GNSS element signal interference suppression method and system based on double complex modeling

Abstract

The invention discloses a GNSS element signal interference suppression method and system based on double complex number modeling, and the method comprises the steps: firstly introducing a double complex number model, carrying out the modeling of a received multi-frequency multi-path signal, and obtaining a GNSS element signal; secondly, carrying out modular operation projection on the GNSS element signal to obtain time-frequency domain analysis of the element signal, and obtaining a ridge value through detection; and correcting the ridge value to obtain instantaneous frequency estimation and instantaneous frequency modulation slope estimation of the element signal interference carrier and subcarrier, and applying the instantaneous frequency estimation and the instantaneous frequency modulation slope estimation to a finite impulse response filter to obtain the finite impulse response filter based on double complex numbers. And finally, performing double complex Kalman filtering on the received element signal to obtain an interference estimation value, removing the interference estimation value from the received element signal, and taking a real part to obtain a GNSS expected signal. According to the method, the high-precision interference is estimated by using the high-precision interference parameter estimation value and the double-complex Kalman filtering, and interference suppression in a multi-frequency multi-path environment is efficiently realized.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and particularly relates to a GNSS meta-signal interference suppression method and system based on dual-complex number modeling. Background Art

[0002] As an important part of GNSS software radio receivers, satellite signal interference suppression technology requires improving the interference suppression ability of satellite navigation receivers due to the extremely weak power of the received satellite signals. Many receivers use interference suppression methods that first estimate interference parameters for the received signals, then reconstruct the interference based on the obtained interference parameter estimates, and finally remove the estimated reconstructed interference from the received signals to achieve interference suppression. This method is only applicable when the desired signal is a single GNSS signal. The concept of GNSS meta-signals is inspired by BOC signals and Alt-BOC signals. By means of coherent synthesis, multiple GNSS signals with different carrier frequencies are combined into a single signal for processing, and this technology provides the benefits of multi-frequency processing. As a type of meta-signal, dual-complex number signals represent the carrier and sub-carrier components in an orthogonal complex form, and this representation can effectively improve the efficiency of GNSS signal interference suppression.

[0003] Therefore, it is necessary to conduct in-depth research on the problem of introducing dual-complex numbers into GNSS signal interference suppression methods and propose a high-precision estimation method for GNSS meta-signal interference parameters based on dual-complex number modeling. Summary of the Invention

[0004] The object of the present invention is to improve the efficiency of the navigation interference estimation method. In the case of receiving multiple signals and combining with the dual-complex number model, a GNSS meta-signal interference suppression method based on dual-complex number modeling is proposed.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A GNSS meta-signal interference suppression method based on dual-complex number modeling includes the following steps:

[0007] S1. Introduce a dual-complex number model to model the received multi-frequency and multi-path signals to obtain GNSS meta-signals;

[0008] S2. Perform modulus operation projection on the GNSS meta-signals based on dual-complex numbers, and then perform dual-complex short-time Fourier transform to obtain the time-frequency domain analysis of the GNSS meta-signals;

[0009] S3. Detect the ridge line of the time-frequency domain analysis of the GNSS meta-signals to obtain the ridge line value;

[0010] S4. Modify the ridge line value to obtain the instantaneous frequency estimation of the GNSS primary signal interfering with the carrier wave and the sub-carrier, and then take the difference between the instantaneous frequency estimation values at adjacent moments to obtain the instantaneous frequency modulation slope estimation;

[0011] S5. Apply the instantaneous frequency estimation and the instantaneous frequency modulation slope estimation to a finite impulse response filter to obtain a finite impulse response filter based on dual complex numbers;

[0012] S6. Perform dual complex Kalman filtering on the received GNSS primary signal, and apply the finite impulse response filter based on dual complex numbers as the state transition matrix to the dual complex Kalman filtering to obtain an accurate interference estimation value;

[0013] S7. Remove the accurate interference estimation value from the received GNSS primary signal and take the real part to obtain the GNSS desired signal, correlate the GNSS desired signal with the local signal, and then calculate the correlation signal-to-noise ratio and the cross ambiguity function.

