An interferometer direction finding resolution unscrambling method, system and medium for a shaped array

Abstract

The application discloses an interferometer direction-finding demodulation method, system and medium for a special-shaped array, and the method comprises the following steps: selecting two pairs of baseline longest antenna elements to form a first interferometer; calculating the maximum ambiguity number of the interferometer measurement phase difference according to the interferometer vector of the first interferometer, and calculating the target unit vector coordinate estimation value corresponding to all ambiguity combination in the range with the maximum ambiguity number as the boundary; forming a corresponding second interferometer with the remaining antenna elements, calculating the cost function corresponding to all ambiguity combinations according to the target unit vector coordinate estimation value and the measurement phase difference of each pair of second interferometers; searching for the minimum value of the cost function, taking the ambiguity combination corresponding to the minimum value of the cost function as the ambiguity number estimation result, obtaining the target unit vector coordinate estimation value corresponding to the ambiguity number estimation result, and calculating the corresponding azimuth and elevation angle estimation value. The application solves the problem of interferometer direction-finding demodulation of a special-shaped array.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal processing, in particular to an interferometer direction finding demodulation method, system and medium for a special-shaped array. BACKGROUND

[0002] Radio direction finding refers to a technology of determining the direction of a signal source by using the propagation characteristics of electromagnetic waves (sound waves) and the signal radiated by the signal source intercepted by a receiver. The technology has wide applications in military and civilian fields such as communication, radar, sonar, etc. There are many radio direction finding methods, among which the interferometer direction finding method refers to receiving the same signal by multiple antenna units at different positions, and since the phase difference between the units caused by the incoming wave in different directions is also different, the direction of the incoming wave can be determined by measuring the phase difference between the units. Compared with other direction finding methods, the interferometer direction finding method has the advantages of high direction finding sensitivity, high accuracy, and fast speed. However, the interferometer direction finding method has the core technical difficulty of phase ambiguity, that is, the phase difference can only change within the range of 0-2π, and when the phase difference exceeds this range, ambiguity will occur. The longer the distance between the units (referred to as the longer baseline), the higher the direction finding accuracy, but the more serious the ambiguity. In order to resolve the ambiguity, relevant scholars have proposed various ambiguity resolution methods, but these methods all have strict restrictions on the positions of the units, for example: it is usually required that the shortest baseline of a linear array is less than the ambiguity-free length, and the ambiguity of the long baseline is resolved by the measurement results of the short baseline; or it is required that the lengths of the baselines of the linear array are relatively prime, and the ambiguity is resolved by the remainder; for a circular array, it is usually required that the units are uniformly arranged.

[0003] In order to break the restriction on the arrangement of the units, relevant scholars have also proposed methods such as ambiguity resolution of uniformly arranged curved array and ambiguity resolution of non-uniform circular array. However, the above methods do not fundamentally break the restriction on the arrangement of the units, cannot be applied to special-shaped arrays, and also have some limitations: first, under the influence of noise, ambiguity resolution failure and other problems are prone to occur; second, for antenna arrays arranged irregularly due to installation environment limitations, such as irregularly arranged arrays along the wings or hulls, ambiguity resolution is still a technical difficulty, and there is still a lack of universally applicable ambiguity resolution means. SUMMARY

[0004] The technical problem to be solved by the present application is that in view of the technical problems existing in the prior art, the present application provides an interferometer direction finding ambiguity resolution method, system and medium for a special-shaped array, which is suitable for irregularly arranged arrays, and is also suitable for uniformly / non-uniformly arranged linear arrays, circular arrays and other arrays, and uses the least square method, cost function minimum value search and other methods, and has good anti-interference ability.

[0005] To solve the above technical problems, the technical solution provided by the present application is:

[0006] An interferometer direction finding ambiguity resolution method for a special-shaped array, comprising the following steps:

[0007] S1) Select two pairs of antenna elements with the longest baselines in the antenna array to form the first interferometer, and calculate the first interferometer vector based on the coordinates of the selected antenna elements.

