Communication signal anti-interference method based on shape parameter estimation and semi-blind source separation

Abstract

The invention discloses a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation, and the method comprises the following steps: S1, a signal receiving device obtains a receiving signal # imgabs0 # through an antenna, and the receiving signal # imgabs1 # is a mixed signal comprising a sending signal # imgabs2 # from a signal sending device and an interference signal # imgabs3 # from other sources; s2, in combination with a deep learning mode and a numerical analysis mode, obtaining an estimated value of a signal shape parameter # imgabs4 # of the received signal; and S3, inputting the received signal # imgabs5 #, the interference signal # imgabs6 # and the estimated value of the signal shape parameter # imgabs7 # into a signal processing system, wherein the signal processing system obtains a target communication signal by adopting a signal shape parameter estimation algorithm and a semi-blind source separation algorithm based on AuxIVA. According to the method, the algorithm accuracy can be improved, so that the anti-interference capability of the same-frequency-band interference signals causing influences in wireless communication is effectively improved.

Description

Technical Field

[0001] The present invention relates to the fields of wireless communication technology and signal processing technology, and in particular to a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation. Background Art

[0002] As wireless communication technologies and wireless networks mature, modern electromagnetic environments have become increasingly complex and dense. These environments are saturated with communication signals across a wide range of frequency bands, modulation formats, and multiple access methods, leading to increasingly severe spectrum resource depletion and electromagnetic pollution. High spectrum density and overlap have led to widespread interference between signals in adjacent frequency bands. As a key approach to improving communication system reliability, interference suppression and elimination have long played a crucial role in signal processing within these systems.

[0003] For communication signals subject to in-band interference, it's difficult to directly suppress and eliminate the interference using frequency-domain filtering. Instead, blind source separation (BSS) can be used to separate and eliminate the interference signal and extract and enhance the target signal. The most commonly used BSS algorithm is Independent Component Analysis (ICA). This type of algorithm requires that the target and interference signals are statistically independent, and that at most one source signal in the mixed signal follows a Gaussian distribution. ICA algorithms inherently suffer from the tendency to cause permutation ambiguity. To address this, independent vector analysis (IVA) algorithms have been developed. To further accelerate convergence, auxiliary function-based independent vector analysis (AuxIVA) algorithms have emerged.

[0004] The main drawback of existing semi-blind source separation algorithms is their inability to accurately estimate the signal's shape parameters, resulting in insufficient signal separation accuracy and weak signal anti-interference capabilities. This algorithm addresses this shortcoming by improving the signal shape parameter estimation step, thereby enhancing signal separation accuracy and anti-interference capabilities. Summary of the Invention

[0005] In response to the problems existing in the prior art, the purpose of the present invention is to provide a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation, which can improve the algorithm accuracy compared with the prior art, and thus effectively improve the anti-interference ability of the same-band interference signals that cause an impact in wireless communications.

[0006] To achieve the above object, the present invention provides a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation, the method comprising the following steps: S1. The signal receiving device obtains a received signal through an antenna, wherein the received signal is a mixed signal including a transmission signal from a signal transmitting device and an interference signal from other sources; S2. Combining deep learning and numerical analysis to obtain signal shape parameters estimated value; S3. The received signal, interference signal and signal shape parameters The estimated value is input into the signal processing system, and the signal processing system adopts the signal shape parameter estimation algorithm and the semi-blind source separation algorithm based on AuxIVA to obtain the target communication signal.

[0007] Furthermore, step S2 specifically includes the following steps: S2.1 obtains a first signal shape parameter estimate using a deep learning method; S2.2 obtains an estimated value of the shape parameter of the second signal using a numerical analysis method; S2.3 takes the average of the first signal shape parameter estimation value and the second signal shape parameter estimation value as the final result of the signal parameter estimation value.

