A direct self-positioning method with same-frequency multi-angle matching, a storage medium and an equipment

Abstract

The application discloses a same-frequency multi-angle matching direct self-positioning method, a storage medium and equipment, and the direct self-positioning method comprises the following steps: a UAV carrying a uniform linear array receives K same-frequency anchor node radiation source signals with known positions, and the azimuths of the K same-frequency radiation sources are estimated; the estimated azimuths of the K same-frequency radiation sources are randomly arranged, and there are K factorial possibilities; meanwhile, for each point in the space domain, the K azimuths of the point relative to the same-frequency radiation sources are calculated; the Euclidean distances between the K azimuths of each point in the space domain relative to the same-frequency radiation sources and the azimuth estimation values of the K same-frequency radiation sources in each arrangement are calculated, and the shortest Euclidean distance is taken as the loss function of the point; and the space domain point with the minimum loss function is taken as the self-positioning result of the UAV. The application solves the problem of high-precision self-positioning by using same-frequency radiation sources under navigation denial, and the positioning can be performed without the correlation between the known azimuths and the positions of the radiation sources.

Description

Technical Field

[0001] This invention belongs to the field of passive positioning technology, specifically relating to a direct self-positioning method, storage medium, and device for multi-angle matching at the same frequency. Background Technology

[0002] Location positioning is the cornerstone of many modern applications, such as crowd sensing, big data analytics, environmental monitoring, and autonomous driving. The location information of devices involved in these applications helps to connect and exchange data more effectively, protect communication security, and reduce the risk of autonomous movement. The Global Navigation Satellite System (GPS) plays a crucial role in location estimation. However, GPS service is often interrupted due to severe signal attenuation in environments such as surrounding hills, buildings, or tunnels. Sometimes, natural or man-made disasters can also cause GPS service failures. With the increasing number and types of devices, scenarios have emerged that utilize distributed data collection using sensing technologies. Compared to satellite navigation systems, positioning technologies based on distributed sensors have received increasing attention.

[0003] Azimuth-based target localization has been a research area that has received considerable attention for decades. It relies on triangulation of azimuth lines emitted by multiple sensors. However, this method requires accurate knowledge of the correlation between azimuth and node positions, and matching is necessary for signals of the same frequency. However, most existing matching algorithms are cumbersome and prone to errors. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a direct self-positioning method, storage medium, and device based on multi-angle matching at the same frequency. This method solves the problem of navigation denial by achieving high-precision self-positioning using a radiation source at the same frequency. Positioning can be performed without knowing the correlation between the azimuth angle and the radiation source position. It has superior performance compared to traditional two-step positioning methods and has significant engineering application value.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a direct self-localization method for multi-angle matching at the same frequency, specifically including the following steps:

[0006] Step 1: The UAV carrying a uniform linear array receives radiation signals from K anchor nodes with known locations and estimates the azimuth angles of the K radiation sources with the same frequency.

[0007] Step 2: Randomly arrange the estimated azimuth angles of the K co-frequency radiation sources, resulting in K factorial possibilities; simultaneously, for each point in the spatial domain, calculate the K azimuth angles of that point relative to the co-frequency radiation source.

[0008] Step 3: For each point in the airspace, calculate the Euclidean distance between the point and the K azimuth angles of the same frequency radiation source relative to the point and the estimated azimuth angles of the K same frequency radiation sources in each arrangement, and use the shortest Euclidean distance as the loss function for that point.

[0009] Step 4: Take the airspace point with the minimum loss function as the self-localization result of the UAV.

[0010] Furthermore, in step 1, the UAV receives radiation source signals x(t) from K anchor nodes with known locations at the same frequency:

[0011] x(t)=A(θ)s(t)+n(t)

[0012] Where A(θ) represents the array manifold matrix, A(θ)=[a(θ1),...,a(θ K )],a(θ k ) represents the guide vector. θ k Let represent the azimuth angle of the radiation source at the k-th anchor node with the same frequency. This represents the position coordinates of the radiation source at the k-th anchor node with the same frequency. The coordinates of the UAV's position are represented by j, the imaginary unit is represented by λ, the wavelength of the received signal is represented by d, the spacing of the linear array is represented by M, and the number of array elements is represented by M. Let n(t) represent the received signal of the linear array, and n(t) represent zero-mean complex Gaussian stationary noise.

