Underwater sound FSK signal blind demodulation method and system

Abstract

The invention relates to the technical field of signal analysis and processing, in particular to an FSK signal blind demodulation method and system in underwater acoustic communication signal analysis and processing, and the method comprises the steps: carrying out the coarse estimation of the bandwidth and carrier frequency of an underwater acoustic FSK signal; performing symbol rate estimation and timing synchronization based on an enhanced digital phase-locked loop; down-converting the received signal to zero intermediate frequency; carrying out discrete short-time Fourier transform to obtain a time-frequency domain distribution matrix of the signal; extracting time-frequency domain distribution in a time dimension by taking a symbol period as an interval, and taking out a frequency point with the strongest power at a corresponding moment to obtain a time-frequency distribution sequence of the signal; clustering the time-frequency distribution sequence by using K-means clustering, and judging the modulation order M of the FSK signal according to the contour value; and clustering the time-frequency distribution sequences into M classes, simultaneously obtaining the class to which the frequency corresponding to each sampling point in the sequences belongs, and judging the class as an M-system symbol until the blind demodulation is finished. According to the invention, blind demodulation of FSK signals with guard intervals and without guard intervals can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of signal analysis and processing, and in particular to a method and system for blind demodulation of FSK signals in underwater acoustic communication signal analysis and processing. Background Art

[0002] Frequency shift keying (FSK) signals typically utilize non-coherent demodulation, which offers advantages such as insensitivity to phase distortion and high tolerance to Doppler shift. This makes them widely used in underwater acoustic communication systems. In the fields of underwater information monitoring and underwater acoustic countermeasures, underwater acoustic signal processing is often required with little or no prior information. Blind demodulation of underwater FSK signals is a crucial aspect of this process.

[0003] Because underwater acoustic communication channels are often sparse multipath channels, FSK signals used for underwater acoustic communication typically include guard intervals to avoid inter-symbol interference (ISI). This means that there are blank periods between the transmitted frequency-shift keying (FSK) symbols. This results in a discontinuous signal waveform and significant differences in the time-domain and frequency-domain characteristics from conventional FSK signals. When the underwater acoustic channel experiences severe frequency-selective fading, the power of different FSK subcarriers varies significantly, and the power spectrum of FSK signals with guard intervals contains many unrelated discrete components. Traditional methods, such as periodogram-based subcarrier frequency estimation, are no longer applicable. Consequently, existing blind demodulation methods and processing flows for FSK signals in landline radios are unsuitable for blind demodulation of underwater FSK signals.

[0004] To date, there are few practical methods for underwater acoustic FSK signal demodulation in the public literature. Most existing public literature only discusses a specific aspect of FSK signal demodulation, such as the estimation of modulation parameters such as the symbol rate. There is a lack of complete and practical FSK signal demodulation methods suitable for complex marine environments. A similar method, identified through patent retrieval and filed in China, includes patent application number CN116032709A. This technical solution uses FFT to estimate subcarrier frequencies, calculates a time-frequency double difference sequence, and estimates the symbol rate before performing FSK blind demodulation. Due to the presence of an interference discrete spectrum in the power spectrum of an FSK signal with a guard interval, the performance of this technique in the first stage of using FFT to estimate subcarrier frequencies is poor. Therefore, this technique is only applicable to conventional FSK signals and is not suitable for blind demodulation of underwater acoustic FSK signals with a guard interval. Summary of the Invention

[0005] The present invention aims to solve the problem that the existing method can only be used for blind demodulation of conventional FSK signals without guard intervals, but cannot perform blind demodulation of FSK signals with guard intervals. A method and system for blind demodulation of underwater acoustic FSK signals are proposed, which can realize blind demodulation of FSK signals with and without guard intervals.

[0006] In order to achieve the above purpose, the technical solutions adopted are:

[0007] The present invention provides a blind demodulation method for underwater acoustic FSK signals, comprising the following steps:

[0008] Roughly estimate the bandwidth and carrier frequency of underwater acoustic FSK signals;

[0009] Symbol rate estimation and timing synchronization based on enhanced digital phase-locked loop;

[0010] Modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering.

