An adaptive angle clustering based circular array interferometer unambiguous method

Abstract

The application discloses a circular array interferometer unambiguous solution method based on adaptive angle clustering, which comprises the following steps: firstly, a two-dimensional uniform circular array interferometer model is constructed, and the position of each antenna array element is calculated; secondly, two elements are randomly selected from the uniform circular array to form a baseline, the ambiguous phase difference of each baseline and the maximum ambiguous number are calculated, two baselines are randomly selected to form a double baseline combination, each double baseline combination is traversed, the azimuth angle and the elevation angle are calculated, and an angle matrix is formed; then, the neighborhood radius parameter of the DBSCAN clustering algorithm is set according to the input frequency, the angle matrix is subjected to clustering analysis, and the cluster with the maximum density is selected; finally, the average value of the data in the maximum density cluster is calculated, and the azimuth angle and the elevation angle of the target are obtained. The method can effectively improve the correct unambiguous solution probability and the direction finding precision of the two-dimensional circular array interferometer.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of phase interferometer direction finding technology, and in particular to a circular array interferometer deambiguating method based on adaptive angle clustering. BACKGROUND

[0002] As a high-precision direction finding device, the phase interferometer plays an important role in modern direction finding systems. It calculates the target direction by measuring the phase difference between multiple received signals in an antenna array, and is characterized by high direction finding accuracy and low computational complexity, and can achieve more reliable target direction finding positioning in complex electromagnetic environments. However, with the continuous expansion of radio spectrum resources and the popularity of wideband signals in practical applications, the limitations of traditional narrowband interferometers in terms of direction finding accuracy and environmental adaptability have gradually emerged, and wideband 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 challenges faced by wideband interferometer technology.

[0003] In wideband interferometric direction finding, a two-dimensional interferometer measures phase differences through two baseline groups, which can provide higher direction finding accuracy, but the deambiguating process is more complex. The existing stereo baseline deambiguating method determines the actual angle of arrival by screening coincident angles through traversing all possible ambiguity numbers and angles. However, under low signal-to-noise ratio and high frequency conditions, the ambiguity number increases, and the number of angles to be compared increases, making deambiguating prone to failure. Therefore, how to improve the accuracy of deambiguating of two-dimensional wideband interferometers under high frequency conditions is a technical problem that needs to be solved urgently.

[0004] The traditional circular array interferometer deambiguating method calculates the phase difference of two baselines and solves the azimuth angle and elevation angle by solving the equations in parallel. However, this method only uses the phase difference information of two baseline groups, resulting in a large error in the direction finding result under low signal-to-noise ratio conditions, especially at high frequencies, where the ambiguity number increases and the number of angles to be compared increases, making deambiguating prone to failure. SUMMARY

[0005] The purpose of the present application is to provide a circular array interferometer deambiguating method based on adaptive angle clustering to improve the correct deambiguating probability and direction finding accuracy of two-dimensional circular array interferometers.

[0006] In order to achieve the above-mentioned task, the present application adopts the following technical solutions:

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

[0008] Modeling a two-dimensional circular array wideband interferometer, determining the unambiguous phase difference between different antenna elements and the relationship between the ambiguous phase differences based on the established model;

[0009] Baselines are constructed based on the combination of different antenna elements of a two-dimensional circular 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 dual-baseline combinations.

[0010] Calculate the unambiguous phase difference for each dual baseline combination. Based on the maximum range of ambiguity numbers corresponding to each baseline in the dual baseline combination, and the relationship between the unambiguous phase difference and the ambiguous phase difference, determine the azimuth and elevation angles corresponding to different ambiguity number combinations in the dual baseline combination, 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 the azimuth and elevation angle combination corresponding to each ambiguity 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 highest density is selected, and the average azimuth and elevation angles of all nodes in the cluster are calculated as the azimuth and elevation angles of the target.