[0014] As a preferred solution, in step S1, the power of the desired signal and the noise in the single-channel RF front-end of the receiver is much smaller than the interference. Considering the effectiveness of anti-interference, the GNSS primary signal based on dual complex numbers is modeled as:

[0015]

[0016] where i and j represent the two imaginary units of the dual complex number, X a [n] = A + S a [n] + W a [n], X b [n] = A b + S b [n] + W b [n], Similarly, S b [n] and W b [n] can be obtained. n represents the sampling point, s a [n] and s b [n] represent two different desired baseband signals, A a and A b represent the amplitudes of two different interferences, w a [n and w b [n] represent additive white Gaussian noise, represents the sub-carrier phase of the interference signal, represents the carrier phase of the interference signal; according to the properties of dual complex numbers, e1 and e2 represent two orthogonal components.

[0017] As a preferred solution, in the step S4, detecting the ridge line of the double complex short-time Fourier transform analysis result to obtain the instantaneous frequency estimation of the interfering carrier wave and the sub-carrier wave, and the linear frequency modulation slope can be obtained through the instantaneous frequency of the interference.

[0018] Aiming at the error existing in the linear interference frequency estimation, a high-precision instantaneous interference frequency estimation is proposed, which can be expressed as:

[0019]

[0020] where represents the largest integer less than or equal to x, and f u0 [n] represents the actual instantaneous frequency. Similarly, Then, according to the estimated values of the carrier wave and the sub-carrier wave, and The corresponding instantaneous phase estimation value can be obtained from the instantaneous frequency estimation value and Taking the difference between the adjacent-time carrier wave instantaneous frequency estimation values to obtain the carrier wave instantaneous frequency modulation slope Similarly, the sub-carrier wave instantaneous frequency modulation slope can be obtained

[0021] As a preferred solution, in the step S5, the finite impulse response filter based on the double complex number:

[0022]

[0023] where L u {·} represents the zero-value filter that suppresses the interference signal u bic [n]. In this example, p = 2, corresponding to the three-coefficient filter, and u bic [n] represents the interference in the form of a double complex number. If a0 = 1, The filter coefficients include the carrier wave instantaneous frequency modulation slope, which is used to consider the characteristics of the instantaneous frequency change of the double complex number interference signal.

[0024] As a preferred solution, in the step S6, combining the state transition matrix based on the double complex number finite impulse response filter:

[0025]

[0026] Combining the above state transition matrix with the received element signal can model the state equation and the observation equation to obtain:

[0027] X n = F n X n-1 + Ge n

[0028] z n = HX n + v n

[0029] where v n represents the observation noise, G represents the process noise e n and the relationship matrix with the state vector X n ; H represents the relationship matrix between the state vector X n and the observed value z n .

[0030] Subsequently, based on the Kalman filter of dual-complex numbers, during the prediction process:

[0031]

[0032] where represents the estimated value of the state estimation vector at the (n - 1)th moment, represents the predicted value of the state vector at the nth moment, represents the process noise variance, represents the predicted state dual-complex covariance matrix, represents the estimated state dual-complex covariance matrix, (·) * represents the dual-complex conjugate. Therefore:

[0033]

[0034] As a preferred solution, in step S7, the estimated value of the interference state vector is removed from the received element signal at the nth moment, and the influence generated by the interference in the dual-complex modeling is removed, obtaining:

[0035]

[0036] where represents the value of the received element signal after removing the interference, represents taking the real part, correlating the obtained desired signal with the local signal, and calculating the cross ambiguity function.

[0037] Next, the cross-correlation function is calculated, the modulus square of the cross-correlation function is taken to obtain the power, and the correlation peak power, noise power, and correlation signal-to-noise ratio are calculated.

