Single-satellite terminal positioning method based on beam gain field pattern

Abstract

The invention relates to the technical field of satellite positioning, in particular to a single-satellite terminal positioning method based on a beam gain field pattern, and aims to solve the problems of insufficient ground network coverage and urgent terminal self-positioning requirements in partial regions. Calculating the same-frequency beam signal gain of the reference point and constructing a beam gain field pattern; then, PCA dimension reduction and K-means clustering algorithms are adopted, received same-frequency beam intensity and field pattern data are matched, and a reference point is searched; and finally, test data is brought in for positioning simulation. According to the method, terminal self-positioning in a Tiantong satellite beam coverage area can be realized, the positioning error is small, and the precision is excellent; the influence of the measurement gain error, the beam pointing error and the field pattern gain error on the positioning precision is analyzed through simulation testing, and an effective solution is provided for the development of the satellite positioning technology.

Description

Technical Field

[0001] The invention relates to the technical field of satellite positioning, and in particular to a single-satellite terminal positioning method based on a beam gain field diagram. Background Art

[0002] Terminal positioning is a key link in network management. In essence, it is a technology that accurately determines the location of network terminal devices in the entire network architecture.

[0003] Among the existing terminal positioning methods, most of them rely on ground-covered wireless networks to obtain some time, angle and other data for positioning through signal transmission between the terminal and the base station or a mobile terminal with a known location. In some areas, the coverage of the ground network has obvious shortcomings, such as remote mountainous areas and vast wilderness. The cost of laying network infrastructure on a large scale in these areas is too high, resulting in fragmented network coverage. Therefore, the demand for terminal autonomous positioning is particularly prominent. With the help of satellite positioning, satellites are not restricted by the complex ground environment, and can easily overcome geographical barriers, make up for the incompleteness of ground network coverage, and build a comprehensive and more reliable positioning service network for users.

[0004] Therefore, this paper proposes a single-satellite terminal positioning method based on the multi-beam gain field diagram of Tiantong satellite. Summary of the invention

[0005] The object of the present invention is to provide a single-satellite terminal positioning method based on a beam gain field diagram to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A single-satellite terminal positioning method based on a beam gain field diagram, the method comprising: S100, deriving the angle relationship between the reference point in the beam coverage area and the center direction of the surrounding n same-frequency beams including the current beam, and calculating the signal gain of each same-frequency beam obtained at the position of all reference points to construct a gain field diagram; S200, using the PCA method to reduce the dimension of the field image data, and using the K-means clustering algorithm to match the received co-frequency beam intensity with the field image data to search for a reference point; S300, input test data and test the positioning result of the method.

[0007] Preferably, the angular relationship between the reference point in the beam coverage area and the center directions of the surrounding n co-frequency beams including the current beam is derived in S100, including: Set in the earth-fixed coordinate system, S is a known satellite with a position of ; Beami The center position is , the known reference point position is ; According to the cosine theorem, the incident direction of the received signal at the reference point is i The beam angle :

[0008] Then, several reference points are selected in the beam coverage area, and the .

[0009] Preferably, calculating the signal gains of each co-frequency beam obtained at all reference point positions in S100 to construct a gain field diagram includes: Calculate the theoretical beam gain according to the theoretical gain formula :

[0010] The gain of the beam signal received at the reference point is It is expressed as the angle between the incident direction of the beam signal and the direction of the spot beam center; where, , and denote the 1st and 3rd order Bessel functions of the first kind, respectively, and the beam center gain ; in, represents the 3dB angle, represents the angle between the incident direction of the signal and the direction of the beam center, D represents the antenna aperture, represents the antenna efficiency, represents the wavelength of the radiated signal; the beam gain of all reference points is the field pattern data matrix U of the beam; and based on this, the gain field pattern of all point beams of the satellite is obtained.

