GNSS matching spectrum interference suppression method based on polarity recovery and orthogonal subspace projection

Abstract

This application relates to a GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection. The method includes: estimating the interference period of a discrete complex signal; dividing the discrete complex signal into multiple consecutive time-segmented signals based on the estimated interference period; sequentially performing down-conversion, inter-segment phase alignment, and coherent merging processing on the segmented signals of the multiple time-segments to obtain a fine estimate of the interference Doppler frequency shift; coherently accumulating the phase-aligned multiple signal segments based on the fine estimate of the interference Doppler frequency shift, and then normalizing the accumulation result to construct a sampling point-level polarity template vector of the interference; constructing an orthogonal complementary projection operator based on the polarity template vector; and using the orthogonal complementary projection operator to perform a projection transformation on the discrete complex signal received in the current time segment to output a GNSS signal after interference suppression. This method significantly reduces computational complexity and exhibits strong robustness.

Description

Technical Field

[0001] This application relates to the field of satellite navigation system signal processing technology, and in particular to a GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection. Background Technology

[0002] With the widespread application of satellite navigation technology in various military and civilian fields, the electromagnetic environment faced by GNSS receivers is becoming increasingly complex. Among them, matched spectrum interference and the near-far effect of pseudo-satellite systems are two particularly challenging types of strong interference. The pseudocode rate and spectral shape of these interference signals are consistent with the weak satellite signals of the target, and their power is usually 40-60 dB higher than the signal, causing conventional time-frequency domain anti-interference methods to fail.

[0003] Existing interference suppression methods mainly include Sequential Interference Cancellation (SIC) and traditional subspace projection. The SIC method requires accurate reconstruction and subtraction of the amplitude, phase, and waveform of the interference signal. Inaccurate amplitude estimation can lead to residual interference or even introduce new noise. Traditional subspace projection uses eigenvalue decomposition (EVD) or singular value decomposition (SVD) to separate the signal subspace from the interference subspace. While it does not require amplitude estimation, the computational cost of decomposing high-dimensional matrices (e.g., 25000×25000) is extremely high, making real-time implementation difficult in engineering. Summary of the Invention

[0004] Therefore, it is necessary to provide a GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection that can solve the problems of existing serial interference cancellation methods being sensitive to interference amplitude reconstruction and amplitude estimation errors, and traditional subspace projection methods relying on high-dimensional covariance matrix eigenvalue decomposition or singular value decomposition resulting in high computational complexity and difficulty in real-time application in broadband high sampling rate receivers.

[0005] A GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection, the method comprising:

[0006] Receive GNSS intermediate frequency analog signals, perform analog-to-digital conversion on the analog signals to obtain discrete complex signals; The interference period of the discrete complex signal is estimated, and the discrete complex signal is divided into segments of multiple consecutive time periods based on the estimated interference period. By sequentially performing down-conversion, inter-segment phase alignment, and coherent merging processing on segmented signals across multiple time periods, a fine estimate of the interference Doppler frequency shift is obtained. Based on the fine estimate of the interference Doppler frequency shift, coherent accumulation is performed on the phase-aligned multi-segment signals, and the accumulation result is normalized to construct the sampling point-level polarity template vector of the interference. An orthogonal complementary projection operator is constructed based on the polar template vector. The orthogonal complementary projection operator is used to perform projection transformation on the discrete complex signal received in the current time period, and outputs the GNSS signal after suppressing interference.