[0038] The present invention also provides a GNSS element signal interference suppression system for dual-complex modeling, which is applied to the GNSS element signal interference suppression method for dual-complex modeling. The GNSS element signal interference suppression system for dual-complex modeling includes:

[0039] A modeling module for performing dual-complex modeling on multiple signals received by a receiver to obtain GNSS element signals;

[0040] The dual-complex short-time Fourier transform module is used to perform modulus operation projection on the GNSS raw signal, perform dual-complex short-time Fourier transform on the signal after modulus operation, and obtain the time-frequency domain analysis of the GNSS raw signal;

[0041] The instantaneous frequency and frequency modulation slope estimation module is used to detect the time-frequency domain analysis of the GNSS raw signal, obtain the instantaneous ridge value and correct it, obtain the estimated values of the instantaneous frequencies of the interfering carrier wave and sub-carrier wave of the GNSS raw signal, and perform subtraction processing on the estimated values of the instantaneous frequencies of the carrier wave and sub-carrier wave at adjacent moments to obtain the estimated values of the instantaneous frequency modulation slopes of the carrier wave and sub-carrier wave;

[0042] The interference estimation module is used to combine the estimated values of the instantaneous frequency and the instantaneous frequency modulation slope with a dual-complex finite impulse response filter to obtain a dual-complex state transition matrix, perform interference estimation on the received GNSS raw signal, and obtain the interference estimation value;

[0043] The calculation and output module is used to subtract the interference estimation value from the received GNSS raw signal and take the real part to obtain the GNSS desired signal, perform cross-correlation processing on the desired signal and the local signal, and perform calculations based on the cross-correlation result to obtain the correlation signal-to-noise ratio and CAF.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] In the present invention, a dual-complex model is adopted in the received signal modeling to combine multiple GNSS signals into a composite signal; the modulus operation projection is used to retain two single-frequency domain signals, and then the time-frequency domain analysis is used to estimate two orthogonal interfering instantaneous frequency parameters, and high-precision instantaneous frequency estimation values are obtained through correction, and the instantaneous frequency modulation slope is directly estimated; in interference suppression, high-precision interference parameter estimation values are used, and dual-complex Kalman filtering is used to estimate high-precision interference, and then the interference is removed; interference suppression in a multi-frequency and multi-path environment is efficiently realized. Description of the Drawings

[0046] Figure 1 It is a flowchart of the GNSS raw signal interference suppression method based on dual-complex modeling according to the embodiment of the present invention;

[0047] FIG. 2(a) is a normalized time-frequency diagram of the dual-complex interfering carrier wave in the dual-complex short-time Fourier transform according to the embodiment of the present invention;

[0048] FIG. 2(b) is a normalized time-frequency diagram of the dual-complex interfering sub-carrier wave in the dual-complex short-time Fourier transform according to the embodiment of the present invention;

[0049] FIG. 2(c) is a normalized instantaneous frequency ridge extraction diagram of the dual-complex interfering carrier wave in the dual-complex short-time Fourier transform according to the embodiment of the present invention;

[0050] Figure 2(d) is the extraction diagram of the double-complex subcarrier normalized instantaneous frequency ridge in the double-complex short-time Fourier transform according to the embodiment of the present invention;

[0051] Figure 3 is the comparison diagram of the signal-to-noise ratio related to the interference suppression method according to the embodiment of the present invention and the case without interference suppression processing;

[0052] Figure 4(a) is the cross ambiguity function diagram of the interference suppression method according to the embodiment of the present invention under the condition of JSR = 60dB;

[0053] Figure 4(b) is the cross ambiguity function diagram without interference suppression processing according to the embodiment of the present invention under the condition of JSR = 60dB;

[0054] Figure 5 is the existing GNSS meta-signal processing method based on the double-complex model. Detailed implementation manners

[0055] To more clearly illustrate the embodiments of the present invention, the specific implementation manners of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings, and other implementation manners can also be obtained.

