A robust satellite navigation fast time-varying flicker interference cancellation method and device

Abstract

This application discloses a robust method and apparatus for canceling fast time-varying flicker interference in satellite navigation. The method involves performing sample-point-by-sample interference pattern recognition on the array signal, recombining the fast time-varying, non-stationary flicker interference into multiple time-stationary interference time periods, and sequentially canceling interference in each time-stationary interference time period to effectively filter out the fast time-varying flicker interference. Then, based on the sampling time points corresponding to the interference recombination, the interference-filtered signals are reassembled to obtain a normally time-ordered and usable satellite navigation signal. This application fully utilizes the spatial sparse distribution of flicker interference and the temporal sparse distribution of outliers to achieve robust time-stationary interference reconstruction, making it more suitable for application in complex electronic warfare scenarios. This application fully utilizes information from individual sampling points, avoiding the anti-interference convergence problem and the problem of exceeding the anti-interference degrees of freedom caused by the time non-stationarity of fast time-varying flicker interference at the algorithmic level.

Description

Technical Field

[0001] This application relates to the field of anti-interference technology, and in particular, to a robust method and apparatus for canceling fast time-varying flicker interference in satellite navigation. Background Technology

[0002] Array interference cancellation is a common technique for achieving robust satellite navigation in interference environments. Based on the fact that the direction of interference is different from the direction of the satellite signal, array interference cancellation weights the received signals from different array elements to create nulls in the direction of the interference, thus achieving robust satellite navigation in strong interference environments. To accurately create nulls in the direction of the interference, traditional array interference cancellation techniques either require using a large amount of antenna array data to calculate the array covariance matrix and obtain the anti-interference weighting value through an open-loop method of matrix inversion, or require using a large amount of antenna array data through an iteratively updated closed-loop method to obtain the anti-interference weighting value. Both methods require the interference signal to satisfy the time-stationary characteristic.

[0003] Fast time-varying flicker interference is specifically designed to address this characteristic of traditional array interference filtering techniques. It causes the interference signals to change rapidly over a short period of time, resulting in an increase in the number of interference signals in the antenna array data over a long period of time. This can exceed the upper limit of the number of interference signals that open-loop methods such as matrix inversion can combat, or disrupt the correct iterative direction of closed-loop methods such as iterative updates. Ultimately, this makes it difficult for traditional array interference filtering techniques to filter out interference signals.

[0004] The principle behind fast time-varying flicker interference (RTF) against traditional array interference filtering techniques is described in detail below: RTF typically manifests as a rapid switching speed from the direction of interference, with switching times usually much shorter than 1 ms, and the switching period and direction remaining random for an extended period. Due to the very short switching time, the sampling data between two switching points is insufficient to support the completion of open-loop iteration or closed-loop covariance matrix calculation. Furthermore, because the switching period and direction are random for an extended period, it is difficult to achieve interference filtering by increasing the open-loop iteration time or the number of sampling points for closed-loop covariance matrix calculation. Therefore, RTF interference leads to a sharp deterioration in the performance of traditional array interference filtering techniques, and may even cause them to malfunction.

[0005] A common solution involves predicting the flickering time and direction of the interference and pre-fixing a null in the direction of the interference to filter it out. The performance of such algorithms is highly dependent on the accuracy of the interference flickering time and direction estimation, especially the accuracy of the interference direction estimation. As the error in the interference direction estimation increases, the interference filtering effect deteriorates sharply.

[0006] To address this issue and improve the robustness of equipment in complex electronic warfare environments, there is an urgent need for a method that can effectively counteract fast time-varying flicker interference. Summary of the Invention

[0007] To address the aforementioned technical problems, this application provides a robust method for canceling fast time-varying flicker interference in satellite navigation, thereby solving the technical problem that existing technologies cannot robustly and effectively combat fast time-varying flicker interference.

[0008] The technical solution adopted in this application is as follows:

[0009] A robust method for canceling fast time-varying flicker interference in satellite navigation is proposed, based on a satellite navigation anti-interference processor employing an array antenna. The array antenna comprises M antenna elements, corresponding to M communication channels, where M is a positive integer and M≥2. The method specifically includes the following steps:

[0010] S1. Control the array antenna to output M analog signals, which are then processed through analog filtering, analog down-conversion, AD conversion, digital filtering, and digital down-conversion to obtain M digital baseband signals.

