Circular array interferometer ambiguity resolution method based on adaptive angle clustering

Abstract

The invention discloses a circular array interferometer ambiguity resolution method based on adaptive angle clustering, and the method comprises the steps: firstly constructing a two-dimensional uniform circular array interferometer model, and calculating the position of each antenna array element; secondly, two units are randomly selected from the uniform circular array to form base lines, the fuzzy phase difference and the maximum fuzzy number of each base line are calculated, two base lines are randomly selected to form double-base-line combinations, each double-base-line combination is traversed, the azimuth angle and the pitch angle are calculated, and an angle matrix is formed; then, a neighborhood radius parameter of a DBSCAN clustering algorithm is set according to the input frequency, clustering analysis is carried out on the angle matrix, and a cluster with the maximum density is selected; and finally, calculating the average value of the data in the maximum density cluster to obtain the azimuth angle and the pitch angle of the target. According to the method, the correct ambiguity resolution probability and the direction finding precision of the two-dimensional circular array interferometer can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of phase interferometer direction finding, and in particular to an adaptive angle clustering circular array interferometer deambiguation method. Background Art

[0002] As a high-precision direction-finding device, phase interferometer occupies an important position in modern direction-finding systems. It calculates the target direction by measuring the phase difference between multiple received signals in the antenna array, and with the characteristics of high direction-finding accuracy and low computational complexity, it can achieve more reliable target direction-finding and positioning in complex electromagnetic environments. However, with the continuous expansion of radio spectrum resources and the popularization of broadband signals in practical applications, the limitations of traditional narrowband interferometers in direction-finding accuracy and environmental adaptability have gradually emerged, and broadband interferometers have gradually become an important research direction in the field of signal direction-finding. Among them, the phase ambiguity problem is one of the most critical problems facing broadband interferometer technology.

[0003] In broadband interferometric direction finding, the two-dimensional interferometer can provide higher direction finding accuracy by measuring the phase difference between two sets of baselines, but its deambiguation process is relatively complicated. The existing stereo baseline deambiguation method determines the actual arrival angle by traversing all possible ambiguity numbers and angles and screening the overlapping angles. However, under low signal-to-noise ratio and high frequency conditions, the number of ambiguity numbers increases, the number of angles to be compared increases, and deambiguation is prone to fail. Therefore, how to improve the accuracy of deambiguation of two-dimensional broadband interferometers under high frequency conditions is a technical problem that needs to be solved urgently.

[0004] The traditional circular array interferometer deambiguation method calculates the phase difference of two baselines and solves the simultaneous equations to obtain the azimuth and elevation angles. However, this method only uses the phase difference information of the two baseline groups, which makes the direction finding results of this method have large errors under low signal-to-noise ratio conditions. Especially at high frequencies, the number of ambiguities increases, and the number of angles to be compared increases, which easily leads to deambiguation failure. Summary of the invention

[0005] The object of the present invention is to provide a circular array interferometer deambiguation method with adaptive angle clustering, so as to improve the correct deambiguation probability and direction finding accuracy of a two-dimensional circular array interferometer.

[0006] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0007] A circular array interferometer deambiguation method based on adaptive angle clustering, comprising:

[0008] The two-dimensional circular array broadband interferometer is modeled, and the relationship between the unambiguous phase difference and the ambiguous phase difference between different antenna array elements is determined based on the established model;

[0009] A baseline is constructed based on a combination of different antenna elements of a two-dimensional circular array broadband interferometer, the unambiguous phase difference corresponding to the antenna elements of each baseline is extracted, and the maximum range of its ambiguity number is determined; the constructed baselines are combined in pairs to generate multiple double baseline combinations;

[0010] Calculate the unambiguous phase difference of each dual baseline combination, and determine the azimuth and elevation angles corresponding to different ambiguity number combinations in the dual baseline combination based on the maximum range value of the ambiguity number corresponding to each baseline in the dual baseline combination and the relationship between the unambiguous phase difference and the ambiguity phase difference, thereby constructing the angle matrix of the dual baseline combination;