[0008] S2) Calculate the maximum ambiguity number of the interferometer measurement phase difference based on the interferometer vector of the first interferometer, and calculate the target unit vector coordinate estimate corresponding to all combinations of ambiguity numbers within the range bounded by the maximum ambiguity number;

[0009] S3) Form a second interferometer by combining each pair of remaining antenna elements in the antenna array. Calculate the cost function corresponding to each pair of ambiguity number combinations based on the estimated target unit vector coordinates and the corresponding phase difference measured by the second interferometer.

[0010] S4) Search for the minimum value among all cost functions, combine the fuzzy array corresponding to the minimum cost function into fuzzy number estimation results, obtain the target unit vector coordinate estimation value corresponding to the fuzzy number estimation result, and use it to calculate the corresponding azimuth and elevation angle estimation values.

[0011] Furthermore, in step S4, after taking the fuzzy array combination corresponding to the minimum value of the cost function as the fuzzy number estimation result, it also includes: if the first fuzzy number estimation result of the current signal processing cycle includes two or more fuzzy number combinations that meet the minimum value condition of the cost function, then reset the combination order of the remaining antenna array elements in the antenna array and return to step S3;

[0012] If the current signal processing cycle includes two or more fuzzy number combinations in two consecutive fuzzy number estimation results, the same fuzzy number combination in the two fuzzy number estimation results is taken as the final fuzzy number estimation result, and the step of obtaining the target unit vector coordinate estimation value corresponding to the fuzzy number estimation result is executed.

[0013] Furthermore, after step S4, the method further includes: if the final ambiguity number estimation result of the current signal processing cycle includes two or more ambiguity number combinations, wait for and obtain the azimuth and elevation angle estimates of the next signal processing cycle, calculate the difference between the azimuth angle estimates and the elevation angle estimates of the two signal processing cycles, select the azimuth and elevation angle estimates of the two signal processing cycles corresponding to the smallest difference, and calculate the average of the selected azimuth angle estimates and the average of the elevation angle estimates as the final estimates of the azimuth and elevation angles.

[0014] Furthermore, in step S2, the expression for calculating the maximum ambiguity number of the interferometer measurement phase difference based on the interferometer vector of the first interferometer is as follows:

[0015]

[0016] Where dk =norm(v ak () represents the baseline length of the interferometer; norm(·) represents the modulus of the vector; f0 is the signal carrier frequency; c is the speed of light, v ak The vector represents the interferometer vector, and k = 1, 2 represents the antenna pair number of the first interferometer.

[0017] Furthermore, the expression for the estimated target unit vector coordinates in step S2 is as follows:

[0018]

[0019] in f0 is the signal carrier frequency, and c is the speed of light. These are the transposes of the antenna vectors of the two pairs of antennas of the first interferometer. Let (i,j) be the phase difference vector after defuzzification under the fuzzy combination (i,j). and Let i and γ be the phase difference values ​​actually measured by the two pairs of first interferometers, respectively, and i ∈ [0, γ]. max,1 ],j∈[0,γ max,2 ].

[0020] Furthermore, in step S3, calculating the cost function corresponding to each pair of ambiguity number combinations based on the estimated target unit vector coordinates and the measured phase difference of the second interferometer composed of each pair of remaining antenna elements specifically includes:

[0021] Calculate the corresponding second interferometer antenna pair vector based on the coordinates of each pair of remaining antenna elements. Multiply the estimated target unit vector coordinates corresponding to each pair of ambiguity combinations with the second interferometer vector and take the modulus of 2π to obtain the theoretical measured phase difference of each second interferometer.

[0022] Obtain the actual measured phase difference of each second interferometer Calculate the actual measured phase difference for each second interferometer. Phase difference from theoretical measurement The difference is the actual measured phase difference of all second interferometers corresponding to each pair of fuzzy number combinations. Phase difference from theoretical measurement The absolute value of the difference is taken and then summed to obtain the cost function corresponding to each pair of fuzzy number combinations.