[0011] S2. Perform sampling point-by-sampling interference pattern identification on the M-channel digital baseband signal to obtain the interference pattern at each sampling point and form the time-varying trajectory of the interference pattern with the sampling point.

[0012] S3. Reconstruct the time-domain data of the interference mode with the time-varying trajectory of the sampling point, and obtain a corresponding reconstructed interference data group for each interference mode.

[0013] S4. Remove outliers from the obtained reconstructed interference data groups. After removal, set all values ​​of the interference data groups corresponding to the outliers to 0.

[0014] S5. Perform interference cancellation on the reconstructed interference data group after outlier removal, filter out the interference in each data group, and obtain the anti-interference data group.

[0015] S6. Reassemble the anti-interference data groups according to the reconstruction time point of the data groups to obtain satellite navigation signals with normal time sequence, which are used to realize satellite navigation and positioning in the environment of fast time-varying flicker interference.

[0016] Furthermore, step S2 specifically includes the following steps:

[0017] S21. Using the xyz coordinate system, arbitrarily select one element of the array antenna as the reference element, and obtain the three-dimensional coordinates of the other i-th element relative to the reference element as: x i y i , z i , where i is a positive integer, and M-1≥i≥1;

[0018] S22. Based on the relative three-dimensional coordinates, the relative phase relationship of interference signals from different directions arriving at each element of the antenna is obtained:

[0019]

[0020] In the formula, This is called interference direction. Corresponding to a 1×M dimensional steering vector, θ is the pitch angle. It is the azimuth angle;

[0021] S23. With azimuth angle 0° and pitch angle 0° as initial values ​​and 1° as the angle change interval, traverse all combinations of azimuth angle 0°~359° and pitch angle 0°~90° to obtain a 32760×M-dimensional interference direction guidance vector dictionary B.

[0022] S24. Extract a 1×M dimensional array signal C from the M-channel digital baseband signals, sampling point by point, and use a convex optimization tool to solve the local optimum solution of the optimization problem, a 1×32760 dimensional vector y:

[0023]

[0024] In the above formula, the 1×32760-dimensional vector y represents the distribution characteristics of the corresponding array signal C in the spatial direction, and is used to determine the interference mode of the corresponding sampling point;

[0025] S25. Arrange the vector y calculated from each sampling point according to the sampling time to obtain the interference mode time distribution table Y = [y1, ..., y2] with a total of T sampling points. t , ..., y T ], where T >> 102400, T ≥ t ≥ 1.

[0026] Furthermore, the specific convex optimization tool's solution process is an iterative solution:

[0027] x(n+1)=x(n)+B(x(n)BC)+(x(n)-y(n))

[0028] y(n+1)=(y(n)-x(n+1))+sign(x(n+1))

[0029] In the above formula, n is the number of iterations, with an initial value of 0; x(n) is an intermediate operation variable, with an initial value of a 1×32760-dimensional all-zero vector; y(n) is an initial value of a 1×32760-dimensional all-zero vector; sign() is the sign function;

[0030] The iteration termination condition for convex optimization tools is that the iteration difference is less than 3 times the noise energy.

[0031]

[0032] Alternatively, the iteration termination condition for convex optimization tools is:

[0033] n≥10

[0034] In the above formula, The value represents the average of the squares of 1000 AD sampling points in an interference-free environment, representing the environmental thermal noise energy.

[0035] Furthermore, step S3 specifically includes the following steps:

[0036] S31. Calculate the difference in interference patterns between two adjacent sampling points: When the difference exceeds 100×M, time t+1 is considered the time of interference flicker, denoted as P. t =t+1, P t To P t+1 -1 sampling time intervals, including P. t and P t+1 -1 sampling points belong to the same interference mode. The data group consisting of all sampling points is defined as the (t+1)th steady-state sampling point data group D. t+1 The first set of steady-state sampling data group D1 is the data group of all sampling points from the first sampling point to the P1-1 sampling point, and the 100×M is the difference threshold.