[0013] Furthermore, the modeling of the two-dimensional circular array broadband interferometer, and the determination of the relationship between the unambiguous phase difference and the ambiguous phase difference between different antenna array elements based on the established model, includes:

[0014] A two-dimensional circular interferometer with radius R has N antenna elements uniformly distributed on its circumference. A spatial rectangular coordinate system oxyz is constructed with the center of the interferometer as the origin o, where the x-axis is the antenna line of sight, and the y and z axes lie in the plane containing the circumference of the interferometer. The antenna elements are located in the yoz plane. The position coordinates (x...) of the nth antenna element are... n ,y n ,z n This can be represented as:

[0015]

[0016] Let the direction of the incident signal be... for The projection onto the xoy plane is defined as the projection. The angle θ between the x-axis and the x-axis is the azimuth angle. The included angle Let θ be the pitch angle, where azimuth θ ∈ (0, 360°). The phase difference of the incident signal between different antenna array elements i and j is:

[0017]

[0018] In the formula, Δφij denotes the unambiguous phase difference between the ith antenna element and the jth antenna element, Δx ij , Δy ij and Δz ij denote the distance difference between the ith antenna element and the jth antenna element in the x-axis, y-axis and z-axis, respectively;

[0019] Phase ambiguity occurs when the phase difference is greater than 2π; let the ambiguity number be m, and the ambiguous phase difference be Then:

[0020]

[0021] Further, the maximum range of the ambiguity number corresponding to the antenna elements of each baseline is determined, comprising:

[0022] Any antenna element i and antenna element j are selected from the two-dimensional circular array broadband interferometer, and the connecting line of the two forms a baseline d ij , and the maximum range value of the ambiguity number m corresponding thereto is:

[0023]

[0024] where λ is the wavelength of the incident signal, denotes the upward rounding; then the value range of the ambiguity number m is an integer between [-M, M].

[0025] Further, the unambiguous phase difference of each double baseline combination is calculated, comprising:

[0026] For the kth baseline and the lth baseline , wherein Δx k , Δy k , Δz k , Δx l , Δy l , Δz l respectively denote the distance difference between the x, y, z-axis coordinates of the two antenna elements contained in the kth and lth baselines.

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

[0028]

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

[0030] Further, based on the maximum range value of the ambiguity number corresponding to each baseline pair in the dual baseline combination, and the relationship between the unambiguous phase difference and the ambiguous phase difference, the azimuth angle and the elevation angle corresponding to different ambiguity number combinations in the dual baseline combination are determined, comprising:

[0031] Since the two-dimensional circular array broadband interferometer can only obtain the ambiguous phase difference and The maximum range value of the ambiguity number corresponding to the kth baseline and the lth baseline is calculated as M k and M l , and all ambiguities are traversed to obtain the unambiguous phase difference as:

[0032]

[0033] Where Δφ k and Δφ l respectively represent the unambiguous phase difference of the kth baseline and the lth baseline when the ambiguity number combination is m k and m l .

[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 for

[0035] (m k , m l ), there is:

[0036]

[0037] Where θ(m k , m l ) and respectively represent the azimuth angle and the elevation angle under the condition that the ambiguity number combination of the dual baseline combination is (m k , m l ), and α and β are process angle parameters, represented as follows:

[0038]

[0039] Finally, the angle matrix corresponding to the dual baseline combination is obtained as:

[0040]

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

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

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

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

[0045] Starting from an unvisited node in the angle point set, the number of nodes within the neighborhood radius of the node is calculated, 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, all nodes within its neighborhood radius are recursively added to the cluster established by the node; if other core points are found in the neighborhood of the node, the neighborhood radius is continued to be expanded, and the nodes in the neighborhood of these core points are also added to the cluster.

[0047] The above process is repeated until all nodes are visited and classified into a certain cluster or marked as noise points, and finally a plurality of density-reachable clustering clusters are obtained, and the nodes that cannot be classified are marked as noise points.

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

[0049] Further, the neighborhood radius of the DBSCAN clustering is set according to the frequency of the incident signal, which is represented as:

[0050]

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

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

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

[0054] The application has the following technical characteristics:

[0055] The application designs a wideband interferometer unambiguous method based on adaptive angle clustering, which fully utilizes the phase difference of each baseline of the circular array to calculate all possible angles.