[0023] Furthermore, the theoretical measured phase difference of each second interferometer The expression is as follows:

[0024]

[0025] Where v akLet k = 3, ..., N / 2 represent the interferometer vector, k = 3, ..., N / 2 represent the sequence number of the second interferometer, and N represent the number of antenna array elements. This represents the estimated target unit vector coordinates corresponding to the fuzzy number combination (i,j), where mod(·,2π) refers to taking the modulus with respect to 2π. f0 is the signal carrier frequency, and c is the speed of light;

[0026] The cost function expression for each pair of fuzzy numbers is as follows:

[0027]

[0028] Where k = 3, ..., N / 2 represents the antenna pair number of the second interferometer, N represents the number of antenna elements, and γ max,1 and γ max,2 These represent the maximum ambiguity number of the interferometer measurement phase difference corresponding to the two pairs of first interferometers.

[0029] Furthermore, the expressions for the estimated azimuth and elevation angles are as follows:

[0030]

[0031] in, This represents the estimated azimuth angle. This represents the estimated pitch angle. This represents the combination of fuzzy numbers corresponding to the minimum value of the cost function. The corresponding target unit vector coordinate estimate The first element, This represents the combination of fuzzy numbers corresponding to the minimum value of the cost function. The corresponding target unit vector coordinate estimate The second element.

[0032] The present invention also proposes an interferometer direction finding and unambiguity resolution system for irregular arrays, including a computer device that is programmed or configured to perform any of the described interferometer direction finding and unambiguity resolution methods for irregular arrays.

[0033] The present invention also proposes a computer-readable storage medium storing a computer program programmed or configured to perform any of the described interferometer direction finding and unambiguity resolution methods for irregular arrays.

[0034] Compared with the prior art, the advantages of the present invention are as follows:

[0035] (1) By selecting the longest baseline combination for direction finding, the accuracy of angle measurement is guaranteed.

[0036] (2) By exhaustively searching the fuzzy numbers through the cost function, the contradiction between angle measurement accuracy and angle measurement fuzziness is resolved to a certain extent. This breaks the limitation of traditional fuzzing methods on array arrangement and is applicable to fuzzing of irregular arrays and non-uniform arrays.

[0037] (3) The least squares method and the cost function search algorithm are used to realize the defuzzification and the estimated angle of arrival. Both methods have good noise resistance performance, so the overall system has strong noise resistance capability. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of the array element distribution of the irregular array in an embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram of the combination of the first interferometer and the second interferometer in an embodiment of the present invention.

[0041] Figure 4 This is a contour plot of the cost function values ​​corresponding to different combinations of fuzzy numbers in an embodiment of the present invention.

[0042] Figure 5 This is the performance curve for estimating the angle of arrival of waves in an embodiment of the present invention. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0044] To address the need for deambiguity resolution in irregularly shaped arrays, this embodiment proposes an interferometer direction-finding deambiguity resolution method for such arrays. This method is applicable to both irregularly arranged arrays and uniformly / non-uniformly arranged linear and circular arrays. Furthermore, due to the use of least squares and cost function search methods, it exhibits good anti-interference capabilities. Figure 1 As shown, in the direction finding process of the interferometer, the method of this embodiment performs the following steps for each signal processing cycle:

[0045] S1) Select two pairs of antenna elements with the longest baselines in the antenna array to form the first interferometer, and calculate the first interferometer vector based on the coordinates of the selected antenna elements.

[0046] S2) Calculate the maximum ambiguity number of the interferometer measurement phase difference based on the interferometer vector of the first interferometer, and calculate the target unit vector coordinate estimate corresponding to all combinations of ambiguity numbers within the range bounded by the maximum ambiguity number;

[0047] S3) Form a second interferometer by combining each pair of remaining antenna elements in the antenna array. Calculate the cost function corresponding to each pair of ambiguity number combinations based on the estimated target unit vector coordinates and the corresponding phase difference measured by the second interferometer.