[0037] S32. Calculate the difference in interference patterns between sampling points at any two interference flickering moments: Where T≥m≠n≥1, when the difference value does not exceed 100×M, the corresponding two sets of steady-state sampling point data groups are considered to belong to the same interference mode, and the sampling point data groups are merged and sorted according to time sequence to realize that one interference mode corresponds to one reconstructed steady-state sampling point data group.

[0038] Furthermore, step S4 specifically includes the following steps:

[0039] S41. Calculate the number of sampling points contained in each reconstructed steady-state data set;

[0040] S42. When the number of sampling points is less than 256×M points, the reconstructed steady-state data set is considered to be an outlier set, and all anti-interference outputs corresponding to the sampling time of the reconstructed steady-state data set are set to 0.

[0041] Furthermore, step S5 specifically includes the following steps:

[0042] S51, for each reconstructed interference data group of each antenna element, according to... The data is segmented by points, where N is the number of signal sampling points for a single antenna element in the reconstructed interference data group. To perform the round-down operation, zeros are padded at the end of the sampling points in the reconstructed interference data set to ensure that the sum of the number of sampling points and the number of padded zeros is equal to the total number of sampling points. A multiple of an integer is denoted as S;

[0043] S52, Perform the following steps on each data segment separately. Point Fast Fourier Transform yields an S×M numerical matrix for each frequency point, denoted as R;

[0044] S53. For each frequency point, perform interference cancellation operation: e = R(R H R) -1 f, where f is an M×1 dimensional all-1 column vector, * H For the conjugate transpose operation, * -1 This is a matrix inversion operation, where e is the S×1 dimensional column vector after interference cancellation;

[0045] S54. Sort e according to the corresponding frequency points, perform inverse fast Fourier transform, and obtain the time domain data after anti-interference.

[0046] S55. Sort and concatenate the time-domain data after interference suppression according to the data segment number to obtain the data group after interference suppression.

[0047] Furthermore, step S6 specifically includes the following steps:

[0048] S61. According to the interference flicker time, reassemble the anti-interference data group corresponding to the reconstructed steady-state data group and the data group with all zeros corresponding to the outlier to obtain the interference-suppressed satellite navigation signal with normal time sequence.

[0049] S62. Use the interference-suppressed satellite navigation signal with normal timing sequence to perform normal satellite navigation signal tracking and acquisition operations, so as to realize satellite navigation positioning in a fast time-varying flicker interference environment.

[0050] This application also provides a robust satellite navigation fast time-varying flicker interference cancellation device, implemented based on a satellite navigation anti-interference processor using an array antenna. The array antenna includes M antenna elements, corresponding to M communication channels, where M is a positive integer and M≥2, specifically including:

[0051] The digital baseband signal acquisition module is used to control the array antenna to output M analog signals. After analog filtering, analog downconversion, AD, digital filtering, and digital downconversion, M digital baseband signals are obtained.

[0052] The interference pattern recognition module is used to perform sampling point-by-sampling interference pattern recognition on M-channel digital baseband signals, obtain the interference pattern at each sampling point, and form the time-varying trajectory of the interference pattern with the sampling point.

[0053] The time-domain data reconstruction module is used to reconstruct the time-domain data of the interference mode with the time-varying trajectory of the sampling point, and obtain a corresponding reconstructed interference data group for each interference mode;

[0054] The outlier removal module is used to remove outliers from the obtained reconstructed interference data groups. After removal, the values ​​of all interference data groups corresponding to the outliers are set to 0.

[0055] The interference cancellation module is used to perform interference cancellation on the reconstructed interference data group after outlier removal, filter out the interference in each data group, and obtain the anti-interference data group.

[0056] The data group stitching module is used to reassemble the anti-interference data groups according to the data group reconstruction time point to obtain satellite navigation signals with normal time sequence, which is used to realize satellite navigation and positioning in fast time-varying flicker interference environment.

[0057] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the robust satellite navigation fast time-varying flicker interference cancellation method.

[0058] This application also provides a storage medium including a stored program that, when the program is executed, controls the device containing the storage medium to perform the steps of the robust satellite navigation fast time-varying flicker interference cancellation method.