[0008] Furthermore, step S2.1 is specifically implemented as follows: a neural network is established for signal shape parameter estimation, the signal shape parameter estimation is based on the time-frequency matrix of the signal, and the overall network architecture includes 2 layers of convolutional neural network layers, 2 layers of average pooling layers, a flattening layer and a fully connected layer.

[0009] Furthermore, the kernel sizes of the CNN layer and the pooling layer are both 3×3.

[0010] Furthermore, before the neural network algorithm for signal shape parameter estimation is run, a set of random signals with specific shape parameter values are generated for network training. After the training is completed, the network is used to estimate the shape parameters of the signal.

[0011] Furthermore, step S2.2 is specifically implemented as follows: Also based on the time-frequency representation matrix of the signal, the algorithm flow includes the following steps: S2.2.1 First, calculate the complex signal kurtosis value of the signal through the time-frequency matrix; S2.2.2 Then according to the CSK value and signal shape parameter The relationship between the values The value is estimated.

[0012] Furthermore, step S3 includes: First, the signal shape parameters are estimated based on the received mixed signal and interference signal, and the final shape parameter estimation result is obtained; then the weight function of the received mixed signal is calculated based on the semi-blind source separation algorithm to obtain the optimal estimation of the target communication signal.

[0013] Furthermore, the signal shape parameter estimation algorithm is as follows: first, the received mixed signal and interference signal are subjected to STFT transformation to obtain their respective time-frequency matrices, and then the time-frequency matrix of the mixed signal is processed, and the shape parameters are estimated by deep learning neural network method and numerical calculation method respectively, and the final shape parameter estimation result is obtained.

[0014] Furthermore, the semi-blind source separation of the signal is implemented by calculating the weight function of the received mixed signal, and iteratively updating the covariance matrix and separation matrix of the signal accordingly, finally calculating the time-frequency representation matrix of the target estimated signal, and obtaining the time domain form of the estimated target communication signal through iSTFT.

[0015] Furthermore, the signal obeys a generalized Gaussian distribution in the time domain.

[0016] The beneficial effects of the present invention are as follows: The present invention improves the accuracy of the algorithm by adopting the structural design of the neural network in the signal shape parameter estimation algorithm, the joint estimation method of the shape parameters of the generalized Gaussian distribution signal based on numerical analysis and neural network, the semi-blind source separation algorithm based on shape parameter estimation and AuxIVA, and the communication signal anti-interference enhancement algorithm based on shape parameter estimation and AuxIVA, thereby effectively improving the anti-interference capability of the algorithm against the interference signals of the same frequency band that cause an impact in wireless communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 2. It is a schematic diagram of an application scenario of the communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to the present invention; Figure 2 This is a schematic diagram of the neural network architecture used for signal shape parameter estimation; Figure 3 2. It is a flow chart of a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to the present invention; Figure 4 It is a comparison chart of frequency band occupancy of two types of communication signals; Figure 5 This is a comparison chart of BER before and after signal enhancement under different SIRs. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0020] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0021] The following combination Figure 1-Figure 5 The specific embodiments of the present invention are described in detail. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0022] Traditional blind source separation algorithms are often used in scenarios involving multiple unknown signal sources. The goal is to separate each source from the mixed signal, aiming for high separation accuracy across all sources. However, in practical communication systems, especially point-to-point communication applications, the receiver is often only concerned with the purity of a specific communication signal and does not need to consider the separation accuracy of other sources. Furthermore, the receiver is often not completely ignorant of the interfering signal. It often has some prior information about the interfering signal, such as data collected when the friendly signal is silent. Signal parameter estimation techniques can be used to determine the frequency band, modulation method, and modulation waveform of the interfering signal. However, the channel through which the interfering signal propagates is unknown. Therefore, in practical communication systems, a semi-blind source separation algorithm, a development of the traditional blind source separation algorithm, can be used to leverage this prior information to estimate the channel function through which the interfering signal propagates from the mixed signal. This effectively reduces or even eliminates the impact of the interfering signal within the mixed signal, significantly enhancing the target signal's anti-interference capability.