[0048] S4) Search for the minimum value among all cost functions, combine the fuzzy array corresponding to the minimum cost function into fuzzy number estimation results, obtain the target unit vector coordinate estimation value corresponding to the fuzzy number estimation result, and use it to calculate the corresponding azimuth and elevation angle estimation values.

[0049] Through the steps described above, the method in this embodiment ensures angle measurement accuracy by selecting the longest baseline combination for direction finding. Furthermore, it resolves the ambiguity by exhaustively searching the ambiguity numbers using a cost function, thus mitigating the conflict between angle measurement accuracy and ambiguity to some extent. This breaks the limitations of traditional deambiguity methods on array arrangement and is applicable to deambiguity of irregular and non-uniform arrays.

[0050] Each step is explained in detail below.

[0051] like Figure 2 As shown, in this embodiment, the irregular array is distributed along the wing edge of the UAV. Step S1) specifically includes:

[0052] Two pairs of antenna elements with the longest baselines are selected to form the first interferometer, and the antenna vectors of the two pairs intersect each other to ensure that incoming waves with azimuth angles from 0° to 360° can obtain relatively good direction-finding performance. Therefore, in this embodiment, antenna pair 1 of the first interferometer is formed by the antenna elements at the root of the right wing and the antenna elements at the tip of the left wing; antenna pair 2 of the first interferometer is formed by the antenna elements at the root of the left wing and the antenna elements at the tip of the right wing. Figure 3 As shown by the solid line in the image.

[0053] To calculate the first interferometer vector, i.e., the interferometer vectors of antenna pair 1 and antenna pair 2 of the first interferometer, in this embodiment, a body coordinate system is constructed with the array geometric center as the origin, the wingspan direction as the X-axis, and the body symmetry axis as the Y-axis. The antenna element coordinates can then be expressed as X... ak =[x ak ,y ak ,z ak ] T k = 1, 2, ..., N, where N represents the number of antenna elements. Assume that the (m,n) ∈ [1,N]th antenna element is selected to form antenna pair 1; the (p,q) ∈ [1,N]th antenna element is selected to form antenna pair 2. The vectors of these two antenna pairs are v. a1 v a2 Then we have v a1 =[x am -x an ,y am -yan ,z am -z an ] T v a2 =[x ap -x aq ,y ap -y aq ,z ap -z aq ] T .here,[·] T This indicates the matrix transpose.

[0054] In step S2 of this embodiment, based on the interferometer vector of the first interferometer selected in step S1, the maximum ambiguity number of the two pairs of phase difference measurements can be obtained. This is also the boundary of the ambiguity number of all interferometer phase difference measurements of the irregular array. Specifically, the expression for calculating the maximum ambiguity number of the interferometer measured phase difference based on the antenna pair vector of the first interferometer in step S2 is as follows:

[0055]

[0056] Where d k =norm(v ak () represents the baseline length of the interferometer; norm(·) represents the modulus of the vector; f0 is the carrier frequency of the signal, which can be obtained through the frequency measurement module; c is the speed of light, v ak The vector represents the interferometer, and k = 1, 2 represents the sequence number of the first interferometer.

[0057] Therefore, the maximum ambiguity number γ of the measured phase difference of antenna pair 1 in the first interferometer can be obtained from equation (1). max,1 The maximum ambiguity number γ of the measured phase difference with antenna pair 2 max,2 .

[0058] Next, step S2 uses the maximum ambiguity number γ of the measured phase difference of antenna pair 1 in the first interferometer. max,1 The maximum ambiguity number γ of the measured phase difference with antenna pair 2 max,2 Using the boundary as the boundary, by exhaustively exhausting the phase ambiguity number, the least squares method is used to obtain all possible estimates of the target unit vector. The derivation process is as follows:

[0059] Assume the target's coordinate vector in the body coordinate system is X. t =[x t ,y t ,z t ] T The unit vector of this coordinate is v. t =X t / norm(X t The relationship between this unit vector and the target azimuth angle α and elevation angle β is vt =[cosαcosβ,sinαcosβ,sinβ] T The phase difference between the unambiguous target signals corresponding to antenna pair 1 and antenna pair 2 is: here Written in matrix form as θ = Kv a ·v t , Right now:

[0060]

[0061] Based on the least squares method, the unit vector estimate of the target coordinates can be obtained.

