Beidou satellite signal anti-interference optimization method, system, equipment and medium

Abstract

The invention discloses a Beidou satellite signal anti-interference optimization method, system and device and a medium, and belongs to the technical field of satellite navigation, and the method comprises the steps: receiving Beidou satellite signals, and generating a four-dimensional digital signal matrix based on the Beidou satellite signals; performing polarization-space-time four-dimensional joint coding on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block; calculating a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, identifying a narrowband interference spectral line and extracting broadband interference covariance features; according to the narrowband interference spectral line and the broadband interference covariance characteristic, generating a space domain null trap, and obtaining an anti-interference baseband signal; fusing the anti-interference baseband signal with the Beidou inter-satellite differential data, and compensating an ionospheric disturbance error to obtain a high-precision positioning enhanced signal; and optimizing a polarization weight and a space-time parameter based on the high-precision positioning enhancement signal, and generating a feedback instruction. According to the invention, different polarization components can be effectively decomposed and processed, and the effectiveness and anti-interference capability of signals are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation technology, in particular to a Beidou satellite signal anti-interference optimization method, system, device and medium. BACKGROUND

[0002] Multi-polarization array improves interference suppression capability through polarization diversity, and space-time processing optimizes beam forming by combining the space-time characteristics of array signals; however, the existing technology mostly adopts fixed polarization base or space-time parameters, which is difficult to adapt to the coordinated suppression requirements of multiple types of interference (such as narrowband sweep, wideband noise and spatially related interference) in electromagnetic environment; for example, the scheme based on fixed polarization coding is prone to failure when the interference polarization changes rapidly, and the traditional N-sigma detection algorithm relies on static threshold, resulting in high false alarm rate of narrowband interference and cumulative deviation of wideband interference covariance estimation.

[0003] In recent years, research has begun to focus on how to combine multi-dimensional signal processing and intelligent algorithms to improve the anti-interference capability and positioning accuracy of signals; although some advanced technologies in the existing technology have improved in terms of accuracy and anti-interference, most methods still face the problem of balancing processing speed and accuracy, and lack adaptability in interference environment, which cannot maintain stable performance in rapidly changing signal environment. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to realize the coordinated suppression of multiple types of interference in various electromagnetic environments, the improvement of Beidou positioning accuracy and the adaptive optimization of polarization weight and space-time parameters, while effectively compensating for ionospheric disturbance errors, through a four-polarization sensitive array antenna and polarization-space-time four-dimensional joint coding, N-sigma interference detection and Kalman filter suppression, and carrier phase double difference algorithm.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a Beidou satellite signal anti-interference optimization method, which comprises receiving a Beidou satellite signal, generating a four-dimensional digital signal matrix based on the Beidou satellite signal; performing polarization-space-time four-dimensional joint coding on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block; calculating a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, identifying narrowband interference spectrum lines and extracting wideband interference covariance characteristics; generating a spatial null according to the narrowband interference spectrum lines and the wideband interference covariance characteristics to obtain an anti-interference baseband signal; fusing the anti-interference baseband signal with Beidou inter-satellite differential data and compensating for ionospheric disturbance errors to obtain a high-precision positioning enhancement signal; optimizing polarization weight and space-time parameters based on the high-precision positioning enhancement signal to generate a feedback instruction.

[0007] As a preferred scheme of the Beidou satellite signal anti-interference optimization method, the receiving Beidou satellite signal and generating a four-dimensional digital signal matrix based on the Beidou satellite signal comprises: receiving the Beidou satellite signal through an array antenna, adjusting the polarization state of each array element to generate a polarization parameter set; adjusting the polarization state of the array antenna based on the polarization parameter set to generate a local oscillator signal; mixing the Beidou satellite signal with the local oscillator signal and performing filtering processing to obtain a down-converted signal; sampling the down-converted signal, constructing an observation matrix and generating an observation vector; decomposing the observation vector to reconstruct a four-dimensional digital signal matrix and outputting the four-dimensional digital signal matrix. The beneficial effect of the preferred technical scheme is that the tensor decomposition reconstruction method combining array antenna polarization state adjustment and observation matrix construction can effectively extract multi-dimensional signal features and construct a high-quality four-dimensional digital signal matrix, laying a solid foundation for subsequent polarization-space-time joint processing.

[0008] As a preferred scheme of the Beidou satellite signal anti-interference optimization method, the polarization-space-time four-dimensional joint coding of the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block comprises: separating a first polarization component and a second polarization component from the four-dimensional digital signal matrix; performing space-time coding on the first polarization component and the second polarization component respectively to generate a space-time two-dimensional signal block; merging the generated space-time two-dimensional signal block into a four-dimensional space-time-polarization tensor according to the polarization dimension; and generating a frequency domain spectrum signal block based on the four-dimensional space-time-polarization tensor.

[0009] As a preferred scheme of the Beidou satellite signal anti-interference optimization method, the calculation of a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, the identification of narrowband interference spectral lines and the extraction of wideband interference covariance features comprise: performing amplitude statistics on the frequency domain spectrum signal block to calculate the mean and standard deviation of each frequency point; calculating a frequency domain spectrum threshold value based on the mean and standard deviation to generate a dynamic threshold value; marking the frequency points with amplitudes exceeding the dynamic threshold value as narrowband interference spectral lines to generate a narrowband interference spectral line position identification matrix; and calculating a space-time covariance matrix based on the frequency point data not marked as interference to output wideband interference covariance features. The beneficial effect of the preferred technical scheme is that the detection method combining dynamic threshold value calculation and narrowband interference spectral line marking can accurately identify multiple types of interference sources in the electromagnetic environment, effectively distinguish narrowband and wideband interference features, and improve the accuracy and robustness of interference detection.

[0010] As a preferred scheme of the Beidou satellite signal anti-interference optimization method, the method comprises: updating time domain weight based on the narrowband interference spectral line position identification matrix, suppressing narrowband interference components, and outputting an intermediate signal; generating a space domain nulling weight value by using the wideband interference covariance characteristic; performing space-time weighting fusion on the intermediate signal and the space domain nulling weight value to generate an anti-interference signal; and performing baseband synchronization processing on the anti-interference signal to output the baseband signal after anti-interference.

[0011] As a preferred scheme of the Beidou satellite signal anti-interference optimization method, the method comprises: fusing the baseband signal after anti-interference with Beidou inter-satellite differential data, and compensating ionospheric disturbance errors to obtain a high-precision positioning enhancement signal; receiving and analyzing Beidou inter-satellite differential data to obtain satellite clock errors, orbit errors and ionospheric correction parameters; performing pseudo-code despreading and carrier phase synchronization on the baseband signal after anti-interference to extract original observation values of each satellite; fusing the baseband signal observation values with the Beidou inter-satellite differential data to generate a joint observation data set and construct a carrier phase double difference model; correcting ionospheric delay errors by using the carrier phase double difference model to generate corrected carrier phase observation values; and inputting the corrected carrier phase observation values into a positioning solution algorithm to output a high-precision Beidou enhanced positioning signal. The preferred technical scheme has the beneficial effects that the carrier phase double difference model and the ionospheric correction parameter fusion processing method can effectively eliminate the influence of satellite clock errors and receiver clock errors, accurately compensate ionospheric delay errors, and improve the Beidou positioning precision and reliability.

[0012] As a preferred scheme of the Beidou satellite signal anti-interference optimization method, the method comprises: optimizing polarization weight and space-time parameters based on the high-precision positioning enhancement signal to generate a feedback instruction; calculating a signal-to-interference ratio of the high-precision positioning enhancement signal to obtain a preset threshold of a Beidou satellite performance index; judging the performance state of the current polarization weight and space-time parameters according to a comparison result of the signal-to-interference ratio and the preset threshold; if the signal-to-interference ratio is not up to standard, generating a polarization weight adjustment instruction and a space-time tap number optimization instruction based on the change rate of the signal-to-interference ratio; and feeding back the polarization weight adjustment instruction and the space-time tap number optimization instruction to polarization encoding and space-time filtering to update parameters and generate a feedback instruction.

[0013] The application provides a Beidou satellite signal anti-interference optimization system.

[0014] To solve the above technical problems, the application provides the following technical scheme: a Beidou satellite signal anti-interference optimization system, comprising: a signal receiving module, configured to receive a Beidou satellite signal and generate a four-dimensional digital signal matrix based on the Beidou satellite signal; a polarization decomposition module, configured to perform polarization-space-time four-dimensional joint coding on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block; an interference detection module, configured to calculate a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, identify a narrowband interference spectrum line and extract a wideband interference covariance feature; a spatial domain suppression module, configured to generate a spatial domain null based on the narrowband interference spectrum line and the wideband interference covariance feature to obtain a baseband signal after anti-interference; a differential fusion module, configured to fuse the baseband signal after anti-interference with Beidou inter-satellite differential data, compensate for ionospheric disturbance errors, and obtain a high-precision positioning enhancement signal; and a real-time tuning module, configured to optimize polarization weights and space-time parameters based on the high-precision positioning enhancement signal to generate a feedback instruction.