[0011] An angle point set is constructed using the angle matrix of all dual baseline combinations; each node in the angle point set is an azimuth and elevation angle combination corresponding to each fuzzy number combination in the angle matrix;

[0012] The nodes in the angle point set are clustered using the adaptive DBSCAN clustering algorithm to obtain clusters; the cluster with the largest density is selected, and the average azimuth angle and the average elevation angle of all the nodes in it are calculated as the azimuth angle and the elevation angle of the target.

[0013] Furthermore, the two-dimensional circular array broadband interferometer is modeled, and the relationship between the unambiguous phase difference and the ambiguous phase difference between different antenna array elements is determined based on the established model, including:

[0014] The radius of the two-dimensional circular array interferometer is R, and there are N antenna array elements evenly distributed on the circumference of the two-dimensional circular array interferometer. Taking the center of the interferometer as the origin o of the coordinate system, a spatial rectangular coordinate system oxyz is constructed, where the x-axis is the antenna line of sight, the y and z-axes are located in the plane where the circumference of the two-dimensional interferometer is located, and the antenna array elements are located in the yoz plane. Then the position coordinates (x n ,y n ,z n ) can be expressed as:

[0015]

[0016] Assume that the direction of the incident signal is for Projection on the xoy surface, define the projection The angle θ with the x-axis is the azimuth angle, Angle is the pitch angle, where the azimuth angle θ∈(0,360°), the pitch angle Then the phase difference of the incident signal between different antenna elements i and j is:

[0017]

[0018] In the formula, Δφij represents the unambiguous phase difference between the ith antenna element and the jth antenna element, Δx ij , Δy ij and Δz ij Respectively represent the distance difference between the ith antenna array element and the jth antenna array element on the x-axis, y-axis and z-axis;

[0019] When the phase difference is greater than 2π, phase ambiguity occurs; let the fuzzy number be m, and the fuzzy phase difference be Then we have:

[0020]

[0021] Furthermore, the maximum range of the fuzzy numbers corresponding to the antenna array elements of each baseline is determined, including:

[0022] Randomly select antenna element i and antenna element j from the two-dimensional circular array broadband interferometer, and the line connecting the two forms the baseline d ij , the corresponding maximum range of the fuzzy number m is:

[0023]

[0024] Where λ is the wavelength of the incident signal, Indicates rounding up; then the value range of the fuzzy number m is an integer between [-M,M].

[0025] Furthermore, the unambiguous phase difference of each dual baseline combination is calculated, including:

[0026] For the kth baseline and the first baseline For this double baseline combination, Δx k ,Δy k ,Δz k , Δx l ,Δy l ,Δz l They respectively represent the distance difference between the x-, y-, and z-axis coordinates of the two antenna array elements included in the kth and lth baselines.

[0027] The unambiguous phase difference expression of the double baseline combination is as follows:

[0028]

[0029] Among them, Δφ k and Δφ l are the phase differences corresponding to the kth baseline and the lth baseline respectively.

[0030] Furthermore, based on the maximum range value of the fuzzy number corresponding to each baseline in the dual baseline combination and the relationship between the unambiguous phase difference and the ambiguous phase difference, the azimuth angle and the pitch angle corresponding to different fuzzy number combinations in the dual baseline combination are determined, including:

[0031] Since the two-dimensional circular array broadband interferometer can only obtain fuzzy phase difference and Calculate the maximum range value of the fuzzy number corresponding to the kth baseline and the lth baseline is M k and M l , traverse all fuzzy numbers, and get the unambiguous phase difference as:

[0032]

[0033] Among them, Δφ k and Δφ l They represent the kth baseline and the lth baseline respectively when the fuzzy number combination is m k and m l The unambiguous phase difference;