[0062]

[0063] However, the actual measured phase difference has a 2π ambiguity. Assuming the actual measured phase difference between antenna pair 1 and antenna pair 2 is... Therefore, it is obvious that the phase difference of the unambiguous target signal corresponding to antenna pair 1 and antenna pair 2 is... Here γ k Let be the actual fuzzy number, k = 1, 2. This fuzzy number is an unknown quantity, but it must be a natural number, and its maximum value does not exceed γ. max,k k = 1, 2. Therefore, for the fuzzy number γ k In [0, γ max,k By searching within the range, for each combination of fuzzy numbers, the corresponding estimated value of the target unit vector coordinates can be obtained based on the least squares method, resulting in:

[0064]

[0065] in f0 is the signal carrier frequency, and c is the speed of light. These are the transposes of the antenna pair vectors of the first interferometer. Let (i,j) be the phase difference vector after defuzzification under the fuzzy combination (i,j). and Let i and y be the phase difference values ​​of antenna pair 1 and antenna pair 2 in the first interferometer, respectively, measured for the current signal processing cycle, and i ∈ [0, γ]. max,1 ],j∈[0,γ max,2 ].

[0066] Obviously, step S2 can obtain γ max,1 ×γ max,2 The estimated unit vector values ​​of the target coordinates.

[0067] In this embodiment, step S3 is based on the estimated target unit vector coordinates. A second interferometer is formed using the remaining antenna pairs, and the phase difference of the second interferometer is used to construct a cost function for each combination of ambiguities. For example... Figure 3 As shown by the dashed line, the interferometer vector of the second interferometer, composed of the remaining antenna elements excluding the m-th and n-th antenna elements and the p-th and q-th antenna elements, is v. ak k = 3, ..., N / 2, basically adopting the method of combining the antenna array elements of the left and right wings.

[0068] The target unit vector coordinates estimated under the fuzzy number combination (i,j) Theoretically, the measurement phase difference of the second interferometer is Here, mod(·, 2π) refers to taking the modulus of 2π, v ak The vector represents the interferometer vector, k = 3, ..., N / 2 represents the antenna pair number of the second interferometer, and N represents the number of antenna elements.

[0069] If the phase difference actually measured by the second interferometer for this signal processing cycle is... For each pair of fuzzy number combinations (i,j), the associated cost function can be constructed as follows:

[0070]

[0071] in, The actual measured phase difference for each second interferometer The theoretical measured phase difference for each second interferometer, k = 3, ..., N / 2 represents the antenna pair number of the second interferometer, N represents the number of antenna elements, γ max,1 and γ max,2 These represent the maximum ambiguity numbers of the interferometer measurement phase difference for antenna pair 1 and antenna pair 2 in the first interferometer, respectively.

[0072] Therefore, in step S3 of this embodiment, calculating the cost function corresponding to each pair of ambiguity number combinations based on the estimated target unit vector coordinates and the measured phase difference of the second interferometer corresponding to each pair of remaining antenna array elements specifically includes:

[0073] Calculate the corresponding second interferometer vector based on the coordinates of each pair of remaining antenna elements. Multiply the estimated target unit vector coordinates corresponding to each pair of ambiguity combinations with the second interferometer vector and take the modulus of 2π to obtain the theoretical measured phase difference of each second interferometer.

[0074] Obtain the actual measured phase difference of each second interferometer Calculate the actual measured phase difference for each second interferometer. Phase difference from theoretical measurement The difference is the actual measured phase difference of all second interferometers corresponding to each pair of fuzzy number combinations. Phase difference from theoretical measurement The absolute values ​​of the differences are then summed to obtain the cost function corresponding to each pair of fuzzy number combinations.