[0007] The aforementioned GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection first receives the GNSS intermediate frequency analog signal and completes analog-to-digital conversion, providing standardized input for all subsequent digital signal processing. This avoids noise interference and format differences in analog signals, laying a solid foundation for accurate processing. The method estimates and segments the discrete complex signal based on interference period. The core principle is to utilize the periodic characteristics of matched spectrum interference—by detecting the main peak interval of the autocorrelation function through time-domain autocorrelation calculation to determine the interference period, and dividing the signal into multiple segments according to the period. This provides common data blocks for subsequent multi-segment coherent merging and improves template consistency through the pre-design of periodic segmentation and cross-segment phase alignment, effectively reducing the impact of frequency offset and phase drift on suppression performance and solving the problem of unstable parameter estimation in traditional methods. Secondly, down-conversion, inter-segment phase alignment, and coherent merging are performed on the multi-segment signals to obtain a fine Doppler frequency shift. A two-step method of coarse and fine estimation is adopted: coarse estimation eliminates the polarity of the BPSK spreading code by squaring point by point, quickly locking the frequency range; fine estimation searches for candidate frequencies near the coarse estimate, and determines the precise frequency through phase compensation and coherent energy accumulation. This method does not rely on interference amplitude information throughout, fundamentally avoiding the residual interference and noise amplification caused by amplitude estimation errors in traditional SIC methods, thus overcoming the pain point of amplitude estimation sensitivity. A polar template vector is constructed based on fine frequency shift estimation. The interference signal-to-noise ratio is enhanced through coherent accumulation of multiple signal segments, and then the interference waveform skeleton is extracted after normalization, completely eliminating the influence of amplitude. It eliminates the need for eigenvalue / singular value decomposition of the high-dimensional covariance matrix, avoiding the high computational and storage overhead of traditional subspace methods and further reducing the risk of residual interference, laying the foundation for low-complexity projection. Finally, an orthogonal complementary projection operator is constructed based on the polar template. The projection calculation can be equivalent to a single inner product and a single vector subtraction, suitable for embedded real-time implementation. This significantly improves the survivability of navigation receivers in scenarios with strong matched spectrum interference and pseudo-satellite near-far effects. The computational load increases linearly with the processing length, perfectly adapting to the requirements of embedded real-time implementation. Meanwhile, this process can be extended to multiple interference scenarios. By constructing a multi-template matrix, it achieves low-rank joint suppression, has strong engineering adaptability, and significantly improves the survivability of navigation receivers in scenarios with strong matched spectrum interference and pseudo-satellite near-far effect, thus comprehensively solving the three core defects of traditional technologies. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection in one embodiment. Figure 2 This is a schematic diagram of interference period estimation in one embodiment; Figure 3 This is a schematic diagram of the coarse-fine estimation process for interference frequency in one embodiment; Figure 4 This is a schematic diagram of polar template construction in another embodiment; Figure 5 This is a schematic diagram illustrating the geometric meaning of orthogonal complementary projection in one embodiment; Figure 6 This is a block diagram of a GNSS anti-jamming receiver / computer device in one embodiment. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0010] In one embodiment, such as Figure 1 As shown, a GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection is provided, including the following steps: Step 102: Receive the GNSS intermediate frequency analog signal, perform analog-to-digital conversion on the analog signal to obtain a discrete complex signal; estimate the interference period of the discrete complex signal, and divide the discrete complex signal into segmented signals of multiple consecutive time periods based on the estimated interference period.

[0011] GNSS intermediate frequency analog signal is the analog signal after down-conversion processing by the radio frequency front end of the satellite navigation receiver, which includes the target satellite signal and the matched spectrum interference signal; analog-to-digital conversion is the process of converting the analog signal into a discrete complex signal sequence, providing input for subsequent digital signal processing; interference period estimation utilizes the periodic characteristics of the matched spectrum interference and determines the time period of the interference through autocorrelation calculation; segmented signal divides the discrete complex signal into continuous time period data according to the interference period, and each segment contains a complete interference period, laying the foundation for subsequent phase alignment and coherent merging.

[0012] Step 104: Perform down-conversion, inter-segment phase alignment and coherent merging processing on the segmented signals of multiple time periods in sequence to obtain a fine estimate of the interference Doppler frequency shift.

[0013] Down-conversion converts the carrier frequency of the segmented signal to the baseband, eliminating the influence of the carrier frequency; inter-segment phase alignment uses the first segment signal as a reference to estimate and compensate for the relative phase of the remaining segments, ensuring that the interference components in each segment have consistent phase; coherent combining superimposes the phase-aligned multi-segment signals to enhance interference characteristics and suppress noise; the fine estimate of the interference Doppler frequency shift is the interference frequency value obtained by optimizing the coarse estimate, used to accurately characterize the frequency characteristics of the interference. Through frequency estimation and phase alignment, the characteristic dispersion problem caused by interference frequency offset and phase drift is solved, providing accurate frequency parameters for constructing a stable interference template.