[0011] According to the blind demodulation method for underwater acoustic FSK signals of the present invention, further, performing a rough estimation of the bandwidth and carrier frequency of the underwater acoustic FSK signal specifically includes:

[0012] Perform Hilbert transform on the received signal r(n) to obtain the complex analytical signal r h (n), n is the discrete time index;

[0013] Calculate the analytical signal r h Welch power spectrum P of (n) w (f);

[0014] P w (f) Perform median filtering, and get

[0015] Search Then find the first and last frequency points that are 10dB below the maximum value, and use the difference between the two frequency points as a rough estimate of the bandwidth. The mean of the two frequency points is used as a rough estimate of the carrier frequency

[0016] According to the blind demodulation method of underwater acoustic FSK signal of the present invention, further, symbol rate estimation and timing synchronization based on the enhanced digital phase-locked loop specifically include:

[0017] The calculation formulas for the output signal y(n) and the error signal e(n) are:

[0018] y(n)=U o (n-1)sin(φ o (n-1))

[0019] e(n)=r(n)-y(n)

[0020] U o (n) = U o (n-1)+μ1T se(n)sin(φ o (n-1))

[0021] Δω o (n) = Δω o (n-1)+μ2T s e(n)cos(φ o (n-1))

[0022] φ o (n) = φ o (n-1)+ω n T s +Δω o (n)T s +μ3T s e(n)cos(φ o (n-1)).

[0023] Among them, f s is the sampling rate, T s =1 / f s is the sampling period, U o (n), Δω o (n) and φ o (n) is an internal variable, and its initial value is set to 0; μ1, μ2, and μ3 are the parameters of the phase-locked loop;

[0024] Then, discrete Fourier transform is used to analyze |e(n)| to obtain the symbol rate estimation and timing estimation of the FSK signal.

[0025] According to the blind demodulation method of underwater acoustic FSK signal of the present invention, further, the values of the parameters μ1, μ2 and μ3 of the phase-locked loop are:

[0026] μ1=μ3=μ

[0027] μ2=2ω r 2

[0028] Where μ = 2ξ1ω n ,ω n is the nominal value of the input signal frequency, which is taken as a rough estimate of the signal carrier frequency. ω r =μ / 4 / ξ2, The value of k ranges from 0.2 to 1.

[0029] According to the blind demodulation method for underwater acoustic FSK signals of the present invention, further, using discrete Fourier transform to analyze |e(n)| to obtain symbol rate estimation and timing estimation of the FSK signal includes:

[0030] Perform N-point discrete Fourier transform on |e(n)| to obtain Fe (n), where N is the length of the discrete signal; the position of the discrete spectral line is N d , the value is Ae jφ , φ represents the phase of the discrete spectral line, A represents the amplitude of the discrete spectral line, then the symbol rate estimate is In addition, the phase of the discrete spectral line contains timing information, so the optimal sampling time is

[0031] According to the blind demodulation method of underwater acoustic FSK signal of the present invention, further, modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering specifically include:

[0032] Down-convert the received signal to zero intermediate frequency;

[0033] Perform discrete short-time Fourier transform on the zero-IF signal to obtain the signal's time-frequency domain distribution matrix X(m,k), where m is the time index and k is the frequency index;

[0034] The time-frequency domain distribution is extracted in the time dimension at intervals of symbol periods, and the frequency point with the strongest power at the corresponding moment is taken out to obtain the signal's time-frequency distribution sequence K(m);

[0035] Use K-means clustering to cluster the time-frequency distribution sequence and determine the modulation order M of the FSK signal based on the contour value;

[0036] The time-frequency distribution sequence is clustered into M categories, and the category to which the frequency corresponding to each sampling point in the sequence belongs, that is, the relative frequency distribution, is obtained. It is then judged into M-ary symbols, and binary bits are obtained through symbol mapping, thus completing the blind demodulation.

[0037] According to the blind demodulation method for underwater acoustic FSK signals of the present invention, further, down-converting the received signal to a zero intermediate frequency comprises:

[0038] The parsed signal r h (n) and frequency are The complex zero intermediate frequency signal is obtained by multiplying the carrier of

[0039]

[0040] According to the blind demodulation method for underwater acoustic FSK signals of the present invention, further, determining the modulation order M of the FSK signal according to the profile value specifically includes:

[0041] First, the possible modulation orders M∈{2,4,8,...,2 n},n∈N + ; Then use K-means clustering to cluster K(m) and calculate its profile value S(M). The one with the smallest profile value This is the estimated value of the modulation order.