[0034] m k =-M k ,-M k +1,…,0,…,M k -1,M k , m l =-M l ,-M l +1,…,0,…,M l ; then

[0035] In a set of fuzzy numbers (m k ,m l ), then:

[0036]

[0037] Where θ(m k ,m l )and They are respectively expressed in the double baseline combination fuzzy number combination (m k ,m l ) conditions, α and β are process angle parameters, expressed as follows:

[0038]

[0039] Finally, the angle matrix corresponding to the double baseline combination is:

[0040]

[0041] The angle matrix corresponding to each dual baseline combination can be calculated by the above method.

[0042] Furthermore, the adaptive DBSCAN clustering algorithm is specifically as follows:

[0043] First, the minimum number of neighborhood points is set according to the requirements, and the neighborhood radius of DBSCAN clustering is set according to the frequency of the incident signal;

[0044] Secondly, the nodes in the angle point set are clustered; when clustering, each set of azimuth and elevation angles in each angle point set is regarded as a node, and all angle matrices together constitute an angle point set;

[0045] Randomly select an unvisited node from the angle point set and calculate the number of nodes within the neighborhood radius of the node. If the number of nodes is greater than or equal to the minimum number of neighborhood points, the node is marked as a core point and a new cluster is created; otherwise, the node is marked as a noise point.

[0046] For each core point, recursively add all nodes within its neighborhood radius to the cluster established with this node; if other core points are found in the neighborhood of this node, continue to expand the neighborhood radius and add the nodes in the neighborhood of these core points to the cluster;

[0047] Repeat the above process until all nodes are visited and classified into a cluster or marked as noise points, and finally obtain multiple density-reachable clusters, and mark the nodes that cannot be classified as noise points;

[0048] The number of nodes in each cluster is counted, and the cluster with the largest density is selected; the average values ​​of the azimuth and elevation angles in the cluster with the largest density are calculated respectively as the azimuth and elevation angles of the target.

[0049] Furthermore, the neighborhood radius of DBSCAN clustering is set according to the frequency of the incident signal, expressed as:

[0050]

[0051] Where k is a constant, ε is the neighborhood radius, and f is the frequency of the incident signal.

[0052] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor executes the computer program, the circular array interferometer deambiguation method based on adaptive angle clustering is implemented.

[0053] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the circular array interferometer deambiguation method based on adaptive angle clustering is implemented.

[0054] The present invention has the following technical features:

[0055] The present invention designs a broadband interferometer deambiguation method based on adaptive angle clustering. This method makes full use of the phase difference of each baseline of the circular array to calculate all possible angles. Then, the adaptive DBSCAN algorithm is used to cluster these angles, and the core point of the cluster with the largest density is selected as the true arrival angle. This improves the correct deambiguation probability and direction finding accuracy of the two-dimensional circular array interferometer. Compared with the prior art:

[0056] 1. On the basis of the traditional stereo baseline, the angle matrix corresponding to multiple groups of dual baselines is further calculated, and the DBSCAN clustering algorithm is used to analyze the data in the angle matrix, screen out the cluster with the largest density, and average the data in the cluster to obtain the measured azimuth and elevation angles. Therefore, the present invention makes full use of all the phase difference information of the interferometer, and can improve the correct deambiguation probability of the broadband interferometer under low signal-to-noise ratio conditions.