[0056] As Figure 1 shown, the GNSS meta-signal interference suppression method based on double-complex modeling according to the embodiment of the present invention includes the following steps:

[0057] S1. At the single-channel RF front-end of the receiver, since the power of the desired signal and noise is much smaller than that of the interference, considering the effectiveness of anti-interference, the GNSS meta-signal based on the double-complex is modeled as:

[0058]

[0059] where i and j represent the two imaginary units of the double-complex, and X a [n] = A a + S a [n] + W a [n], X b [n] = A b + S b [n] + W b [n], Similarly, S b [n] and W b [n] can be obtained. s a [n and s b [n] represent two different desired baseband signals, and A a and A b represent the amplitudes of two different interferences. Indicates the subcarrier phase of the interference signal Indicates the carrier phase of the interference signal, w a [n] and w b [n] represents additive white Gaussian noise, and n represents the sampling point. Expanding:

[0060]

[0061] Among them, f usub [n] represents the subcarrier frequency of the interference signal, f u0 [n] represents the subcarrier frequency of the interference signal, f s Represents the sampling rate, f ua [n] and f ub [n] represents the instantaneous frequencies of two different interference signals Represents the instantaneous interference frequency f ua The phase of [n] Represents the instantaneous frequency f of the desired signal sa The phase of [n]. Similarly, it can be obtained that

[0062] According to the properties of dual complex numbers, e1 and e2 represent two orthogonal components, and have the following characteristics:

[0063] k = ij = ji

[0064]

[0065] i 2 =-1, j 2 =-1, k 2 =1

[0066] Perform modulus operation projection on the GNSS elementary signal:

[0067]

[0068]

[0069] Among them,

[0070] Take the square root of the modulus operation result and perform the dual complex short-time Fourier transform, BSSTFT j and BSSTFT j Are respectively the dual complex short-time Fourier transforms in the carrier domain and the subcarrier domain:

[0071]

[0072] Among them, c u0 and c usubrespectively represent the frequency indices of the carrier and the subcarrier, \(g[m]\) represents the window function, which is selected as the Hamming window. The window function moves on the time axis, and the window length is \(M\). Using and to implement the conversion of the dual-complex time-domain signal to the orthogonal frequency domain:

[0073] First, detect the ridge line of the dual-complex short-time Fourier transform analysis result to obtain the instantaneous frequency estimates of the interfering carrier and the subcarrier:

[0074]

[0075] Among them, and respectively represent the values at the maximum energy of the time-frequency analysis of the carrier and the subcarrier at time \(n\), \(\arg\{\cdot\}\) represents the frequency estimate value of the ridge line, and represent the frequency points at the maximum energy at time \(n\), \(\Delta\) f =(2M) -1 represents the normalized frequency resolution, and the chirp rate can be obtained from the interfering instantaneous frequency.

[0076] Second, aiming at the error in the linear interference frequency estimate, a high-precision interfering instantaneous frequency estimate is proposed, which can be expressed as:

[0077]

[0078] Among them, represents the largest integer less than or equal to \(x\), \(f\) u0 [n] represents the actual instantaneous frequency. Similarly, Then, according to the estimates of the carrier and the subcarrier, and The corresponding instantaneous phase estimates can be obtained from the instantaneous frequency estimates and

[0079] The above is the high-precision frequency estimation process of the dual-complex interfering carrier and the subcarrier.

[0080] Take the difference between the adjacent-time carrier instantaneous frequency estimates to obtain the carrier instantaneous chirp rate:

[0081]

[0082] Similarly, the subcarrier instantaneous chirp rate can be obtained S1. Dual-complex Kalman filtering

[0083] Finite impulse response filter based on dual-complex numbers:

[0084]

[0085] Among them, L u {·} represents a zero-value filter for suppressing the interference signal u bic [n], and u bic [n] represents the interference in the form of a dual complex number. In this example, p = 2, corresponding to a three-coefficient filter. If a0 = 1, we can obtain The filter coefficients include the instantaneous frequency modulation slope of the carrier, which is used to consider the characteristics of the instantaneous frequency change of the dual complex interference signal. Combining with the state transition matrix of the dual complex finite impulse response filter:

[0086]

[0087] Combining the above state transition matrix with the received element signal can model the state equation and the observation equation:

[0088] X n = F n X n-1 + Ge n

[0089] z n = HZ n + v n

[0090] Among them, v n represents the observation noise, G represents the process noise e n and the relationship matrix with the state vector X n ; H represents the relationship matrix between the state vector X n and the observed value z n , and the specific representation is as follows:

[0091]

[0092] After that, the Kalman filtering process based on dual complex numbers is as follows:

[0093] First, the prediction process:

[0094]

[0095] Among them, represents the estimated value of the state estimation vector at the (n - 1)th moment, represents the predicted value of the state vector at the nth moment, represents the process noise variance, represents the predicted state dual complex covariance matrix, represents the estimated state dual complex covariance matrix, (·) * represents the dual complex star conjugate. Therefore:

[0096]

[0097] Second, the update process:

[0098]

[0099] Among them, represents the measurement noise variance, and K n represents the dual-complex Kalman gain.