[0015] The application provides a computer device, comprising a memory and a processor, and the memory stores a computer program, characterized in that the processor implements the steps of the Beidou satellite signal anti-interference optimization method when executing the computer program.

[0016] The application provides a computer readable storage medium, which stores a computer program, characterized in that the computer program is executed by a processor to implement the steps of the Beidou satellite signal anti-interference optimization method.

[0017] The application has the beneficial effects that: the application receives signals through a four-polarization sensitive array antenna and performs polarization-space-time four-dimensional joint coding, can effectively decompose and process different polarization components, and improves the effectiveness and anti-interference ability of signals; narrowband interference spectrum lines are identified by using an N-sigma algorithm, interference suppression is performed by combining a Kalman filtering algorithm, and the robustness in an electromagnetic environment is enhanced; Beidou inter-satellite differential data is fused, an ionospheric disturbance error is compensated for by using a carrier phase double difference algorithm, and the positioning precision is further improved; polarization weights and space-time parameters are optimized, real-time adjustment settings can be made for different interference environments, and the anti-interference effect and positioning performance are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort. Among them:

[0019] Figure 1 A flowchart of a Beidou satellite signal anti-interference optimization method provided by an embodiment of the application is shown in the figure.

[0020] Figure 2 A module diagram of a Beidou satellite signal anti-interference optimization system is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may, however, be practiced in a variety of ways beyond the specific embodiments described herein without departing from the scope of the present application. It should be noted that the present application is not limited to the specific embodiments described herein, but also covers other embodiments that fall within the scope of the present application.

[0023] Secondly, the term "one embodiment" or "an embodiment" as used herein means that a particular implementation can include a particular feature, structure, or characteristic, but every embodiment can not necessarily include the particular feature, structure, or characteristic. Moreover, such features, structures, or characteristics can be combined in one or more implementations. Therefore, appearance of the term "in one embodiment" or "in an embodiment" at various places in the specification does not necessarily refer to the same embodiment. Furthermore, particular features, structures, or characteristics can be implemented in software, hardware, or a combination thereof.

[0024] Embodiment 1, Reference Figure 1 For an embodiment of the present application, the embodiment provides a Beidou satellite signal anti-interference optimization method, comprising:

[0025] S100: receiving a Beidou satellite signal, and generating a four-dimensional digital signal matrix based on the Beidou satellite signal.

[0026] S200: performing polarization-space-time four-dimensional joint coding on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block.

[0027] S300: calculating a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, identifying a narrowband interference spectrum line, and extracting a wideband interference covariance feature.

[0028] S400: generating a spatial null based on the narrowband interference spectrum line and the wideband interference covariance feature to obtain a baseband signal after anti-interference.

[0029] S500: fusing the baseband signal after anti-interference with Beidou inter-satellite differential data, and compensating ionospheric disturbance error to obtain a high-precision positioning enhancement signal.

[0030] S600: optimizing polarization weight and space-time parameters based on the high-precision positioning enhancement signal to generate a feedback instruction.

[0031] It should be noted that the Beidou satellite signal is susceptible to multiple types of interference in the electromagnetic environment, and the existing technology adopts a fixed polarization base or static space-time parameters, which is difficult to adapt to the coordinated suppression requirements of narrowband sweep, wideband noise and spatial domain related interference in the electromagnetic environment; while the scheme based on fixed polarization coding is prone to failure when the interference polarization changes rapidly, and the traditional N-sigma detection algorithm relies on a static threshold, resulting in high false alarm rate of narrowband interference and cumulative deviation of wideband interference covariance estimation, thereby affecting the positioning accuracy; at the same time, due to the balance problem between processing speed and accuracy in signal processing under interference environment, the system may also fail to maintain stable performance in rapidly changing signal environment due to insufficient adaptability.

[0032] Therefore, in view of the above existing interference suppression and positioning accuracy problems, through the steps of S100-S600, the frequency domain signal characteristics and interference detection mechanism under polarization-space-time four-dimensional joint coding are obtained by receiving the Beidou satellite signal through the four-polarization sensitive array antenna, realizing accurate identification and coordinated suppression of narrowband and wideband interference; by calculating the spatial nulling weight, the interference signal is effectively suppressed, realizing real-time anti-interference processing of the baseband signal; at the same time, based on the carrier phase double difference algorithm and the closed-loop optimization of polarization weight and space-time parameters, high-precision positioning enhancement and adaptive optimization of performance of the Beidou satellite signal are realized.

[0033] Embodiment 2, refer to Figure 1 For an embodiment of the present application, a Beidou satellite signal anti-interference optimization method is provided based on the previous embodiment, comprising:

[0034] In the embodiment of the present application, the Beidou satellite signal is received in step S100, and a four-dimensional digital signal matrix is generated based on the Beidou satellite signal, comprising the following steps A1-A5:

[0035] A1: receiving the Beidou satellite signal through the array antenna, adjusting the polarization state of each array element, and generating a polarization parameter set.

[0036] A2: adjusting the polarization state of the array antenna based on the polarization parameter set, and generating a local oscillator signal.

[0037] A3: mixing the Beidou satellite signal with the local oscillator signal and performing filtering processing to obtain a down-converted signal.

[0038] A4: sampling the down-converted signal, constructing an observation matrix and generating an observation vector.

[0039] A5: decomposing the observation vector to reconstruct a four-dimensional digital signal matrix and outputting.

[0040] Specifically, in step A1, the Beidou satellite signal is received through the array antenna, the polarization state of each array element is adjusted, and a polarization parameter set is generated, comprising:

[0041] The Beidou signal is received by the quad-polarization sensitive array antenna.

[0042] The polarization inclination and azimuth of each independent array element are adjusted in real time to generate a polarization parameter set.

[0043] It should be noted that the quad-polarization sensitive array antenna is a core component that can receive Beidou satellite signals and adjust the polarization state of each array element in real time. Quad-polarization means that signals can be transmitted and received in multiple polarization modes, including horizontal, vertical, and two diagonal polarization modes. By precisely adjusting the polarization state of each array element, different signal environments can be addressed, the receiving sensitivity can be improved, and interference can be reduced.

[0044] Secondly, the polarization parameter set is generated by adjusting the polarization inclination and azimuth of the array elements, and contains polarization characteristic information of different array elements under different conditions, which is crucial for signal processing. By flexibly adjusting the polarization parameter set, effective information in the Beidou satellite signal can be effectively extracted in a multipath propagation environment.

[0045] Further, the polarization parameter set generated in step A1 includes:

[0046] The array antenna uses the principles of spatial diversity and polarization diversity. With different polarization modes of the array elements, different polarized signals can be received simultaneously, thereby improving the receiving sensitivity and anti-interference ability of the signals. The array antenna integrates an electronic control unit, which adjusts the polarization state of the array elements by controlling the current and voltage of the array elements, to optimize the receiving quality of the signals.

[0047] By adding electronic control elements (such as micro electric motors and MEMS gyroscopes) to each array element, the real-time position changes of the satellite can be determined. Through feedback, the inclination and azimuth of each array element are adjusted so that the array always maintains the best receiving direction.

[0048] High-precision sensors (such as accelerometers, gyroscopes, and electronic magnetometers) are used to monitor the angle changes of the array elements. The polarization state (polarization inclination, azimuth) of each array element is collected and stored. By aggregating the polarization state data of all array elements, a complete polarization parameter set is formed.

[0049] Specifically, in step A2, the polarization state of the array antenna is adjusted based on the polarization parameter set to generate the local oscillator signal, including: based on the polarization parameter set, the polarization state of the quad-polarization sensitive array antenna is adjusted to generate a nonlinear local oscillator signal.

[0050] It should be noted that in step A2, based on the polarization parameter set, the polarization state of each array element of the array antenna is adjusted by the electronic control element, ensuring that the polarization mode of each array element is consistent with the polarization mode of the received Beidou satellite signal. Each array element in the antenna array is equipped with a feedback mechanism, which detects the quality of the received signal (such as the signal-to-noise ratio index) in real time, and further adjusts the polarization state according to the feedback information. The adjustment method is to adjust the polarization tilt angle and azimuth angle of the antenna by controlling the current and voltage of the array element.

[0051] Secondly, the nonlinear local oscillator signal is modulated by a nonlinear method (such as quadratic modulation or cubic modulation), so that its output frequency characteristic is different from that of the traditional linear local oscillator signal. The traditional local oscillator signal is generally a linear signal with constant frequency and no special change. For example, the nonlinear local oscillator signal is processed using a nonlinear circuit (such as a nonlinear mixer or a high-order nonlinear element), thereby generating frequency modulation and amplitude modulation components, forming a local oscillator signal with nonlinear characteristics.