[0075] Obviously, step S3 can obtain γ max,1 ×γ max,2 Each cost function value.

[0076] In step S4 of this embodiment, the minimum value of the cost function is first searched to determine the estimated value of the fuzzy number. Specifically, for γ... max,1 ×γ max,2 The cost function value P ij Perform a search to find the minimum value P of the cost function. min Corresponding fuzzy number combinations Right now Then fuzzy number combination This is the fuzzy number estimate. For example... Figure 4 As shown in the figure, the horizontal axis represents all possible ambiguities for antenna pair 1; the vertical axis represents all possible ambiguities for antenna pair 2, and the contour lines for different cost function values ​​are filled with different colors. It can be seen from the figure that the cost function value is minimized under the ambiguity combination (4,5).

[0077] After obtaining the fuzzy number estimation results, the azimuth and elevation angle estimates can be calculated based on the target unit vector coordinate estimates corresponding to the fuzzy number combination in equation (4). The corresponding azimuth and elevation angle estimates are: here This represents the k-th element of the coordinate vector.

[0078] In this embodiment, since all antenna elements are arranged along the wing edge, the Z-axis coordinates of all antenna elements are the same, meaning all antenna elements are located in a plane parallel to XOY. Therefore, That is, it is impossible to obtain an estimated value of the target's Z-axis coordinate, but it can be known using the unit vector property. Therefore, the estimated values ​​of azimuth and elevation angles are expressed as follows:

[0079]

[0080] in, This represents the estimated azimuth angle. This represents the estimated pitch angle. This represents the combination of fuzzy numbers corresponding to the minimum value of the cost function. The corresponding target unit vector coordinate estimate The first element, This represents the combination of fuzzy numbers corresponding to the minimum value of the cost function. The corresponding target unit vector coordinate estimate The second element.

[0081] In step S4 of this embodiment, if multiple fuzzy number combinations have the same minimum cost function when searching for the minimum cost function, then all fuzzy number combinations that meet the minimum value condition are combined into a fuzzy number estimate. Since the fuzzy number estimation result includes two or more fuzzy number combinations, it is necessary to recalculate the fuzzy number estimation result for the current signal processing cycle. Therefore, step S4 of this embodiment also includes: if the initial fuzzy number estimation result for the current signal processing cycle includes two or more fuzzy number combinations that meet the minimum cost function condition, then the combination order of the remaining antenna elements in the antenna array is reset. For example, the combination order of the remaining antenna elements "ab" and "cd" is shuffled and reset to "ac" and "bd". After resetting the combination order of each pair of remaining antenna elements in the antenna array, the process returns to step S3.

[0082] If there is no case where the cost function corresponding to multiple fuzzy number combinations is at its minimum in the second fuzzy number estimation result of the current signal processing cycle, then the azimuth and elevation angle estimates corresponding to the fuzzy number combinations in the second fuzzy number estimation result are directly used as the final estimation results.

[0083] To reduce computational load, in step S4 of this embodiment, after combining the fuzzy array corresponding to the minimum value of the cost function into a fuzzy number estimation result, and before obtaining the target unit vector coordinate estimation value corresponding to the fuzzy number estimation result, the aforementioned steps are performed. Thus, only the azimuth and elevation angle estimation values ​​corresponding to the fuzzy number combination of the second fuzzy number estimation result need to be calculated.

[0084] If the second fuzzy number estimation result of the current signal processing cycle also contains multiple fuzzy number combinations whose corresponding cost functions are all at their minimum values, that is, if the two consecutive fuzzy number estimation results of the current signal processing cycle both include two or more fuzzy number combinations, the same fuzzy number combination in the two fuzzy number estimation results is taken as the final fuzzy number estimation result. The step of obtaining the target unit vector coordinate estimation value corresponding to the fuzzy number estimation result is then executed. The azimuth and elevation angle estimation values ​​corresponding to the fuzzy number combination of the final fuzzy number estimation result are taken as the final estimation result.

