Method for suppressing random distribution array beam comprehensive feature space-variant characteristics

Abstract

The invention belongs to the technical field of antenna design, and particularly relates to a method for suppressing random distribution array beam comprehensive feature space-variant characteristics. The method comprises the steps of 1, determining a task airspace beam scanning range; 2, establishing a mapping relation from each sub-array plane coordinate system to a random array polar coordinate system, and selecting a sub-array plane meeting a beam space coverage range according to a task airspace beam scanning range and the mapping relation; step 3, for each wave beam arranged in the wave beam scanning range, carrying out wave beam synthesis according to the selected sub-array plane; step 4, judging whether the comprehensive wave beam meets a constraint condition or not, if so, entering step 6, and otherwise, entering step 5; 5, adjusting the number and the distribution mode of the sub-array planes, carrying out beam synthesis again, and returning to the step 4; step 6, outputting a beam synthesis result corresponding to the current beam; step 7, judging whether other beams still exist in the beam scanning range, if so, entering step 3, otherwise, entering step 8; and step 8, counting sub-array surface resource allocation results corresponding to different beams in the task.

Description

Technical Field

[0001] The invention belongs to the technical field of antenna design, and in particular relates to a method for suppressing spatial variability of comprehensive characteristics of randomly distributed array beams. Background Art

[0002] Currently, phased array antenna arrays are mostly uniform linear arrays and planar arrays. Linear arrays can actually only cover an azimuth angle of about 120°, and the gain and radiation pattern characteristics they provide change with different scanning angles. These shortcomings have greatly limited their scope of use. Planar arrays also have problems such as narrow beam scanning range, increasing beam width with increasing scanning angle, and the mutual coupling effect between array elements being a function of the scanning angle.

[0003] The synthesis of random array patterns arbitrarily distributed in three-dimensional space has the potential to solve the above problems. There are three main structural forms of array antennas that meet full airspace coverage: multi-faceted arrays, curved or conformal arrays, and lens array antennas. Conformal array antennas often need to achieve low sidelobe or ultra-low sidelobe patterns, but the conventional amplitude-weighted conformal array antenna form will have a very high excitation amplitude dynamic ratio, and the corresponding requirements on the accuracy of feeding are stringent. In order to obtain a wider multi-beam coverage space to improve the intercept probability of the radar, an idea of ​​​​combining a multi-faceted array antenna with multiple rectangular planar array antennas can be adopted, and the beam coverage domain formed by multiple arrays can be combined to expand the beam coverage domain of the planar array. On the basis of this idea, the spatial arrangement of multiple arrays is specifically designed using the knowledge of solid geometry, and then a wide coverage multi-faceted array multi-beam forming method is formed according to the multi-beam forming algorithm. The multi-faceted splicing structure is adopted to reduce the number of planar sub-arrays, while maintaining the wide-area coverage performance, further reducing the difficulty of implementation.

[0004] Although randomly distributed multi-faceted arrays have advantages such as wide spatial coverage, their spatial distribution has greater uncertainty than traditional planar arrays, resulting in greater "spatial variability" in their beam synthesis performance. Summary of the invention

[0005] The purpose of the present invention is to propose a method for suppressing the spatial variability of the beam synthesis characteristics of a randomly distributed array, so as to solve the problem that the shape and beam gain of the formed beam vary with the scanning angle in the beam synthesis of complex distributed arrays such as conformally distributed opportunity arrays, randomly distributed conformal arrays, and randomly distributed multi-faceted arrays of radar system platforms.

[0006] The technical solution of the present invention:

[0007] A method for suppressing spatial variability of a random distribution array beam synthesis characteristic, comprising the following steps:

[0008] Step 1: Determine the beam scanning range of the mission airspace;

[0009] Step 2: Establish a mapping relationship between each sub-array coordinate system and the random array polar coordinate system, and select the sub-array that meets the beam space coverage range according to the task spatial beam scanning range and the mapping relationship;

[0010] Step 3: For each beam arranged in the beam scanning range, beam synthesis is performed according to the selected sub-array surface;

[0011] Step 4: Determine whether the integrated beam meets the constraint conditions. If so, proceed to step 6; otherwise, proceed to step 5.