[0052] Specifically, in step A3, the Beidou satellite signal is mixed with the local oscillator signal and filtered to obtain a downconverted signal, including: after mixing the Beidou signal with the nonlinear local oscillator signal, passing through an adaptive band-stop filter to suppress out-of-band interference, and then outputting a downconverted signal.

[0053] It should be noted that the use of nonlinear local oscillator signals and the characteristics of adaptive band-stop filters help to process narrowband and wideband interference. The adaptive band-stop filter can optimize performance and improve interference rejection capability in a constantly changing signal environment by adjusting the suppression of specific interference frequency bands.

[0054] Further, in step A3, the received Beidou satellite signal and the generated nonlinear local oscillator signal are input into a mixer. The mixer is used to nonlinearly modulate two frequency signals to generate new spectral components. A high-order mixer is used to process the spectrum of the nonlinear local oscillator signal, and the frequency of the output signal is adjusted. Through this process, the original Beidou signal frequency is reduced to a baseband or near-baseband frequency range, facilitating subsequent processing.

[0055] Secondly, the filter uses an adaptive algorithm (such as the LMS algorithm or the RLS algorithm) to continuously update the filter coefficients, allowing it to adjust the filter parameters based on real-time changes in the mixed signal. In particular, when the signal is significantly affected by interference, the adaptive band-stop filter can actively adjust its stopband position to suppress out-of-band interference in real time. After the filter output, appropriate gain adjustment and output buffering are performed to generate a stable downconverted signal.

[0056] Specifically, the step A4 includes: performing compressed sensing sampling on the down-converted signal, constructing a partial Fourier observation matrix, and generating an observation vector.

[0057] It should be noted that the compressed sensing can reduce the data acquisition amount while ensuring the integrity of the signal by greatly reducing the signal sampling rate, and the observation vector is generated in combination with the partial Fourier observation matrix, which can improve the sampling efficiency, reduce the demand for storage and processing capacity, while maintaining the accuracy of signal reconstruction, and is suitable for complex signal processing tasks, such as efficient decoding of satellite navigation signals.

[0058] Specifically, the step A4 includes: performing compressed sensing sampling on the down-converted signal, constructing a partial Fourier observation matrix, and generating an observation vector.

[0059]

[0060] wherein, Φ m,n is a measurement value in compressed sensing, m is an element of the mth row of the observation matrix, n is an element of the nth column of the observation matrix, is a normalization factor, M is an observation dimension, is a summation of four elements of a four-polarized array, i is the ith element, is an integral of a time-varying parameter in a time interval [0, U], U is a signal period, e -j2π(·) is a complex exponential function, k i is a polarization state weight of the element i, -j is a right-hand polarization, ω r is an angular frequency of the rth frequency point, τ is an integral time variable, Q is a total number of sampling points of an original signal, β i (τ) is a dynamic interference suppression coefficient of the element i, θ i (τ) is a polarization tilt angle of the element i at time τ, ω v is an angular frequency of the vth observation frequency component, f BDS is a Beidou satellite reference frequency, e -ητ is a time attenuation factor, η is a control historical signal weight decay rate, is a rectangular window function, is a two-norm square of a polarization parameter set, dτ is an integral variable differential operator.

[0061] Specifically, the step A5 includes: decomposing the observation vector, reconstructing a four-dimensional digital signal matrix, and outputting the four-dimensional digital signal matrix.

[0062] It should be noted that high-order tensor decomposition provides a more accurate mathematical tool for high-dimensional reconstruction of signals, making signal extraction and processing more efficient, automatically identifying effective signal features in high-dimensional data, and accurately reconstructing them, thereby significantly improving signal processing accuracy. Compared with traditional two-dimensional or one-dimensional signal processing methods, the reconstruction of four-dimensional signal matrices can more comprehensively restore the structure of the signal and improve overall performance.

[0063] Further, a tensor is a multi-dimensional data structure, which is a high-order generalization of a matrix. If two-dimensional data can be represented by a matrix, three-dimensional or higher-dimensional data can be represented by a tensor. The goal of high-order tensor decomposition is to map the observation vector back to the original four-dimensional signal matrix.

[0064] In an optional embodiment, the Beidou satellite signal is received in step S100, and a four-dimensional digital signal matrix is generated based on the Beidou satellite signal. The signal can also be received by a smart antenna array combined with a beam forming technology, using a multiple-input multiple-output antenna system. The antenna pattern is adjusted according to the angle of arrival and polarization characteristics of the signal, and digital signal processing technology is used for real-time preprocessing of the received signal. A digital filter is used to replace an analog filter to achieve more accurate frequency selection. A software-defined radio platform is used to realize flexible configuration of signal processing algorithms, and a machine learning algorithm is introduced to optimize the signal reconstruction process. The reconstruction parameters are automatically adjusted according to historical data and real-time signal characteristics to improve the reconstruction accuracy and processing efficiency of the four-dimensional digital signal matrix.

[0065] In another optional embodiment, the Beidou satellite signal is received in step S100, and a four-dimensional digital signal matrix is generated based on the Beidou satellite signal. The signal can also be processed by a distributed antenna system. Multiple antenna nodes are deployed in different locations to improve signal reception quality using spatial diversity technology. A coherent synthesis method is used to optimally combine signals received by each node. Cognitive radio technology is introduced to realize spectrum sensing and spectrum allocation. The optimal working frequency band is selected according to the changes in the electromagnetic environment. An edge computing platform is used to realize distributed execution of signal processing tasks. The computing load is distributed to multiple processing nodes. Parallel processing technology is used to accelerate the generation process of the four-dimensional digital signal matrix, improving the real-time performance and scalability of the four-dimensional digital signal matrix generation.

[0066] It should be explained that the application receives signals through a four-polarization sensitive array antenna and performs polarization-space-time four-dimensional joint coding, which can effectively decompose and process different polarization components, improve the effectiveness and anti-interference ability of signals; compared with the receiving mode of a single polarization antenna or fixed polarization configuration in the prior art, the application can maintain signal reconstruction accuracy while reducing the sampling rate through compressed sensing sampling and high-order tensor decomposition, ensure the high-quality generation of the four-dimensional digital signal matrix, not only improve the reception quality and processing efficiency of the Beidou satellite signal, but also provide a reliable data basis for subsequent polarization decomposition, interference detection and positioning enhancement, effectively improve the overall performance in the interference environment.

[0067] In the embodiment of the application, the polarization-space-time four-dimensional joint coding of the four-dimensional digital signal matrix in step S200 generates a frequency domain spectrum signal block, including the following steps B1-B4:

[0068] B1: separating a first polarization component and a second polarization component from the four-dimensional digital signal matrix.

[0069] B2: performing space-time coding on the first polarization component and the second polarization component respectively to generate a space-time two-dimensional signal block.

[0070] B3: merging the generated space-time two-dimensional signal block into a four-dimensional space-time-polarization tensor according to the polarization dimension.

[0071] B4: generating a frequency domain spectrum signal block based on the four-dimensional space-time-polarization tensor.

[0072] In the embodiment of the application, the first polarization component in step B1 is a horizontal polarization component; and the second polarization component is a vertical polarization component.

[0073] Specifically, the separation of the first polarization component and the second polarization component from the four-dimensional digital signal matrix in step B1 includes:

[0074] Separating the horizontal polarization component and the vertical polarization component from the four-dimensional digital signal matrix, and realizing polarization dimension decomposition through the formula of the polarization projection operator.

[0075] It should be noted that the four-dimensional digital signal matrix refers to the signal data matrix after preprocessing, which contains multiple dimensions of signal information such as time, space, frequency and polarization. The representation form of the four-dimensional matrix can more comprehensively capture the multi-dimensional characteristics of the signal and provide a structured data basis for subsequent processing. Polarization is a property that describes the direction of electromagnetic wave propagation. In satellite communication, signals usually have two polarization modes: horizontal polarization (H) and vertical polarization (V). These two polarization components carry different signal information, and separating them helps improve signal resolution and anti-interference capability. The polarization projection operator decomposes the polarization dimension in the four-dimensional signal matrix into horizontal and vertical polarization components, extracts and separately processes the signals of each polarization mode.

[0076] Further, the formula of the polarization projection operator refers to using linear transformation to decouple the polarization dimension of the signal matrix through the projection matrix to obtain the horizontal polarization and vertical polarization components. Through the formula of the polarization projection operator, the horizontal polarization component and the vertical polarization component can be accurately separated from the four-dimensional digital signal matrix, the polarization information of the signal is effectively decoupled, and each polarization component signal can be independently processed, thereby improving the processing efficiency, anti-interference and recovery capability of the signal.