[0100] The above is the dual-complex Kalman interference estimation process.

[0101] Remove the estimated value of the interference state vector from the received element signal at time n, and remove the influence of interference generated in the dual-complex modeling to obtain:

[0102]

[0103] Among them, represents the value of the received element signal after removing interference, represents taking the real part, correlating the obtained desired signal with the local signal, and calculating the cross ambiguity function to obtain:

[0104]

[0105] Among them, CAF(τ, f d ) represents the cross ambiguity function, r local [n] represents the local signal, (·) * represents the complex conjugate, τ represents the time delay, and f d represents the Doppler frequency shift.

[0106] Next, calculate the cross-correlation function to obtain:

[0107]

[0108] Among them, R(·) represents the cross-correlation function. Take the modulus square of the cross-correlation function to obtain the power:

[0109] P(τ) = |R(τ)| 2

[0110] Calculate the correlation peak power and noise power according to the above formula:

[0111] τ peak = arg max P(τ)

[0112] P peak = P(τ peak )

[0113]

[0114] Among them, CSNR represents the relevant signal-to-noise ratio, and τ peak represents the peak position, and P peak represents the peak power, and N noise represents the number of samples of the noise, and P noise represents the noise power.

[0115] In summary, the estimated cross ambiguity function CAF(τ, f d ) and the relevant signal-to-noise ratio CSNR can be obtained.

[0116] As Figures 2(a) to 2(d) shown, it is the normalized time-frequency domain analysis diagram of the dual-complex short-time Fourier transform method of the present invention for the carrier instantaneous frequency and subcarrier instantaneous frequency of dual-complex interference. JSR = 60dB, the frequency sweep period of interference a is 0.1ms, and the frequency sweep period of interference b is 0.2ms. Clear normalized time-frequency domain analysis diagrams can be seen from Figures 2(a) and 2(b). The bright ridge lines in the figures are the time-frequency information of the interference signals. The instantaneous frequency estimates obtained by detecting the ridge lines can be seen from Figures 2(c) and 2(d). The frequency sweep interference frequency periods of the carrier and subcarrier are 0.2ms.

[0117] In summary, since the method of the embodiment of the present invention composes multi-frequency and multi-path signals into a single signal form, the dual-complex short-time Fourier transform and ridge line detection can be realized through modulo operation projection, and the carrier and subcarrier instantaneous frequency estimates can be realized.

[0118] As Figure 3 shown, it is the comparison diagram of the relevant signal-to-noise ratio between the existing GNSS primitive signal technology method and the dual-complex interference suppression method of the present invention. JSR takes 40 - 60dB, the input SNR is -20dB, and the desired signals are B1I and B1C baseband signals. In the figure, good interference suppression effects can be achieved through the dual-complex interference suppression method, the relevant signal-to-noise ratio is close to the ideal signal-to-noise ratio, and the relevant signal-to-noise ratio without interference suppression processing is much smaller than the ideal signal-to-noise ratio. Therefore, the GNSS primitive signal interference suppression based on dual-complex numbers can be realized through the method proposed by the present invention.

[0119] As shown in Figure 4, it is the comparison diagram of the cross ambiguity function between the existing GNSS primitive signal technology method and the dual-complex interference suppression method of the present invention. JSR takes 60dB, the input SNR is -20dB, and the desired signals are B1I and B1C baseband signals. In Figure 4(a), there is no peak in the CAF without interference suppression processing, which is caused by the influence of strong interference. In Figure 4(b), clear peaks can be seen, which is because the method of the embodiment of the present invention suppresses the interference.