[0023] The application scenarios targeted by the method of the present invention are as follows Figure 1 As shown, the present invention mainly considers the interference signal elimination algorithm. For example, the electromagnetic field, which is currently widely affected by resource pollution and spectrum interference, is widely used in radio electromagnetic wave communications. Due to the intensive use of radio frequency signal resources, mutual interference between communication signals in adjacent frequency bands has become the norm; what's more, in certain specific scenarios, unknown devices may maliciously send signals in the same frequency band to achieve interference. The communication scenario targeted by this algorithm is a typical point-to-point communication. Both the communication sending and receiving devices are radio frequency signal processing devices. The number of antennas is 1. The time domain transmission signal emitted by the sending end is , the time domain received signal obtained by the receiver is There is an interference signal source in the area, and the time domain interference signal emitted is , the impulse response of the interference signal through the multipath channel is The transmitted signal and the interference signal are additively mixed at the receiving end, and the received signal at the receiving end can be expressed as ,in The ultimate goal of this algorithm is to and interference signal multipath channel In unknown cases, by receiving the signal and the prior information of the interference signal , for communication signals Perform enhancement and recovery to improve the anti-interference capability of the overall communication system.

[0024] Based on this, the present invention proposes a communication signal anti-interference method based on shape parameter estimation and semi-blind source separation, the method comprising the following steps: S1. The signal receiving device obtains the received signal through the antenna , the received signal The signal transmission device includes a signal , interference signals from other sources .

[0025] S2. Combine deep learning and numerical analysis to obtain the estimated value of the signal shape parameter of the received signal; the signal shape parameter is the shape parameter of the probability density function of the generalized Gaussian distribution that the signal obeys, that is, the parameter .

[0026] S3. Will receive the signal and interference signals The signal shape parameter estimation value is input into the signal processing system, and the signal processing system adopts a semi-blind source separation algorithm based on the shape parameter estimation algorithm and AuxIVA to obtain the target communication signal.

[0027] The present invention first estimates the shape parameters of the signal, and then applies the semi-blind source separation algorithm based on the estimated shape parameter values to obtain the estimated target communication signal. After obtaining the target communication signal, the original downsampling, mixing, demodulation, decoding and other operations of the receiving end are performed. The target communication signal finally obtained in step S3 is the transmitted signal after system processing. An approximate estimate of the signal.

[0028] The anti-interference method proposed in this paper is a semi-blind source separation algorithm that combines AuxIVA and deep learning. Based on the ICA algorithm, the AuxIVA algorithm expands the signal processing domain from the time domain to the time-frequency domain, which can effectively improve the connection between the frequency components in the same signal to solve the permutation ambiguity problem that occurs during signal separation, and introduces auxiliary functions to improve the convergence speed. The shape parameter is required in this algorithm. , shape parameter The value of is an uncertain value. When using this parameter in the algorithm of the prior art, the empirical value is often adopted, such as 0.4, which limits the accuracy of the algorithm. Therefore, in step S2 of the present invention, the shape parameter How to accurately determine the value of shape parameter? A shape parameter estimation algorithm combining deep learning and numerical analysis is proposed to achieve shape parameter This improvement in parameter value estimation accuracy further improves the algorithm's accuracy and computational accuracy, achieving improved target communication signal extraction accuracy and enhanced anti-interference capabilities. The signal shape parameter estimation is achieved by first performing an STFT transform on the received mixed signal and interference signal to obtain their respective time-frequency matrices. The time-frequency matrices of the mixed signal are then processed, and shape parameter estimation is performed using a deep learning neural network method and a numerical calculation method, respectively, to obtain the final shape parameter estimation result. This specifically includes the following steps: S2.1 uses deep learning to obtain the first signal shape parameter estimation value. Specifically: The neural network architecture used for signal shape parameter estimation is as follows: Figure 2 As shown, signal shape parameter estimation is based on the signal's time-frequency matrix. The overall network architecture consists of two convolutional neural network (CNN) layers, two average pooling layers, a flattening layer, and a fully connected layer. The kernel size of both the CNN and pooling layers is 3×3. Before the algorithm runs, a set of random signals with specific shape parameter values is generated for network training. After training, the network is used to estimate the signal's shape parameters.

