Underwater acoustic communication receiving method based on superposed interference signals

Abstract

The invention relates to the technical field of underwater acoustic communication, in particular to an underwater acoustic communication receiving method based on superimposed interference signals. According to prior information of interference signals, a sparse channel time domain signal reconstruction scheme based on a fast soft threshold iterative algorithm (FISTA) is provided, and the scheme has higher reconstruction accuracy for sonar interference signals; aiming at the influence of a residual sonar interference signal, a multipath effect and environmental noise, a VTRM equalization method based on FISTA sparse estimation and a non-parametric Viterbi decoding algorithm based on a Gaussian kernel are provided, so that the influence caused by the multipath effect and the environmental noise can be well solved; and the performance of underwater acoustic communication and the signal receiving precision are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater acoustic communication, and in particular to an underwater acoustic communication receiving method based on superimposed interference signals. Background Art

[0002] Traditional superposition communication receiver technology uses the time domain orthogonal superposition of communication signals and sonar signals. The superposition signal received at the receiving end does not consider any signal separation method, and then the communication receiver demodulates and judges the received signal. Although the traditional method uses orthogonal signals to try to avoid the influence between signals, the serious interference between the superposition signals still makes it impossible for the receiving end to resolve.

[0003] The Viterbi Decoder algorithm is a maximum likelihood decoding algorithm based on the convolutional code trellis graph. The process can be divided into two directions: forward and backtracking. The backtracking method searches for the maximum likelihood path of the trellis graph through the path metric (PM) and calculates each possible path in the trellis graph. They are saved as branch metrics (BM) respectively, and then added to the PM. In order to ensure that there is only one optimal path, only the PM path with the minimum value is retained and passed on in each iteration.

[0004] In the Viterbi decoding algorithm, the performance of the system will be greatly affected due to the serious multipath effect and non-Gaussian noise characteristics of the ocean channel. Therefore, the receiver's signal processing for multipath equalization and non-Gaussian noise signal processing is also an indispensable link. The accuracy of channel estimation is directly related to the restoration of sonar signals and the anti-multipath fading performance of the communication system. In order to improve the anti-multipath fading of the communication system, the receiving end uses channel estimation and equalization technology to improve the signal-to-noise ratio of the transmission channel; at the same time, various impulse noise suppression technologies are proposed to suppress ocean impulse noise to improve the signal-to-noise ratio of the communication system.

[0005] The overall performance of marine environmental noise is non-Gaussian noise (Non-Gaussian Noise), which is widely present in the ocean, especially in shallow waters. Because the ocean is a complex scattering channel, the sources of marine environmental noise are extensive and extremely complex. Most noise environments (including wind and rain noise, shipping noise, biological noise, seismic noise and industrial noise, noise generated by biological activities such as krill, and various geophysical sources such as Arctic ice, seismic activity, etc.) cause the overall noise to be severely non-Gaussian, mainly impulse noise (Impulsive Boise), which is characterized by a very short duration, strong burstiness, and high amplitude. Compared with traditional Gaussian noise, the energy of impulse noise is almost dozens of times that of traditional Gaussian noise. In addition, the probability density function of non-Gaussian noise is extremely complex and has a serious tailing effect (Heavy tail).

[0006] Channel estimation and equalization, channel estimation technology is to estimate the parameters of a channel, such as a wireless channel or underwater acoustic channel, based on known transmit and receive data. The accuracy of channel estimation will directly affect the performance of the entire system. Channel equalization is an anti-fading method to improve the transmission performance of a communication system in a fading channel. It uses known channel estimation results to reduce the impact of multipath delay during underwater acoustic communication and compensate for system characteristics.