[0014] Step 106: Based on the fine estimate of the interference Doppler frequency shift, coherently accumulate the phase-aligned multi-segment signals, and then normalize the accumulation result to construct the sampling point-level polarity template vector of the interference.

[0015] Coherent accumulation, under precise frequency estimation, superimposes multiple phase-aligned signal segments to further enhance the stable structural characteristics of the interference. Normalization divides the accumulation result by its magnitude, eliminating the influence of amplitude fluctuations. The polarity template vector, a unit vector representing the interference direction / waveform skeleton, reflects only the phase and waveform structure of the interference, without amplitude information, thus avoiding the influence of amplitude estimation errors. By extracting the structural features of the interference, an interference fingerprint is constructed for subsequent projection suppression, laying the foundation for accurate interference removal.

[0016] Step 108: Construct an orthogonal complementary projection operator based on the polar template vector, and use the orthogonal complementary projection operator to perform projection transformation on the discrete complex signal received in the current time period, and output the GNSS signal after suppressing interference.

[0017] The orthogonal projection operator, a linear transformation operator based on polar template vectors, decomposes the currently received signal into two components: one parallel to the interference direction and the other perpendicular to it. The projection transformation removes the component parallel to the interference direction through operator operations, retaining the target satellite signal component perpendicular to the interference direction. The clean GNSS signal after interference suppression, after removing matched spectrum interference, can be directly input into the receiver's acquisition and tracking modules for further processing. Separating interference and target signals through orthogonal projection eliminates the need for high-dimensional matrix decomposition, resulting in low computational complexity and suitability for real-time processing requirements.

[0018] The aforementioned GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection first receives the GNSS intermediate frequency analog signal and completes analog-to-digital conversion, providing standardized input for all subsequent digital signal processing. This avoids noise interference and format differences in analog signals, laying a solid foundation for accurate processing. The method estimates and segments the discrete complex signal based on interference period. The core principle is to utilize the periodic characteristics of matched spectrum interference—by detecting the main peak interval of the autocorrelation function through time-domain autocorrelation calculation to determine the interference period, and dividing the signal into multiple segments according to the period. This provides common data blocks for subsequent multi-segment coherent merging and improves template consistency through the pre-design of periodic segmentation and cross-segment phase alignment, effectively reducing the impact of frequency offset and phase drift on suppression performance and solving the problem of unstable parameter estimation in traditional methods. Secondly, down-conversion, inter-segment phase alignment, and coherent merging are performed on the multi-segment signals to obtain a fine Doppler frequency shift. A two-step method of coarse and fine estimation is adopted: coarse estimation eliminates the polarity of the BPSK spreading code by squaring point by point, quickly locking the frequency range; fine estimation searches for candidate frequencies near the coarse estimate, and determines the precise frequency through phase compensation and coherent energy accumulation. This method does not rely on interference amplitude information throughout, fundamentally avoiding the residual interference and noise amplification caused by amplitude estimation errors in traditional SIC methods, thus overcoming the pain point of amplitude estimation sensitivity. A polar template vector is constructed based on fine frequency shift estimation. The interference signal-to-noise ratio is enhanced through coherent accumulation of multiple signal segments, and then the interference waveform skeleton is extracted after normalization, completely eliminating the influence of amplitude. It eliminates the need for eigenvalue / singular value decomposition of the high-dimensional covariance matrix, avoiding the high computational and storage overhead of traditional subspace methods and further reducing the risk of residual interference, laying the foundation for low-complexity projection. Finally, an orthogonal complementary projection operator is constructed based on the polar template. The projection calculation can be equivalent to a single inner product and a single vector subtraction, suitable for embedded real-time implementation. This significantly improves the survivability of navigation receivers in scenarios with strong matched spectrum interference and pseudo-satellite near-far effects. The computational load increases linearly with the processing length, perfectly adapting to the requirements of embedded real-time implementation. Meanwhile, this process can be extended to multiple interference scenarios. By constructing a multi-template matrix, it achieves low-rank joint suppression, has strong engineering adaptability, and significantly improves the survivability of navigation receivers in scenarios with strong matched spectrum interference and pseudo-satellite near-far effect, thus comprehensively solving the three core defects of traditional technologies.