[0085] Furthermore, the final ambiguity number estimation result of the current signal processing cycle may also have two or more ambiguity number combinations. Then, obtain the azimuth and elevation angle estimates for the next signal processing cycle, calculate the difference between each azimuth angle estimate of the current signal processing cycle and each azimuth angle estimate of the next signal processing cycle, and calculate the difference between each elevation angle estimate of the current signal processing cycle and each elevation angle estimate of the next signal processing cycle. Find the azimuth angle estimates of the two signal processing cycles corresponding to the minimum difference in azimuth angle estimates, and the elevation angle estimates of the two signal processing cycles corresponding to the minimum difference in elevation angle estimates. Take the average of the selected pair of azimuth angle estimates as the final azimuth angle estimate, and take the average of the selected pair of elevation angle estimates as the final elevation angle estimate.

[0086] Therefore, after step S4 in this embodiment, the method further includes: if the final ambiguity number estimation result of the current signal processing cycle includes two or more ambiguity number combinations, return to step S1, wait and obtain the azimuth and elevation angle estimates of the next signal processing cycle, calculate the difference between the azimuth angle estimates and the elevation angle estimates of the two signal processing cycles, select the azimuth and elevation angle estimates of the two signal processing cycles corresponding to the smallest difference, and calculate the average value of the selected azimuth angle estimates and the average value of the elevation angle estimates as the final estimates of the azimuth and elevation angles.

[0087] In this embodiment, the performance curves for the estimated angle of arrival (AHA) obtained by the above method under different signal processing cycles are shown in Figure 5. The horizontal axis represents the interferometer's phase difference measurement error, varying from 0° to 10°; the vertical axis represents the estimated azimuth and elevation angles of the incoming wave. In the two curves, * indicates the azimuth measurement error; Δ indicates the elevation measurement error. As can be seen from the figure, when the interferometer's phase difference measurement error is <9°, the positioning error is <0.5° and increases very slowly. In contrast, the phase difference measurement error of a real system typically does not exceed 10°, demonstrating that this method has good angle estimation performance in practical applications.

[0088] This embodiment also proposes an interferometer direction finding and deambiguity resolution system for irregular arrays, including a computer device that is programmed or configured to execute the interferometer direction finding and deambiguity resolution method for irregular arrays described in this embodiment.

[0089] This embodiment also proposes a computer-readable storage medium storing a computer program programmed or configured to execute the interferometer direction finding and deambiguity method for irregular arrays described in this embodiment.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. An interferometer direction-finding deambiguity resolution method for irregular arrays, characterized in that, Includes the following steps: S1) Select two pairs of antenna elements with the longest baselines in the antenna array to form the first interferometer, and calculate the first interferometer vector based on the coordinates of the selected antenna elements. S2) Calculate the maximum ambiguity number of the interferometer measurement phase difference based on the interferometer vector of the first interferometer, and calculate the target unit vector coordinate estimate corresponding to all combinations of ambiguity numbers within the range of this maximum ambiguity number as the boundary; S3) Form a second interferometer by combining each pair of remaining antenna elements in the antenna array. Based on the estimated target unit vector coordinates and the corresponding phase difference measured by the second interferometer, calculate the cost function corresponding to each pair of ambiguity number combinations, specifically including: Calculate the corresponding second interferometer antenna pair vector based on the coordinates of each pair of remaining antenna elements. Multiply the estimated target unit vector coordinates corresponding to each pair of ambiguity combinations with the second interferometer vector and take the modulus of 2π to obtain the theoretical measured phase difference of each second interferometer. Obtain the actual measured phase difference of each second interferometer Calculate the actual measured phase difference for each second interferometer. Phase difference from theoretical measurement The difference is the actual measured phase difference of all second interferometers corresponding to each pair of fuzzy number combinations. Phase difference from theoretical measurement The absolute values ​​of the differences are then summed to obtain the cost function corresponding to each pair of fuzzy number combinations. S4) Search for the minimum value among all cost functions, and combine the fuzzy arrays corresponding to the minimum cost function into fuzzy number estimation results. If the first fuzzy number estimation result of the current signal processing cycle includes two or more fuzzy number combinations that meet the minimum cost function condition, then reset the combination order of the remaining antenna array elements in the antenna array and return to step S3. If the current signal processing cycle includes two or more fuzzy number combinations in two consecutive fuzzy number estimation results, the same fuzzy number combination in the two fuzzy number estimation results is taken as the final fuzzy number estimation result. The target unit vector coordinate estimation value corresponding to the fuzzy number estimation result is obtained, and the corresponding azimuth and elevation angle estimation values ​​are calculated based on this.