[0007] Existing underwater acoustic channel estimation usually uses the existing conventional least squares (LS) channel estimation. The LS algorithm is based on the characteristics of the newly received channel, and has the characteristics of poor estimation accuracy and susceptibility to noise. Summary of the invention

[0008] The technical problem to be solved by the present invention is: to propose an underwater acoustic communication receiving method based on superimposed interference signals, which can solve the interference problem between superimposed interference signals and reduce the impact of ocean multipath delay and non-Gaussian noise on the receiver, thereby effectively improving the performance of underwater acoustic communication and signal reception accuracy.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0010] A method for receiving underwater acoustic communication based on superimposed interference signals comprises the following steps:

[0011] S101, the bit data is coded by a convolutional code channel and then BPSK digitally modulated and carrier modulated to obtain a communication signal, and a sonar signal with the same frequency and at the same time is generated;

[0012] S102, superimposing the communication signal with the sonar signal, and adding a synchronization header for receiving signal synchronization. At this time, the signal passes through the actual underwater acoustic channel to add ocean environmental noise;

[0013] S103, performing sonar interference signal elimination on the received signal sequence;

[0014] S104, according to the channel estimation result, using a time reversal mirror to perform VTRM channel equalization on the signal after the sonar interference is eliminated, so as to enhance the energy of the main path;

[0015] S105, using an impulse noise suppression algorithm based on Gaussian kernel soft limiting combined with a Viterbi decoder to improve the signal-to-noise ratio of the receiving system.

[0016] Furthermore, in step S101, the ocean channel and environmental noise in the target waters are firstly collected to establish an environmental noise model and a channel model under underwater acoustic communication;

[0017] The generated bit data is then encoded with a (2, 1, 3) convolutional code and digitally modulated using BPSK to generate a BPSK communication signal, which is then carrier modulated and superimposed with a simultaneous and co-frequency sonar signal to serve as the transmitting end data.

[0018] Furthermore, the BPSK communication signal is superimposed on the LMF signal of the sonar after pulse shaping, and its mathematical formula is expressed as follows:

[0019] s(n)=s b (n)+s c (n)

[0020] Among them, [S(0), S(1), …, S(N-1)] T is the transmitted time domain superposition signal, S b =[S b (0),S b (1),...,S b (N-1)] T is the BPSK communication signal after convolutional coding, S c =[S c (0),S c (1),…,S c (N-1)] T The sonar Chirp signal sent.

[0021] Further, in step S102, the transmitting end signal passes through the generated BELLHOP underwater acoustic channel and the Middleton Class A impulse noise sequence to obtain a received signal.

[0022] Furthermore, in step S103, the step of eliminating sonar interference signals from the received signal sequence includes:

[0023] Firstly, the synchronization header is extracted from the receiving signal. Then, the FISTA algorithm is used to estimate the receiving signal to obtain the estimated value of the BELLOP underwater acoustic channel. The estimated value is convolved with the sonar signal to obtain the reconstructed sonar interference signal. Finally, the reconstructed sonar interference signal is subtracted from the received data.

[0024] Furthermore, the process steps of the FISTA algorithm include:

[0025] 1) Initialize q (0) =0,y (1) =q (0) ,t (1) =1;

[0026] 2)

[0027] 3)

[0028] 4)y (n+1) =q (n) +((t (k) -1) / t (k+1) )(q (n) -q (n-1) );

[0029] Among them, ρ + is the Euclidean projection into the non-negative quadrant, is the descent gradient, L is the Lipschis continuous gradient operator, which is numerically equal to A T The largest eigenvalue of A.

[0030] Furthermore, in step S104, the use of a time reversal mirror to perform VTRM channel equalization on the signal after the sonar interference is eliminated includes:

[0031] S41, signal reception: receiving the signal after sonar interference elimination, and performing pre-processing;

[0032] S42, time reversal: performing time reversal processing on the received signal to offset the distortion caused by the channel;

[0033] S43, spectrum equalization: performing spectrum equalization processing on the inverted signal to enhance the energy of the main path of the signal;

[0034] S44, demodulation and data recovery: demodulate and recover the equalized signal to obtain a processed data transmission result.

[0035] Furthermore, in step S105, the impulse noise suppression algorithm based on Gaussian kernel soft limiting includes:

[0036] S51, preprocessing: performing filtering preprocessing on the data transmission result;

[0037] S52, Gaussian kernel smoothing: using a Gaussian kernel to smooth the preprocessed signal, specifically convolving the Gaussian kernel with the preprocessed signal;

[0038] S53, wavelet transform and soft limiting: performing wavelet transform on the smoothed signal to obtain wavelet coefficients of different scales, and removing noise components in the wavelet coefficients by setting a preset threshold;

[0039] S54, inverse wavelet transform: perform inverse wavelet transform on the processed wavelet coefficients to obtain a denoised signal.