[0013] Furthermore, the process of estimating the azimuth angles of the K co-frequency radiation sources in step 1 is as follows:

[0014] Step S1.1: Calculate the covariance matrix of the radiation signals from K known anchor nodes at the same frequency received by the UAV. in, H represents the sign of the mean, and H represents the conjugate transpose.

[0015] Step S1.2: Adjust the covariance matrix. Perform eigenvalue decomposition. Where Λ represents the eigenvalue matrix, Λ=diag{λ1,...,λ M}, the diagonal elements of Λ are λ1,...,λ M Covariance matrix eigenvalues, λ1≥...≥λ M ; Represents the characteristic matrix, e1,...,e M For λ1,...,λ M One-to-one correspondence of eigenvectors;

[0016] Step S1.3: Take the eigenvectors corresponding to the first K eigenvalues ​​to form the signal subspace E. s =[e1,...,e K ], take the signal subspace E s The first M-1 rows and the last M-1 rows respectively form matrix E x and E y ;

[0017] Step S1.4: For the matrix Eigenvalue decomposition yields the eigenmatrix E, which is then decomposed into K×K submatrices:

[0018]

[0019] Step S1.5: Calculate the auxiliary matrix And find the eigenvalues ​​λ of the auxiliary matrix. k The estimated azimuth angle is obtained.

[0020] Furthermore, in step 2, the estimated azimuth angles of the K co-frequency radiation sources are randomly arranged and represented as a set:

[0021]

[0022] in, Each element in is One arrangement method.

[0023] Furthermore, the process of determining the azimuth angle of each point in the spatial domain relative to the same-frequency radiation source in step 2 is as follows:

[0024]

[0025] in, Let (x, y) represent the azimuth angle of a point in the spatial domain relative to the k-th source of radiation with the same frequency, and let (x, y) represent the position coordinates of that point in the spatial domain. This represents the position coordinates of the radiation source of the k-th same-frequency anchor node.

[0026] Furthermore, the loss function is:

[0027]

[0028] in, Represents a set of random permutations The l-th element, It represents the azimuth angle of a point in the airspace relative to the k-th source of radiation with the same frequency.

[0029] Furthermore, the self-localization results of the drone

[0030] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program that causes a computer to execute the aforementioned direct self-localization method for multi-angle matching at the same frequency.

[0031] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the aforementioned direct self-localization method for multi-angle matching at the same frequency.

[0032] Compared with existing technologies, the present invention, employing the above technical solutions, has the following technical advantages: The direct self-localization method using multi-angle matching of the same frequency estimates the azimuth angles of K radiation sources at the same frequency anchor nodes. Localization can be performed without knowing the correlation between the estimated azimuth angles and the positions of the radiation sources at the same frequency anchor nodes. In contrast, traditional azimuth angle localization algorithms require knowledge of the radiation sources corresponding to the measured azimuth angles when there are multiple radiation sources. Furthermore, the direct self-localization method using multi-angle matching of the same frequency can achieve high-precision localization without relying on satellite signals, thus enabling its application in satellite navigation denial scenarios. The loss function constructed by the direct self-localization method using multi-angle matching of the same frequency utilizes the idea of ​​direct localization, integrating multiple azimuth angle measurement information to directly construct a mapping from azimuth angle measurements to the localization result. Compared with traditional two-step localization algorithms, it eliminates the need to construct pseudo-linear equations, avoiding the risk of unavoidable bias in obtaining the localization result, and thus achieving higher localization performance. Attached Figure Description

[0033] Figure 1 This is a flowchart of the direct self-localization method for multi-angle matching at the same frequency according to the present invention;

[0034] Figure 2 This is a positioning scene diagram for the present invention;

[0035] Figure 3 This is a comparison chart of the self-localization performance of the direct self-localization method of the same frequency multi-angle matching of the present invention as the signal-to-noise ratio changes;

[0036] Figure 4 This is a comparison chart showing the self-localization performance of the direct self-localization method of the same frequency multi-angle matching of the present invention as the number of snapshots changes. Detailed Implementation

[0037] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0038] like Figure 1 This is a flowchart of the direct self-localization method for multi-angle matching at the same frequency according to the present invention. The direct self-localization method specifically includes the following steps:

[0039] Step 1: The UAV carrying a uniform linear array receives radiation signals from K anchor nodes with known positions and at the same frequency, and estimates the azimuth angles of the K radiation sources with the same frequency to provide prior information for subsequent angle matching.

