A navigation satellite signal direction simulation method based on hybrid clustering algorithm

Abstract

The present invention discloses a navigation satellite signal simulation method based on a hybrid clustering algorithm: step 1: calculating satellite coordinate values ​​in a geodetic coordinate system; step 2: projecting them into a northeastern celestial coordinate system and calculating the distance between any two satellites; step 3: setting the number of clusters, azimuth angle threshold, and distance threshold; step 4: clustering using the DBSCAN algorithm; step 5: calculating the horizontal and vertical coordinate values ​​of the center point of each cluster; step 6: clustering each satellite point using the K-means algorithm; step 7: determining an optimized clustering result; step 8: determining whether the maximum azimuth angle difference is less than or equal to the azimuth angle threshold, if so, proceeding to step 9; otherwise, updating the optimized clustering result using the K-means algorithm and returning to step 8; step 9: outputting the current optimized clustering result. The present invention can significantly reduce the difference in incident angle between false satellite signals and real satellite signals, more realistically simulate the state of navigation satellites, and reduce the possibility of false satellite signals being identified.

Description

Technical Field

[0001] The invention belongs to the field of electronic information technology and relates to a navigation satellite signal simulation method based on a hybrid clustering algorithm. Background Art

[0002] With the continuous development of array antennas and array signal processing technologies, array-based satellite navigation anti-interference technology has become increasingly popular. To better test the anti-interference capabilities of array antennas, a new requirement has emerged: a simulated test environment with virtually no noise and navigation signal conditions similar to those of real satellite signals. The "Starry Sky" microwave anechoic chamber, a specially designed electromagnetically isolated environment, features walls, ceiling, and floor covered in highly effective electromagnetic wave absorbing materials, creating an ideal test space virtually free of electromagnetic interference. Multiple signal transmitting antennas installed in the chamber's ceiling can simulate navigation satellite signals from various directions, providing the necessary conditions for testing key performance characteristics such as array antenna beamforming and spatiotemporal interference mitigation. However, deploying a separate transmitting antenna for each visible satellite significantly increases cost and implementation complexity. Therefore, a common practice is to install a small number of antennas in the chamber's ceiling, with each transmitting antenna transmitting signals from several satellites. The optimal number of antennas and the specific navigation satellite signals each antenna simulates significantly impact the effectiveness of the simulated navigation signal direction. However, a sound calculation method for this problem remains, leaving significant room for optimization. The existing "Starry Sky" microwave anechoic chamber transmit antenna layout lacks scientific accuracy, typically determining its distribution using simple calculations of angle differences. Therefore, there is an urgent need to develop a more scientific and efficient method for simulating the direction of navigation satellite signals to enhance the microwave anechoic chamber's ability to simulate the real-world satellite signal environment. This method can also be used in the field of navigation decoy technology for array antenna receivers, reducing the likelihood of decoys being identified by array antenna signal direction estimation techniques. Summary of the Invention

[0003] The purpose of the present invention is to propose a navigation satellite signal arrival simulation method based on a hybrid clustering algorithm to solve the problem that the existing microwave anechoic chamber test method is difficult to accurately simulate the actual satellite signal arrival state.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A navigation satellite signal direction simulation method based on a hybrid clustering algorithm specifically comprises the following steps:

[0006] Step 1: According to the needs of the simulation scene, enter the required simulation time and the current coordinate value of the satellite navigation receiver; obtain and analyze the ephemeris data through the satellite navigation receiver, online or offline, extract the satellite number and orbit parameter information, and calculate the satellite coordinate value in the geodetic coordinate system;

[0007] Step 2: Based on the current coordinate value of the satellite navigation receiver in step 1 and the satellite coordinate value in the earth-fixed coordinate system, the distance between any two satellites in the two-dimensional Cartesian coordinate system is obtained, including the distance in azimuth and elevation.