[0059] Compared with the prior art, this application has the following advantages:

[0060] (1) The robust satellite navigation fast time-varying flicker interference cancellation method proposed in this application reconstructs the fast time-varying flicker interference into a time-stationary interference group through interference pattern recognition at each sampling point. Interference cancellation is then performed on each time-stationary interference group, effectively filtering out the fast time-varying flicker interference. The filtered signal is then reassembled based on the interference reconstruction time point, enabling normal satellite navigation positioning. Compared with traditional array interference filtering techniques, this method effectively solves the problem of robust satellite navigation operation in complex electronic environments where fast time-varying flicker interference exists.

[0061] (2) The robust satellite navigation fast time-varying scintillation interference cancellation method proposed in this application fully utilizes the characteristics of sparse spatial distribution of scintillation interference and sparse temporal distribution of outliers to adaptively achieve interference pattern extraction and outlier removal. Therefore, this application does not require prediction or estimation of the scintillation time and direction of the fast time-varying scintillation interference. Compared with the fixed null interference filtering method, this application requires fewer preconditions, has more robust performance, and is more suitable for application in practical electronic warfare scenarios;

[0062] (3) The robust satellite navigation fast time-varying flicker interference cancellation method proposed in this application makes full use of the information of a single sampling point, and avoids the anti-interference convergence problem and the problem of exceeding the anti-interference degree of freedom caused by the non-stationary characteristics of fast time-varying flicker interference at the algorithm level;

[0063] (4) The robust satellite navigation fast time-varying flicker interference cancellation method proposed in this application is easy to modularize and encapsulate, has good real-time computation performance, is simple to implement in engineering, and has good portability between platforms.

[0064] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. The application will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0065] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0066] Figure 1 A schematic flowchart of a robust satellite navigation fast time-varying flicker interference cancellation method provided in a preferred embodiment of this application;

[0067] Figure 2 This is a detailed flowchart illustrating the implementation process of step S2, point-by-point pattern recognition, in the 7-channel robust satellite navigation fast time-varying flicker interference cancellation method in this embodiment of the application.

[0068] Figure 3 This is a detailed flowchart illustrating the implementation process of step S5, fast time-varying flicker interference cancellation, in the 7-channel robust satellite navigation fast time-varying flicker interference cancellation method in this embodiment of the application.

[0069] Figure 4 This is a schematic diagram of a robust satellite navigation fast time-varying flicker interference cancellation device module according to a preferred embodiment of this application;

[0070] Figure 5 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application;

[0071] Figure 6 This is an internal structural diagram of a computer device according to a preferred embodiment of this application. Detailed Implementation

[0072] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0073] Reference Figure 1 A preferred embodiment of this application provides a robust method for canceling fast time-varying flicker interference in satellite navigation, implemented based on a satellite navigation anti-interference processor using an array antenna. The array antenna includes M antenna elements, corresponding to M communication channels, where M is a positive integer and M≥2. In this embodiment, M is preferably 7. The specific steps include:

[0074] S1. Control the array antenna to output M analog signals, which are then processed through analog filtering, analog down-conversion, AD conversion, digital filtering, and digital down-conversion to obtain M digital baseband signals.

[0075] S2. Perform sampling point-by-sampling interference pattern identification on the M-channel digital baseband signal to obtain the interference pattern at each sampling point, form the time-varying trajectory of the interference pattern with the sampling point, and obtain the time distribution table of the interference pattern.

[0076] S3. Reconstruct the time-domain data of the interference mode with the time-varying trajectory of the sampling point, and obtain a corresponding reconstructed interference data group for each interference mode.

[0077] S4. Remove outliers from the obtained reconstructed interference data groups. After removal, set all values ​​of the interference data groups corresponding to the outliers to 0.

[0078] S5. Perform interference cancellation on the reconstructed interference data group after outlier removal, filter out the interference in each data group, and obtain the anti-interference data group.

[0079] S6. Reassemble the anti-interference data groups according to the reconstruction time point of the data groups to obtain satellite navigation signals with normal time sequence, which are used to realize satellite navigation and positioning in the environment of fast time-varying flicker interference.

[0080] The robust satellite navigation fast time-varying flicker interference cancellation method provided in this embodiment identifies the interference pattern at each sampling point, reconstructs the fast time-varying flicker interference into a time-stationary interference group, and cancels the interference of each time-stationary interference group to effectively filter out the fast time-varying flicker interference. Based on the interference reconstruction time point, the interference-filtered signal is reassembled to finally obtain the normal time-ordered satellite navigation signal.