[0012] Step 5: Adjust the number and distribution of sub-arrays, re-perform beam synthesis, and return to step 4;

[0013] Step 6: Output the beam synthesis result corresponding to the current beam;

[0014] Step 7: Determine whether there are other beams in the beam scanning range. If so, proceed to step 3; otherwise, proceed to step 8;

[0015] Step 8: Count the sub-array resource allocation results corresponding to different beams in the task.

[0016] Furthermore, in step 1, the azimuth range of the mission airspace beam scanning range is The pitch angle range is [θ min ,θ max ].

[0017] Further, in step 2, the process is as follows:

[0018] The array distribution of a typical aircraft is modeled, including eight sub-arrays, and the beam space coverage corresponding to different arrays is fixed;

[0019] Select sub-arrays or sub-array combinations that overlap with the mission airspace beam coverage requirements;

[0020] The entire coverage of the beam space where the task is located is achieved by scanning I beams at different times, and the beam synthesis result of the i-th (1≤i≤I) beam is optimized;

[0021] The mapping relationship establishment process is as follows:

[0022] First, the azimuth-elevation angle of the i-th beam in the random array polar coordinate system is Mapped to the random array rectangular coordinate system (x i ,y i ,z i ), and then transform it to the rectangular coordinate system of the sub-array surface (x i (u),y i (u),zi (u)), and finally transformed to the sub-array polar coordinate system

[0023] and θ i are the azimuth and elevation angles of the beam in the random array polar coordinate system, and satisfy θ imin ≤θ i ≤θ imax ;x i ,y i 、z i are the coordinate values ​​of the random array in the rectangular coordinate system; x i (u), y i (u), z i (u) is the coordinate value of the u-th sub-array in the rectangular coordinate system; θ i (u), The azimuth-elevation angle of the u-th sub-array in the polar coordinate system;

[0024] set up and θ Be are the 3dB beamwidths in azimuth and elevation of the array element pattern in the sub-array plane, respectively. If the u-th sub-array plane meets the following conditions:

[0025]

[0026] Then it is judged that the sub-array surface can participate in beam synthesis;

[0027] make Through statistics in In different variation ranges, the number of sub-array faces that meet the condition (1) on a certain array face. When the number of sub-array faces that meet the condition is greater than the minimum sub-array face number threshold required for beam synthesis, it is determined that the beam formed by the corresponding array face can cover the spatial angle

[0028] Furthermore, in step 3, the process of beam synthesis is as follows:

[0029] Assume that the randomly distributed array has U planar sub-arrays with normals pointing in different directions, the position of each sub-array is randomly variable, the direction of the sub-array surface normal changes with the curvature of the aircraft surface, and each sub-array surface in the U sub-arrays is a regular array composed of N*M array elements;

[0030] For the planar array with constant amplitude feeding, assuming that the radar transmits narrowband signals, the array antenna elements are arranged at intervals of d = λ0 / 2, and λ0 = c / f0;

[0031] If we consider that the normal direction of the u-th sub-array is When the random array composed of U sub-arrays needs to be in the beam pointing direction To form a beam, let the amplitude weight coefficient of each subarray / element of the random array be

[0032] Then the antenna beam gain of the randomly distributed array can be expressed as:

[0033]

[0034] In the formula, is the pointing angle of the formed beam, which is the angle in spherical coordinates; U is the number of sub-array planes; M and N are the number of rows and columns of array elements in each sub-array plane, respectively; For the beam forming direction The weighting coefficients related to the sub-array surface u, the array element numbers m and n in the sub-array; α(u) and β(u) are the normal directions of the u-th sub-array. The two associated angle parameters represent the random array in the direction of beam formation. Transform to the azimuth and elevation angles in the u-th sub-array;

[0035] For the array antenna pattern design implemented by the beam synthesis method, the influence of the side lobe on the beam gain cannot be ignored; if the normalized amplitude of the main lobe of the beam is a0, the normalized amplitude of the P larger side lobe levels is a p (p=1,2,…,P); then the main lobe reception and radiation efficiency of the array antenna can be expressed as η p :

[0036]

[0037] In the same sub-array Since cos[α(u)] and cos[β(u)] do not change with n and m, equation (2) can be expressed as:

[0038]

[0039] Furthermore, in step 4, the constraints include:

[0040] 1) The beam gain of the random array beam synthesis is not less than G0;

[0041] 2) The maximum main-sidelobe ratio of random array beam synthesis is not less than a p0 .