[0029] S2.2 uses numerical analysis to obtain the estimated value of the second signal shape parameter. Also based on the signal's time-frequency representation matrix, the algorithm flow is as follows, including the following steps: S2.2.1 Calculate the complex signal kurtosis (CSK) value of the signal using the time-frequency matrix. The calculation method is as follows: ; in, Represents the time domain signal The time-frequency representation of Indicates averaging the signal.

[0030] S2.2.2 According to CSK value and shape parameters The relationship between the values The specific relationship is as follows: ; So far, the shape parameter estimation by using numerical analysis method has been completed.

[0031] S2.3 takes the average of the first shape parameter estimation value and the second shape parameter estimation value as the final result of the signal parameter estimation value.

[0032] In step S3, the communication signal anti-interference enhancement algorithm proposed in the present invention includes a shape parameter estimation algorithm and a semi-blind source separation algorithm based on AuxIVA (independent vector analysis based on auxiliary functions). The shape parameter estimation algorithm includes a deep learning method and a numerical calculation method. The final result of the shape parameter estimation is the average of the deep learning and numerical calculation results. The flowchart of the overall algorithm is as follows: Figure 3 As shown in . Figure 3 In this method, the received communication signal, after being interfered with, undergoes two steps: signal shape parameter estimation and semi-blind source separation. This process then yields an optimal estimate of the target communication signal. The goal of the semi-blind source separation algorithm is to achieve this by calculating the weight function of the received mixed signal and iteratively updating the signal covariance matrix and separation matrix accordingly. Ultimately, the time-frequency representation matrix of the target estimated signal is calculated, and the time-domain representation of the estimated target communication signal is obtained through iSTFT.

[0033] Specifically, step S3 includes: Signal shape parameter estimation is as follows: Use time-frequency matrices to represent the time domain received signal , time domain interference signal And the estimated time domain target communication signal , by performing Short-Time Fourier Transform (STFT), we can get the time-frequency representation matrix of the three signals: ; Accordingly, the generation expression of the mixed signal in the time-frequency domain can be expressed as: ; in, The first STFT result of the interference signal The influence coefficient of the frame signal on the current frame signal; is the coefficient subscript number, specifically the first The coefficient subscript number that affects the STFT result of the received signal of the current frame; is the influence coefficient of the current frame in the STFT result of the interference signal on the current frame of the STFT result of the received signal; is the original transmitted signal; is the number of adjacent frames that need to be considered simultaneously. By constructing the extended matrix, we obtain the representation of the generalized mixed signal and the generalized target signal: (The purpose of this step is to expand the computational process of the semi-blind source separation algorithm and convert it into a form that can directly apply the blind source separation algorithm.) ; The STFT result of the interference signal is the current frame and the most recent A vector consisting of the STFT results of the received signal of the frame and the coefficient values that affect the current frame.

[0034] It can be seen that the expansion matrix and Both The column vector of . Define the generalized target signal The calculation method is: (The meaning of this formula is: for the extended vectors x and y, the separation matrix W after iterative estimation is applied to calculate and obtain the extended vector x) ; in, is the time-frequency representation of the separation matrix, which is a The complex field matrix can be decomposed into: ; in Is a long , a column vector of all zeros, is a dimensional identity matrix, the goal is to The first line The parameters used in this algorithm are the received signal in the time domain and interference signals in the time domain The main calculation results are the target estimation signal in the time domain ,The purpose of this step is to estimate the final target signal, so the algorithm is a global algorithm.