[0011] Preferably, in S200, the PCA method is used to reduce the dimension of the field image data, including: S21-1. Standardize the data according to the centralization formula so that the mean of each feature is 0 and the variance is 1:

[0012] in, represents the centralized data matrix, U represents the original data matrix, μ represents the data mean, σ represents the standard deviation of the data; S21-2. Calculate the covariance matrix of the data V :

[0013] Where m represents the number of samples; S21-3. Covariance matrix V The row eigenvalue decomposition is performed to obtain the eigenvalues ​​and corresponding eigenvectors, and the eigenvalues ​​are arranged in descending order. The eigenvectors corresponding to the first k eigenvalues ​​are taken as the basis after dimensionality reduction to form a dimensionality reduction matrix. , where the dimensionality reduction representation of the data is calculated:

[0014] in, Represents the data after dimensionality reduction.

[0015] Preferably, in S200, a K-means clustering algorithm is used to match the received co-frequency beam intensity with the field map data to search for reference points, including: S22-1. Select K data points as initial cluster centers , where K is determined by the elbow rule; S22-2. Get each data from the dimension reduction Data points , calculate its distance to each cluster center, and assign it to the cluster with the closest distance:

[0016] in, represents the current cluster center, Represents each cluster after allocation; S22-3. For each cluster , calculate the new cluster center, which is the mean of all points in the cluster:

[0017] in, Representation Cluster The number of data points in ; S22-4, repeat the steps of allocating clusters and updating cluster centers until the cluster centers no longer change or the predetermined number of iterations is reached, the algorithm terminates, and the final clustering result is output.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned single-satellite terminal positioning method based on a beam gain field diagram.

[0019] A computer device comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the program, the steps in the above-mentioned single-satellite terminal positioning method based on beam gain field diagram are implemented.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are: The terminal positioning method provided by the present invention can enable a terminal with an unknown position to realize autonomous positioning within a satellite beam coverage area, with small positioning error and excellent accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 Schematic diagram of 7 co-frequency beams provided by the present invention.

[0022] Figure 2 This is a matching search flow chart provided by the present invention.

[0023] Figure 3 The present invention provides seven co-frequency beam gain field diagrams.

[0024] Figure 4 The positioning error simulation results are shown when the measurement gain error follows a Gaussian distribution with a mean of 0.5db and a variance of 0.1db.

[0025] Figure 5 The positioning error simulation results are shown when the beam pointing error follows a Gaussian distribution with a mean of 0.01 and a variance of 0.005.

[0026] Figure 6 The positioning error simulation results are shown when the field pattern gain error follows a Gaussian distribution with a mean of 0.5db and a variance of 0.1db.

[0027] Figure 7 This is a time comparison chart of searching 100 times using the K-means algorithm and using PCA combined with the K-means algorithm. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1-Figure 7 , the present invention provides a technical solution:

[0030] Embodiment 1: like Figure 1, calculate the signal gains of all co-frequency beams obtained by the reference points within the coverage area of ​​central beam 1.

[0031] S100, deriving the angle relationship between the reference point in the beam coverage area and the center direction of the surrounding n same-frequency beams including the current beam, and calculating the signal gain of each same-frequency beam obtained at the position of all reference points to construct a gain field diagram; Preferably, S101, derive the angle relationship between a known reference point in the coverage area of ​​beam 1 and the center direction of n surrounding same-frequency beams including the beam. The derivation steps are as follows: In the Earth-fixed coordinate system, S is a known satellite with a position of ; represents the angle between the incident direction of the received signal at the reference point and the i-th beam; the center position of beam i is , the known reference point position is According to the cosine theorem, we can get:

[0032] Take several reference points in the beam coverage area and calculate the .

[0033] S102: Calculate the signal gains of each co-frequency beam obtained at all reference point positions.

[0034] Calculate the theoretical beam gain according to the theoretical gain formula ; The gain of the beam signal received at the reference point It is expressed as the angle between the incident direction of the beam signal and the direction of the spot beam center, as follows:

[0035] in, , and are the 1st and 3rd order first kind Bessel functions, beam center gain . is the 3dB angle, It represents the angle between the incident direction of the signal and the direction of the beam center, D is the antenna aperture, is the antenna efficiency, is the wavelength of the radiated signal. The beam gain of all reference points is the gain field data matrix U(m*n) of beam 1; Among them, m represents the number of reference points, n represents the number of co-frequency beams including the current beam, and the latitude of the matrix U is m*n.