[0042] Furthermore, in step 5, the process of adjusting the number of sub-array surfaces is as follows:

[0043] When determining the number of sub - arrays participating in synthesis, the constraint condition of the allowable range of beam gain variation is used as the verification and discrimination condition, and the bisection method is combined to reduce the search range of variables such as the number of sub - arrays participating in beam synthesis, so as to reduce the computational complexity of the algorithm.

[0044] Let the beam gain be a fixed value G0, as follows:

[0045]

[0046] Among them, is the number of sub - array surfaces participating in beam synthesis that varies with the beam pointing angle ;

[0047] The process of using the bisection method to solve Equation (4) so that the beam gain of the beam synthesis result in the direction is G0 can be described as follows:

[0048] (1) The first - step half - interval verification calculation

[0049] Let initially, the number of sub - array surfaces U participating in beam synthesis ∈ [U1, U2]. To further narrow the value range of U, divide [U1, U2] into two halves, calculate the mid - point x1=(U1 + U2) / 2 of the number of sub - array surfaces and let U = x1 for beam synthesis, or look up the beam synthesis result table of the relevant array surface. Then, according to the beam main - lobe gain value obtained from the beam synthesis or table - looking result, calculate the beam gain value G i ;

[0050] (2) The second - step half - interval verification calculation

[0051] If G i < G0, then take x1 = U1 and let x2=(x1 + U2) / 2;

[0052] If G i > G0, then take x1 = U2 and let x2=(x1 + U1) / 2;

[0053] Let U = x2 for beam synthesis again, and then according to the beam main - lobe gain value G i obtained from the beam synthesis; iterate in turn;

[0054] (k) The k - step half - interval verification calculation

[0055] If G i < G0, then take x k-1 = U1 and let x k =(x k-1 + U2) / 2;

[0056] If G i > G0, then take x k-1 = U2 and let xk =(x k-1 +U1) / 2;

[0057] Let U = x k Perform beam synthesis, and then obtain the beam main lobe gain value G according to the beam synthesis i ; Until the condition is met: |G i -G0|≤ε, or the number of iterations K exceeds the set value, the iterative calculation ends;

[0058] Through the above iterative process based on the dichotomy method, the maximum possible variation range of the number of sub-array surfaces that meets the main lobe beam gain under different scanning angle conditions can be obtained, and the sub-array surface search interval can be effectively reduced without reducing the credibility of the sub-array surface number distribution;

[0059] Due to the randomness of the working state of the sub-array surface position and the normal direction of the array surface of the randomly distributed array, the beam synthesis result sometimes cannot completely achieve the optimization goal. Therefore, the beam synthesis result is optimized when the credibility of the constraint condition is not less than the pre-given confidence level; the problem of the beam forming a specified gain changing with the beam pointing angle in this step is regarded as a problem of dynamic optimization allocation of sub-array surface resources of a randomly distributed array and solved;

[0060] use It represents the deviation of the beam gain after beam synthesis from the expected value:

[0061]

[0062] An uncertainty constraint for beam synthesis optimization can be expressed as

[0063]

[0064] In the formula, is the number of sub-arrays involved in beam synthesis;

[0065] Fuzzy variables are used to describe the number of units involved in pattern synthesis;

[0066] For a pre-given confidence level α, it is hoped that the number of sub-array planes / array elements participating in the pattern synthesis meets the constraint condition with a reliability of not less than α and a probability of not less than β;

[0067] Therefore, the fuzzy chance constrained programming model for multi-array pattern synthesis is obtained as follows:

[0068]

[0069] Where α, β∈[0,1] are predetermined chance measures, representing the credibility and probability of satisfying the constraints; ch{} is the chance measure;

[0070] In order to effectively use equation (7) to solve the uncertainty problem in the beam synthesis of randomly distributed array antennas, the number of sub-array planes or array elements involved in beam synthesis ξ is taken as an interval random variable, and its variation interval is Changes with the beam pointing angle, determined according to the pre-simulation calculation; interval The larger it is, the higher the confidence that the constraints are met;

[0071] exist In the interval, ξ is a random variable that satisfies a certain probability distribution; in this way, by determining the value interval and probability statistical law of the number of sub-array surfaces ξ, the uncertainty of ξ is described, and the computational complexity brought by the complex hybrid intelligent algorithm is reduced.

[0072] Furthermore, in step six, the parameters included in the output result are as follows:

[0073] 1) Number of sub-arrays involved in beam synthesis

[0074] 2) Beam main lobe gain value G i ;

[0075] 3) The credibility α and probability β of the beam synthesis result.