[0019] In one embodiment, interference period estimation of the discrete complex signal includes: Perform time-domain autocorrelation operation on the discrete complex signal to obtain the autocorrelation function. Use the number of sampling points corresponding to the main peak interval of the autocorrelation function as the estimated number of sampling points per period. Divide multiple time periods based on the estimated number of sampling points per period.

[0020] Specifically, matched-spectrum interference exhibits stable periodicity, and its autocorrelation function displays a distinct main peak. The interval between these main peaks represents the interference period. Time-domain autocorrelation calculations amplify the periodicity of the signal and suppress noise interference, making the estimation of the interference period more reliable. Multiplying the estimated number of sampling points per period by the sampling period yields the interference time period. Dividing the signal into time segments ensures that each signal segment contains a complete interference period, providing a guarantee for the phase alignment and coherent merging of subsequent signal segments. This application requires no prior interference information; it achieves period estimation solely through the signal's own autocorrelation characteristics, demonstrating strong robustness and solving the problem of unknown interference period.

[0021] In one embodiment, the autocorrelation function is:

[0022] in, The autocorrelation function representing a discrete complex signal. k Indicates the number of lag steps. N This represents the total number of sampling points for a discrete complex signal. The first discrete complex signal represents the second... n The meaning of each sample value This represents the (n+k)th sample value of a discrete complex signal. express .

[0023] Specifically, such as Figure 2 As shown in the figure, the autocorrelation function exhibits a periodic main peak. The lag step interval between adjacent main peaks corresponds to the number of sampling points for the interference period. By detecting this interval, the estimated number of sampling points per period can be directly determined, providing a basis for signal segmentation. This intuitively presents the core logic of autocorrelation calculation in capturing the periodicity of interference. The autocorrelation function calculation method fully utilizes the phase and amplitude information of the complex signal, accurately capturing the periodicity of interference and providing a rigorous mathematical basis for period estimation.

[0024] In one embodiment, before obtaining a fine estimate of the interference Doppler frequency shift, a coarse estimate of the interference Doppler frequency shift is also included. The coarse estimate process includes performing point-by-point squaring operations on the discrete complex signal to eliminate the polarity of the BPSK spreading code, performing frequency domain spectral peak detection on the squared signal, and taking half of the frequency corresponding to the spectral peak as the coarse estimate of the interference Doppler frequency shift. The expression for point-by-point squaring is: in, For time-domain sampled values ​​of discrete complex signals, This is the signal value after being squared point by point.

[0025] Specifically, the coarse estimation method utilizes the characteristics of the BPSK spreading code to initially extract the interference frequency. It is simple to operate and highly efficient, providing a reliable initial value for subsequent fine estimation.

[0026] In one embodiment, segmented signals across multiple time periods are sequentially subjected to down-conversion, inter-segment phase alignment, and coherent combining processes to obtain a fine estimate of the interference Doppler frequency shift, including: Construct a set of candidate frequencies near the coarse estimate, perform downconversion and periodic segmentation on each candidate frequency; use the first segmented signal as a reference, estimate and compensate for the relative phase of the remaining segments using the inter-segment correlation inner product; calculate the energy of the coherent cumulative waveform of each segment after phase compensation as a consistency measure; traverse the set of candidate frequencies and select the frequency that maximizes the consistency measure as the fine estimate.