[0035] A semi-blind source separation algorithm based on the AuxIVA algorithm. The parameters used by this algorithm are the received signal in the time-frequency domain. , and also uses the trained shape parameters The main calculation results obtained by the neural network are the shape parameters obtained by both the neural network and the numerical calculation method. The purpose of this step is to estimate the shape parameters by applying the shape parameter estimation algorithm proposed by the present invention. The details are as follows: In the AuxIVA algorithm, the signal obeys the generalized Gaussian distribution (GGD) in the time domain, and its probability density function expression is as follows: ; in, is the symbol for the independent variable in the probability density expression, the signal shape parameter This is the focus of signal parameter estimation in this algorithm. The signal shape parameters are obtained through step S2. estimated value; and are the mean and variance of the signal, and the parameters , The calculation method is as follows: ; is the gamma function, which is a universal function used to represent the GGD probability density function. Its expression is: ; is an integration variable, is the natural logarithm, is the negative power of the natural logarithm.

[0036] Assume that the signal obeys the complex domain spherical GGD, that is: ; When referring to a temporal representation of a target signal, any estimated time domain signal of the target signal may be referred to.

[0037] The contrast function and weight function are defined as follows: ; in, For signal 2-norm of ; It is a meaningless identifier symbol. yes The first derivative of ; ; , represents the L2 norm, It is the auxiliary variable in AuxIVA. The contrast function and weight function are used to iteratively calculate the covariance matrix. , and calculate the separation matrix .

[0038] The weight covariance matrix is calculated as follows and separation matrix : ; in, is the weight function, is the conjugate transpose of the matrix, The vector Multiply it with its own conjugate transpose and take the mean in the time domain; is the forgetting factor. By iteratively separating the matrix Update and finally take the matrix The first row of the final separation matrix The first line in , so far the separation matrix is completed Estimates.

[0039] By estimating the separation matrix Compute the time-frequency matrix representation of the source signal , and obtain the time domain representation of the source signal through the inverse short-time Fourier transform (Inverse STFT, ISTFT) The source signal here refers to the optimal estimate of the transmitted signal, that is, the target communication signal.

[0040] At this point, the semi-blind source separation algorithm based on the AuxIVA algorithm is completed. This algorithm can be used in point-to-point communication systems to enhance the anti-interference of communication signals.

[0041] The algorithm of the present invention was tested on a mixed signal of two wireless communication signals. Binary Phase Shift Keying (BPSK) and Gaussian Filtered Minimum Shift Keying (GMSK) are two widely used communication modulation methods, and therefore were used in the technical effect test. The sampling frequency of the two communication signals is 40MHz, the radio frequency frequency is 10MHz, and the intermediate frequency is 1MHz. The length of the two communication modulated signals is about 1 second, the code rate of the BPSK modulated signal is 4.9kbps, and the code rate of the GMSK modulated signal is 2.4kbps. The frequency bands occupied by the two communication radio frequency signals are compared. Figure 4 As shown in the figure, it can be seen that the frequency bands occupied by the two types of signals are both within the range of 10.987MHz to 11.013MHz, and the communication band occupancy is highly overlapping. When the two communication signals are transmitted simultaneously, the BPSK communication signal at a certain power will cause serious interference to the demodulation of the GMSK signal. Under different signal-to-interference ratios (SIR), the bit error rate (BER) of the demodulated GMSK signal before and after applying the signal enhancement algorithm proposed in this invention is compared. Figure 5As shown in the figure, it can be seen that when the SIR is less than 10dB, the interference signal has a significant impact on the demodulation of the communication signal, and the BER is high. However, after the signal is processed by the signal enhancement algorithm proposed in this invention, the BER is effectively improved and can be maintained at an extremely low level. When the SIR is high and reaches above 10dB, the interference signal is weak, and the effect of applying the algorithm of this invention is not obvious. The above results prove that the method proposed by this invention has an effective anti-interference ability against the interference signals in the same frequency band that cause interference in wireless communication.