[0081] This embodiment fully leverages the spatial sparseness of scintillation interference and the temporal sparseness of outliers during interference pattern recognition, achieving robust time-stationary interference reconstruction, making it more suitable for complex electronic warfare scenarios. This embodiment fully utilizes information from individual sampling points, avoiding the anti-interference convergence problem and the problem of exceeding the degrees of freedom caused by the time-non-stationary nature of fast time-varying scintillation interference at the algorithm level. This embodiment is easy to modularize and encapsulate, has good real-time computation performance, is simple to implement in engineering, and has good portability across platforms.

[0082] Preferably, such as Figure 2 As shown, step S2 specifically includes the following steps:

[0083] S21. Using the xyz coordinate system, arbitrarily select one element of the array antenna as the reference element, and obtain the three-dimensional coordinates of the other i-th element relative to the reference element as: x i y i , z i, where i is a positive integer, and M-1≥i≥1;

[0084] S22. Based on the relative three-dimensional coordinates, the relative phase relationship of interference signals from different directions arriving at each element of the antenna is obtained:

[0085]

[0086] In the formula, This is called interference direction. Corresponding to a 1×M dimensional steering vector, θ is the pitch angle. It is the azimuth angle;

[0087] S23. With azimuth angle 0° and pitch angle 0° as initial values ​​and 1° as the angle change interval, traverse all combinations of azimuth angle 0°~359° and pitch angle 0°~90° to obtain a 32760×M-dimensional interference direction guidance vector dictionary B.

[0088] S24. Extract a 1×M dimensional array signal C from the M-channel digital baseband signals, sampling point by point, and use a convex optimization tool to solve the local optimum solution of the optimization problem, a 1×32760 dimensional vector y:

[0089]

[0090] In the above formula, the 1×32760-dimensional vector y represents the distribution characteristics of the corresponding array signal C in the spatial direction, and is used to determine the interference mode of the corresponding sampling point;

[0091] Specifically, the solution process of the concrete convex optimization tool is an iterative solution:

[0092] x(n+1)=x(n)+B(x(n)BC)+(x(n)-y(n))

[0093] y(n+1)=(y(n)-x(n+1))+sign(x(n+1))

[0094] In the above formula, n is the number of iterations, with an initial value of 0; x(n) is an intermediate operation variable, with an initial value of a 1×32760-dimensional all-zero vector; y(n) is an initial value of a 1×32760-dimensional all-zero vector; sign() is the sign function;

[0095] The iteration termination condition for convex optimization tools is that the iteration difference is less than 3 times the noise energy.

[0096]

[0097] Alternatively, the iteration termination condition for convex optimization tools is:

[0098] n≥10

[0099] In the above formula, The environmental thermal noise energy is the average of the sum of squares of 1000 AD sampling points when the specific source is an interference-free environment.

[0100] S25. Arrange the vector y calculated from each sampling point according to the sampling time to obtain the interference mode time distribution table Y = [y1, ..., y2] with a total of T sampling points. t , ..., y T ], where T >> 102400, T ≥ t ≥ 1.

[0101] Preferably, step S3 specifically includes the following steps:

[0102] S31. Calculate the difference in interference patterns between two adjacent sampling points: When the difference exceeds 100×M, time t+1 is considered the time of interference flicker, denoted as P. t =t+1, P t To P t+1 -1 sampling time intervals, including P. t and P t+1 -1 sampling points belong to the same interference mode. The data group consisting of all sampling points is defined as the (t+1)th steady-state sampling point data group D. t+1 The first set of steady-state sampling data group D1 is the data group of all sampling points from the first sampling point to the P1-1 sampling point, and the 100×M is the difference threshold.

[0103] S32. Calculate the difference in interference patterns between sampling points at any two interference flickering moments: Where T≥m≠n≥1, when the difference value does not exceed 100×M, the corresponding two sets of steady-state sampling point data groups are considered to belong to the same interference mode, and the sampling point data groups are merged and sorted according to time sequence to realize that one interference mode corresponds to one reconstructed steady-state sampling point data group.