[0076] Furthermore, in step seven, the constraints include: the beam number i is equal to the total number of beams I in the mission airspace coverage range, i=I.

[0077] Further, in step eight, the output result is as follows:

[0078] 1) Beam pointing of different beams in the mission airspace coverage;

[0079] 2) beam gain for each beam pointing;

[0080] 3) The number and distribution of required sub-array surfaces corresponding to each beam;

[0081] 4) The credibility and probability of each beam.

[0082] Beneficial effects of the present invention:

[0083] The present invention provides a method for suppressing the "space variability" of beam comprehensive performance based on the "dichotomy" and the optimization of the number and spatial layout of randomly distributed array sub-arrays, solving the problem that the beam shape and beam gain of a randomly distributed array remain unchanged over a wide angle range. The conclusion of the present invention has important guiding significance for improving the comprehensive performance of randomly distributed array beams and realizing multi-functions and multi-tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1The present invention is a flow chart of a method for suppressing the spatial variability of the comprehensive performance of a randomly distributed array beam.

[0085] Figure 2 It is a schematic diagram of the spatial random distribution of array elements / sub-arrays on a typical aircraft body.

[0086] Figure 3 It is a schematic diagram of the change of the array beam gain on the left side of the aircraft nose with the beam pointing.

[0087] Figure 4 It is a schematic diagram of the change of array beam gain on the left side of the aircraft fuselage with beam pointing.

[0088] Figure 5 This is a simulation diagram of the beam synthesis results of a randomly distributed array. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] A method to suppress the spatial variability of the comprehensive performance of randomly distributed array beams, such as Figure 1 As shown, the specific steps are as follows:

[0091] (1) Method for determining beam spatial coverage of randomly distributed arrays

[0092] Typical aircraft and other platforms have complex geometric structures, and the sub-arrays in different areas are located on the left and right sides of the aircraft's nose, cockpit, fuselage, and wings. Because the positions and normal directions of the sub-arrays in the randomly distributed array have a certain degree of randomness, the resulting beam gain and beam width parameters have a complex mapping relationship with the number and distribution of sub-arrays involved in beam synthesis. Figure 2 shown.

[0093] For each of the eight distributed arrays on the aircraft, the simulation calculation results (forming a data table) under different numbers of sub-arrays and different beam pointing angles are first obtained through the beam synthesis algorithm; then the sub-array allocation results that meet the constraints of the main lobe beam gain (beam width) and sidelobe level under different scanning angles (beam pointing) are obtained through table lookup and interpolation. It is not necessary to fully meet the constraints for each wave position within the beam scanning angle range, but only the probability and credibility of meeting the constraints within the scanning angle range (several discrete wave positions) must meet the requirements. The result of this treatment is a significant reduction in the number of sub-arrays involved in beam synthesis.

[0094] The beam coverage area of ​​each array is different. The directional angle of the beam to be formed in the randomly distributed array coordinate system is transformed by coordinate transformation. Find this Mapped to the sub-array coordinate system of each antenna unit in the sub-array plane Coordinate rotation transformation can be divided into The conversion is carried out in three steps, namely, firstly, transforming from the random array polar coordinate system to the random array rectangular coordinate system, then transforming from the random array rectangular coordinate system to the rectangular coordinate system of the sub-array surface, and finally transforming from the rectangular coordinate system of the sub-array surface to the polar coordinate system of the sub-array surface.

[0095] Assume that the half-power beamwidth of the array element in each sub-array is BWe, if the u-th sub-array satisfies the following conditions:

[0096]

[0097] It is judged that the sub-array surface can participate in beam synthesis.

[0098] make By calculating the statistics of the algorithm In different variation ranges, the number of sub-array faces that meet the condition (1) on a certain array face. When the number of sub-array faces that meet the condition is greater than the minimum number of sub-array faces (threshold value) required for beam synthesis, it is determined that the beam formed by the corresponding array face can cover the spatial angle

[0099] (2) "Dichotomy" to determine the number and distribution of sub-arrays involved in beam synthesis

[0100] Aiming at the problem that the beam width and beam gain of the array antenna obtained based on the beam synthesis method vary with the beam pointing, the gain of the formed beam is kept unchanged by changing the number of array elements participating in beam synthesis, thereby achieving the goal of suppressing the "space variability" of the array beam width and beam gain.