[0040] Compared with the prior art, the present invention has the following main advantages:

[0041] According to the prior information of the interference signal, the present invention proposes a sparse channel time domain signal reconstruction scheme based on the fast soft threshold iterative algorithm (FISTA), which has higher reconstruction accuracy for the sonar interference signal; in view of the influence of the residual sonar interference signal, multipath effect and environmental noise, a VTRM equalization method based on FISTA sparse estimation and a non-parametric Viterbi decoding algorithm based on Gaussian kernel are proposed, which can well solve the influence of multipath effect and environmental noise, thereby effectively improving the performance of underwater acoustic communication and the signal reception accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A block diagram of an underwater acoustic communication receiver system based on superimposed interference signals in an embodiment of the present invention;

[0043] Figure 2 It is a specific principle block diagram of the sonar elimination solution based on the FISTA sparse channel estimation algorithm in an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of the BELLHOP underwater acoustic channel impulse response in an embodiment of the present invention;

[0045] Figure 4 Schematic diagram of underwater acoustic channel estimation results based on matching correlation estimation in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the underwater acoustic channel estimation result based on the FISTA sparse algorithm in an embodiment of the present invention;

[0047] Figure 6 It is a block diagram of the VTRM equalization principle based on FISTA sparse estimation in an embodiment of the present invention;

[0048] Figure 7 4 is a block diagram of a Viterbi decoding algorithm based on Gaussian kernel soft limiting in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0051] Embodiment 1: This embodiment provides an underwater acoustic communication receiving method based on superimposed interference signals, which mainly includes:

[0052] 1) In order to solve the serious problem of superimposed signal self-interference, a sparse channel time domain signal reconstruction scheme based on the fast soft threshold iterative algorithm (FISTA) was proposed based on the prior information of the interference signal and combined with the full-duplex digital domain self-interference elimination theory and compressed sensing theory to eliminate the interference of the sonar signal.

[0053] 2) In order to address the influence of residual sonar interference signals, underwater acoustic channel multipath effects and non-Gaussian noise, a VTRM equalization scheme based on FISTA sparse estimation and a non-parametric Viterbi decoding algorithm based on Gaussian kernel are proposed. Considering both strong and weak non-Gaussian conditions, the effect of sonar elimination is measured by the reliability index of the communication system.

[0054] In order to verify the proposed technology, the sea trial and simulation experiments are as follows:

[0055] S1: Collect ocean channels and environmental noise in the target waters, and establish environmental noise models and channel models under underwater acoustic communication;

[0056] S2: The generated bit data is encoded with (2, 1, 3) convolution code, and BPSK modulation is adopted to generate BPSK signal, and carrier modulation is performed. At the same time, the sonar signal with the same frequency is superimposed as the transmitting end data.

[0057] S3: The transmitted data is passed through the real ocean channel and ocean noise to obtain the received data, which is then extracted and processed by signal processing such as sonar interference signal separation, VTRM channel equalization, and impulse noise suppression to obtain the bit data before judgment;

[0058] S4: The signal-processed data is demodulated, detected, and Viterbi decoded to restore the bit data, thereby calculating the bit error rate.

[0059] In step S1, the generated BPSK communication signal is pulse-shaped and then superimposed with the LMF signal of the sonar. The mathematical formula is as follows:

[0060] s(n)=s b (n)+s c (n)(1)

[0061] Among them, [S(0), S(1), …, S(N-1)] T is the transmitted time domain superposition signal, S b =[S b (0),S b (1),…,Sb (N-1)] T is the BPSK communication signal after convolutional coding, S c =[S c (0),S c (1),…,S c (N-1)] T is the transmitted sonar chirp signal, and the received signal after passing through the underwater acoustic channel y=[y(0),y(1),…,y(N-1)] T It can be expressed as:

[0062]

[0063] Where y is the signal received by the receiving end.