[0077] Specifically, the first polarization component and the second polarization component are respectively space-time encoded in step B2 to generate a space-time two-dimensional signal block, including: the horizontal polarization component and the vertical polarization component are respectively space-time encoded to generate a space-time two-dimensional signal block.

[0078] It should be noted that space-time encoding refers to encoding signals in the time and space domains, which can improve the transmission reliability and anti-interference capability of signals. The horizontal and vertical polarization components of the signal are respectively space-time encoded, thereby providing more stable and anti-interference signal blocks for subsequent signal processing.

[0079] Secondly, the space-time two-dimensional signal block refers to the result of encoding the signal in the time and space domains, which is usually a two-dimensional matrix. One dimension is the time dimension, and the other dimension is the space dimension (antenna array). The two-dimensional matrix stores the encoded signals sent in multiple time slots and antenna elements. For example, assuming that a signal is encoded through a 2x2 antenna array, the two-dimensional signal block formed after time encoding may look like a 2x2 matrix, and each element represents the signal sent through a specific antenna at a specific time.

[0080] Further, the step B2 of generating a space-time two-dimensional signal block includes:

[0081] By using multiple antenna arrays (such as 2x2 or 4x4 arrays) for spatial encoding, multiple copy signals are sent simultaneously using different transmission paths, and these copy signals are combined at the receiving end to improve the receiving quality. First, the input horizontal polarization signal and the vertical polarization signal are respectively time domain encoded, and at the same time, the signals are spatially encoded in multiple antenna arrays. For example, in a 2x2 array, the encoded signals are alternately transmitted between two antenna units, increasing the spatial redundancy and ensuring that multiple signal copies reach the receiving end through different paths.

[0082] Secondly, in each time slot, different copies of the signal will be alternately transmitted in different antenna arrays, ensuring the redundancy distribution of the signal. After space-time encoding, the horizontal polarization component and the vertical polarization component are respectively encoded to obtain the encoded signal. The encoded signal will form a space-time two-dimensional signal block.

[0083] It should be noted that the step B3 of merging the generated space-time two-dimensional signal block into a four-dimensional space-time-polarization tensor according to the polarization dimension means that the signal information of different dimensions can be integrated into a unified data structure by merging the space-time encoded signal block into a four-dimensional space-time-polarization tensor according to the polarization dimension. The multi-dimensional characteristics of the signal can be more fully utilized to improve the signal recovery and processing efficiency. Through this tensor representation method, the multi-dimensional mode of the signal can be more efficiently captured and analyzed, thereby providing more rich feature information for subsequent signal enhancement, filtering and positioning.

[0084] Specifically, the step B4 of generating a frequency domain spectrum signal block based on the four-dimensional space-time-polarization tensor means performing a fast Fourier transform on the four-dimensional space-time-polarization tensor to generate a frequency domain spectrum signal block.

[0085] It should be noted that the Fourier transform is a core tool in signal processing, which can convert time domain signals to frequency domain and reveal their frequency components. Fast Fourier transform is an optimization of traditional Fourier transform, which can greatly reduce the computational complexity.

[0086] Further, the step B4 of generating a frequency domain spectrum signal block includes:

[0087] After space-time encoding, the horizontal polarization component and the vertical polarization component respectively obtain two two-dimensional signal blocks, each signal block has a size of 2xT. In order to convert from time domain to frequency domain, a fast Fourier transform (FFT) is performed on the space-time signal block. The horizontal signal block is subjected to a fast Fourier transform. The input of the FFT is the signal in the time domain, i.e. each table represents the signal sample in time. The FFT will convert these samples to frequency domain samples. Similarly, the vertical polarization signal block is subjected to FFT conversion to obtain the frequency domain spectrum signal.

[0088] The frequency domain spectrum signal block is obtained by merging the obtained frequency domain spectrum signals according to the polarization dimension through FFT conversion of the signal of each polarization component.

[0089] In an optional embodiment, the polarization-space-time four-dimensional joint encoding is performed on the four-dimensional digital signal matrix in step S200 to generate the frequency domain spectrum signal block, and wavelet transform can be used instead of fast Fourier transform to perform time-frequency analysis, a multi-scale decomposition method is used to obtain the time domain and frequency domain characteristics of the signal, wavelet packet decomposition technology is used to finely process the signals of different frequency bands, an independent component analysis method is introduced to perform blind source separation on the polarization components, the contributions of different signal sources are automatically identified and separated, a sparse coding technology is used to compress the signal representation, a dictionary learning method is used to construct the sparse representation base of the signal, the data storage requirement is reduced and the calculation efficiency of subsequent processing is improved, and finally a filter bank is used to realize parallel processing of multi-band signals.

[0090] In another optional embodiment, the polarization-space-time four-dimensional joint encoding is performed on the four-dimensional digital signal matrix in step S200 to generate the frequency domain spectrum signal block, and a deep learning network can be used for end-to-end feature extraction and encoding, a convolutional neural network is used to learn the spatial features of the signal, a recurrent neural network is used to capture the time dependence of the signal, an attention mechanism is designed to highlight important signal features, a graph neural network is introduced to model the spatial relationship between antenna arrays, a graph convolution operation is used to process non-Euclidean structured signal data, a variational autoencoder is used to learn the latent representation of the signal, dimensionality reduction and feature compression of the signal are realized, an adversarial generative network is used to enhance the quality and robustness of the signal, and the resistance to noise and interference is improved.

[0091] It should be noted that the polarization projection operator is used to realize polarization dimension decomposition in the present application, a two-dimensional signal block is generated by combining space-time encoding technology, and a frequency domain spectrum signal block is obtained by tensor merging and fast Fourier transform, which can fully utilize the multi-dimensional characteristics of the signal, improve the accuracy and efficiency of signal processing; compared with the single-dimensional signal processing or simple polarization separation in the prior art, the four-dimensional joint encoding in the present application solves the problem that the traditional method cannot simultaneously process time, space, frequency and polarization multi-dimensional information, the space-time encoding can enhance the transmission reliability and anti-interference ability of the signal, the tensor representation can better maintain the structural information of the signal, not only improves the analysis accuracy and processing efficiency of the signal, but also provides more rich feature information for subsequent interference detection and suppression, and thus more accurate signal reconstruction and feature extraction can be realized in the electromagnetic environment, and the overall performance of the Beidou satellite signal processing is effectively improved.

[0092] In the embodiment of the present application, the frequency domain spectrum threshold value is calculated based on the frequency domain spectrum signal block in step S300, narrowband interference spectrum lines are identified and wideband interference covariance features are extracted, including the following steps C1-C4:

[0093] C1: Perform amplitude statistics on the frequency domain spectrum signal block, and calculate the mean and standard deviation of each frequency point.

[0094] C2: Calculate the frequency domain spectrum threshold value based on the mean and standard deviation, and generate a dynamic threshold value.

[0095] C3: Mark the frequency points whose amplitudes exceed the dynamic threshold value as narrowband interference spectrum lines, and generate a narrowband interference spectrum line position identification matrix.

[0096] C4: Calculate the space-time covariance matrix based on the frequency point data not marked as interference, and output the wideband interference covariance features.

[0097] Specifically, in step C1, the amplitude statistics of the frequency domain spectrum signal block are performed, and the mean and standard deviation of each frequency point are calculated, including:

[0098] The amplitude statistics of the frequency domain spectrum signal block are performed, that is, the amplitude of each frequency point is calculated to obtain its distribution characteristics in the frequency domain.

[0099] The statistical characteristics (i.e., the central value and fluctuation amplitude) of each frequency point are obtained by calculating the mean and standard deviation of the amplitude data.

[0100] Specifically, in step C2, the frequency domain spectrum threshold value is calculated based on the mean and standard deviation, and a dynamic threshold value is generated, which means that the frequency domain spectrum threshold value is calculated by the N-sigma algorithm, and a dynamic threshold value is generated.

[0101] It should be noted that the N-sigma algorithm is a dynamic threshold calculation method based on statistical principles, which is used to identify abnormal values in signals. The N-sigma algorithm calculates the mean and standard deviation of the frequency points, sets a threshold value to identify interference frequencies, and the N value represents the multiple of the standard deviation. When the amplitude of the signal exceeds this threshold value, the frequency point is marked as a potential interference signal. According to the real-time frequency domain spectrum, the interference detection threshold is adjusted to avoid false positives caused by fixed threshold values.

[0102] Specifically, in step C2, the dynamic threshold value is generated, including:

[0103] The signal is converted to the frequency domain by spectrum analysis, and the amplitude (i.e., the size of the signal) of each frequency point is calculated. The amplitude value reflects the intensity distribution of the signal at different frequencies.

