelope signal; The envelope signal is subjected to low-pass filtering and spectrum analysis to obtain a DEMON spectrum.

7. A DEMON spectrum analysis frequency band selection device based on holographic Hilbert spectrum analysis, characterized in that: The device comprises: A downsampling module is used to perform spectrum analysis on the acquired underwater target radiation noise signal to obtain the main frequency range of the radiation noise signal, and to perform downsampling processing on the radiation noise signal to obtain a simplified radiation noise signal; A signal interception module, used to perform time-frequency analysis on the simplified radiation noise signal through short-time Fourier transform, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal; A first analysis module is used to decompose the intercepted radiation noise signal into multiple eigenmode functions based on empirical mode decomposition in holographic Hilbert spectrum analysis, and perform Hilbert spectrum analysis on each of the eigenmode functions to obtain an amplitude change function and a carrier frequency change function of each of the eigenmode functions; A second analysis module is used to calculate the envelope of each of the eigenmode functions, and perform empirical mode decomposition and Hilbert spectrum analysis on each of the envelopes to obtain an envelope amplitude variation function and a modulation frequency variation function; An integration module, used for integrating the time variables of the carrier frequency variation function, the modulation frequency variation function and the envelope amplitude variation function to obtain a holographic Hilbert amplitude modulation spectrum; A third analysis module is used to obtain the corresponding relationship between the modulation frequency and the carrier frequency of the intercepted radiation noise signal based on the holographic Hilbert amplitude modulation spectrum, determine the frequency band range of the bandpass filter in the DEMON spectrum analysis, and perform DEMON spectrum analysis on the intercepted radiation noise signal to obtain a DEMON spectrum; The feature extraction module is used to extract features from the DEMON spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater target propeller.

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