[0008] Step 3: Set the number of clusters based on the satellite signals in the simulated scene and the number of false signal transmission antennas required for the simulation. k、 Azimuth threshold Th and distance threshold EPS ;

[0009] Step 4: Use the DBSCAN algorithm based on density distribution to cluster each satellite data point using the azimuth and elevation distances between any two satellites calculated in step 2. The clustering results include the number of clusters and each cluster; secondly, determine whether the number of clusters is equal to the number of clusters set in step 3 k If yes, execute step 5, otherwise use binary search to update the distance threshold EPS , and repeat step 4;

[0010] Step 5: Based on the clustering results of step 4, calculate the horizontal and vertical coordinate values ​​of the center point of each cluster;

[0011] Step 6: Use the center point of each cluster obtained in step 5 as the initial center point, use the K-means algorithm, and use the azimuth and elevation angle distances between any two satellites calculated in step 2 to cluster the satellite data points to obtain the clustering results;

[0012] Step 7: Calculate the average intra-cluster distance of the clustering results of step 4 and step 6 respectively, and select the clustering result corresponding to the smaller average intra-cluster distance as the optimized clustering result; the optimized clustering result includes the clustering of the navigation satellite data points, the horizontal and vertical coordinates of the center point of each cluster, the average distance of each satellite point to the center point of its cluster, and the maximum difference in the elevation angle and azimuth angle of the satellites in each cluster;

[0013] Step 8: Calculate the maximum azimuth difference of each cluster in the optimized clustering result, and determine the maximum difference, which is recorded as , where and Indicates the m The azimuth of the two satellite signals in the cluster relative to the satellite navigation receiver; determine the maximum value of the azimuth difference Is it less than or equal to the azimuth angle threshold set in step 3? Th If yes, go to step 9; otherwise, use the K-means algorithm to find the maximum value of the azimuth angle. The corresponding clusters are re-clustered, and the re-clustered clusters are merged with all other clusters in the current optimized clustering result as the new optimized clustering result, and the process returns to step 8.

[0014] Step 9: Output the current optimized clustering result as the final clustering result.

[0015] Furthermore, step 2 specifically includes the following sub-steps:

[0016] Step 21, convert the current coordinate value of the satellite navigation receiver in step 1 and the satellite coordinate value in the earth-fixed coordinate system to the northeast sky coordinate system with the position of the satellite navigation receiver as the origin; calculate the azimuth and pitch angle of each satellite relative to the satellite navigation receiver in this coordinate system, and then convert it to a two-dimensional Cartesian coordinate system for representation; specifically, assuming that The azimuth of the satellite is , the pitch angle is , in azimuth As the horizontal axis, the pitch angle As the vertical axis, The direction angles of the satellite signals are projected into a two-dimensional Cartesian coordinate system;

[0017] Step 22: In a two-dimensional Cartesian coordinate system, let the satellite data set to be clustered be ,in, , is the number of visible satellites relative to the satellite navigation receiver; calculate and The distance between , 、 Represents the satellite data set to be clustered X Any two of them represent the azimuth and elevation information of the satellite, represents the 2-norm.

[0018] Furthermore, in step 22, two data points are found in a two-dimensional Cartesian coordinate system. 、 For the pitch angle, the norm of the two satellites is directly taken after the difference between the pitch angles. For the azimuth angle, if the azimuth angle difference between the two satellites is less than or equal to 180°, the distance between the azimuth angles of the two satellites is ,otherwise, .

[0019] Furthermore, in step 5, for the pitch angle, the average of the maximum and minimum pitch angles of all satellites in each cluster is calculated as the vertical coordinate of the center point of the cluster; for the azimuth angle, based on the difference between the two azimuth angles of all satellites in each cluster, the two satellite points with the largest azimuth angle difference are used as the left and right boundary points of the cluster, and then the horizontal coordinate of its center point is calculated based on the left and right boundary points.

[0020] In a second aspect, the present invention provides an electronic device, comprising:

[0021] a memory for storing executable instructions;

[0022] The processor is configured to implement the method according to any one of claims 1 to 4 when executing the executable instructions or computer program stored in the memory.

[0023] In a third aspect, the present invention provides a computer-readable storage medium storing executable instructions or a computer program, wherein the executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 4.

[0024] Compared with the prior art, the present invention has the following technical effects:

[0025] The present invention can simulate the needs of navigation satellite signals, group satellite signals from similar satellites into the same cluster, determine the optimal false signal emission angle, and reduce the difference in the angle of incidence between real and false satellite signals. At the same time, when implementing cluster division, it combines the K-means and DBSCAN algorithms, organically integrating the two and adding threshold restrictions. Specific advantages include the following:

[0026] (1) This method automatically clusters the incoming directions of navigation satellite signals and calculates the center point of each cluster for arranging the transmitting antennas for false signals. Using the calculation results of this method, the difference in the angle of incidence between false satellite signals and real satellite signals can be significantly reduced. On the one hand, it can more realistically simulate the state of navigation satellites and provide a realistic environment for array antenna testing. On the other hand, it can reduce the possibility of false satellite signals being identified by array antenna technology.