[0064]

[0065] Where h is the time domain impulse response of the underwater acoustic channel, M is the length of the channel impulse response, and h i is the channel tap coefficient, τ is the delay spread, because the tap coefficient h i The non-zero value of is far less than m, so the impulse response is said to be sparse with sparsity M, and is randomly distributed. represents the convolution symbol, and w is the ocean environmental noise. The above formula can be expressed in the form of a circulant matrix-vector:

[0066] y=X·h+w(3)

[0067] Among them, X is a circulant matrix with a Toeplitz structure transformed from the pilot signal to be estimated (the insufficient dimensions are padded with zeros), and y and w are M+L-1 dimensional data sequences. At this moment, all formula operations are in the time domain, and the matrix X is a square matrix with a dimension of M+L-1:

[0068]

[0069] In step S3, the specific principle of the proposed sonar elimination scheme is attached. Figure 2 As shown, it shows the reconstruction block diagram based on time domain sparse multipath channel signals. The channel estimation value is obtained by the proposed time-domain sparse multipath channel reconstruction algorithm based on the FISTA algorithm. The known sonar signal is convolved with the estimated value to generate a reconstructed sonar interference signal. The reconstructed sonar estimation signal S I It can be expressed as:

[0070]

[0071] The sonar estimation signal is subtracted from the receiving end signal to obtain the residual communication signal, which can be written as:

[0072]

[0073] It can be seen from the above formula that, given the circulant matrix X, the channel tap coefficient h can be solved by the receiving end observation data y i . And h is a sparse vector with sparsity M. Solving this problem is transformed into a signal reconstruction problem under the theory of compressed sensing. The solution vector h can be written as:

[0074]

[0075] Where ||h||0 is the L0 norm of the vector. Minimizing the L0 norm is an NP-hard problem. Regularization is a method that can alleviate ill-conditioned characteristics, stabilize matrix inversion, and improve the accuracy of sparse estimation. We use the L1 norm as the regularization term, and the solution to this problem can be transformed into:

[0076]

[0077] Among them, λ represents the regularization factor. The advantage of L0 norm as a regularization term is that it can produce sparse solutions and is insensitive to outliers. Solving this formula is a convex optimization problem. Considering the computational complexity, the commonly used solution is the gradient-based method, which has a simple structure and low complexity. The fast soft threshold iterative algorithm (FISTA) with low complexity and good convergence is used.

[0078] The basic principles of the FISTA algorithm are as follows:

[0079]

[0080] Where ρ+ is the Euclidean projection (non-negative quadrant), is the descent gradient, L is the Lipschis continuous gradient operator, which is numerically equal to A T The maximum eigenvalue of A. The gradient can be expressed as:

[0081]

[0082] The core idea of ​​the projection algorithm is to use an algorithm that makes the vector field drop fastest along the negative gradient direction, so that the objective function can repeatedly iterate and search in the negative gradient direction according to a specific step size to obtain the q value.

[0083] Attached Figure 4 The channel estimation results based on matching correlation are given, and the estimation of the amplitude and multipath delay based on the matching correlation estimation scheme and the real Bellhop channel impulse response are described. Figure 3Given). When the signal-to-noise ratio (SNR) is taken to 5dB, it can be seen that the peak of the matching correlation estimation almost coincides with the position of the channel impulse response, but there are still some deviations in the delay estimation, and the deviation of the amplitude estimation increases with the increase of the delay. This is the result of the influence of noise, and the integrity of the Chirp signal cycle will also affect the delay of the matching estimation. For example, there are multiple energy paths between the main path and the first delay, which will have a serious impact on the elimination of the sonar signal.

[0084] Attached Figure 5 The channel estimation results based on the FISTA sparse estimation algorithm are given, describing the estimation of the system multipath delay and amplitude by FISTA sparse estimation and the real Bellhop channel impulse response. The signal-to-noise ratio SNR is 5dB and the regularization parameter λ is 0.1. It can be seen from the figure that the peak of the FISTA sparse estimation is almost coincident with the position of the channel impulse response, the amplitude estimation is more accurate, less sensitive to noise, and is not affected by the periodic integrity of the Chirp signal. Therefore, it is more suitable as a parameter estimation scheme based on VTRM channel equalization.

[0085] Further, the FISTA sparse channel estimation VTRM equalization principle block diagram proposed in step S3 is attached Figure 6 The Gaussian kernel soft-limited Viterbi decoding algorithm is given in the appendix. Figure 7 Given in.