[0056] 1. On the basis of the traditional stereoscopic baseline, the angle matrix corresponding to multiple groups of double baselines is further calculated, and the DBSCAN clustering algorithm is used to analyze the data in the angle matrix, the cluster with the maximum density is screened out, and the average value of the data in the cluster is obtained to obtain the measured azimuth angle and pitch angle. Therefore, the application fully utilizes all the phase difference information of the interferometer, and can improve the correct unambiguous probability of the wideband interferometer under low signal-to-noise ratio conditions.

[0057] 2. Since under low frequency conditions, the baseline length is relatively short compared with the signal wavelength, the angle measurement result is relatively divergent, while under high frequency conditions, the baseline length is relatively long compared with the signal wavelength, the ambiguity number is larger, the angle measurement accuracy is higher, and the clustering points are relatively concentrated, therefore, the application adaptively adjusts the neighborhood radius of the DBSCAN algorithm according to different signal frequencies, so as to optimize the clustering process and improve the correct unambiguous probability of the interferometer under high frequency conditions. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a flowchart of the method of the application;

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

[0060] Figure 3 is a diagram of angle clustering analysis results of different frequency signals using the DBSCAN algorithm of the application, wherein (a) is the case when the frequency is 1GHz, (b) is the case when the frequency is 3GHz, and (c) is the case when the frequency is 6GHz;

[0061] Figure 4 is a simulation result of the direction finding and unambiguous performance of the application with frequency variation; wherein (a) is the lateral error, and (b) is the correct unambiguous probability;

[0062] Figure 5 is a simulation comparison diagram of the direction finding and unambiguous performance of the application and the traditional method, wherein (a) is the 1GHz lateral error, (b) is the 1GHz correct unambiguous probability, (c) is the 3GHz lateral error, (d) is the 3GHz correct unambiguous probability, (e) is the 6GHz lateral error, and (f) is the 6GHz correct unambiguous probability. Detailed Implementation

[0063] The basic concept of this invention is as follows: First, a two-dimensional uniform circular array interferometer model is constructed, and the position of each antenna element is calculated. Second, two elements are randomly selected from the uniform circular array to form a baseline. The ambiguity phase difference and maximum ambiguity number of each baseline are calculated, and two baselines are randomly selected to form a dual-baseline combination. Each dual-baseline combination is iterated over to calculate the azimuth and elevation angles, forming an angle matrix. Then, the neighborhood radius parameter of the DBSCAN clustering algorithm is set according to the input frequency, and cluster analysis is performed on the angle matrix to select the cluster with the highest density. Finally, the average value of the data within the highest density cluster is calculated to obtain the azimuth and elevation angles of the target.

[0064] See Figure 1 The present invention provides a method for defuzzifying a circular interferometer 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] Let the radius of the two-dimensional circular interferometer be R, and let N antenna elements be uniformly distributed on the circumference of the two-dimensional circular interferometer; see [link to relevant documentation]. Figure 2 Using the center of the interferometer as the origin o, construct a spatial rectangular coordinate system oxyz, where the x-axis is the antenna line of sight, and the y and z axes lie in the plane containing the circumference of the two-dimensional interferometer. The antenna elements are located in the yoz plane. Then, the position coordinates (x...) of the nth antenna element... n ,y n ,z n This can be represented as:

[0067]

[0068] Assume the direction of the incident signal is for The projection onto the xoy plane is defined as the projection. The angle θ between the x-axis and the x-axis is the azimuth angle. The included angle Let θ be the pitch angle, where azimuth θ ∈ (0, 360°). The phase difference of the incident signal between different antenna array elements i and j is:

[0069]

[0070] In the formula, Δφ ij Δx represents the unambiguous phase difference between the i-th antenna element and the j-th antenna element. ij Δy ij and Δzij respectively represent the distance difference of the i-th antenna element and the j-th antenna element on the x-axis, y-axis and z-axis.

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

[0072]

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

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

[0075] From the two-dimensional circular array wideband interferometer, select antenna element i and antenna element j at random, and the connecting line of the two forms a baseline d ij , and the maximum range value of the ambiguity number m corresponding to it is:

[0076]

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

[0078] From the traversal of all antenna element combinations, obtain baselines; extract the ambiguous phase difference corresponding to the antenna elements of each baseline, and determine the maximum range value of the ambiguity number corresponding to the antenna elements of each baseline according to the formula of step 1 and step 2.