[0027] (2) The DBSCAN algorithm and the K-means algorithm are organically combined. On the one hand, this solves the problem that the DBSCAN algorithm is sensitive to the neighborhood radius and the minimum number of samples within a cluster and cannot directly specify the number of clusters; on the other hand, it also solves the problem that the K-means algorithm is sensitive to the location of the initial cluster center. Based on the processing of the clustering module, the present invention can provide a transmit antenna layout and signal transmission distribution scheme that conforms to the actual situation according to the number of existing antennas and the accuracy required by the experiment, meeting the requirements of the accuracy of the incoming angle simulation under different experimental tasks.

[0028] (3) The present invention is mainly implemented through data preprocessing and hybrid clustering algorithm. This method does not require the deployment of additional hardware equipment, and the method process is streamlined, the calculation is efficient, the demand for computing resources is low, and it is easy to deploy and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is the cluster analysis process;

[0030] Figure 2 It is a comparison of the clustering effects of three algorithms: K-means algorithm, DBSCAN algorithm, and hybrid clustering algorithm.

[0031] The present invention is further explained below with reference to the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0032] like Figure 1 As shown, the present invention proposes a navigation satellite signal simulation method based on a hybrid clustering algorithm, which specifically includes the following steps:

[0033] Step 1: According to the needs of the simulation scene, enter the required simulation time and the current coordinate value of the satellite navigation receiver; obtain and analyze the ephemeris data through the satellite navigation receiver, online or offline, extract the satellite number and orbit parameter information, and calculate the satellite coordinate value in the Earth-fixed coordinate system.

[0034] Step 2: Based on the current coordinate values ​​of the satellite navigation receiver in step 1 and the satellite coordinate values ​​in the earth-fixed coordinate system, obtain the distance between any two satellites in the two-dimensional Cartesian coordinate system, including the distance in azimuth and elevation. This specifically includes the following sub-steps:

[0035] Step 21, convert the current coordinate value of the satellite navigation receiver in step 1 and the satellite coordinate value in the earth-fixed coordinate system to the northeast sky coordinate system with the position of the satellite navigation receiver as the origin; calculate the azimuth and pitch angle of each satellite relative to the satellite navigation receiver in this coordinate system, and then convert it to a two-dimensional Cartesian coordinate system for representation. Specifically, assuming that The azimuth of the satellite is , the pitch angle is , in azimuth As the horizontal axis, the pitch angle As the vertical axis, The direction angles of the satellite signals are projected into a two-dimensional Cartesian coordinate system;

[0036] Step 22: In a two-dimensional Cartesian coordinate system, let the satellite data set to be clustered be ,in, , is the number of visible satellites relative to the satellite navigation receiver; calculate and The distance between , 、 Represents the satellite data set to be clustered X Any two of them represent the azimuth and elevation information of the satellite, represents the 2-norm.

[0037] Specifically, find two data points in a two-dimensional Cartesian coordinate system 、 The distance between them is: for the elevation angle, the angle and angle difference range is 0°~90°, and the norm can be directly obtained by taking the difference between the elevation angles of the two satellites; for the azimuth angle, the azimuth angle range is -180°~180°, and the angle difference range is 0°~180°. Therefore, when calculating the distance for the azimuth angle, if the azimuth angle difference between the two satellites is less than or equal to 180°, then the distance between the azimuth angles of the two satellites is: ,otherwise, .

[0038] Step 3: Set the number of clusters based on the satellite signals in the simulated scene and the number of false signal transmission antennas required for the simulation. k、 Azimuth threshold Th and distance threshold EPS .

[0039] Step 4: Use the DBSCAN algorithm based on density distribution to cluster each satellite data point using the distance between any two satellites calculated in step 2 (including the distance in azimuth and elevation). The clustering result includes the number of clusters and each cluster. Secondly, determine whether the number of clusters is equal to the number of clusters set in step 3. k If yes, execute step 5, otherwise use binary search to update the distance threshold EPS , and repeat step 4.

[0040] Step 5: Based on the clustering results of step 4, calculate the horizontal and vertical coordinate values ​​of the center point of each cluster.