[0104] Preferably, step S4 specifically includes the following steps:

[0105] S41. Calculate the number of sampling points contained in each reconstructed steady-state data set;

[0106] S42. When the number of sampling points is less than 256×M points, the reconstructed steady-state data set is considered to be an outlier set, and all anti-interference outputs corresponding to the sampling time of the reconstructed steady-state data set are set to 0.

[0107] Preferably, such as Figure 3 As shown, step S5 specifically includes the following steps:

[0108] S51, for each reconstructed interference data group of each antenna element, according to... The data is segmented by points, where N is the number of signal sampling points for a single antenna element in the reconstructed interference data group. To perform the round-down operation, zeros are padded at the end of the sampling points in the reconstructed interference data set to ensure that the sum of the number of sampling points and the number of padded zeros is equal to the total number of sampling points. A multiple of an integer is denoted as S;

[0109] S52, Perform the following steps on each data segment separately. Point Fast Fourier Transform yields an S×M numerical matrix for each frequency point, denoted as R;

[0110] S53. For each frequency point, perform interference cancellation operation: e = R(R H R) -1 f, where f is an M×1 dimensional all-1 column vector, * H For the conjugate transpose operation, * -1 This is a matrix inversion operation, where e is the S×1 dimensional column vector after interference cancellation;

[0111] S54. Sort e according to the corresponding frequency points, perform inverse fast Fourier transform, and obtain the time domain data after anti-interference.

[0112] S55. Sort and concatenate the time-domain data after interference suppression according to the data segment number to obtain the data group after interference suppression.

[0113] Preferably, step S6 specifically includes the following steps:

[0114] S61. According to the interference flicker time, reassemble the anti-interference data group corresponding to the reconstructed steady-state data group and the data group with all zeros corresponding to the outlier to obtain the interference-suppressed satellite navigation signal with normal time sequence.

[0115] S62. Use the interference-suppressed satellite navigation signal with normal timing sequence to perform normal satellite navigation signal tracking and acquisition operations, so as to realize satellite navigation positioning in a fast time-varying flicker interference environment.

[0116] like Figure 4 As shown, this application also provides a robust satellite navigation fast time-varying flicker interference cancellation device, implemented based on a satellite navigation anti-interference processor using an array antenna. The array antenna includes M antenna elements, corresponding to M communication channels, where M is a positive integer and M≥2. In this application, M is taken as 7, specifically including:

[0117] The digital baseband signal acquisition module is used to control the array antenna to output M analog signals. After analog filtering, analog downconversion, AD, digital filtering, and digital downconversion, M digital baseband signals are obtained.

[0118] The interference pattern recognition module is used to perform sampling point-by-sampling interference pattern recognition on M-channel digital baseband signals, obtain the interference pattern at each sampling point, and form the time-varying trajectory of the interference pattern with the sampling point.

[0119] The time-domain data reconstruction module is used to reconstruct the time-domain data of the interference mode with the time-varying trajectory of the sampling point, and obtain a corresponding reconstructed interference data group for each interference mode;

[0120] The outlier removal module is used to remove outliers from the obtained reconstructed interference data groups. After removal, the values ​​of all interference data groups corresponding to the outliers are set to 0.

[0121] The interference cancellation module is used to perform interference cancellation on the reconstructed interference data group after outlier removal, filter out the interference in each data group, and obtain the anti-interference data group.

[0122] The data group stitching module is used to reassemble the anti-interference data groups according to the data group reconstruction time point to obtain satellite navigation signals with normal time sequence, which is used to realize satellite navigation and positioning in fast time-varying flicker interference environment.

[0123] The modules in the aforementioned satellite navigation fast time-varying flicker interference cancellation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0124] like Figure 5 As shown, a preferred embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the robust satellite navigation fast time-varying flicker interference cancellation method described in the above embodiments.

[0125] like Figure 6 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the aforementioned robust satellite navigation fast time-varying flicker interference cancellation method.

[0126] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] A preferred embodiment of this application also provides a storage medium including a stored program that, when the program is executed, controls the device where the storage medium is located to perform the robust satellite navigation fast time-varying flicker interference cancellation method described in the above embodiments.