[0057] 2. Under low-frequency conditions, the baseline length is shorter than the signal wavelength, and the angle measurement results are more divergent. Under high-frequency conditions, the baseline length is longer than the signal wavelength, the fuzzy number is larger, the angle measurement accuracy is higher, and the clustering points are relatively concentrated. Therefore, the present invention adaptively adjusts the neighborhood radius of the DBSCAN algorithm for different signal frequencies to optimize the clustering process and improve the probability of correct deambiguation of the interferometer under high-frequency conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0059] Figure 2 It is a schematic diagram of an eight-element uniform circular array interferometer;

[0060] Figure 3 The present invention uses the DBSCAN algorithm to analyze the angle clustering results of signals of different frequencies, wherein (a) is the case when the frequency is 1 GHz, (b) is the case when the frequency is 3 GHz, and (c) is the case when the frequency is 6 GHz;

[0061] Figure 4 is the simulation result of the direction finding and deambiguation performance of the present invention as it changes with frequency; wherein (a) is the lateral error, and (b) is the probability of correct deambiguation;

[0062] Figure 5 It is a simulation comparison diagram of the direction finding and deambiguation performance between the present invention and the traditional method, wherein (a) is the 1 GHz lateral error, (b) is the 1 GHz correct deambiguation probability, (c) is the 3 GHz lateral error, (d) is the 3 GHz correct deambiguation probability, (e) is the 6 GHz lateral error, and (f) is the 6 GHz correct deambiguation probability. DETAILED DESCRIPTION

[0063] The basic concept of the present invention is as follows: First, a two-dimensional uniform circular array interferometer model is constructed, and the position of each antenna array element is calculated. Secondly, two units are randomly selected from the uniform circular array to form a baseline, the fuzzy phase difference and the maximum fuzzy number of each baseline are calculated, and two baselines are randomly selected to form a double baseline combination. Each double baseline combination is traversed to calculate the azimuth and elevation angles to form an angle matrix. Then, the neighborhood radius parameter of the DBSCAN clustering algorithm is set according to the input frequency, and the angle matrix is ​​clustered and analyzed to select the cluster with the largest density. Finally, the average value of the data in the maximum density cluster is calculated to obtain the azimuth and elevation angles of the target.

[0064] See also Figure 1 The present invention provides a circular array interferometer deambiguation method based on adaptive angle clustering, comprising:

[0065] Step 1: Model the two-dimensional circular array broadband interferometer, and determine the relationship between the unambiguous phase difference and the ambiguous phase difference between different antenna array elements based on the established model.

[0066] Assume that the radius of the two-dimensional circular array interferometer is R, and there are N antenna array elements evenly distributed on the circumference of the two-dimensional circular array interferometer; see Figure 2 , taking the center of the interferometer as the origin o of the coordinate system, construct the spatial rectangular coordinate system oxyz, where the x-axis is the antenna line of sight, the y and z-axes are located in the plane where the circumference of the two-dimensional interferometer is located, and the antenna array element is located in the yoz plane. Then the position coordinate (x n ,y n ,z n ) can be expressed as:

[0067]

[0068] Assume that the direction of the incident signal is for Projection on the xoy surface, define the projection The angle θ with the x-axis is the azimuth angle, Angle is the pitch angle, where the azimuth angle θ∈(0,360°), the pitch angle Then the phase difference of the incident signal between different antenna elements i and j is:

[0069]

[0070] In the formula, Δφ ij represents the unambiguous phase difference between the ith antenna element and the jth antenna element, Δx ij , Δy ij and Δzij They represent the distance differences between the ith antenna array element and the jth antenna array element on the x-axis, y-axis, and z-axis respectively.

[0071] When the phase difference is greater than 2π, phase ambiguity occurs; let the fuzzy number be m, and the fuzzy phase difference be Then we have:

[0072]

[0073] In the direction-finding system of a two-dimensional circular array broadband interferometer, when the phase difference is greater than 2π, due to the limitation of the phase detector, only the phase difference information within 2π can be obtained, that is, the fuzzy phase difference For example, the current phase detector gives The phase difference between antenna element i and antenna element j is 2.5π, so the unambiguous phase difference is Δφ ij =2.5π, the fuzzy phase difference is Fuzzy number m=1.

[0074] Step 2: construct a baseline based on a combination of different antenna elements of a two-dimensional circular array broadband interferometer, extract the unambiguous phase difference corresponding to the antenna element of each baseline, and determine the maximum range of its ambiguity number; combine the constructed baselines in pairs to generate multiple double baseline combinations.