[0040] like Figure 2 Based on the positioning scenario, the UAV receives radiation signals x(t) from K known anchor nodes at the same frequency:

[0041] x(t)=A(θ)s(t)+n(t)

[0042] Where A(θ) represents the array manifold matrix, A(θ)=[a(θ1),…,a(θ K )],a(θ k ) represents the guide vector. θ k Let represent the azimuth angle of the radiation source at the k-th anchor node with the same frequency. This represents the position coordinates of the radiation source at the k-th anchor node with the same frequency. The coordinates of the UAV's position are represented by j, the imaginary unit is represented by λ, the wavelength of the received signal is represented by d, the spacing of the linear array is represented by M, and the number of array elements is represented by M. Let n(t) represent the received signal of the linear array, and n(t) represent zero-mean complex Gaussian stationary noise.

[0043] The complexity can be reduced by estimating the azimuth angles of K co-frequency radiation sources using the ESPRIT fast algorithm. The specific process is as follows:

[0044] Step S1.1: Calculate the covariance matrix of the radiation signals from K known anchor nodes at the same frequency received by the UAV. in, H represents the sign of the mean, and H represents the conjugate transpose.

[0045] Step S1.2: Adjust the covariance matrix. Perform eigenvalue decomposition. Where Λ represents the eigenvalue matrix, Λ=diag{λ1,...,λ M}, the diagonal elements of Λ are λ1,...,λ M Covariance matrix eigenvalues, λ1≥...≥λ M ; Represents the characteristic matrix, e1,...,e M For λ1,...,λ M One-to-one correspondence of eigenvectors;

[0046] Step S1.3: Take the eigenvectors corresponding to the first K eigenvalues ​​to form the signal subspace E. s=[e1,...,e K ], take the signal subspace E s The first M-1 rows and the last M-1 rows respectively form matrix E x and E y ;

[0047] Step S1.4: For the matrix Eigenvalue decomposition yields the eigenmatrix E, which is then decomposed into K×K submatrices:

[0048]

[0049] Step S1.5: Calculate the auxiliary matrix And find the eigenvalues ​​λ of the auxiliary matrix. k , to the estimated azimuth angle

[0050] Step 2: Randomly arrange the estimated azimuth angles of the K co-frequency radiation sources. There are K factorial possibilities. Represent the random arrangement of the estimated azimuth angles of the K co-frequency radiation sources as a set:

[0051]

[0052] in, Each element in is One arrangement method;

[0053] For each point in the spatial domain, calculate the K azimuth angles of that point relative to a radiation source of the same frequency.

[0054] in, Let (x, y) represent the azimuth angle of a point in the spatial domain relative to the k-th source of radiation with the same frequency, and let (x, y) represent the position coordinates of that point in the spatial domain. This represents the position coordinates of the radiation source of the k-th same-frequency anchor node.

[0055] Step 3: For each point in the airspace, calculate the Euclidean distance between the K azimuth angles of the point relative to the same frequency radiation source and the estimated azimuth angles of the K same frequency radiation sources in each arrangement. Use the shortest Euclidean distance as the loss function for that point. There is no need to know the correspondence between multiple azimuth angles and radiation sources, thus eliminating the angle matching process of traditional azimuth angle positioning.

[0056] The loss function in this invention is:

[0057]

[0058] in, Represents a set of random permutations The l-th element, It represents the azimuth angle of a point in the airspace relative to the k-th source of radiation with the same frequency.

[0059] Step 4: Take the airspace point with the minimum loss function as the UAV's self-localization result.

[0060] Example

[0061] The direct self-localization method of the present invention, using multi-angle matching at the same frequency, was simulated. Simulation parameters were set as follows: the positions of the three radiation sources at the same frequency were... and The drone's position coordinates are The unit is meters (m).

[0062] Figure 3 The graph shows a comparison of the self-localization performance of the direct self-localization method of the present invention with the change of signal-to-noise ratio. It can be seen that as the signal-to-noise ratio increases, the direct self-localization error of the present invention continuously decreases, and compared with the classic two-step localization algorithm, it does not require source azimuth angle matching and has higher accuracy. Figure 4 The graph shows a comparison of the self-localization performance of the direct self-localization method of the present invention with the number of snapshots. It can be seen that as the number of snapshots increases, the direct self-localization error of the present invention continuously decreases, and compared with the classic two-step localization algorithm, it does not require source azimuth angle matching and has higher accuracy.

[0063] In one technical solution of the present invention, a computer-readable storage medium is provided, which stores a computer program that causes a computer to execute the direct self-localization method of same-frequency multi-angle matching.