[0042] Furthermore, the present invention also provides an underwater acoustic FSK signal blind demodulation system for implementing the above-mentioned underwater acoustic FSK signal blind demodulation method, comprising a bandwidth and carrier frequency estimation module, a symbol rate and timing estimation module, and an identification and judgment module, wherein:

[0043] Bandwidth and carrier frequency estimation module, used to roughly estimate the bandwidth and carrier frequency of underwater acoustic FSK signals;

[0044] A symbol rate and timing estimation module for performing symbol rate estimation and timing synchronization based on an enhanced digital phase-locked loop;

[0045] The identification and decision module is used for modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering.

[0046] The beneficial effects achieved by adopting the above technical solution are:

[0047] The present invention proposes a blind demodulation method and system for underwater acoustic FSK signals, which can achieve blind parameter estimation and demodulation for FSK signals with or without guard intervals. The symbol rate and timing estimation module of the present invention works independently and does not rely on other modules. According to the method of the present invention, there is no need to perform subcarrier frequency estimation and subband filtering, which avoids the influence of the interference discrete spectrum on the subcarrier frequency estimation when FSK has a guard interval. Therefore, it is possible to achieve blind demodulation of FSK signals with guard intervals. Conventional FSK signals are special cases with a guard interval of zero, and can also achieve blind demodulation. Overall, the present invention has a simple structure, a certain degree of independence between the modules, and good noise resistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention. The drawings are only used to illustrate some embodiments of the present invention, but not to limit all embodiments of the present invention thereto.

[0049] Figure 1 This is an overall framework diagram of the underwater acoustic FSK signal blind demodulation method according to the first embodiment of the present invention;

[0050] Figure 2 This is a flow chart of bandwidth and carrier frequency rough estimation according to the first embodiment of the present invention;

[0051] Figure 3 is a flowchart of symbol rate and timing estimation according to the first embodiment of the present invention;

[0052] Figure 4 Schematic diagram of |e(n)| output by EPLL sampling point by sampling point according to the first embodiment of the present invention;

[0053] Figure 5 The discrete Fourier transform of |e(n)| at N points is performed to obtain F e (n) spectrum;

[0054] Figure 6 Schematic diagram of the time-frequency distribution of the FSK signal according to the first embodiment of the present invention;

[0055] Figure 7 is the power spectrum of the FSK signal with a modulation order of 4 in the test of the second embodiment of the present invention;

[0056] Figure 8 is the NRMSE performance curve of the second embodiment of the present invention;

[0057] Figure 9 is a bit error rate performance curve of the second embodiment of the present invention;

[0058] Figure 10 is the power spectrum of the test FSK signal of embodiment 3 of the present invention;

[0059] Figure 11 This is the bit error rate performance curve of the third embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the accompanying drawings of specific embodiments of the present invention to clearly and completely describe the exemplary embodiments of the present invention. Unless otherwise defined, technical or scientific terms used in the present invention should be given the common meanings understood by people with ordinary skills in the relevant field.

[0061] Example 1

[0062] This embodiment discloses a method for blind demodulation of underwater acoustic FSK signals. The method flow is as follows: Figure 1 As shown, the following steps are included:

[0063] Step S1: perform a rough estimation of the bandwidth and carrier frequency of the underwater acoustic FSK signal. This step specifically includes sub-steps S101-S104. The process is as follows: Figure 2 shown.

[0064] Step S101: Perform Hilbert transform on the received signal r(n) to obtain the complex analytical signal r h (n), where n is the discrete time index.

[0065] Step S102: Calculate the analytical signal r h Welch power spectrum P of (n) w (f).

[0066] Step S103: w(f) Perform median filtering with a sliding window size of 20Hz to 50Hz to convert P w The discrete components in (f) are filtered out and the median filter is used to obtain

[0067] Step S104: Find Then find the first and last frequency points that are 10dB below the maximum value, and subtract the minimum frequency point from the maximum frequency point to get a rough estimate of the bandwidth. The mean of the two frequency points is used as a rough estimate of the carrier frequency

[0068] Step S2: symbol rate estimation and timing synchronization based on enhanced digital phase-locked loop. The process is as follows: Figure 3 shown.