[0075] Randomly select antenna element i and antenna element j from the two-dimensional circular array broadband interferometer, and the line connecting the two forms the baseline d ij , the corresponding maximum range of the fuzzy number m is:

[0076]

[0077] Where λ is the wavelength of the incident signal, Indicates rounding up; then the value range of the fuzzy number m is an integer between [-M,M].

[0078] By traversing all antenna array element combinations, we can obtain extract the fuzzy phase difference corresponding to the antenna array element of each baseline, and determine the maximum range value of the fuzzy number corresponding to the antenna array element of each baseline according to the formulas of steps 1 and 2.

[0079] Generate a dual baseline combination: From Two baselines are randomly selected from the baselines to generate multiple different dual baseline combinations.

[0080] Step 3, calculate the unambiguous phase difference of each dual baseline combination, based on the maximum range value of the fuzzy number corresponding to each baseline in the dual baseline combination, and the relationship between the unambiguous phase difference and the fuzzy phase difference, determine the azimuth and elevation angles corresponding to different fuzzy number combinations in the dual baseline combination, thereby constructing the angle matrix of the dual baseline combination.

[0081] Take any double baseline combination as an example: for the kth baseline and the first baseline For this double baseline combination, Δx k ,Δy k ,Δz k , Δx l ,Δy l ,Δz l They respectively represent the distance difference between the x-, y-, and z-axis coordinates of the two antenna array elements included in the kth and lth baselines.

[0082] The unambiguous phase difference expression of the double baseline combination is as follows:

[0083]

[0084] Among them, Δφ k and Δφ l are the phase differences corresponding to the kth baseline and the lth baseline respectively.

[0085] The process of determining the azimuth and elevation angles of a dual baseline combination is as follows:

[0086] According to the established two-dimensional circular array broadband interferometer model, let Δx in the above formula k =Δx l = 0, simplifying the above formula, the azimuth angle θ and pitch angle corresponding to the above double baseline combination can be calculated for:

[0087]

[0088] Among them, α and β are process angle parameters, and their expressions are as follows:

[0089]

[0090] Since the two-dimensional circular array broadband interferometer can only obtain fuzzy phase difference and According to step 2, the maximum range value of the fuzzy number corresponding to the kth baseline and the lth baseline is calculated to be M k and M l , traverse all fuzzy numbers in steps of 1, and the unambiguous phase difference is obtained as:

[0091]

[0092] Among them, Δφ k and Δφ l They represent the kth baseline and the lth baseline respectively when the fuzzy number combination is m k and m l Unambiguous phase difference.

[0093] m k =-M k ,-M k +1,…,0,,M k -1,M k , m l =-M l ,-M l +1,…,0,…,M l ; then

[0094] In a set of fuzzy numbers (m k ,m l ), then:

[0095]

[0096] Where θ(m k ,m l )and They are respectively expressed in the double baseline combination fuzzy number combination (m k ,m l ) conditions.

[0097] Finally, the angle matrix corresponding to the double baseline combination is:

[0098]

[0099] The angle matrix corresponding to each dual baseline combination can be calculated by the above method.

[0100] Step 4: construct an angle point set using the angle matrix of all dual baseline combinations; each node in the angle point set is the azimuth and elevation angle combination corresponding to each fuzzy number combination in the angle matrix; for example (θ(m k ,m l ), ) is a node;

[0101] The nodes in the angle point set are clustered using the adaptive DBSCAN clustering algorithm to obtain clusters; the cluster with the largest density is selected, and the average azimuth angle and the average elevation angle of all the nodes in it are calculated as the azimuth angle and the elevation angle of the target.

[0102] The adaptive DBSCAN clustering algorithm is specifically as follows:

[0103] First, the minimum number of neighborhood points is set according to the requirements, and the neighborhood radius ε of the DBSCAN clustering is set according to the frequency f of the incident signal:

[0104]

[0105] Where k is a constant.