[0041] Specifically, for the pitch angle, the average of the maximum and minimum pitch angles of all satellites in each cluster is calculated as the vertical coordinate of the center point of the cluster; for the azimuth angle, based on the difference between the two azimuth angles of all satellites in each cluster, the two satellite points with the largest azimuth angle difference are used as the left and right boundary points of the cluster, and then the horizontal coordinate of its center point is calculated based on the left and right boundary points.

[0042] Step 6: Use the center point of each cluster obtained in step 5 as the initial center point, use the K-means algorithm, and use the distance between any two satellites calculated in step 2 (including the distance in azimuth and elevation) to cluster the satellite data points to obtain the clustering results.

[0043] Step 7: Calculate the average intra-cluster distances for the clustering results from Step 4 and Step 6, respectively. Select the clustering result with the smaller average intra-cluster distance as the optimized clustering result. The optimized clustering result includes the clustering of the navigation satellite data points, the horizontal and vertical coordinates of the center point of each cluster, the average distance from each satellite point to the center point of its cluster, and the maximum difference in the elevation and azimuth angles of the satellites in each cluster.

[0044] Step 8: Calculate the maximum azimuth difference of each cluster in the optimized clustering result, and determine the maximum difference, which is recorded as , where and Indicates the m The azimuth of the two satellite signals in the cluster relative to the satellite navigation receiver; determine the maximum value of the azimuth difference Is it less than or equal to the azimuth angle threshold set in step 3? Th If yes, go to step 9; otherwise, use the K-means algorithm to find the maximum value of the azimuth angle. The corresponding clusters are re-clustered, and the re-clustered clusters are merged with all other clusters in the current optimized clustering result as the new optimized clustering result, and the process returns to step 8.

[0045] Step 9: Output the current optimized clustering result as the final clustering result.

[0046] In order to demonstrate the feasibility and effectiveness of the present invention, a simulation experiment was conducted using 120 sets of ephemeris data, and experimental results were obtained.

[0047] The current mainstream array antenna is four-element or seven-element array. The experiment sets the number of clusters. k 5, azimuth threshold Th Set to 72°, distance threshold EPSThe value of the K-means clustering algorithm was set to 100, and 120 different sets of ephemeris data were clustered using K-means, DBSCAN, and the method of the present invention. The results show that both K-means and DBSCAN can successfully cluster into 5 clusters. However, in the K-means clustering results, the satellite corresponding to the azimuth angle of -136.9° and the elevation angle of 21.6° and the two satellites to its right are actually far apart and are mistakenly classified as one cluster. In the DBSCAN clustering results, the 5 satellites with azimuth angles between -65° and 45° have a large angular span and are mistakenly classified as the same cluster. The hybrid clustering algorithm of the present invention effectively solves the above problems, and the clustering effect is significantly improved.

[0048] In order to quantitatively analyze the effect of the proposed clustering method, the average intra-cluster distance of the clustering results of 120 groups of data using the three methods is statistically analyzed as follows: Figure 2 shown. Figure 2 The curve A represents the average intra-cluster distance of the clustering results of the K-means algorithm, the curve B represents the DBSCAN algorithm, and the curve C represents the average intra-cluster distance of the hybrid clustering algorithm. The smaller the value, the higher the intra-cluster similarity, that is, the better the clustering effect. Figure 2 As can be seen from the figure, compared with the K-means and DBSCAN algorithms, the hybrid clustering algorithm achieves smaller average intra-cluster distances, higher intra-cluster similarities, and closer satellite signal heading angles across all 120 data sets. Therefore, the false signals transmitted based on the hybrid clustering results are more realistic than the true satellite signal headings, demonstrating the effectiveness of the proposed method.