[0104] The frequency domain signal is statistically analyzed to calculate the mean value (average amplitude) and standard deviation (amplitude fluctuation degree) of each frequency point. Through the N-sigma algorithm, the threshold value of each frequency point is dynamically calculated, and the threshold value can be adaptively adjusted to avoid the defect that the fixed threshold value cannot adapt to the change of the signal environment. For example, in the case of complex signal environment and large interference fluctuation, the dynamic threshold value can be flexibly adjusted to ensure that the abnormal interference signal can be accurately detected.

[0105] It should be noted that the frequency points with amplitudes exceeding the dynamic threshold value in step C3 are narrowband interference spectral lines, and generating a narrowband interference spectral line position identification matrix means marking the frequency points with amplitudes exceeding the dynamic threshold value as narrowband interference spectral lines to generate a corresponding frequency position identification matrix. The identification matrix provides accurate interference source positions for subsequent processing. Narrowband interference spectral lines have high amplitudes and relatively concentrated characteristics in the frequency domain, and these interference frequency points can be accurately identified and marked.

[0106] Secondly, the frequency point data not marked as interference in step C4 is used to calculate the space-time covariance matrix, and the covariance characteristics of the wideband interference are output based on the space-time covariance matrix. The space-time covariance matrix can effectively describe the spatial and temporal correlation of the signal, especially when the signal contains multiple signal sources or interference sources, which can help identify the characteristics of the wideband interference, extract the statistical characteristics of the wideband interference, and provide a basis for interference suppression and signal recovery.

[0107] Specifically, in step C4, the space-time covariance matrix is calculated based on the frequency point data not marked as interference, and the wideband interference covariance characteristics are output, including:

[0108] According to the received signal, a signal vector is constructed in time and space. For each time point, the signals of multiple receiving antennas can be integrated into a vector.

[0109] For all time points, the expected value (i.e. average signal) of the received signal is calculated.

[0110] The space-time covariance matrix is calculated according to the signal vector and the expected value.

[0111] Since the eigenvalues and eigenvectors of the space-time covariance matrix can usually help analyze the spatial and temporal characteristics of the signal, the wideband interference covariance characteristics are obtained by performing eigenvalue decomposition on the covariance matrix.

[0112] In an optional embodiment, the frequency domain spectrum threshold value is calculated based on the frequency domain spectrum signal block in step S300, the narrowband interference spectrum line is identified, and the wideband interference covariance feature is extracted, and the interference detection can also be performed by a machine learning method, a support vector machine or a random forest algorithm is used to train an interference identification model, a feature vector is constructed by using historical interference data, and the distinguishing features of normal signals and interference signals are automatically learned, a deep learning network is introduced for end-to-end interference detection, local features of the frequency domain signal are extracted by a convolutional neural network, the time sequence change mode of the interference is captured by using a long short-term memory network, and an unsupervised learning method is used for anomaly detection, the representation of the normal signal is learned by using a self-encoder, and frequency points with large reconstruction errors are marked as interference, so that the accuracy and robustness of interference detection are improved.

[0113] In another optional embodiment, the frequency domain spectrum threshold value is calculated based on the frequency domain spectrum signal block in step S300, the narrowband interference spectrum line is identified, and the wideband interference covariance feature is extracted, and the detection precision can also be improved by using a multi-threshold detection method, a plurality of threshold values with different confidence levels are set, a hierarchical detection strategy is used to classify and identify interference of different intensities, time domain and frequency domain joint analysis is introduced, the signal time-frequency features are combined for comprehensive judgment, multi-scale analysis is performed by using a wavelet transform, interference modes on different time scales are identified, the spectral resolution is improved by using a spectrum estimation technique, and an adaptive threshold updating mechanism is established, so that the detection parameters are adjusted in real time according to the change of the signal environment, and the adaptability to the interference environment is improved.

[0114] It should be noted that, by using the N-sigma algorithm to calculate the threshold value and combining the amplitude statistics and the covariance feature extraction, the narrowband interference and the wideband interference can be accurately identified, the effective detection of multiple types of interference in the electromagnetic environment is realized, compared with the fixed threshold value or the single detection method in the prior art, the dynamic threshold calculation solves the problems that the traditional method is difficult to adapt to the change of the signal environment and is prone to false alarm and missed detection, the N-sigma algorithm can adjust the detection threshold according to the real-time frequency domain spectrum change, the covariance feature extraction can effectively distinguish the statistical characteristics of the narrowband and wideband interference, the accuracy and reliability of the interference detection are improved, accurate position information and feature parameters are provided for subsequent interference suppression, different types of interference can be suppressed by using corresponding suppression strategies, and the anti-interference performance of the Beidou satellite signal in the interference environment is effectively improved.

[0115] In the embodiment of the application, the spatial null is generated according to the narrowband interference spectrum line and the wideband interference covariance feature in step S400, and the baseband signal after interference suppression is obtained, including the following steps D1-D4:

[0116] D1: The time domain weight is updated based on the narrowband interference spectrum line position identification matrix, the narrowband interference component is suppressed, and the intermediate signal is output.

[0117] D2: generating spatial nulling weight values using wideband interference covariance characteristics.

[0118] D3: spatio-temporally weighting and fusing the intermediate signal with the spatial nulling weight values to generate an anti-interference signal.

[0119] D4: performing baseband synchronization processing on the anti-interference signal to output an anti-interference baseband signal.

[0120] Specifically, the step D1 of updating the time domain weight values based on the narrowband interference spectral line position identification matrix, suppressing the narrowband interference components, and outputting an intermediate signal comprises:

[0121] updating the time domain weight values recursively using Kalman filtering based on the narrowband interference spectral line position identification matrix, suppressing the narrowband interference components, and outputting an intermediate signal.

[0122] It should be noted that the narrowband interference spectral line position identification matrix is used to mark which frequency points in the frequency domain are the spectral line positions of the narrowband interference, and the element values of the matrix are usually 1 indicating that the position is an interference frequency point, and 0 indicating a non-interference frequency point. The narrowband interference is concentrated in a specific frequency domain range.

[0123] Secondly, Kalman filtering is a recursive estimation method, which calculates the interference component weight in the time domain in real time and optimizes and updates according to the marked narrowband interference spectral line position, and can effectively suppress the influence of the interference signal through adjustment.

[0124] Further, the role of the narrowband interference spectral line position identification matrix in step D1 is to identify which frequency points are the sources of narrowband interference and determine the exact positions of these frequency points. If the amplitude of each frequency point exceeds a dynamic threshold value, it is marked as a narrowband interference frequency point. After processing by the Kalman filter, the influence of the narrowband interference has been weakened, and the useful information in the signal has been preserved. The frequency points coinciding with the interference spectral lines are suppressed. After recursive updating of the time domain weight values and Kalman filtering for interference suppression, the intermediate signal is output.

[0125] Specifically, the step D2 of generating spatial nulling weight values using wideband interference covariance characteristics refers to generating spatial nulling weight values using wideband interference covariance characteristics through linearly constrained beamforming algorithm.

[0126] It should be noted that the signal weighting values of different antenna arrays are adjusted by the space-time beam forming algorithm, so that the signal is enhanced along a specific direction while the interference signal in other directions is suppressed, and the transmission direction of the signal is ensured not to be affected by the interference by generating a spatial null to directionally suppress the interference signal; wherein the spatial null weight value refers to the weight value calculated by the beam forming algorithm to achieve zero reception signal in a specific spatial region, to eliminate the influence of the interference signal on the received signal, and the spatial null refers to shielding the interference signal in a specific direction or spatial region, so that the interference signal cannot enter the processing process of the received signal.

[0127] It should be noted that in step D3, the spatial null weight value is fused with the intermediate signal by space-time weighting, so that the null generated by the space-time beam forming can be effectively combined into the actual signal, and the suppression of the interference signal requires accurate weighting fusion. Separate time domain or spatial domain weighting may not effectively synchronize the changes of the interference signal, and by fusing the time domain and spatial domain weighting, the signal quality can be more accurately controlled to avoid the influence of the interference signal on the signal chain.

[0128] Specifically, in step D3, the intermediate signal is fused with the spatial null weight value by space-time weighting to generate an anti-interference signal, including:

[0129] The frequency points affected by narrowband interference in the frequency domain are identified by the narrowband interference spectrum line position identification matrix, and at this time, the weight value of the time domain signal is updated using the Kalman filtering algorithm.

[0130] The anti-interference signal is optimized by space-time beam forming, and the spatial null weight value is generated using a linearly constrained beam forming algorithm according to the covariance characteristics of the wideband interference.

[0131] After the space-time beam forming is completed, the intermediate signal is fused with the generated spatial null weight value by weighting, and the finally generated anti-interference signal can effectively reduce the influence of interference components while enhancing the quality of useful signals by space-time weighting fusion.

[0132] Specifically, in step D4, the anti-interference signal is subjected to baseband synchronization processing to output the anti-interference baseband signal, including: the generated anti-interference signal is subjected to baseband synchronization processing, and the baseband synchronization processing includes frequency synchronization, phase synchronization and time synchronization to ensure accurate alignment of the signal.