[0069] The calculation formulas for the output signal y(n) and the error signal e(n) are:

[0070] y(n)=U o (n-1)sin(φ o (n-1))

[0071] e(n)=r(n)-y(n)

[0072] U o (n) = U o (n-1)+μ1T s e(n)sin(φ o (n-1))

[0073] Δω o (n) = Δω o (n-1)+μ2T s e(n)cos(φ o (n-1))

[0074] φ o (n) = φ o (n-1)+ω n T s +Δω o (n)T s +μ3T s e(n)cos(φ o (n-1)).

[0075] Among them, f s is the sampling rate, T s =1 / f s is the sampling period, U o (n), Δω o (n) and φ o(n) is an internal variable, and its initial value is set to 0; μ1, μ2, and μ3 are the parameters of the phase-locked loop, and the values of μ1, μ2, and μ3 are:

[0076] μ1=μ3=μ

[0077] μ2=2ω r 2

[0078] Where μ = 2ξ1ω n ,ω n is the nominal value of the input signal frequency, which is taken as the rough estimate of the signal carrier frequency in this embodiment. ω r =μ / 4 / ξ2, The value of k ranges from 0.2 to 1.

[0079] For FSK signals, due to the frequent frequency jumps between symbols, the tracking error |e(n)| will have periodic maximum values at the frequency jump time during the frequency tracking process of the extended phase-locked loop (EPLL). Figure 4 Therefore, the symbol rate estimation of the FSK signal can be obtained by analyzing |e(n)| using discrete Fourier transform (DFT), as shown in Figure 5 As shown. At the same time, since EPLL is calculated in a sampling point-by-sample mode, the position where |e(n)| has an extreme value is the starting point and end point of each FSK code symbol. When performing DFT analysis, the phase part of the discrete spectrum contains timing information. Performing N-point discrete Fourier transform on |e(n)| yields F e (n), where N is the length of the discrete signal; the position of the discrete spectral line is N d , the value is Ae j φ , φ represents the phase of the discrete spectral line, A represents the amplitude of the discrete spectral line, then the symbol rate estimate is The best sampling time is

[0080] Step S3, modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering, this step specifically includes sub-steps S301-S304.

[0081] Step S301: down-convert the received signal to zero intermediate frequency. h (n) and frequency are Multiplying the carrier by can get the complex zero intermediate frequency signal, and its formula can be expressed as .

[0082]

[0083] Step S302: Perform discrete short-time Fourier transform on the zero-IF signal to obtain a time-frequency domain distribution matrix of the signal.

[0084] Short-time Fourier transform (STFT) is a common time-frequency analysis method with impressive time-frequency resolution performance and low computational complexity, making it easy to implement in engineering. The expression of STFT is as follows:

[0085]

[0086] Where x(t) is the input signal, f and τ are the frequency and time delay, and w(t) is the window function. The corresponding discrete short-time Fourier transform (DSTFT) for digital implementation is:

[0087]

[0088] Where X(m,k) represents the DSTFT result at time index m and frequency index k. x(n) is the input signal, where x(n) = r0(n). w(n-mR) is the window function, which is applied to a local region of the signal. Here, m is the window's time index, and R is the window's step size, which determines the window's position within the signal. It is the core expression of the discrete Fourier transform, which represents the complex exponential function of frequency k, where N is the number of DFT points.

[0089] Step S303: extracting the time domain and frequency domain to obtain the relative distribution sequence of the FSK signal in the time domain and frequency domain.

[0090] Specifically, we extract X(m,k) in the time domain according to the symbol period to obtain X'(m,k). Then we query the maximum position in the frequency domain to obtain the time-frequency distribution sequence:

[0091]

[0092] Figure 6 This is a schematic diagram of the time-frequency distribution of an FSK signal with a modulation order of 4.

[0093] Step S304: Use K-means clustering to cluster the time-frequency distribution sequence, and determine the modulation order M of the FSK signal based on the profile value; cluster the time-frequency distribution sequence into M categories, and at the same time obtain the category to which the frequency corresponding to each sampling point in the sequence belongs, that is, the relative frequency distribution, and judge it into M-ary symbols. After symbol mapping, binary bits are obtained, and blind demodulation is completed.

[0094] Specifically, we first preset the possible modulation order M, which is usually M∈{2,4,8,...,2 n},n∈N +; Then use K-means clustering to cluster K(m) and calculate its profile value S(M). The one with the smallest profile value This is the estimated value of the modulation order. Output When K(m) is the sampling point, the serial number of the category s∈{0,1,...,M-1} is obtained through symbol mapping to obtain binary bits, and the blind demodulation is completed.