[0036] Repeat the above steps to obtain the gain field diagrams of all spot beams of the satellite.

[0037] like Figure 2 , use the method in this paper to achieve terminal positioning.

[0038] S200, using the PCA method to reduce the dimension of the field image data, and using the K-means clustering algorithm to match the received co-frequency beam intensity with the field image data to search for reference points.

[0039] Preferably, S201, PCA dimensionality reduction: determining a main beam according to the strongest beam signal received, and reducing the dimensionality of the field pattern data matrix U of the main beam: S21-1. Centralized data: Standardize the data so that the mean of each feature is 0 and the variance is 1. is the data mean, is the standard deviation of the data, is the original data matrix, is the centralized data matrix.

[0040]

[0041] S21-2. Calculate the covariance matrix: Calculate the covariance matrix V of the data:

[0042] S21-3. Solve eigenvalues ​​and eigenvectors: Perform eigenvalue decomposition on the covariance matrix V to obtain eigenvalues ​​and corresponding eigenvectors. The eigenvalues ​​are arranged in descending order, and the eigenvectors corresponding to the first k eigenvalues ​​are taken as the basis after dimensionality reduction to form a reduced dimension matrix , calculate the reduced dimension representation of the data:

[0043] Preferably, S202, using a K-means clustering algorithm, matching the received co-frequency beam intensity with the field map data to search for a reference point; S22-1. Initialization: Select K data points as the initial cluster centers , K is determined by the elbow rule.

[0044] S22-2. Assign clusters: For each data point (From the reduced dimensionality data ), calculate its distance to each cluster center (usually Euclidean distance), and assign it to the cluster with the closest distance. is the current cluster center, are the clusters after allocation.

[0045]

[0046] S22-3. Update cluster center: For each cluster , calculate the new cluster center, which is the mean of all points in the cluster. It is a cluster The number of data points in .

[0047]

[0048] S22-4, Iteration: Repeat the steps of allocating clusters and updating cluster centers until the cluster centers no longer change or the predetermined number of iterations is reached, the algorithm terminates, and the final clustering result is output.

[0049] S300, input test data and perform positioning result test.

[0050] Preferably, Tiantong-1 01 satellite adopts 7-color frequency multiplexing technology. Assume that the unknown terminal is located in the coverage area of ​​beam 1 and can receive 6 surrounding same-frequency beam signals (n=7) at the same time. Bring in the test data, beam 1 (115.783, 27.555), beam 2 (112.368, 33.414), beam 3 (119.727, 37.096), beam 4 (124.142, 30.974), beam 5 (120.72, 19.872), beam 6 (111.448, 17.876), beam 7 (108.54, 24.766). In the coverage area, a reference point is taken every 0.01° of longitude and latitude. PCA dimensionality reduction retains k=4 principal components, and K-means initializes K=2. The gain distribution field diagram produced on this basis is as follows: Figure 3 shown.

[0051] like Figure 4 , when the measured receiving gain error obeys a Gaussian distribution with a mean of 0.5db and a variance of 0.1db, the impact of different locations of the terminal in the area on the autonomous positioning accuracy.

[0052] like Figure 5 , when the beam pointing error obeys a Gaussian distribution with a mean of 0.01 and a variance of 0.005, the impact on the autonomous positioning accuracy of the terminal at different locations in the area.

[0053] like Figure 6 ,When the field pattern gain error obeys a Gaussian distribution with a mean of 0.5db and a variance of 0.1db, it affects the accuracy of autonomous positioning of the terminal at different locations in the area.

[0054] like Figure 7 For 100 identical unknown points, the cumulative time of matching search using K-means algorithm alone is compared with the cumulative time of matching search using PCA combined with K-means algorithm. In terms of overall efficiency, the latter can improve the search time by about 10% compared with the former.

[0055] Embodiment 2: The computer-readable storage medium of this embodiment stores a computer program thereon, and when the program is executed by a processor, the steps in the single-satellite terminal positioning method based on the beam gain field diagram of embodiment 1 are implemented.

[0056] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0057] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0058] Embodiment 3: The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in a single-satellite terminal positioning method based on a beam gain field diagram in Embodiment 1 are implemented.