[0120] As Figure 5As shown in the figure, it is the existing GNSS primary signal technology method. The broadband front end converts the received analog signal into a digital intermediate frequency signal, then constructs it into a double complex signal, performs correlation with the local code, local carrier, and local subcarrier, and finally calculates the correlation signal-to-noise ratio and CAF.

[0121] In summary, the correlation signal-to-noise ratio of the method in the embodiment of the present invention is much higher than that without interference processing, and can significantly present the CAF peak. Therefore, the interference suppression in the GNSS primary signal can be achieved through the double complex interference suppression method.

[0122] Based on the GNSS primary signal interference suppression method with double complex modeling in the embodiment of the present invention, the embodiment of the present invention also provides a GNSS primary signal interference suppression system with double complex modeling, including:

[0123] A modeling module, configured to perform double complex modeling on multiple signals received by the receiver to obtain GNSS primary signals;

[0124] A double complex short-time Fourier transform module, configured to perform modulus operation projection on the GNSS primary signal, and perform double complex short-time Fourier transform on the signal after the modulus operation to obtain the time-frequency domain analysis of the GNSS primary signal;

[0125] An instantaneous frequency and frequency modulation slope estimation module, configured to detect the time-frequency domain analysis of the GNSS primary signal, obtain the instantaneous ridge value and correct it to obtain the estimated value of the instantaneous frequency of the interference carrier and subcarrier of the GNSS primary signal, and perform subtraction processing on the estimated values of the instantaneous frequency of the carrier and subcarrier at adjacent moments to obtain the estimated value of the instantaneous frequency modulation slope of the carrier and subcarrier;

[0126] An interference estimation module, configured to combine the estimated value of the instantaneous frequency and the estimated value of the instantaneous frequency modulation slope with a double complex finite impulse response filter to obtain a double complex state transition matrix, and perform interference estimation on the received GNSS primary signal to obtain an interference estimation value;

[0127] A calculation and output module, configured to subtract the interference estimation value from the received GNSS primary signal and take the real part to obtain the GNSS desired signal, perform cross-correlation processing on the desired signal and the local signal, and calculate according to the cross-correlation result to obtain the correlation signal-to-noise ratio and CAF. The specific execution processes of all the above modules can refer to the detailed description in the method steps and will not be elaborated here.

[0128] The above is only a detailed description of the preferred embodiments and principles of the present invention. For those of ordinary skill in the art, according to the idea provided by the present invention, there will be changes in the specific implementation manners, and these changes should also be regarded as the protection scope of the present invention.

Claims

1. A GNSS meta-signal interference suppression method based on dual-complex number modeling, characterized in that It includes the following steps: S1. Introduce a dual-complex number model to model the received multi-frequency and multi-channel signals to obtain GNSS elementary signals; S2. Perform modulus operation projection on the GNSS elementary signals to obtain the time-frequency domain analysis of the GNSS elementary signals; S3. Detect the ridge line of the time-frequency domain analysis of the GNSS elementary signals, obtain the ridge line value and correct it to obtain the instantaneous frequency estimation and instantaneous frequency modulation slope estimation of the interference carrier and sub-carrier of the GNSS elementary signals; S4. Apply the instantaneous frequency estimation and instantaneous frequency modulation slope estimation to a finite impulse response filter to obtain a finite impulse response filter based on dual-complex numbers; S5. Perform dual-complex Kalman filtering on the received GNSS elementary signals, and apply the finite impulse response filter based on dual-complex numbers as the state transition matrix to the dual-complex Kalman filtering to obtain the interference estimation value; S6. Remove the interference estimation value from the received GNSS elementary signals and take the real part to obtain the GNSS desired signal, correlate the GNSS desired signal with the local signal, and then calculate the correlation signal-to-noise ratio and cross ambiguity function.

2. The GNSS raw signal interference suppression method based on dual-complex number modeling according to claim 1, wherein The specific implementation of step S1 is as follows: The GNSS elementary signal modeling based on the dual-complex number model is: where i and j represent the two imaginary units of the dual complex number, X a [n] = A a + S a [n] + W a [n], X b [n] = A b + S b [n] + W b [n], Similarly, S b [n] and W b [n], n represents the sampling point, s a [n] and s b [n] represents two different desired baseband signals, A a and A b represent the amplitudes of two different interferences, w a [n] and w b [n] represents additive white Gaussian noise, represents the subcarrier phase of the interference signal, represents the carrier phase of the interference signal.