[0095] Corresponding to the above method, this embodiment also proposes an underwater acoustic FSK signal blind demodulation system, including a bandwidth and carrier frequency estimation module, a symbol rate and timing estimation module, and an identification and judgment module, wherein:

[0096] The bandwidth and carrier frequency estimation module is used to roughly estimate the bandwidth and carrier frequency of the underwater acoustic FSK signal.

[0097] The symbol rate and timing estimation module is used to perform symbol rate estimation and timing synchronization based on an enhanced digital phase-locked loop.

[0098] The identification and decision module is used for modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering.

[0099] Example 2

[0100] The simulation signal uses an FSK signal with a modulation order of 4, a sampling rate of 100 kHz, a carrier frequency of 6.8 kHz, a signal with a guard interval, a pulse width of 1 ms, a symbol rate of 400 Baud, and a carrier frequency interval of 1.2 kHz. The simulation channel uses a typical underwater acoustic multipath channel:

[0101] h=0.04+z -736 +0.283z -1342 +0.508z -1188

[0102] The power spectrum of the FSK signal is tested as follows: Figure 7 As shown in the figure, it can be found that due to the presence of guard intervals in the signal, many discrete spectral lines are generated, which makes it difficult to search for subcarriers. This experiment uses the normalized root mean square error (NRMSE) as the evaluation criterion to analyze the modulation parameter estimation performance, which is defined as:

[0103]

[0104] Where L is the number of Monte Carlo simulation experiments, the true value of the parameter to be estimated is X, and the parameter estimate for the kth time is

[0105] For symbol rate estimation, the NRMSE performance curve under current conditions is as follows Figure 8As shown in Figure 1, where SNR is the power signal-to-noise ratio. The results show that the signal-to-noise ratio when the symbol rate estimation result achieves the best accuracy is lower than the signal-to-noise ratio required for the demodulation. The bit error rate under different signal-to-noise ratios is shown in Figure 1. Figure 9 As shown in the figure, when the signal-to-noise ratio is greater than 0dB, the proposed method can achieve a bit error rate of 10E-3, which has high communication reliability.

[0106] Example 3

[0107] The simulation signal uses an FSK signal with a modulation order of 4, a sampling rate of 100 kHz, a carrier frequency of 10 kHz, no guard interval, a symbol rate of 800 Baud, and a carrier frequency interval of 800 Hz. The simulation channel uses a typical underwater acoustic multipath channel:

[0108] h=0.32+0.1z -102 +z -127 +0.05z -558

[0109] The power spectrum of the FSK signal is tested as follows: Figure 10 As shown in Figure 2. For FSK signals without guard intervals, the discrete components in their power spectrum correspond to the subcarrier frequencies. Therefore, the traditional method can correctly separate the subcarriers. The proposed method does not rely on subband filtering and is also applicable to this situation. In this case, the proposed method was tested and the bit error rate performance curves obtained by performing 200 independent repeated tests at different signal-to-noise ratios are shown in Figure 2. Figure 11 As shown in the figure, when the signal-to-noise ratio is greater than -1dB, the proposed method can achieve a bit error rate of 10E-3, which has high communication reliability.

[0110] Unless otherwise specifically stated, the components, steps, numerical expressions and values set forth in these embodiments do not limit the scope of the present invention.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0112] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0113] Those skilled in the art will appreciate that all or part of the steps in the above method can be performed by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps in the above embodiment can be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or software functional modules. The present invention is not limited to any specific combination of hardware and software.

[0114] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A blind demodulation method for underwater acoustic FSK signals, characterized in that: The following steps are involved: Roughly estimate the bandwidth and carrier frequency of underwater acoustic FSK signals; Symbol rate estimation and timing synchronization based on enhanced digital phase-locked loop; Modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering.