[0064] In another technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the direct self-localization method of same-frequency multi-angle matching.

[0065] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A direct self-localization method of same-frequency multi-angle matching, characterized in that, Specifically comprising the following steps: Step 1: Receiving data from a UAV carrying a uniform linear array. The radiation source signal of the anchor node with known location and frequency, and the ... Estimate the azimuth angle of each radiation source with the same frequency; Step 2, for the estimated The azimuth angles of the same frequency radiation sources are randomly arranged, resulting in a total of The factorial number of possibilities; simultaneously, for each point in the spatial domain, find the factorial number of that point relative to the same-frequency radiation source. One azimuth angle; Step 3, calculate the Euclidean distance of each point in the space to the azimuth estimation value of each arrangement of the same frequency radiation source, and take the shortest Euclidean distance as the loss function of the point. Step 3, calculate the Euclidean distance of each point in the space to the azimuth estimation value of each arrangement of the same frequency radiation source, and take the shortest Euclidean distance as the loss function of the point.​ The loss function is: wherein, represents a randomly arranged set th element in the set, represents the azimuth angle of a certain point in the spatial domain relative to the k th same frequency radiation source; Step 4, the spatial point with the minimum loss function is taken as the self-positioning result of the unmanned aerial vehicle.

2. The method of claim 1, wherein, The UAV receives in step 1 a co-located anchor node radiation source signal is: wherein, denotes an array flow pattern matrix, , , denotes a steering vector, , denotes the azimuth of the k th co-frequency anchor node radiation source, , denotes the position coordinates of the k th co-frequency anchor node radiation source, denotes the position coordinates of the UAV, denotes the imaginary unit, denotes the wavelength of the received signal, d denotes the pitch of the linear array, M denotes the number of array elements; denotes the received signal of the linear array, denotes a zero-mean complex Gaussian stationary noise.

3. The method of claim 2, wherein, In step 1, for The process of estimating the azimuth angle of a single frequency radiation source is as follows: Step S1.1, obtaining a covariance matrix of a same-frequency anchor node radiation source signal with known position received by the UAV wherein wherein denotes the mean symbol, H denotes the conjugate transpose; Step S1.2, Eigenvalue decomposition of the covariance matrix is performed, wherein, denotes the eigenvalue matrix, , the diagonal elements of the eigenvalue matrix are the eigenvalues of the covariance matrix , ; denotes the eigenvector matrix, , are the eigenvectors corresponding one-to-one to the eigenvalues ; Step S1.3, take the eigenvector corresponding to the first eigenvalue to constitute the signal subspace K , take the eigenvector corresponding to the second eigenvalue to constitute the noise subspace , take the eigenvector corresponding to the third eigenvalue to constitute the signal subspace , take the eigenvector corresponding to the fourth eigenvalue to constitute the noise subspace M -1 row and the last M -1 row respectively constitute the matrix and ; Step S1.4, Eigenvalue decomposition of the matrix results in the eigenmatrix and the decomposition of the eigenmatrix into submatrices of ​ Step S1.5, computing the auxiliary matrix and finding the eigenvalues of the auxiliary matrix to obtain the azimuth estimate .

4. The method of claim 3, wherein, The estimated azimuth angles of the individual co-frequency radiation sources in step 2 are randomly arranged in a set: {θ1, θ2, θ3, θ4, θ5, θ6, θ7, θ8, θ9, θ10, θ11, θ12, θ13, θ14, θ15 wherein, each element in the set of elements is one permutation of the set of elements.

5. The method of claim 1, wherein, The azimuth angle of each point in the spatial domain relative to the same frequency radiation source in step 2 is calculated as follows: wherein, denotes the azimuth angle of a certain point in the airspace with respect to the first k co-located radiating source, denotes the position coordinates of a certain point in the airspace, denotes the position coordinates of the first k co-located anchor node radiating source.

6. The method of claim 5, wherein, The self-positioning result of the unmanned aerial vehicle .

7. A computer readable storage medium storing a computer program, characterized in that, The computer program enables the computer to execute the direct self-positioning method of the same frequency multi-angle matching according to any one of claims 1-6.

8. An electronic device, comprising: Comprise: The memory, the processor and the computer program stored in the memory and executable on the processor, when the processor executes the computer program, the direct self-positioning method of the same frequency multi-angle matching according to any one of claims 1-6 is realized.

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