[0128] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0129] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this application's embodiments that contribute to the prior art or the technical solutions can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0130] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A robust method for canceling fast time-varying flicker interference in satellite navigation, implemented based on a satellite navigation anti-interference processor using an array antenna, wherein the array antenna comprises M antenna elements corresponding to M communication channels, where M is a positive integer and M≥2, characterized in that: The specific steps include: S1. Control the array antenna to output M analog signals, which are then processed through analog filtering, analog down-conversion, AD conversion, digital filtering, and digital down-conversion to obtain M digital baseband signals. S2. Perform sampling point-by-sampling interference pattern identification on the M-channel digital baseband signal to obtain the interference pattern at each sampling point and form the time-varying trajectory of the interference pattern with the sampling point. S3. Reconstruct the time-domain data of the interference mode with the time-varying trajectory of the sampling point, and obtain a corresponding reconstructed interference data group for each interference mode. S4. Remove outliers from the obtained reconstructed interference data groups. After removal, set all values ​​of the interference data groups corresponding to the outliers to 0. S5. Perform interference cancellation on the reconstructed interference data group after outlier removal, filter out the interference in each data group, and obtain the anti-interference data group. S6. Reassemble the anti-interference data groups according to the reconstruction time point of the data groups to obtain satellite navigation signals with normal time sequence, which are used to realize satellite navigation and positioning in the environment of fast time-varying flicker interference.

2. The robust satellite navigation fast time-varying scintillation interference cancellation method according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Using the xyz coordinate system, arbitrarily select one element of the array antenna as the reference element, and obtain the three-dimensional coordinates of the other i-th element relative to the reference element as: x i ,y i ,z i , where i is a positive integer, and M-1≥i≥1; S22. Based on the relative three-dimensional coordinates, the relative phase relationship of interference signals from different directions arriving at each element of the antenna is obtained: In the formula, This is called interference direction. Corresponding to a 1×M dimensional steering vector, θ is the pitch angle. It is the azimuth angle; S23. With azimuth angle 0° and pitch angle 0° as initial values ​​and 1° as the angle change interval, traverse all combinations of azimuth angle 0°~359° and pitch angle 0°~90° to obtain a 32760×M-dimensional interference direction guidance vector dictionary B. S24. Extract a 1×M dimensional array signal C from the M-channel digital baseband signals, sampling point by point, and use a convex optimization tool to solve the local optimum solution of the optimization problem, a 1×32760 dimensional vector y: In the above formula, the 1×32760-dimensional vector y represents the distribution characteristics of the corresponding array signal C in the spatial direction, and is used to determine the interference mode of the corresponding sampling point; S25. Arrange the vector y calculated from each sampling point according to the sampling time to obtain the interference mode time distribution table Y = [y1, ..., y2] with a total of T sampling points. t ,…,y T ], where T >> 102400, T ≥ t ≥ 1.

3. A robust method for canceling fast time-varying scintillation interference in satellite navigation according to claim 2, characterized in that: The specific solution process of the convex optimization tool is iterative: x(n+1)=x(n)+B(x(n)BC)+(x(n)-y(n)) y(n+1)=(y(n)-x(n+1))+sign(x(n+1)) In the above formula, n is the number of iterations, with an initial value of 0; x(n) is an intermediate operation variable, with an initial value of a 1×32760-dimensional all-zero vector; y(n) is an initial value of a 1×32760-dimensional all-zero vector; sign() is the sign function; The iteration termination condition for convex optimization tools is that the iteration difference is less than 3 times the noise energy. Alternatively, the iteration termination condition for convex optimization tools is: n≥10 In the above formula, The value represents the average of the squares of 1000 AD sampling points in an interference-free environment, representing the environmental thermal noise energy.

4. A robust method for canceling fast time-varying scintillation interference in satellite navigation according to claim 2, characterized in that: Step S3 specifically includes the following steps: S31. Calculate the difference in interference patterns between two adjacent sampling points: When the difference exceeds 100×M, time t+1 is considered the time of interference flicker, denoted as P. t =t+1, P t To P t+1 -1 sampling time intervals, including P. t and P t+1 -1 sampling points belong to the same interference mode. The data group consisting of all sampling points is defined as the (t+1)th steady-state sampling point data group D. t+1 The first set of steady-state sampling data group D1 is the data group of all sampling points from the first sampling point to the P1-1 sampling point, and the 100×M is the difference threshold. S32. Calculate the difference in interference patterns between sampling points at any two interference flickering moments: Where T≥m≠n≥1, when the difference value does not exceed 100×M, the corresponding two sets of steady-state sampling point data groups are considered to belong to the same interference mode, and the sampling point data groups are merged and sorted according to time sequence to realize that one interference mode corresponds to one reconstructed steady-state sampling point data group.