[0106] Secondly, the nodes in the angle point set are clustered; when clustering, each set of azimuth and elevation angles in each angle point set is regarded as a node, and all angle matrices together constitute an angle point set;

[0107] Randomly select an unvisited node from the angle point set and calculate the number of nodes within the neighborhood radius of the node. If the number of nodes is greater than or equal to the minimum number of neighborhood points, the node is marked as a core point and a new cluster is created; otherwise, the node is marked as a noise point, and all nodes within its neighborhood radius are placed in the candidate set for subsequent processing;

[0108] For each core point, recursively add all nodes within its neighborhood radius to the cluster established with this node; if other core points are found within the neighborhood radius of this node, continue to expand the neighborhood and add the nodes in the neighborhood of these core points to the cluster; this process will be recursive until there are no more nodes to be added;

[0109] The above process is repeated until all nodes are visited and classified into a cluster or marked as noise points, and finally multiple density-reachable clusters are obtained, and the nodes that cannot be classified are marked as noise points.

[0110] The number of nodes in each cluster is counted, and the cluster with the largest density is selected; the average values ​​of the azimuth and elevation angles in the cluster with the largest density are calculated respectively as the azimuth and elevation angles of the target.

[0111] Example:

[0112] In one embodiment of the present invention, an eight-element uniform circular array broadband interferometer is taken as an example. Figure 2 As shown, the radius of the circular array is R = 0.4m.

[0113] Example 1: Assume that the frequencies f of the far-field signals are 1 GHz, 3 GHz, and 6 GHz, respectively, the incident azimuth angle is θ = 30°, and the incident elevation angle is The signal-to-noise ratio is 10dB. Set the neighborhood radius of the DBSCAN clustering algorithm to 10 9 / f, the minimum number of neighborhood points is 3, and the angle clustering simulation results of the signals with frequencies of 1 GHz, 3 GHz and 6 GHz are shown in the figure below. Figure 3As shown in the figure, for the 1GHz signal, the number of clusters with the largest clustering density is 210, and the estimated angle obtained by averaging the data within the cluster is (29.56°, 9.66°). For the 3GHz signal and the 6GHz signal, the number of clusters with the largest clustering density is 210, and the estimated angles are (30.02°, 9.93°) and (30.00°, 9.99°) respectively. It can be seen that the present invention can effectively realize the angle estimation in the frequency range of 1 to 6GHz.

[0114] Example 2: Assume that the incident azimuth angle of the far-field signal is θ = 30° and the incident elevation angle is The signal-to-noise ratio is 10 dB. The number of Monte Carlo experiments is 100, and the signal frequency varies from 1 to 6 GHz. The neighborhood radius of the DBSCAN clustering algorithm is set to 10 9 / f, the minimum number of neighborhood points is 3, and the defuzzification capability of the present invention in different frequency ranges is obtained as follows Figure 4 As shown in the figure, it can be seen that the present invention can achieve correct deambiguation in the frequency range of 1 to 6 GHz, and the correct deambiguation probability exceeds 90%.

[0115] Example 3: Assume that the frequencies f of the far-field signals are 1 GHz, 3 GHz, and 6 GHz, respectively, the incident azimuth angle is θ = 30°, and the incident elevation angle is The signal-to-noise ratio varies in the range of -10dB to 25dB. The traditional stereo baseline method is set to select the longest vertical double baseline group, and the neighborhood radius of the DBSCAN clustering algorithm of the improved method is 10 9 / f, the minimum number of neighborhood points is 3, and the defuzzification performance comparison of the methods before and after improvement is shown in the figure Figure 5 As shown in the figure, the improved algorithm has significantly better direction finding accuracy and ambiguity resolution performance than the traditional algorithm under low signal-to-noise ratio conditions. Especially under high-frequency signal conditions, such as Figure 5 As shown in (a), when the frequency is 6 GHz and the signal-to-noise ratio is in the range of 10-15 dB, the traditional method almost fails to resolve the ambiguity, while the improved method has a direction finding performance better than 1° and a correct resolution probability close to 100%. However, for low-frequency signals, such as Figure 5 As shown in (e), the difference between the direction finding performance and ambiguity resolution performance of the improved method is small. This result shows that the improved algorithm has significant advantages under high-frequency conditions, while maintaining a performance level comparable to that of the traditional method under low-frequency conditions.