[0133] The signal after baseband synchronization processing is finally output as the anti-interference baseband signal.

[0134] It should be noted that the baseband synchronization processing can effectively synchronize the anti-interference signal in time, so that the frequency and phase of the signal can be calibrated, further improving the signal quality.

[0135] In an alternative embodiment, the spatial null is generated according to the narrow-band interference spectrum and the wide-band interference covariance characteristics in step S400 to obtain the anti-interference baseband signal, and a filtering algorithm can be used instead of Kalman filtering for interference suppression, a minimum mean square error filter or a recursive least square filter is used to update the time domain weight, a blind source separation technology is used to separate the interference and the useful signal, an artificial intelligence algorithm is introduced to optimize the beam forming process, a genetic algorithm or a particle swarm optimization algorithm is used to search for the optimal spatial null weight, a reinforcement learning method is used to train the intelligent interference suppression strategy, the suppression parameters are automatically adjusted according to the change of the interference environment, and a deep neural network is combined for end-to-end interference suppression, thereby improving the adaptability and suppression effect.

[0136] In another alternative embodiment, the spatial null is generated according to the narrow-band interference spectrum and the wide-band interference covariance characteristics in step S400 to obtain the anti-interference baseband signal, and a multi-domain joint processing technology can be used to simultaneously suppress the interference in the time domain, the frequency domain and the spatial domain, a time-frequency joint filtering method is used in combination with wavelet transform and short-time Fourier transform for multi-scale interference analysis, an air-frequency processing technology is used to realize frequency-selective spatial null formation, a cooperative anti-interference mechanism is introduced, the interference suppression performance is improved through the cooperative processing of multiple receiving nodes, a distributed beam forming technology is used to realize efficient processing of large-scale antenna arrays, and an interference prediction model is established to predict the future interference trend according to the historical interference mode, so that the suppression strategy is adjusted in advance to realize active interference protection.

[0137] It should be noted that the present application uses Kalman filtering to recursively update the time domain weight to suppress narrow-band interference, combines a linearly constrained beam forming algorithm to generate spatial null weight to suppress wide-band interference, and realizes the cooperative suppression of multiple types of interference in the electromagnetic environment through space-time weighting fusion and baseband synchronous processing; through the classification processing of narrow-band and wide-band interference, the problem that the traditional method cannot effectively suppress different types of interference at the same time is solved; secondly, the recursive updating mechanism of Kalman filtering can track the interference change in real time, the accurate formation of spatial null can shield the interference source in a directional manner, and the space-time weighting fusion can fully utilize the information advantage of the time domain and the spatial domain, thereby not only improving the precision and efficiency of interference suppression, but also maintaining the integrity of the useful signal, and further obtaining a high-quality baseband signal in a strong interference environment, thereby effectively improving the anti-interference performance of the Beidou satellite signal.

[0138] In the embodiment of the present application, the anti-interference baseband signal is fused with the Beidou inter-satellite differential data in step S500, and the ionospheric disturbance error is compensated to obtain a high-precision positioning enhanced signal, including the following steps E1-E5:

[0139] E1: receiving and analyzing the Beidou inter-satellite differential data to obtain satellite clock error, orbit error and ionospheric correction parameters.

[0140] E2: Pseudo code de-spreading and carrier phase synchronization are performed on the anti-interference baseband signal to extract the original observation values of each satellite.

[0141] E3: The baseband signal observation values are fused with the Beidou inter-satellite differential data to generate a joint observation dataset and construct a carrier phase double difference model.

[0142] E4: The ionospheric delay error is corrected using the carrier phase double difference model to generate corrected carrier phase observation values.

[0143] E5: The corrected carrier phase observation values are input into a positioning solution algorithm to output high-precision Beidou enhanced positioning signals.

[0144] It should be noted that the Beidou inter-satellite differential data in step E1 refers to parameters corrected through the observation information difference between multiple Beidou satellites, including satellite clock error, orbit error, and ionospheric correction parameters.

[0145] Specifically, in step E1, the Beidou inter-satellite differential data is received and analyzed to obtain satellite clock error, orbit error, and ionospheric correction parameters, including:

[0146] By receiving Pseudorange measurement data in the satellite signal, the base station or receiver can obtain the time information of each satellite. After receiving the satellite signal, the receiver compares it with the known reference signal (for example, the accurate time obtained by the ground station) to obtain the satellite clock error.

[0147] By receiving the precise satellite orbit data provided by the ground station, the orbit error is calculated based on the real-time position and speed of the satellite in the orbit.

[0148] By receiving the delay data of the satellite signal through the Klobuchar model and the NeQuick model, the delay effect of the ionosphere is calculated using the ionospheric model.

[0149] It should be noted that the pseudo code de-spreading in step E2 refers to obtaining the pseudo range information of the signal by processing the pseudo random noise code (PRN code) in the satellite signal. The pseudo code is usually generated by a specific code transmitted by the satellite (for example: GPS C / A code, L2 code). Through the de-spreading process, the receiver can extract the time information of the signal to calculate the distance from the satellite. Carrier phase synchronization refers to improving the positioning accuracy by accurately synchronizing the carrier phase of the satellite signal. The carrier phase is a measure of the relative position within the signal waveform period, which provides higher accuracy than pseudo range measurement.

[0150] Specifically, in step E2, the pseudo code de-spreading is performed on the anti-interference baseband signal to extract the original observation values of each satellite, including:

[0151] The receiver receives the satellite broadcast signal.

[0152] Based on the characteristic sequence of the PRN code used by each satellite, the pseudo code (i.e. pseudo-random noise code) is extracted from the signal and despread by matching the received signal with a locally generated satellite PRN code.

[0153] After the pseudo code is despread, the receiver obtains the pseudo range by calculating the time delay of the received signal and the local pseudo-random noise code, wherein the pseudo range information extracted by each satellite through the pseudo code despread process is the original observation value of the satellite.

[0154] Specifically, the carrier phase synchronization of the anti-interference baseband signal in step E2 extracts the original observation value of each satellite, including:

[0155] The receiver receives the satellite carrier signal.

[0156] The receiver synchronizes the carrier in the satellite signal through a phase-locked loop (PLL), and the PLL adjusts the frequency of the local oscillator to ensure that the local carrier of the receiver is consistent with the carrier phase of the satellite. Once synchronization is successful, the receiver can measure the phase difference of the satellite signal, and the phase difference of the carrier (relative to the local clock of the receiver) provides a higher precision observation value.

[0157] The carrier phase information obtained by synchronization extracts the phase observation value (i.e. carrier phase difference) of each satellite.

[0158] It should be noted that the baseband signal observation value in step E3 is the original data obtained through the pseudo code despread and carrier phase synchronization process of the satellite signal, including: pseudo range observation value and carrier phase observation value, and the Beidou inter-satellite differential data is obtained through the relative observation between multiple satellites, and is used to correct satellite clock error, orbit error, ionospheric delay error.

[0159] Specifically, the baseband signal observation value and the Beidou inter-satellite differential data are fused in step E3 to generate a joint observation data set and construct a carrier phase double difference model, including:

[0160] The baseband signal observation value and the Beidou inter-satellite differential data are time-aligned and formatted, synchronized to ensure that the two types of data can be fused under the same time reference.

[0161] The satellite clock error information in the inter-satellite differential data is used to correct the baseband signal observation value, the orbit error obtained from the inter-satellite differential data is used to correct the baseband signal observation value, especially the satellite position error in the pseudo-range observation, and the ionospheric delay information provided by the inter-satellite differential data is used to correct the ionospheric effect in the baseband signal.

[0162] After the data correction, the baseband signal observation value (including the corrected pseudo-range and carrier phase) is combined with other information (such as clock error, orbit error, ionospheric error) in the inter-satellite differential data to form a joint observation data set.

[0163] Further, the carrier phase double difference model in step E3 can be specifically represented as:

[0164]

[0165] wherein, is a double difference carrier phase observation value, is a double difference geometric distance, and λ is a signal wavelength, is a double difference integer ambiguity, is an anti-interference processed double difference observation noise.

[0166] It should be noted that the carrier phase double difference model in step E4 is obtained by double difference operation on the carrier phase observation values of multiple satellites, and the satellite difference is the carrier phase difference of the satellite relative to the reference satellite, and the receiver difference is the carrier phase difference of the receiver relative to the reference receiver. In the ionospheric delay error correction process, the carrier phase observation values of multiple satellites and multiple receivers are difference calculated, which can effectively eliminate the influence of the ionosphere.

[0167] Specifically, the carrier phase double difference model in step E4 is used to correct the ionospheric delay error to generate corrected carrier phase observation values, including:

[0168] The carrier phase observation values L1 and L2 frequencies are selected. Since the ionosphere has different effects on signals of different frequencies, the difference between the carrier phase observation values of the two frequencies can be used to correct the ionospheric delay error.