[0027] Specifically, the candidate frequency set is a series of frequency points selected near the coarse estimate to cover the actual interference frequency. Down-conversion is performed on each candidate frequency to convert the interference signal to baseband, and then the signal is segmented by period. The inter-segment correlation inner product estimates the relative phase by calculating the mean of the conjugate products of the two signal segments, and compensation is achieved through phase rotation. The consistency metric reflects the phase consistency of multiple signal segments at the candidate frequency; the higher the energy, the closer the candidate frequency is to the actual interference frequency. By traversing the candidate frequencies and selecting the frequency with the highest consistency metric, accurate estimation of the interference Doppler shift is achieved, solving the problem of insufficient accuracy in the coarse estimate. The coarse-fine estimation process for the interference frequency is as follows: Figure 3 As shown.

[0028] In one embodiment, the expression for the consistency measure is: ,in, , Indicates the total number of segments. Indicates the first m Segment signal at candidate frequency f The processed vector below, Indicates the first m Segment signal at candidate frequency f The phase compensation amount below.

[0029] Specifically, this consistency metric can accurately select the optimal candidate frequency, providing high-precision frequency parameters for subsequent template construction.

[0030] In one embodiment, the expression for constructing the polar template vector is:

[0031] in, This is a unit polarity template vector used to characterize the main directions of the interference subspace. For unnormalized polar template vectors, This is a fine estimate of the interference Doppler frequency shift.

[0032] Specifically, the polarity template vector retains only the polarity skeleton (phase and waveform structure) of the interference, excluding amplitude information, thus avoiding the impact of amplitude estimation errors on interference suppression. It accurately represents the orientation of the interference subspace, providing a core basis for subsequent orthogonal projection suppression. A schematic diagram of polarity template construction is shown below. Figure 4 As shown.

[0033] In one embodiment, the orthogonal complement projection operator is constructed based on the polar template vector. in, It is the identity matrix. It is a polar template vector. It is the conjugate transpose of u.

[0034] Specifically, this operator can be constructed without high-dimensional matrix decomposition, only through vector outer products, resulting in low computational complexity. It is well-suited for embedded real-time processing requirements and solves the high complexity problem of traditional subspace projection methods. A schematic diagram illustrating the geometric meaning of orthogonal complementary projection is shown below. Figure 5 As shown.

[0035] In one embodiment, an orthogonal projection operator is used to perform a projection transformation on the discrete complex signal received in the current time period, outputting a GNSS signal with suppressed interference, including: The orthogonal projection operator is used to perform a projection transformation on the discrete complex signal received in the current time period, and the output GNSS signal after interference suppression is:

[0036] in, This is the currently received discrete complex signal vector. It is a polar template vector. This is the conjugate transpose of u. This is the orthogonal complementary projection operator.

[0037] Specifically, the above projection transformation can be achieved through only one inner product and one vector subtraction. The computational load increases linearly with the signal length, avoiding the explicit generation of a large projection matrix. Interference cancellation can be achieved through only vector inner product and subtraction operations, which can meet the real-time processing requirements of broadband high sampling rate receivers.

[0038] In one embodiment, the polar template vector is obtained using a sliding update method, including a length of... L Processing is done in units of blocks, for the nearest M Each period of data undergoes phase alignment and coherent merging to update. And perform a projection transformation on the current block.

[0039] Specifically, by using a sliding window approach to periodically update the polarity template vector u, the method can adapt to scenarios where the interference frequency and phase change slowly over time, ensuring that the template vector always accurately represents the characteristics of the current interference. This not only guarantees the timeliness of interference suppression but also maintains the stability of the template through coherent merging of multi-period data, thus improving the robustness of the method under dynamic interference scenarios.

[0040] In a specific embodiment, such as Figure 6 The diagram shown is a block diagram of a GNSS anti-jamming receiver / computer device in one embodiment. Each module communicates sequentially through a data bus to form a complete hardware processing link, thus clarifying the engineering implementation architecture of the method.