[0086] Embodiment 2: This embodiment provides an underwater acoustic communication receiving method based on superimposed interference signals, such as Figure 1 As shown, the following steps are included:

[0087] S101: After the bit data is channel-coded by a convolutional code, it is digitally modulated by BPSK and then modulated by a carrier wave to obtain a communication signal, and a sonar signal with the same frequency and at the same time is generated.

[0088] In a specific embodiment, the sampling frequency is 100KHz, the symbol length is 1ms, the transmission rate is 1000bps, the carrier is 30KHz, the sonar signal length is also 1ms, and the pulse interval is 1ms.

[0089] S102: The communication signal is superimposed on the sonar signal, and a synchronization header is added for synchronization of the received signal. At this time, the signal passes through the actual underwater acoustic channel to add ocean environmental noise.

[0090] In the specific implementation simulation, the transmitter signal passes through the generated BELLHOP underwater acoustic channel and Middleton Class A pulse noise sequence to obtain the received signal; in addition, in the sea trial semi-physical simulation experiment, the transmitter obtains the received signal through the actual ocean channel and ocean noise.

[0091] S103: Eliminate the sonar interference signal from the received signal sequence. The specific solution is to first extract the synchronization header, perform FISTA sparse estimation on the synchronization header, obtain the channel estimation result, reconstruct the sonar interference signal at the receiving end, and finally subtract the reconstructed sonar interference signal from the received data.

[0092] In the simulation example, the receiving end signal (including the noise signal) is estimated by the FISTA algorithm to obtain the estimated value of the BELLOP underwater acoustic channel, which is convolved with the sonar signal to obtain the reconstructed sonar interference signal. The estimated value of the BELLOP underwater acoustic channel can be compared with the original BELLOP channel, thus reflecting the accuracy of the algorithm. In the semi-physical simulation experiment of the sea trial, the receiving end signal (including the noise signal) is estimated by the FISTA algorithm to obtain the estimated value of the actual underwater acoustic channel, which is convolved with the sonar signal to obtain the reconstructed sonar interference signal.

[0093] In the simulation, the performance of FISTA algorithm channel estimation is related to the signal-to-noise ratio. In a low signal-to-noise ratio environment, the estimation result is poor and the sonar interference signal recovery result is poor. In a medium and high signal-to-noise ratio environment, the channel estimation result and the sonar interference signal recovery result are better.

[0094] S104: According to the channel estimation result, a time reversal mirror is used to perform VTRM channel equalization on the signal after the sonar interference is eliminated, so as to enhance the energy of the main path.

[0095] In a specific embodiment, the channel estimation results based on matching correlation estimation and the channel estimation results based on FISTA sparse algorithm are analyzed to verify the superiority of the proposed algorithm and the accuracy of channel estimation under the same signal-to-noise ratio.

[0096] S105: Due to the influence of non-Gaussian noise in the ocean, the signal is still affected by impulse noise after equalization. Therefore, we use an impulse noise suppression algorithm based on Gaussian kernel soft limiting combined with a Viterbi decoder to improve the signal-to-noise ratio of the system.

[0097] In the specific embodiment, the performance of the Viterbi decoding hard decision algorithm, the Viterbi decoding soft decision algorithm, the LLR-based impulse noise suppression algorithm, and the Gaussian core soft limiting-based impulse noise suppression algorithm are analyzed. The simulation results and sea trial results verify the superiority of the Gaussian core soft limiting scheme.

[0098] Furthermore, all parts involved in this application that are not described in detail are the same as the prior art or are implemented using the prior art.

[0099] In summary:

[0100] According to the prior information of the interference signal, the present invention proposes a sparse channel time domain signal reconstruction scheme based on the fast soft threshold iterative algorithm (FISTA), which has higher reconstruction accuracy for the sonar interference signal; in view of the influence of the residual sonar interference signal, multipath effect and environmental noise, a VTRM equalization method based on FISTA sparse estimation and a non-parametric Viterbi decoding algorithm based on Gaussian kernel are proposed, which can well solve the influence of multipath effect and environmental noise, thereby effectively improving the performance of underwater acoustic communication and the signal reception accuracy.