[0169] Based on the selected reference satellite, the phase difference between the two satellites is calculated, based on the reference receiver, the phase difference between the two receivers is calculated, and by double difference operation on the L1 and L2 carrier phase observation values, the difference value of the ionospheric delay is obtained.

[0170] It should be noted that the corrected carrier phase observation in step E5 refers to the accurate observation obtained by compensating the carrier phase observation data by correcting the ionospheric delay, satellite clock error and orbit error; the corrected carrier phase observation includes the carrier phase of each satellite signal and the corresponding pseudorange information, and the accurate position of the receiver is calculated by solving by the least square method, and the result obtained after completing the positioning solution is the accurate position of the receiver.

[0171] In an optional embodiment, the anti-interference baseband signal and the inter-satellite differential data of the Beidou are fused in step S500, and ionospheric disturbance error is compensated to obtain a high-precision positioning enhanced signal. In addition, the observation data of multiple satellite navigation systems such as GPS, GLONASS and Galileo can be simultaneously used by using a multi-navigation fusion technology, a weighted fusion method is used to allocate weights according to different signal qualities, and a Kalman filtering technology is used to perform optimal fusion of multiple sources of data.

[0172] In another optional embodiment, the anti-interference baseband signal and the inter-satellite differential data of the Beidou are fused in step S500, and ionospheric disturbance error is compensated to obtain a high-precision positioning enhanced signal. In addition, the positioning precision can be improved by using a real-time difference technology, a local reference station network is established to provide real-time difference correction data, and a network RTK technology is used to realize large-scale high-precision positioning service.

[0173] It should be noted that, by fusing inter-satellite differential data, carrier phase double difference algorithm and ionospheric error compensation, combining with pseudocode despreading and carrier phase synchronization technology, high-precision processing and positioning enhancement of the anti-interference baseband signal are realized. Through multi-source data fusion and multi-layer error compensation, the problem that the traditional method cannot simultaneously eliminate the influence of multiple error sources is solved, for example, the influence of satellite clock error and receiver clock error can be effectively eliminated by using a carrier phase double difference model, ionospheric delay error can be accurately compensated by using ionospheric correction parameters, and orbit error can be corrected by using inter-satellite differential data, thereby improving the precision and reliability of Beidou positioning, enabling the receiver to obtain centimeter-level or even millimeter-level positioning precision, and effectively improving the overall performance of Beidou satellite navigation.

[0174] In the embodiment of the application, the polarization weight and space-time parameter are optimized based on the high-precision positioning enhanced signal in step S600 to generate a feedback instruction, including the following steps F1-F4:

[0175] F1: Calculate the signal-to-interference ratio of the high-precision positioning enhanced signal, and obtain a preset threshold of the performance index of the Beidou satellite.

[0176] F2: According to the comparison result of the signal-to-interference ratio and the preset threshold, the performance state of the current polarization weight and space-time parameter is judged.

[0177] F3: If the signal-to-interference ratio does not meet the standard, generate a polarization weight adjustment instruction and a space-time tap number optimization instruction based on the change rate of the signal-to-interference ratio.

[0178] F4: Feedback the polarization weight adjustment instruction and the space-time tap number optimization instruction to the polarization encoding and space-time filtering, update the parameters, and generate a feedback instruction.

[0179] It should be noted that the signal-to-interference ratio in step F1 is an important indicator in wireless communication, used to measure the ratio between the strength of the signal and the interference signal and noise. By evaluating the quality of the satellite positioning signal using the signal-to-interference ratio, especially in environments with severe multipath effects, interference, or noise, the signal-to-interference ratio reflects the clarity and effectiveness of the signal.

[0180] Specifically, in step F1, the signal-to-interference ratio of the high-precision positioning enhanced signal is calculated, and the preset threshold of the Beidou satellite performance index is obtained, including:

[0181] The Beidou satellite signal is received in real time by the four-element polarization sensitive array antenna. The received signal may be affected by interference and noise, and needs to be filtered, signal synchronized, and amplitude analyzed for preprocessing.

[0182] After the signal is received and preprocessed, the effective strength and interference noise of the signal are calculated respectively to obtain the signal power and noise power, and the signal-to-interference ratio is calculated based on the signal power and noise power.

[0183] The preset threshold of the Beidou satellite performance index is obtained, which needs to consider the performance standards of the Beidou satellite signal. The quality of the satellite signal is affected by various factors, including the power of the satellite, the signal frequency, the interference environment (such as multipath effect, noise, antenna distortion, etc.), and the relative position between the satellite and the receiver. The preset threshold is determined based on the analysis of these performance standards. By calculating and analyzing the performance standards of the satellite based on various influencing factors, a performance target value is set as the preset threshold of the performance index of the Beidou satellite.

[0184] Specifically, in step F2, the performance state of the current polarization weight and space-time parameters is determined based on the comparison result of the signal-to-interference ratio and the preset threshold, including:

[0185] Compare the real-time calculated signal-to-interference ratio with the preset threshold of the performance index. If the signal-to-interference ratio is greater than or equal to the preset threshold, it means that the current signal quality meets the requirements and can continue to run and perform normal operations.

[0186] If the signal-to-interference ratio is less than the preset threshold, it means that the signal quality is not good, and the parameter optimization needs to be triggered, such as adjusting the polarization weight and space-time parameters.

[0187] It should be noted that according to the comparison between the signal-to-interference ratio and the preset threshold, the performance state of the current polarization weight and space-time parameter can be judged, and timely feedback and corresponding optimization decision can be made when the performance is not up to standard; by judging the performance state, it is avoided to continue to use the current configuration in the case of poor signal quality, thereby ensuring the accuracy of the positioning result.

[0188] It should be noted that the polarization weight in step F3 refers to the adjustment parameter of the four-element polarization sensitive array antenna, which enhances the signal reception effect by changing the polarization state of the antenna array. Different polarization weight adjustments will affect the reception quality of the signal, especially in the presence of interference. Reasonable polarization weight can effectively suppress unnecessary interference components; the space-time parameter refers to the processing configuration of the antenna array in space and time, such as space-time coding and beam forming, which enhances the spatial resolution of the signal and suppresses noise interference, and improves the anti-interference ability by optimizing the space-time processing. If the signal-to-interference ratio does not reach the preset performance standard, it means that the current signal reception quality is not ideal, there may be strong interference or signal attenuation, and parameter optimization needs to be triggered, such as adjusting the polarization weight and space-time parameter.

[0189] Specifically, step F4 feeds back the polarization weight adjustment instruction and space-time tap number optimization instruction to polarization encoding and space-time filtering, updates the parameters, and generates feedback instructions, including:

[0190] Based on the comparison between the real-time calculated signal-to-interference ratio and the preset threshold, the polarization weight adjustment instruction and the space-time tap number optimization instruction are generated. If the signal-to-interference ratio does not reach the preset performance standard, the adjustment instruction will be generated according to the change rate of the signal-to-interference ratio. After the polarization weight adjustment instruction and the space-time tap number optimization instruction are generated, they will be fed back to the polarization encoding and space-time filtering.

[0191] According to the polarization weight adjustment instruction, the polarization inclination and azimuth angle of each independent element are adjusted to achieve the best signal reception effect.

[0192] According to the space-time tap number optimization instruction, the tap number of the space-time filter is adjusted, and increasing the tap number helps to improve the spectral resolution.

[0193] According to the adjusted instruction, the parameters are updated to generate new feedback instructions.

[0194] In an optional implementation, the polarization weight and space-time parameter are optimized based on the high-precision positioning enhanced signal in step S600 to generate feedback instructions. The parameter optimization model can also be trained by a machine learning algorithm, and the training samples are constructed using historical signal quality data and parameter adjustment records. The neural network is used to learn the mapping relationship between the signal-to-interference ratio and the optimal parameter configuration.

[0195] In another alternative embodiment, the step S600 of optimizing the polarization weight and the space-time parameter based on the high-precision positioning enhancement signal can also consider multiple indexes such as signal quality, power consumption and calculation complexity simultaneously through a multi-objective optimization algorithm, and search for a Pareto optimal solution set by using a genetic algorithm or a particle swarm optimization algorithm, so as to select the most suitable parameter configuration according to application requirements and environmental conditions.

[0196] It should be noted that the present application realizes the closed-loop adaptive optimization of the polarization weight and the space-time parameter by monitoring the signal-to-interference ratio in real time and comparing it with a preset threshold, and generating an accurate parameter adjustment instruction based on the change rate analysis; wherein the problem of the traditional method that is difficult to accurately track the signal environment change and the parameter adjustment lag is solved through the signal-to-interference ratio change rate analysis; the polarization state of each array element can be optimized in real time through the polarization weight adjustment instruction, the processing precision of the filter can be adjusted through the space-time tap number optimization instruction, and the signal reception quality can be continuously improved through the closed-loop feedback mechanism, thereby not only improving the adaptive ability and robustness of the Beidou satellite signal processing, but also enabling the receiver to maintain stable high-quality signal reception in the electromagnetic environment, and providing a reliable self-adjusting ability for the entire anti-interference optimization process.