[0041] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0042] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A GNSS matched spectrum interference suppression method based on polarity recovery and orthogonal subspace projection, characterized in that, The method includes: Receive GNSS intermediate frequency analog signals, perform analog-to-digital conversion on the analog signals to obtain discrete complex signals; The discrete complex signal is subjected to interference period estimation, and the discrete complex signal is divided into segmented signals of multiple consecutive time periods based on the estimated interference period. The segmented signals from the multiple time periods are sequentially subjected to downconversion, inter-segment phase alignment, and coherent merging processes to obtain a fine estimate of the interference Doppler frequency shift. Based on the fine estimate of the interference Doppler frequency shift, coherent accumulation is performed on the phase-aligned multi-segment signals, and the accumulation result is normalized to construct the sampling point-level polarity template vector of the interference. An orthogonal complementary projection operator is constructed based on the polarity template vector. The orthogonal complementary projection operator is used to perform projection transformation on the discrete complex signal received in the current time period, and outputs the GNSS signal after suppressing interference.

2. The method according to claim 1, characterized in that, Estimating the interference period of the discrete complex signal includes: Perform time-domain autocorrelation operation on the discrete complex signal to obtain the autocorrelation function. Use the number of sampling points corresponding to the main peak interval of the autocorrelation function as the estimated number of sampling points per period. Divide the multiple time periods according to the estimated number of sampling points per period.

3. The method according to claim 2, characterized in that, The autocorrelation function is: in, The autocorrelation function representing a discrete complex signal. k Indicates the number of lag steps. N This represents the total number of sampling points for a discrete complex signal. The first discrete complex signal represents the second... n The meaning of each sample value This represents the (n+k)th sample value of a discrete complex signal. express .

4. The method according to claim 1, characterized in that, Before obtaining a fine estimate of the interference Doppler frequency shift, a coarse estimate of the interference Doppler frequency shift is also included. The coarse estimate process includes performing point-by-point squaring operations on the discrete complex signal to eliminate the polarity of the BPSK spreading code, performing frequency domain spectral peak detection on the squared signal, and taking half of the frequency corresponding to the spectral peak as the coarse estimate of the interference Doppler frequency shift. The expression for the point-by-point squaring operation is: in, These are time-domain sampled values ​​of a discrete complex signal. This is the signal value after being squared point by point.

5. The method according to claim 4, characterized in that, The segmented signals across the multiple time periods are sequentially subjected to down-conversion, inter-segment phase alignment, and coherent combining processes to obtain a fine estimate of the interference Doppler frequency shift, including: A set of candidate frequencies is constructed near the coarse estimate. For each candidate frequency, downconversion and periodic segmentation are performed. Using the first segmented signal as a reference, the relative phase of the remaining segments is estimated and compensated using the inter-segment correlation inner product. The energy of the coherent cumulative waveform of each segment after phase compensation is calculated as a consistency measure. The set of candidate frequencies is traversed, and the frequency that maximizes the consistency measure is selected as the fine estimate.

6. The method according to claim 5, characterized in that, The expression for the consistency measure is: ,in, , Indicates the total number of segments. Indicates the first m Segment signal at candidate frequency f The processed vector below, Indicates the first m Segment signal at candidate frequency f The phase compensation amount below.

7. The method according to claim 1, characterized in that, The expression for constructing the polar template vector is: in, The unit polarity template vector is used to characterize the main directions of the interference subspace. For unnormalized polar template vectors, This is a fine estimate of the interference Doppler frequency shift.

8. The method according to claim 1, characterized in that, Based on the polar template vector, an orthogonal complement projection operator is constructed as follows: in, It is the identity matrix. It is a polar template vector. It is the conjugate transpose of u.

9. The method according to claim 1, characterized in that, The orthogonal projection operator is used to perform a projection transformation on the discrete complex signal received in the current time period, and outputs a GNSS signal after interference suppression, including: The orthogonal projection operator is used to perform a projection transformation on the discrete complex signal received in the current time period, and the output GNSS signal after interference suppression is... in, This is the currently received discrete complex signal vector. It is a polar template vector. This is the conjugate transpose of u. This is the orthogonal complementary projection operator.

10. The method according to claim 7, characterized in that, The polarity template vector is obtained using a sliding update method, including vectors of length 1. L Processing is done in units of blocks, for the nearest M Each period of data performs the phase alignment and coherent merging to update. And perform a projection transformation on the current block.

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