[0059] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0060] Those skilled in the art will appreciate that the contents disclosed in the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware. Moreover, the present solution may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0061] The present solution is described with reference to the method according to the embodiment of the present solution and the flowchart and / or block diagram of the computer program product. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 one or more processes and / or methods Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 one or more processes and / or methods Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 one or more processes and / or methods Figure 1 The steps for the functions specified in one or more boxes.

[0064] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0065] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A single-satellite terminal positioning method based on beam gain field diagram, characterized in that: The method comprises: S100, deriving the angle relationship between the reference point in the beam coverage area and the center direction of the surrounding n same-frequency beams including the current beam, and calculating the signal gain of each same-frequency beam obtained at the position of all reference points to construct a gain field diagram; S200, using the PCA method to reduce the dimension of the field image data, and using the K-means clustering algorithm to match the received co-frequency beam intensity with the field image data to search for a reference point; S300, input test data and perform positioning result test.

2. A single-satellite terminal positioning method based on beam gain field diagram as claimed in claim 1, characterized in that: The angular relationship between the reference point in the beam coverage area and the center directions of the surrounding n co-frequency beams including the current beam is derived in S100, including: Set in the earth-fixed coordinate system, S is a known satellite with a position of ; Beam i The center position is , the known reference point position is ; According to the cosine theorem, the incident direction of the received signal at the reference point is i The beam angle : ； Then, several reference points are selected in the beam coverage area, and the .

3. The single-satellite terminal positioning method based on beam gain field diagram according to claim 1, characterized in that: The step S100 calculates the gain of each co-frequency beam signal obtained at all reference point positions and constructs a gain field diagram, including: Calculate the theoretical beam gain according to the theoretical gain formula : ； The gain of the beam signal received at the reference point is It is expressed as the angle between the incident direction of the beam signal and the direction of the spot beam center; where, , and denote the 1st and 3rd order Bessel functions of the first kind, respectively, and the beam center gain ; in, represents the 3dB angle, represents the angle between the incident direction of the signal and the direction of the beam center, D represents the antenna aperture, represents the antenna efficiency, represents the wavelength of the radiated signal; the beam gain of all reference points is the field diagram data matrix U of the beam; and based on this, the gain field diagram of all point beams of the satellite is obtained.

4. The single-satellite terminal positioning method based on beam gain field diagram according to claim 1, characterized in that: In S200, the PCA method is used to reduce the dimension of the field image data, including: S21-1. Standardize the data according to the centralization formula so that the mean of each feature is 0 and the variance is 1: ； in, represents the centralized data matrix, U represents the original data matrix, μ represents the data mean, σ represents the standard deviation of the data; S21-2. Calculate the covariance matrix of the data V : ； Where m represents the number of samples; S21-3. Covariance matrix V The row eigenvalue decomposition is performed to obtain the eigenvalues ​​and corresponding eigenvectors, and the eigenvalues ​​are arranged in descending order. The eigenvectors corresponding to the first k eigenvalues ​​are taken as the basis after dimensionality reduction to form a dimensionality reduction matrix. , where the dimensionality reduction representation of the data is calculated: ； in, Represents the data after dimensionality reduction.

5. The single-satellite terminal positioning method based on beam gain field diagram according to claim 1, characterized in that: The K-means clustering algorithm is used in S200 to match the received co-frequency beam intensity with the field map data to search for reference points, including: S22-1. Select K data points as initial cluster centers , where K is determined by the elbow rule; S22-2. Get each data from the dimension reduction Data points , calculate its distance to each cluster center, and assign it to the cluster with the closest distance: ； in, represents the current cluster center, Represents each cluster after allocation; S22-3. For each cluster , calculate the new cluster center, which is the mean of all points in the cluster: ； in, Representation Cluster The number of data points in ; S22-4, repeat the steps of allocating clusters and updating cluster centers until the cluster centers no longer change or the predetermined number of iterations is reached, the algorithm terminates, and the final clustering result is output.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in a single-satellite terminal positioning method based on a beam gain field diagram as described in any one of claims 1 to 5 are implemented.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps in the single-satellite terminal positioning method based on beam gain field diagram as described in any one of claims 1-5 are implemented.

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