[0079] Generate double baseline combinations: randomly select two from baselines to generate multiple different double baseline combinations.

[0080] Step 3, calculating the unambiguous phase difference of each double baseline combination, determining the azimuth angle and the elevation angle corresponding to different ambiguity number combinations in the double baseline combination based on the maximum range value of the ambiguity number corresponding to each baseline in the double baseline combination, the relationship between the unambiguous phase difference and the ambiguous phase difference, and thus constructing the angle matrix of the double baseline combination.

[0081] Taking any one double baseline combination as an example: for the kth baseline and the lth baseline , the azimuth angle and the elevation angle of this double baseline combination are determined as follows: k , Δy k , Δz k , Δx l , Δy l , Δz l respectively represent the distance difference between the x, y, z axis coordinates of the two antenna elements contained in the kth and lth baselines.

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

[0083]

[0084] where 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 angle and the elevation angle of the double baseline combination is as follows:

[0086] According to the established two-dimensional circular array wideband interferometer model, Δx k = Δx l = 0 in the above formula, the above formula is simplified, and the azimuth angle θ and the elevation angle corresponding to the above double baseline combination can be calculated as follows:

[0087]

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

[0089]

[0090] Since the two-dimensional circular array wideband interferometer can only obtain the ambiguous phase difference and , the maximum range values of the ambiguity numbers corresponding to the kth baseline and the lth baseline are calculated as M k and M l according to step 2, and the unambiguous phase difference is obtained by traversing all ambiguity numbers with a step of 1:

[0091]

[0092] where Δφ k and Δφ l represent the unambiguous phase difference of the kth baseline and the lth baseline respectively, when the ambiguity array is combined as m k and m l .

[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 for

[0094] a set of ambiguity numbers (m k , m l ), there are:

[0095]

[0096] where θ(m k , m l ) and represent the azimuth angle and the elevation angle respectively under the condition that the ambiguity array is combined as (m k , m l ) by the double baseline combination.

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

[0098]

[0099] Through the above method, the angle matrix corresponding to each double baseline combination can be calculated.

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

[0101] Using the adaptive DBSCAN clustering algorithm to cluster the nodes in the angle point set, to obtain a clustering cluster; selecting the clustering cluster with the largest density, calculating the average of the azimuth angle and the average of the elevation angle of all nodes in the clustering cluster as the azimuth angle and the elevation angle of the target.

[0102] wherein the adaptive DBSCAN clustering algorithm is as follows:

[0103] First, set the minimum number of neighborhood points according to the requirements, and set the neighborhood radius ε of DBSCAN clustering 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; during clustering, each set of azimuth and elevation angles in each angle point set is treated as a node, and all angle matrices together constitute the angle point set;

[0107] Starting with any unvisited node in the set of angle points, calculate the number of nodes within the radius of that node's neighborhood. If the number of nodes is greater than or equal to the minimum number of neighborhood points, then 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 radius are added to the candidate set for further processing.

[0108] For each core point, recursively add all nodes within its neighborhood radius to the cluster built around that node; if other core points are found within the neighborhood radius of that node, continue to expand the neighborhood and add the nodes in the neighborhood of these core points to the cluster as well; this process will continue to recursively until there are no more nodes to add.

[0109] Repeat the above process until all nodes have been visited and classified into a cluster or marked as noise points, ultimately resulting in multiple density-reachable clusters, and marking unclassifiable nodes as noise points.

[0110] The number of nodes in each cluster is counted, and the cluster with the highest density is selected. The average azimuth and elevation angles within the cluster with the highest density are calculated and used 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 used as an example, such as... Figure 2 As shown, the radius of the circular array is R = 0.4m.

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

[0114] Example Two: Assuming that the incident azimuth angle of the far-field signal is θ = 30°, the incident elevation angle is , and the signal-to-noise ratio is 10dB. The number of Monte Carlo times is 100, and the signal frequency varies in the range of 1-6GHz. The neighborhood radius of the DBSCAN clustering algorithm is set to 10 9 / f, and the minimum number of neighborhood points is 3. The deambiguating ability of the application in different frequency ranges is shown in Figure 4 . It can be seen from the figure that the application can realize correct deambiguating in the frequency range of 1-6GHz, and the correct deambiguating probability is more than 90%.