[0042] Any process or method described in the flowchart of the present invention or in other ways herein can be understood as representing a module, segment or portion of code including one or more executable instructions for implementing specific logical functions or process steps, which can be implemented in any computer-readable medium for use by an instruction execution system, device or apparatus. The computer-readable medium can be any medium that stores, communicates, propagates or transmits a program for use by an execution system, device or apparatus, including read-only memory, magnetic disk or optical disk, etc.

[0043] Throughout this specification, reference to terms such as "embodiment" and "example" indicates that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, those skilled in the art may combine or integrate different embodiments or examples described in this specification, as well as features therein, without creating any inconsistency.

[0044] Although the above content has shown and described the embodiments of the present invention, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can perform update operations such as changes, modifications, replacements and variations on the above embodiments within the scope of the present invention.

Claims

1. A communication signal anti-interference method based on shape parameter estimation and semi-blind source separation, characterized in that: The method comprises the following steps: S1. The signal receiving device obtains a received signal through an antenna, wherein the received signal is a mixed signal including a transmission signal from a signal transmitting device and an interference signal from other sources; S2. Combining deep learning and numerical analysis to obtain signal shape parameters estimated value; S3. The received signal, interference signal and signal shape parameters The estimated value is input into the signal processing system, and the signal processing system adopts the signal shape parameter estimation algorithm and the semi-blind source separation algorithm based on AuxIVA to obtain the target communication signal.

2. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 1, characterized in that: Step S2 specifically includes the following steps: S2.1 obtains a first signal shape parameter estimate using a deep learning method; S2.2 obtains an estimated value of the shape parameter of the second signal using a numerical analysis method; S2.3 takes the average of the first signal shape parameter estimation value and the second signal shape parameter estimation value as the final result of the signal parameter estimation value.

3. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 2, characterized in that: The specific implementation method of step S2.1 is as follows: establish a neural network for signal shape parameter estimation, and the signal shape parameter estimation is based on the time-frequency matrix of the signal. The overall network architecture includes 2 layers of convolutional neural network layers, 2 layers of average pooling layers, a flattening layer and a fully connected layer.

4. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 3, characterized in that: The kernel sizes of the CNN layer and the pooling layer are both 3×3.

5. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 4, characterized in that: Before the neural network algorithm for signal shape parameter estimation is run, a set of random signals with specific shape parameter values are generated for network training. After the training is completed, the network is used to estimate the shape parameters of the signal.

6. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 2, characterized in that: The specific implementation of step S2.2 is as follows: Based on the time-frequency representation matrix of the signal, the algorithm flow includes the following steps: S2.2.1 First, calculate the complex signal kurtosis value of the signal through the time-frequency matrix; S2.2.2 Then according to the CSK value and signal shape parameter The relationship between the values The value is estimated.

7. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 1, characterized in that: Step S3 includes: First, the signal shape parameters are estimated based on the received mixed signal and interference signal, and the final shape parameter estimation result is obtained; then the weight function of the received mixed signal is calculated based on the semi-blind source separation algorithm to obtain the optimal estimation of the target communication signal.

8. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 7, characterized in that: The signal shape parameter estimation algorithm is as follows: first, the received mixed signal and interference signal are subjected to STFT transformation to obtain their respective time-frequency matrices, and then the time-frequency matrix of the mixed signal is processed, and the shape parameters are estimated by deep learning neural network method and numerical calculation method respectively, and the final shape parameter estimation result is obtained.

9. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to claim 7, characterized in that: The semi-blind source separation of the signal is implemented as follows: the weight function of the received mixed signal is calculated, and the covariance matrix and separation matrix of the signal are iteratively updated accordingly. Finally, the time-frequency representation matrix of the target estimated signal is calculated, and the time domain form of the estimated target communication signal is obtained through iSTFT.

10. The communication signal anti-interference method based on shape parameter estimation and semi-blind source separation according to any one of claims 1 to 9, characterized in that: The signal obeys a generalized Gaussian distribution in the time domain.

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