Claims

1. A navigation satellite signal direction simulation method based on a hybrid clustering algorithm, characterized in that: The specific steps include: Step 1: According to the needs of the simulation scene, enter the required simulation time and the current coordinate value of the satellite navigation receiver; obtain and analyze the ephemeris data through the satellite navigation receiver, online or offline, extract the satellite number and orbit parameter information, and calculate the satellite coordinate value in the geodetic coordinate system; Step 2: Based on the current coordinate value of the satellite navigation receiver in step 1 and the satellite coordinate value in the earth-fixed coordinate system, the distance between any two satellites in the two-dimensional Cartesian coordinate system is obtained, including the distance in azimuth and elevation. Step 3: According to the satellite signals in the simulated scene, the number of false signal transmission antennas required for angular simulation and the number of available ones, set the number of clusters k, the azimuth angle threshold Th, and the distance threshold Eps; Step 4: Use the density-based DBSCAN algorithm to cluster each satellite data point using the azimuth and elevation distances between any two satellites calculated in Step 2. The clustering result includes the number of clusters and each cluster. Next, determine whether the number of clusters is equal to the number of clusters k set in Step 3. If so, proceed to Step 5. Otherwise, use the binary search method to update the distance threshold Eps and repeat Step 4. Step 5: Based on the clustering results of step 4, calculate the horizontal and vertical coordinate values ​​of the center point of each cluster; Step 6: Use the center point of each cluster obtained in step 5 as the initial center point, use the K-means algorithm, and use the azimuth and elevation angle distances between any two satellites calculated in step 2 to cluster the satellite data points to obtain the clustering results; Step 7: Calculate the average intra-cluster distance of the clustering results of step 4 and step 6 respectively, and select the clustering result corresponding to the smaller average intra-cluster distance as the optimized clustering result; the optimized clustering result includes the clustering of the navigation satellite data points, the horizontal and vertical coordinates of the center point of each cluster, the average distance of each satellite point to the center point of its cluster, and the maximum difference in the elevation angle and azimuth angle of the satellites in each cluster; Step 8: Calculate the maximum azimuth difference of each cluster in the optimized clustering result, and determine the maximum difference, which is recorded as Where, α mi With α mj represents the azimuth angles of the two satellite signals in the mth cluster relative to the satellite navigation receiver; determining whether the maximum azimuth angle difference M is less than or equal to the azimuth angle threshold Th set in step 3, if so, proceeding to step 9; Otherwise, use the K-means algorithm to re-cluster the cluster corresponding to the maximum azimuth angle M, merge the re-clustered cluster with all other clusters in the current optimized clustering result as the new optimized clustering result, and return to step 8; Step 9: Output the current optimized clustering result as the final clustering result.

2. The navigation satellite signal direction simulation method based on the hybrid clustering algorithm according to claim 1, characterized in that: Step 2 specifically includes the following sub-steps: Step 21, convert the current coordinate value of the satellite navigation receiver in step 1 and the satellite coordinate value in the earth-fixed coordinate system to the northeast sky coordinate system with the position of the satellite navigation receiver as the origin; calculate the azimuth and pitch angle of each satellite relative to the satellite navigation receiver in this coordinate system, and then convert it to a two-dimensional Cartesian coordinate system for representation; specifically, assuming that the azimuth angle of the i-th satellite is α i , the pitch angle is β i , with an azimuth angle α i As the horizontal axis, the pitch angle β i As the vertical coordinate, project the direction angle of the i-th satellite signal into the two-dimensional Cartesian coordinate system; Step 22: In a two-dimensional Cartesian coordinate system, let the satellite data set to be clustered be X = {x i |x i ∈R 2 ,i=1,2,…,n}, where x i =(α i ,β i ), n is the number of visible satellites relative to the satellite navigation receiver; calculate x i with x j The distance d(x i ,x j )=||x i -x j ||,x i 、x j represents the azimuth and elevation information of any two representative satellites in the satellite data set X to be clustered, and ||·|| represents the 2-norm.

3. The navigation satellite signal direction simulation method based on the hybrid clustering algorithm according to claim 2, characterized in that: In step 22, find the two data points x in the two-dimensional Cartesian coordinate system. i 、x j For the pitch angle, the norm of the two satellites is directly taken after the difference between the pitch angles. For the azimuth angle, if the azimuth angle difference between the two satellites is less than or equal to 180°, the distance between the azimuth angles of the two satellites is d(α i ,α j )=||α i -α j ||, otherwise, d(α i ,α j )=360-||α i -α j ||.

4. The navigation satellite signal direction simulation method based on the hybrid clustering algorithm according to claim 1, characterized in that: In step 5, for the pitch angle, the average of the maximum and minimum pitch angles of all satellites in each cluster is calculated as the vertical coordinate of the center point of the cluster; For azimuth, according to the difference between the two azimuths of all satellites in each cluster, the two satellite points with the largest azimuth difference are used as the left and right boundary points of the cluster, and then the horizontal coordinate of its center point is calculated based on the left and right boundary points.

5. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the method according to any one of claims 1 to 4 when executing the executable instructions or computer programs stored in the memory.

6. A computer-readable storage medium storing executable instructions or a computer program, characterized in that: When the executable instructions are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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