[0115] Example Three: Assuming that the frequency f of the far-field signal is 1GHz, 3GHz and 6GHz 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-25dB. The longest vertical double-baseline group is selected by the traditional stereo baseline method, and the neighborhood radius of the DBSCAN clustering algorithm of the improved method is set to 10 9 / f. The deambiguating performance comparison chart of the improved method before and after is shown in Figure 5 . It can be seen from the figure that the direction-finding accuracy and deambiguating performance of the improved algorithm under the condition of low signal-to-noise ratio are significantly better than those of the traditional algorithm. Especially in the condition of high frequency signal, as shown in (a) of Figure 5 , when the frequency is 6GHz and the signal-to-noise ratio is in the range of 10-15dB, the traditional method almost fails to deambiguate, while the direction-finding performance of the improved method is better than 1°, and the correct deambiguating probability is close to 100%. However, for low frequency signals, as shown in (e) of Figure 5 , the difference between the direction-finding performance and deambiguating performance of the improved method before and after is small. The results show that the improved algorithm has a significant advantage under the condition of high frequency, and still maintains the performance level comparable to the traditional method under the condition of low frequency.

[0116] The above examples are only used to illustrate the technical solutions of the present application, but not limit the same; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An adaptive angle clustering based circular array interferometer unambiguousness method, characterized in that, The method comprises the following steps: modeling a two-dimensional circular array wideband interferometer, determining the unambiguous phase difference between different antenna elements and the relationship between the ambiguous phase differences based on the established model; constructing baselines based on different combinations of antenna elements of the two-dimensional circular array wideband interferometer, extracting the unambiguous phase difference of the antenna elements corresponding to each baseline, and determining the maximum range of the ambiguity number; combining the constructed baselines in pairs to generate multiple double baseline combinations; calculating the unambiguous phase difference of each double baseline combination, determining the azimuth angle and the elevation angle corresponding to different ambiguity combinations in the double baseline combination based on the maximum range value of the ambiguity number corresponding to each baseline in the double baseline combination, the unambiguous phase difference and the relationship between the ambiguous phase differences, and constructing an angle matrix of the double baseline combination; constructing an angle point set using the angle matrices of all double baseline combinations; each node in the angle point set is a combination of the azimuth angle and the elevation angle corresponding to each ambiguity combination in the angle matrix; clustering the nodes in the angle point set using an adaptive DBSCAN clustering algorithm to obtain a clustering cluster; selecting a clustering cluster with the largest density, calculating the average value of the azimuth angle and the average value of the elevation angle of all nodes in the clustering cluster as the target azimuth angle and the target elevation angle.

2. The adaptive angle clustering based circular array interferometer unambiguous method of claim 1, wherein, The method of modeling the two-dimensional circular array wideband interferometer, determining the unambiguous phase difference between different antenna elements and the relationship between the ambiguous phase differences based on the established model comprises: A two-dimensional circular array interferometer has a radius R and a total of N antenna elements uniformly distributed on the circumference of the two-dimensional circular array interferometer; the center of the interferometer is taken as the origin o of the coordinate system, and a space rectangular coordinate system oxyz is constructed, wherein the x axis is the antenna boresight direction, the y and z axes are located in the plane of the circumference of the two-dimensional interferometer, and the antenna elements are located in the yoz plane, so that the position coordinates (x n ,y n ,z n ) of the nth antenna element can be represented as: Let the direction of the incident signal be For The projection on the xoy plane, define the projection The angle θ with the x-axis is the azimuth angle, and the angle with the z-axis is the elevation angle The azimuth angle θ ∈ (0, 360°), and the elevation angle Then the phase difference of the incident signal between different antenna elements i and antenna elements j is: where Δφ ij represents the unambiguous phase difference between the ith antenna element and the jth antenna element, Δx ij , Δy ij , and Δz ij represent the distance difference between the ith antenna element and the jth antenna element in the x-axis, y-axis, and z-axis, respectively. Phase ambiguity occurs when the phase difference is greater than 2π; let the ambiguity number be m, and the ambiguous phase difference be Then we have:

3. The adaptive angle cluster based circular array interferometer unambiguous method of claim 1, wherein, determining the maximum range of the ambiguity number corresponding to the antenna elements of each baseline comprises: From the two-dimensional circular array broadband interferometer, any antenna array element i and antenna array element j are selected, and the connecting line of the two forms a baseline d ij The maximum range value of the corresponding ambiguity number m is: where λ is the wavelength of the incident signal, denotes the ceiling function; the value of the fuzzy number m is an integer between -M and M.