[0116] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A circular array interferometer deambiguation method based on adaptive angle clustering, characterized in that: include: The two-dimensional circular array broadband interferometer is modeled, and the relationship between the unambiguous phase difference and the ambiguous phase difference between different antenna array elements is determined based on the established model; The baseline is constructed based on the combination of different antenna elements of the two-dimensional circular array broadband interferometer, the unambiguous phase difference corresponding to the antenna elements of each baseline is extracted, and the maximum range of its ambiguity number is determined; Combine the constructed baselines in pairs to generate multiple double baseline combinations; Calculate the unambiguous phase difference of each dual baseline combination, and determine the azimuth and elevation angles corresponding to different ambiguity number combinations in the dual baseline combination based on the maximum range value of the ambiguity number corresponding to each baseline in the dual baseline combination and the relationship between the unambiguous phase difference and the ambiguity phase difference, thereby constructing the angle matrix of the dual baseline combination; An angle point set is constructed using the angle matrix of all dual baseline combinations; each node in the angle point set is an azimuth and elevation angle combination corresponding to each fuzzy number combination in the angle matrix; The nodes in the angle point set are clustered using the adaptive DBSCAN clustering algorithm to obtain clusters; the cluster with the largest density is selected, and the average azimuth angle and the average elevation angle of all the nodes in it are calculated as the azimuth angle and the elevation angle of the target.

2. The circular array interferometer deambiguation method based on adaptive angle clustering according to claim 1, characterized in that: The two-dimensional circular array broadband interferometer is modeled, and the relationship between the unambiguous phase difference and the ambiguous phase difference between different antenna array elements is determined based on the established model, including: The radius of the two-dimensional circular array interferometer is R, and there are N antenna array elements evenly distributed on the circumference of the two-dimensional circular array interferometer. Taking the center of the interferometer as the origin o of the coordinate system, a spatial rectangular coordinate system oxyz is constructed, where the x-axis is the antenna line of sight, the y and z-axes are located in the plane where the circumference of the two-dimensional interferometer is located, and the antenna array elements are located in the yoz plane. Then the position coordinates (x n ,y n ,z n ) can be expressed as: Assume that the direction of the incident signal is for Projection on the xoy surface, define the projection The angle θ with the x-axis is the azimuth angle, Angle is the pitch angle, where the azimuth angle θ∈(0,360°), the pitch angle Then the phase difference of the incident signal between different antenna elements i and j is: In the formula, Δφ ij represents the unambiguous phase difference between the ith antenna element and the jth antenna element, Δx ij , Δy ij and Δz ij Respectively represent the distance difference between the ith antenna array element and the jth antenna array element on the x-axis, y-axis and z-axis; When the phase difference is greater than 2π, phase ambiguity occurs; let the fuzzy number be m, and the fuzzy phase difference be Then we have:

3. The circular array interferometer deambiguation method based on adaptive angle clustering according to claim 1, characterized in that: Determine the maximum range of ambiguity numbers corresponding to the antenna array elements of each baseline, including: Randomly select antenna element i and antenna element j from the two-dimensional circular array broadband interferometer, and the line connecting the two forms the baseline d ij , the corresponding maximum range of the fuzzy number m is: Where λ is the wavelength of the incident signal, Indicates rounding up; then the value range of the fuzzy number m is an integer between [-M,M].