3. The GNSS meta-signal interference suppression method based on dual-complex number modeling according to claim 2, wherein, The specific implementation of step S2 is as follows: Perform modulus operation projection on the GNSS elementary signals, take the square root of the modulus operation result, and perform dual-complex short-time Fourier transform.

4. The GNSS primary signal interference suppression method based on dual-complex number modeling according to claim 3, characterized in that The finite impulse response filter based on dual-complex numbers is: where, L u {·} represents a zero-valued filter for suppressing the interference signal u bic [n], corresponding to a three-coefficient filter, u bic [n] represents the interference in the form of a dual complex number; if a0 = 1, we get is the instantaneous frequency estimation of the interference carrier,[[]] is the instantaneous frequency estimation of the subcarrier, k is a coefficient,[[]] is the instantaneous frequency modulation slope of the interference carrier,[[]] is the instantaneous frequency modulation slope of the subcarrier. The filter coefficients include the instantaneous frequency modulation slope of the carrier, which is used to consider the characteristics of the instantaneous frequency change of the dual complex interference signal; the corresponding instantaneous phase estimation value can be obtained from the instantaneous frequency estimation value and 5. The GNSS primary signal interference suppression method based on dual-complex number modeling according to claim 4, wherein The specific implementation process of step S5 is as follows: The state transition matrix based on the dual-complex finite impulse response filter Combined with the received elemental signal, model the state equation and the observation equation, and then perform Kalman filtering based on dual-complex numbers on the machine; In the prediction process of Kalman filtering based on dual-complex numbers, the dual-complex star conjugate in the predicted state dual-complex covariance matrix is expressed as 6. The GNSS primary signal interference suppression method based on dual-complex number modeling according to claim 5, characterized in that The specific implementation process of step S6 is as follows: Remove the estimated value of the interference state vector from the received elementary signal at time n, remove the influence of interference generated in the dual-complex modeling, and obtain: Among them, represents the value after the received meta-signal removes interference, represents taking the real part, e1 and e2 represent two orthogonal components, correlate the obtained desired signal with the local signal, calculate the cross ambiguity function, then obtain the cross-correlation function, take the modulus square of the cross-correlation function to get the power, calculate the correlation peak power, noise power and correlation signal-to-noise ratio according to the power.

7. A GNSS meta-signal interference suppression system based on dual-complex number modeling, which is used to implement the method described in any one of claims 1 to 6, and is characterized in that It includes the following modules: A modeling module, which is used to perform dual-complex modeling on the multi-channel signals received by the receiver to obtain GNSS elementary signals; A dual-complex short-time Fourier transform module, which is used to perform modulus operation projection on the GNSS elementary signals, perform dual-complex short-time Fourier transform on the signal after the modulus operation to obtain the time-frequency domain analysis of the GNSS elementary signals; An instantaneous frequency and frequency modulation slope estimation module, which is used to detect the time-frequency domain analysis of the GNSS elementary signals, obtain the instantaneous ridge line value and correct it to obtain the instantaneous frequency estimation value of the interference carrier and sub-carrier of the GNSS elementary signals, and perform subtraction processing on the instantaneous frequency estimation values of the carrier and sub-carrier at adjacent moments to obtain the instantaneous frequency modulation slope estimation values of the carrier and sub-carrier; An interference estimation module, which is used to combine the instantaneous frequency estimation value and the instantaneous frequency modulation slope estimation value with the dual-complex finite impulse response filter to obtain a dual-complex state transition matrix, perform interference estimation on the received GNSS elementary signals to obtain the interference estimation value; A calculation and output module, which is used to subtract the interference estimation value from the received GNSS elementary signals and take the real part to obtain the GNSS desired signal, perform cross-correlation processing on the desired signal and the local signal, and calculate according to the cross-correlation result to obtain the correlation signal-to-noise ratio and CAF.

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