[0101] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for receiving underwater acoustic communication based on superimposed interference signals, characterized in that: The steps include: S101, the bit data is coded by a convolutional code channel and then BPSK digitally modulated and carrier modulated to obtain a communication signal, and a sonar signal with the same frequency and at the same time is generated; S102, superimposing the communication signal with the sonar signal, and adding a synchronization header for receiving signal synchronization. At this time, the signal passes through the actual underwater acoustic channel to add ocean environmental noise; S103, performing sonar interference signal elimination on the received signal sequence; S104, according to the channel estimation result, using a time reversal mirror to perform VTRM channel equalization on the signal after the sonar interference is eliminated, so as to enhance the energy of the main path; S105, using an impulse noise suppression algorithm based on Gaussian kernel soft limiting combined with a Viterbi decoder to improve the signal-to-noise ratio of the receiving system.

2. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 1 is characterized in that In step S101, firstly, the ocean channel and environmental noise in the target waters are collected, and an environmental noise model and a channel model under underwater acoustic communication are established; The generated bit data is then encoded with a (2, 1, 3) convolutional code and BPSK digital modulation is used to generate a BPSK communication signal, which is then carrier modulated and superimposed with a simultaneous and co-frequency sonar signal to serve as the transmitting end data.

3. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 2 is characterized in that: The BPSK communication signal is superimposed on the LMF signal of the sonar after pulse shaping, and its mathematical formula is expressed as follows: s(n)=s b (n)+s c (n) Among them, [S(0), S(1), ..., S(N-1)] T is the transmitted time domain superposition signal, S b =[S b (0),S b (1),…,S b (N-1)] T is the BPSK communication signal after convolutional coding, S c =[S c (0),S c (1),…,S c (N-1)] T The sonar Chirp signal sent.

4. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 1 is characterized in that In step S102, the transmitting end signal passes through the generated BELLHOP underwater acoustic channel and the Middleton Class A impulse noise sequence to obtain a received signal.

5. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 4 is characterized in that In step S103, the sonar interference signal elimination of the received signal sequence includes: Firstly, the synchronization header is extracted from the receiving signal. Then, the FISTA algorithm is used to estimate the receiving signal to obtain the estimated value of the BELLOP underwater acoustic channel. The estimated value is convolved with the sonar signal to obtain the reconstructed sonar interference signal. Finally, the reconstructed sonar interference signal is subtracted from the received data.

6. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 5 is characterized in that: The process steps of the FISTA algorithm include: 1) Initialize q (0) =0,y (1) =q (0) ,t (1) =1; 2) 3) 4)y (n+1) =q (n) +((t (k) -1) / t (k+1) )(q (n) -q (n-1) ); Among them, ρ + is the Euclidean projection into the non-negative quadrant, is the descent gradient, L is the Lipschis continuous gradient operator, which is numerically equal to A T The largest eigenvalue of A.

7. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 1 is characterized in that In step S104, the time reversal mirror is used to perform VTRM channel equalization on the signal after the sonar interference is eliminated, including: S41, signal reception: receiving the signal after sonar interference elimination, and performing pre-processing; S42, time reversal: performing time reversal processing on the received signal to offset the distortion caused by the channel; S43, spectrum equalization: performing spectrum equalization processing on the inverted signal to enhance the energy of the main path of the signal; S44, demodulation and data recovery: demodulate and recover the equalized signal to obtain a processed data transmission result.

8. The underwater acoustic communication receiving method based on superimposed interference signals according to claim 7 is characterized in that In step S105, the impulse noise suppression algorithm based on Gaussian kernel soft limiting includes: S51, preprocessing: performing filtering preprocessing on the data transmission result; S52, Gaussian kernel smoothing: using a Gaussian kernel to smooth the preprocessed signal, specifically convolving the Gaussian kernel with the preprocessed signal; S53, wavelet transform and soft limiting: performing wavelet transform on the smoothed signal to obtain wavelet coefficients of different scales, and removing noise components in the wavelet coefficients by setting a preset threshold; S54, inverse wavelet transform: perform inverse wavelet transform on the processed wavelet coefficients to obtain a denoised signal.

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