2. The underwater acoustic FSK signal blind demodulation method according to claim 1, characterized in that: The rough estimation of bandwidth and carrier frequency of underwater acoustic FSK signal specifically includes: Perform Hilbert transform on the received signal r(n) to obtain the complex analytical signal r h (n), n is the discrete time index; Calculate the analytical signal r h Welch power spectrum P of (n) w (f); P w (f) Perform median filtering, and get Search Then find the first and last frequency points that are 10dB below the maximum value, and use the difference between the two frequency points as a rough estimate of the bandwidth. The mean of the two frequency points is used as a rough estimate of the carrier frequency 3. The underwater acoustic FSK signal blind demodulation method according to claim 2, characterized in that: Symbol rate estimation and timing synchronization based on an enhanced digital phase-locked loop specifically include: The calculation formulas for the output signal y(n) and the error signal e(n) are: y(n)=U o (n-1)sin(φ o (n-1)) e(n)=r(n)-y(n) U o (n)=U o (n-1)+μ1T s e(n)sin(φ o (n-1)) Give o (n)=See o (n-1)+μ2T s e(n)cos(φ o (n-1)) f o (n)=φ o (n-1)+ω n T s +See o (n)T s +μ3T s e(n)cos(φ o (n-1)). Among them, f s is the sampling rate, T s =1 / f s is the sampling period, U o (n), Δω o (n) and φ o (n) is an internal variable, and its initial value is set to 0; μ1, μ2, and μ3 are the parameters of the phase-locked loop; Then, discrete Fourier transform is used to analyze |e(n)| to obtain the symbol rate estimation and timing estimation of the FSK signal.

4. The underwater acoustic FSK signal blind demodulation method according to claim 3, characterized in that: The values of the phase-locked loop parameters μ1, μ2, and μ3 are: μ1=μ3=μ μ2=2ω r 2 Where μ = 2ξ1ω n ,ω n is the nominal value of the input signal frequency, which is taken as a rough estimate of the signal carrier frequency. ω r =μ / 4 / ξ2, The value of k ranges from 0.2 to 1.

5. The underwater acoustic FSK signal blind demodulation method according to claim 3, characterized in that: Using discrete Fourier transform to analyze |e(n)| to obtain symbol rate and timing estimates of FSK signals includes: Perform N-point discrete Fourier transform on |e(n)| to obtain F e (n), where N is the length of the discrete signal; the position of the discrete spectral line is N d , the value is Ae jφ , φ represents the phase of the discrete spectral line, A represents the amplitude of the discrete spectral line, then the symbol rate estimate is In addition, the phase of the discrete spectral line contains timing information, so the optimal sampling time is 6. The underwater acoustic FSK signal blind demodulation method according to claim 2, characterized in that: Modulation order recognition and symbol decision based on discrete short-time Fourier transform and mean clustering specifically include: Down-convert the received signal to zero intermediate frequency; Perform discrete short-time Fourier transform on the zero-IF signal to obtain the signal's time-frequency domain distribution matrix X(m,k), where m is the time index and k is the frequency index; The time-frequency domain distribution is extracted in the time dimension at intervals of symbol periods, and the frequency point with the strongest power at the corresponding moment is taken out to obtain the signal's time-frequency distribution sequence K(m); Use K-means clustering to cluster the time-frequency distribution sequence and determine the modulation order M of the FSK signal based on the contour value; The time-frequency distribution sequence is clustered into M categories, and the category to which the frequency corresponding to each sampling point in the sequence belongs, that is, the relative frequency distribution, is obtained. It is then judged into M-ary symbols, and binary bits are obtained through symbol mapping, thus completing the blind demodulation.

7. The underwater acoustic FSK signal blind demodulation method according to claim 6, characterized in that: Down-converting the received signal to zero IF involves: The parsed signal r h (n) and frequency are The complex zero intermediate frequency signal is obtained by multiplying the carrier of 8. The underwater acoustic FSK signal blind demodulation method according to claim 6, characterized in that: The modulation order M of the FSK signal is determined according to the profile value. First, the possible modulation orders M∈{2,4,8,...,2 n },n∈N + ; Then use K-means clustering to cluster K(m) and calculate its profile value S(M). The one with the smallest profile value This is the estimated value of the modulation order.

9. An underwater acoustic FSK signal blind demodulation system, characterized in that: The method for blind demodulation of underwater acoustic FSK signals according to any one of claims 1 to 8 comprises a bandwidth and carrier frequency estimation module, a symbol rate and timing estimation module, and an identification and judgment module, wherein: Bandwidth and carrier frequency estimation module, used to roughly estimate the bandwidth and carrier frequency of underwater acoustic FSK signals; A symbol rate and timing estimation module for performing symbol rate estimation and timing synchronization based on an enhanced digital phase-locked loop; The identification and decision module is used for modulation order identification and symbol decision based on discrete short-time Fourier transform and mean clustering.

10. 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 method according to any one of claims 1 to 8 are implemented.

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