2. The interferometer direction finding and deambiguity resolution method for irregular arrays according to claim 1, characterized in that, Step S4 is followed by: if the final ambiguity number estimation result of the current signal processing cycle includes two or more ambiguity number combinations, wait for and obtain the azimuth and elevation angle estimates of the next signal processing cycle, calculate the difference between the azimuth angle estimates and the elevation angle estimates of the two signal processing cycles, select the azimuth and elevation angle estimates of the two signal processing cycles corresponding to the smallest difference, and calculate the average of the selected azimuth angle estimates and the average of the elevation angle estimates as the final estimates of the azimuth and elevation angles.

3. The interferometer direction finding and deambiguation method for irregular arrays according to claim 1, characterized in that, In step S2, the expression for calculating the maximum ambiguity number of the interferometer measurement phase difference based on the interferometer vector of the first interferometer is as follows: Where d k =norm(v ak () represents the baseline length of the interferometer; norm(·) represents the modulus of the vector; f0 is the signal carrier frequency; c is the speed of light, v ak The vector represents the interferometer vector, and k = 1, 2 represents the antenna pair number of the first interferometer.

4. The interferometer direction finding and deambiguity resolution method for irregular arrays according to claim 3, characterized in that, The expression for the estimated target unit vector coordinates in step S2 is as follows: in f0 is the signal carrier frequency, and c is the speed of light. These are the transposes of the two pairs of antenna vectors of the first interferometer. Let (i,j) be the phase difference vector after defuzzification under the fuzzy combination (i,j). and Let i and γ be the phase difference values ​​actually measured by the two pairs of first interferometers, respectively, and i ∈ [0, γ]. max,1 ],j∈[0,γ max,2 ].

5. The interferometer direction finding and deambiguity resolution method for irregular arrays according to claim 1, characterized in that, The theoretical measured phase difference of each second interferometer The expression is as follows: Where v ak Let k = 3, ..., N / 2 represent the interferometer vector, k = 3, ..., N / 2 represent the antenna pair number of the second interferometer, and N represent the number of antenna elements. This represents the estimated target unit vector coordinates corresponding to the fuzzy number combination (i,j), where mod(·,2π) refers to taking the modulus with respect to 2π. f0 is the signal carrier frequency, and c is the speed of light; The cost function expression for each pair of fuzzy numbers is as follows: Where k = 3, ..., N / 2 represents the antenna pair number of the second interferometer, N represents the number of antenna elements, and γ max,1 and γ max,2 These represent the maximum ambiguity number of the interferometer measurement phase difference corresponding to the first interferometer.

6. The interferometer direction finding and deambiguity resolution method for irregular arrays according to claim 1, characterized in that, The estimated values ​​of the azimuth and elevation angles are expressed as follows: in, This represents the estimated azimuth angle. This represents the estimated pitch angle. This represents the combination of fuzzy numbers corresponding to the minimum value of the cost function. The corresponding target unit vector coordinate estimate The first element, This represents the combination of fuzzy numbers corresponding to the minimum value of the cost function. The corresponding target unit vector coordinate estimate The second element.

7. An interferometer direction-finding and deambiguity-resolving system for irregular arrays, characterized in that, Includes a computer device, which is programmed or configured to perform the interferometer direction finding and deambiguity method for irregular arrays as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program programmed or configured to perform the interferometer direction finding and deambiguity resolution method for irregular arrays as described in any one of claims 1 to 6.

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