5. A robust method for canceling fast time-varying scintillation interference in satellite navigation according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41. Calculate the number of sampling points contained in each reconstructed steady-state data set; S42. When the number of sampling points is less than 256×M points, the reconstructed steady-state data set is considered to be an outlier set, and all anti-interference outputs corresponding to the sampling time of the reconstructed steady-state data set are set to 0.

6. A robust method for canceling fast time-varying scintillation interference in satellite navigation according to claim 1, characterized in that: Step S5 specifically includes the following steps: S51, for each reconstructed interference data group of each antenna element, according to... The data is segmented by points, where N is the number of signal sampling points for a single antenna element in the reconstructed interference data group. To perform the round-down operation, zeros are padded at the end of the sampling points in the reconstructed interference data set to ensure that the sum of the number of sampling points and the number of padded zeros is equal to the total number of sampling points. A multiple of an integer is denoted as S; S52, Perform the following steps on each data segment separately. Point Fast Fourier Transform yields an S×M numerical matrix for each frequency point, denoted as R; S53. For each frequency point, perform interference cancellation operation: e = R(R H R) -1 f, where f is an M×1 dimensional all-1 column vector, * H For the conjugate transpose operation, * -1 This is a matrix inversion operation, where e is the S×1 dimensional column vector after interference cancellation; S54. Sort e according to the corresponding frequency points, perform inverse fast Fourier transform, and obtain the time domain data after anti-interference. S55. Sort and concatenate the time-domain data after interference suppression according to the data segment number to obtain the data group after interference suppression.

7. A robust method for canceling fast time-varying scintillation interference in satellite navigation according to claim 1, characterized in that: Step S6 specifically includes the following steps: S61. According to the interference flicker time, reassemble the anti-interference data group corresponding to the reconstructed steady-state data group and the data group with all zeros corresponding to the outlier to obtain the interference-suppressed satellite navigation signal with normal time sequence. S62. Use the interference-suppressed satellite navigation signal with normal timing sequence to perform normal satellite navigation signal tracking and acquisition operations, so as to realize satellite navigation positioning in a fast time-varying flicker interference environment.

8. A robust satellite navigation fast time-varying flicker interference cancellation device, implemented based on a satellite navigation anti-interference processor using an array antenna, wherein the array antenna comprises M antenna elements corresponding to M communication channels, where M is a positive integer and M≥2, characterized in that: Specifically, it includes: The digital baseband signal acquisition module is used to control the array antenna to output M analog signals. After analog filtering, analog downconversion, AD, digital filtering, and digital downconversion, M digital baseband signals are obtained. The interference pattern recognition module is used to perform sampling point-by-sampling interference pattern recognition on M-channel digital baseband signals, obtain the interference pattern at each sampling point, and form the time-varying trajectory of the interference pattern with the sampling point. The time-domain data reconstruction module is used to reconstruct the time-domain data of the interference mode with the time-varying trajectory of the sampling point, and obtain a corresponding reconstructed interference data group for each interference mode; The outlier removal module is used to remove outliers from the obtained reconstructed interference data groups. After removal, the values ​​of all interference data groups corresponding to the outliers are set to 0. The interference cancellation module is used to perform interference cancellation on the reconstructed interference data group after outlier removal, filter out the interference in each data group, and obtain the anti-interference data group. The data group stitching module is used to reassemble the anti-interference data groups according to the data group reconstruction time point to obtain satellite navigation signals with normal time sequence, which is used to realize satellite navigation and positioning in fast time-varying flicker interference environment.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the robust satellite navigation fast time-varying flicker interference cancellation method as described in any one of claims 1 to 7.

10. A storage medium comprising a stored program, characterized in that, When the program is running, it controls the device containing the storage medium to perform the steps of the robust satellite navigation fast time-varying flicker interference cancellation method as described in any one of claims 1 to 7.

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