4. The adaptive angle cluster based circular array interferometer unambiguous method of claim 1, wherein, calculating the unambiguous phase difference of each double baseline combination comprises: for the kth baseline and the lth baseline for this dual baseline combination, where Δx k , Δy k , Δz k , Δx l , Δy l , Δz l represent the differences in x, y, z coordinates between the two antenna elements contained in the kth, lth baseline, respectively. The unambiguous phase difference expression of the double baseline combination is as follows: where Δφk and Δφl are the phase differences corresponding to the kth baseline and the lth baseline, respectively. k where Δφk and Δφl are the phase differences corresponding to the kth baseline and the lth baseline, respectively. l where Δφk and Δφl are the phase differences corresponding to the kth baseline 5. The adaptive angle cluster based circular array interferometer unambiguous method of claim 1, wherein, determining the azimuth angle and the elevation angle corresponding to different ambiguity combinations in the double baseline combination based on the maximum range value of the ambiguity number corresponding to each baseline in the double baseline combination, the unambiguous phase difference and the relationship between the ambiguous phase differences comprises: Because the two-dimensional circular array broadband interferometer can only obtain ambiguous phase difference and The maximum range value of the ambiguity number corresponding to the kth baseline and the lth baseline is calculated as M k and M l , all the ambiguity numbers are traversed, and the unambiguous phase difference is obtained as: where Δφ k and Δφ l represent the unambiguous phase difference of the kth baseline and the lth baseline, respectively, when the ambiguities are combined into m k and m l , respectively. m k = -M k , -M k + 1, 0,..., M k - 1, M k , m l = -M l , -M l + 1, 0,..., M l ; then for In a set of fuzzy numbers (m k ,m l ), then we have: where θ(m k ,m l ) and represent the azimuth and elevation angles, respectively, under the condition that the dual-baseline combined ambiguity vector is combined as (m k ,m l ), and α and β are process angle parameters, which are represented as follows: The angle matrix corresponding to the double baseline combination is finally obtained as follows: The angle matrix corresponding to each double baseline combination can be calculated by the above method.

6. The adaptive angle-clustering based circular array interferometer unambiguous method of claim 1, wherein, The adaptive DBSCAN clustering algorithm is as follows: First, set the minimum number of neighborhood points according to the requirements, and set the neighborhood radius of the DBSCAN clustering according to the frequency of the incident signal; Second, cluster the nodes in the angle point set; when clustering, each group of azimuth angle and elevation angle in each angle point set is taken as a node, and all angle matrices together constitute an angle point set; Start with an unvisited node selected arbitrarily from the angle point set, 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 by the node; If other core points are found in the neighborhood of the node, continue to expand the neighborhood radius, and add the nodes in the neighborhood of these core points to the cluster; Repeat the above process until all nodes are visited and classified into a cluster or marked as a noise point, finally obtain multiple density-reachable clustering clusters, and mark the nodes that cannot be classified as noise points; The number of nodes of each cluster is counted, and the cluster with the largest density is selected; the average values of the azimuth and the elevation in the cluster with the largest density are calculated as the target azimuth and the target elevation.

7. The adaptive angle-clustering based circular array interferometer unambiguous method of claim 6, wherein, The neighborhood radius of DBSCAN clustering is set according to the frequency of the incident signal, denoted as: wherein 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, The processor executes the computer program to implement the adaptive angle clustering based circular array interferometer ambiguity resolution method according to any one of claims 1-7.

9. A computer readable storage medium having stored therein a computer program; characterized in that, The computer program is executed by the processor to implement the adaptive angle clustering based circular array interferometer ambiguity resolution method according to any one of claims 1-7.

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