[0197] In summary, the present application can effectively decompose and process different polarization components, improve the effectiveness and anti-interference ability of the signal, and enhance the robustness in the electromagnetic environment by receiving the signal through the four-element polarization sensitive array antenna and performing polarization-space-time four-dimensional joint coding; the ionospheric disturbance error is further improved by fusing with the Beidou interstellar differential data and using the carrier phase double difference algorithm; the interference environment is adjusted in real time by optimizing the polarization weight and the space-time parameter, so as to achieve the anti-interference effect and positioning performance.

[0198] Embodiment 3, refer to Figure 2 As an embodiment of the present application, the embodiment provides a Beidou satellite signal anti-interference optimization system, comprising: a signal receiving module, configured to receive a Beidou satellite signal and generate a four-dimensional digital signal matrix based on the Beidou satellite signal; a polarization decomposition module, configured to perform polarization-space-time four-dimensional joint coding on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block; an interference detection module, configured to calculate a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, identify a narrowband interference spectrum line and extract a wideband interference covariance feature; a spatial domain suppression module, configured to generate a spatial domain null based on the narrowband interference spectrum line and the wideband interference covariance feature to obtain an anti-interference baseband signal; a differential fusion module, configured to fuse the anti-interference baseband signal with Beidou interstellar differential data and compensate for ionospheric disturbance error to obtain a high-precision positioning enhancement signal; and a real-time tuning module, configured to optimize a polarization weight and a space-time parameter based on the high-precision positioning enhancement signal to generate a feedback instruction.

[0199] Embodiment 4, which is an embodiment of the present application, is different from the previous three embodiments in that: the function, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0200] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can take instructions from an instruction execution system, apparatus, or device, or in conjunction with these instructions. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instructions.

[0201] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting, or otherwise processing, if necessary, in other suitable ways, to be electronically obtained, and then stored in the computer memory.

[0202] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0203] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing anti-interference of BeiDou satellite signals, characterized in that: Comprising, Receiving a Beidou satellite signal, generating a four-dimensional digital signal matrix based on the Beidou satellite signal; Polarization-space-time four-dimensional joint coding is performed on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block; Based on the frequency domain spectrum signal block, a frequency domain spectrum threshold value is calculated, narrowband interference spectral lines are identified, and wideband interference covariance characteristics are extracted; According to the narrowband interference spectral lines and the wideband interference covariance characteristics, a spatial null is generated, and an anti-interference baseband signal is obtained; Fuse the anti-interference baseband signal with the Beidou inter-satellite differential data, and compensate the ionospheric disturbance error to obtain a high-precision positioning enhancement signal; Based on the high-precision positioning enhancement signal, the polarization weight and the space-time parameter are optimized to generate a feedback instruction.

2. The method of claim 1, wherein the method comprises: The receiving of the Beidou satellite signal and the generation of the four-dimensional digital signal matrix based on the Beidou satellite signal comprise: Receiving the Beidou satellite signal through an array antenna, adjusting the polarization state of each array element, and generating a polarization parameter set; Adjusting the polarization state of the array antenna based on the polarization parameter set to generate a local oscillator signal; Mixing the Beidou satellite signal with the local oscillator signal and performing filtering processing to obtain a down-converted signal; Sampling the down-converted signal, constructing an observation matrix and generating an observation vector; Decomposing the observation vector to reconstruct a four-dimensional digital signal matrix and outputting the four-dimensional digital signal matrix.

3. The method for optimizing BeiDou satellite signal anti-interference as described in claim 2, characterized in that: The polarization-space-time four-dimensional joint coding of the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block comprises: Separating a first polarization component and a second polarization component from the four-dimensional digital signal matrix; Space-time encoding the first polarization component and the second polarization component respectively to generate a space-time two-dimensional signal block; Merging the generated space-time two-dimensional signal block into a four-dimensional space-time-polarization tensor according to the polarization dimension; Generating a frequency domain spectrum signal block based on the four-dimensional space-time-polarization tensor.

4. The method of claim 3, wherein the method further comprises: determining a signal-to-noise ratio (SNR) of the received signal; and determining the threshold based on the SNR. Based on the frequency domain spectrum signal block, a frequency domain spectrum threshold value is calculated, narrowband interference spectral lines are identified, and wideband interference covariance characteristics are extracted, comprising: Statistically analyzing the amplitude of the frequency domain spectrum signal block to calculate the mean and standard deviation of each frequency point; Based on the mean and standard deviation, a frequency domain spectrum threshold value is calculated to generate a dynamic threshold value; Marking the frequency points with amplitudes exceeding the dynamic threshold value as narrowband interference spectral lines to generate a narrowband interference spectral line position identification matrix; Based on the frequency point data not marked as interference, a space-time covariance matrix is calculated, and a wideband interference covariance characteristic is output.

5. The method of claim 4, wherein the method further comprises: determining a signal-to-noise ratio (SNR) of the received signal; and determining the threshold based on the SNR. According to the narrowband interference spectral lines and the wideband interference covariance characteristics, a spatial null is generated, and an anti-interference baseband signal is obtained, comprising: Updating the time domain weight based on the narrowband interference spectral line position identification matrix to suppress the narrowband interference component and output an intermediate signal; Generating a spatial null weight value using the wideband interference covariance characteristics; Fusing the intermediate signal with the spatial null weight value through space-time weighting to generate an anti-interference signal; Performing baseband synchronization processing on the anti-interference signal to output an anti-interference baseband signal.

6. The method of claim 5, wherein the method further comprises: determining a signal-to-noise ratio (SNR) of the received signal; and determining the threshold based on the SNR. Fuse the anti-interference baseband signal with the Beidou inter-satellite differential data, and compensate the ionospheric disturbance error to obtain a high-precision positioning enhancement signal, comprising: Receiving and analyzing the Beidou inter-satellite differential data to obtain satellite clock error, orbit error and ionospheric correction parameters; The baseband signal after interference is subjected to code despreading and carrier phase synchronization, and original observation values of each satellite are extracted; The baseband signal observation values are fused with inter-BD2 satellite differential data to generate a joint observation dataset, and a carrier phase double difference model is constructed; The carrier phase double difference model is used to correct ionospheric delay errors, and corrected carrier phase observation values are generated; The corrected carrier phase observation values are input into a positioning solution algorithm, and a high-precision BD2 enhanced positioning signal is output.

7. The method of claim 6, wherein the method further comprises: determining a signal-to-noise ratio (SNR) of the received signal; and determining the threshold based on the SNR. Based on the high-precision positioning enhancement signal, polarization weight and space-time parameters are optimized to generate feedback instructions, including: The signal-to-interference ratio of the high-precision positioning enhancement signal is calculated to obtain a preset threshold of the performance index of the BD2 satellite; According to the comparison result of the signal-to-interference ratio and the preset threshold, the performance state of the current polarization weight and space-time parameters is judged; If the signal-to-interference ratio does not meet the standard, polarization weight adjustment instructions and space-time tap number optimization instructions are generated based on the change rate of the signal-to-interference ratio; The polarization weight adjustment instructions and space-time tap number optimization instructions are fed back to polarization encoding and space-time filtering to update the parameters and generate feedback instructions.

8. A Beidou satellite signal anti-jamming optimization system, applying a Beidou satellite signal anti-jamming optimization method according to any one of claims 1-7, characterized in that, It includes: A signal receiving module is configured to receive a BD2 satellite signal and generate a four-dimensional digital signal matrix based on the BD2 satellite signal; A polarization decomposition module is configured to perform polarization-space-time four-dimensional joint encoding on the four-dimensional digital signal matrix to generate a frequency domain spectrum signal block; An interference detection module is configured to calculate a frequency domain spectrum threshold value based on the frequency domain spectrum signal block, identify narrowband interference spectral lines, and extract wideband interference covariance characteristics; A spatial domain suppression module is configured to generate a spatial domain null based on the narrowband interference spectral lines and the wideband interference covariance characteristics to obtain a baseband signal after interference; A differential fusion module is configured to fuse the baseband signal after interference with inter-BD2 satellite differential data, and compensate for ionospheric disturbance errors to obtain a high-precision positioning enhancement signal; A real-time tuning module is configured to optimize polarization weight and space-time parameters based on the high-precision positioning enhancement signal to generate feedback instructions. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the BD2 satellite signal anti-interference optimization method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the BD2 satellite signal anti-interference optimization method of any one of claims 1 to 7.

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