4. The circular array interferometer deambiguation method based on adaptive angle clustering according to claim 1, characterized in that: Calculate the unambiguous phase difference for each dual baseline combination, including: For the kth baseline and the first baseline For this double baseline combination, Δx k ,Δy k ,Δz k , Δx l ,Δy l ,Δz l They respectively represent the distance difference between the x-, y-, and z-axis coordinates of the two antenna array elements included in the kth and lth baselines. The unambiguous phase difference expression of the double baseline combination is as follows: Among them, Δφ k and Δφ l are the phase differences corresponding to the kth baseline and the lth baseline respectively.

5. The circular array interferometer deambiguation method based on adaptive angle clustering according to claim 1, characterized in that: Based on the maximum range value of the fuzzy number corresponding to each baseline in the dual baseline combination and the relationship between the unambiguous phase difference and the ambiguous phase difference, the azimuth and elevation angles corresponding to different fuzzy number combinations in the dual baseline combination are determined, including: Since the two-dimensional circular array broadband interferometer can only obtain fuzzy phase difference and Calculate the maximum range value of the fuzzy number corresponding to the kth baseline and the lth baseline to be M k and M l , traverse all fuzzy numbers, and get the unambiguous phase difference as: Among them, Δφ k and Δφ l They represent the kth baseline and the lth baseline respectively when the fuzzy number combination is m k and m l The unambiguous phase difference; m k =-M k ,-M k +1,…,0,…,M k -1,M k , m l =-M l ,-M l +1,…,0,…,M l ; then In a set of fuzzy numbers (m k ,m l ), then: Where θ(m k ,m l )and They are respectively expressed in the double baseline combination fuzzy number combination (m k ,m l ) conditions, α and β are process angle parameters, expressed as follows: Finally, the angle matrix corresponding to the double baseline combination is: The angle matrix corresponding to each dual baseline combination can be calculated by the above method.

6. The circular array interferometer deambiguation method based on adaptive angle clustering according to claim 1, characterized in that: The adaptive DBSCAN clustering algorithm is specifically as follows: First, the minimum number of neighborhood points is set according to the requirements, and the neighborhood radius of DBSCAN clustering is set according to the frequency of the incident signal; Secondly, the nodes in the angle point set are clustered; when clustering, each set of azimuth and elevation angles in each angle point set is regarded as a node, and all angle matrices together constitute an angle point set; Randomly select an unvisited node from the angle point set and calculate the number of nodes within the neighborhood radius of the node. If the number of nodes is greater than or equal to the minimum number of neighborhood points, the node is marked as a core point and a new cluster is created; otherwise, the node is marked as a noise point. For each core point, recursively add all nodes within its neighborhood radius to the cluster established with this node; If other core points are found in the neighborhood of the node, the neighborhood radius will continue to be expanded, and the nodes in the neighborhood of these core points will also be added to the cluster; Repeat the above process until all nodes are visited and classified into a cluster or marked as noise points, and finally obtain multiple density-reachable clusters, and mark the nodes that cannot be classified as noise points; The number of nodes in each cluster is counted, and the cluster with the largest density is selected; the average values ​​of the azimuth and elevation angles in the cluster with the largest density are calculated respectively as the azimuth and elevation angles of the target.

7. The circular array interferometer deambiguation method based on adaptive angle clustering according to claim 6, characterized in that: The neighborhood radius of DBSCAN clustering is set according to the frequency of the incident signal, expressed as: Where k is a constant, ε is the neighborhood radius, and f is the frequency of the incident signal.

8. A terminal device comprising a processor, a memory and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the circular array interferometer deambiguation method based on adaptive angle clustering according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, wherein a computer program is stored in the medium; characterized in that: When the computer program is executed by a processor, the circular array interferometer deambiguation method based on adaptive angle clustering according to any one of claims 1 to 7 is implemented.

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