[0101] To ensure the performance of the array antenna, beam synthesis must first ensure that the beam shape and beam gain in the array beam scanning area remain basically unchanged and the performance degradation is not obvious. When determining the number of sub-arrays participating in the synthesis, the constraint condition of the allowable value of the variation range of the beam gain can be used as a verification and judgment condition, and the search interval of variables such as the number of sub-arrays participating in the beam synthesis can be reduced by combining the dichotomy method to reduce the amount of algorithm calculation.

[0102] By solving the following equation, the value of U can be obtained while ensuring that the beam gain is basically constant:

[0103]

[0104] Wherein, M and N are the number of array elements in the x-direction and y-direction on each sub-array plane.

[0105] In the same sub-array cos[α i (u)] and cos[β i (u)] does not change with n and m, so the above formula can be changed to:

[0106]

[0107] Assume that the beam gain is a fixed value G0, and let

[0108]

[0109] in, is the beam pointing angle Variable number of participating beam synthesis sub-arrays.

[0110] The idea of ​​linear optimization bisection is based on solving the root of the objective function: first determine the value interval of the root of the objective function and divide the value interval into two equal parts. By judging the value of the objective function, gradually narrow the interval where the function root is distributed until the interval where the function root is distributed is small enough, and then find the approximate root that meets the accuracy requirements.

[0111] Solve equation (4) by bisection method so that The process of obtaining the beam synthesis result with beam gain G0 can be described as follows.

[0112] (1) Step 1: Verify the calculation by dividing the interval into half

[0113] Assume that at the beginning, the number of sub-arrays participating in beam synthesis is U∈[U1,U2]. In order to further narrow the value range (variation interval) of U, [U1,U2] is divided into two halves, the midpoint of the number of sub-arrays is calculated as x1=(U1+U2) / 2, and U=x1 is set to perform beam synthesis (or look up the beam synthesis result table of the relevant arrays). Then, the beam gain value G is calculated based on the beam main lobe gain value obtained by beam synthesis (table lookup result).i 。

[0114] (2) Step 2: Half - interval verification calculation

[0115] If G i < G0, then take x1 = U1, and let x2 = (x1 + U2) / 2. This step is actually a process of increasing the number of sub - arrays (moving the value of the number of sub - arrays towards the upper limit of the interval);

[0116] If G i > G0, then take x1 = U2, and let x2 = (x1 + U1) / 2. This step is actually a process of decreasing the number of sub - arrays (moving the value of the number of sub - arrays towards the lower limit of the interval);

[0117] Let U = x2 again for beam synthesis, and then according to the beam main - lobe gain value G i value obtained from beam synthesis. Iterate in turn.

[0118] (k) Step k: Half - interval verification calculation

[0119] If G i < G0, then take x k-1 = U1, and let x k = (x k-1 + U2) / 2;

[0120] If G i > G0, then take x k-1 = U2, and let x k = (x k-1 + U1) / 2;

[0121] Let U = x k for beam synthesis again, and then according to the beam main - lobe gain value G i . Until the condition: |G i - G0| ≤ ε is satisfied, where ε is the allowable deviation. At this time, the iterative calculation can be ended. Or according to the above method, when the number of iterations K is large enough, the iteration can also be ended.

[0122] Through the above iterative process based on the bisection method, the maximum - likelihood change interval of the number of sub - arrays that satisfies the main - lobe beam gain (beam width) under different scanning angles (beam pointing) can be obtained, effectively reducing the sub - array search interval without reducing the credibility of the sub - array number distribution.

[0123] According to the relationship between the number of sub - arrays required in beam synthesis of the left - hand side array of the aircraft nose and the left - hand side array of the fuselage implemented by the above bisection method and the beam - pointing angle, refer to Figure 3 and Figure 4 shown respectively.

[0124] (3) Methods to reduce the “spatial variability” of beam gain and beam shape in randomly distributed arrays

[0125] In this module, a subarray resource allocation method for optimizing beam synthesis performance is proposed. The problem that the number of array elements / subarrays required for the formed beam changes with the beam pointing angle is regarded as a problem of dynamic optimization allocation of array element / subarray resources for a randomly distributed array. Since the subarray position, array surface normal direction, occlusion and other factors of the distributed random array lead to the randomness of the subarray working state, the beam synthesis result sometimes cannot fully achieve the optimization goal. Therefore, optimization is set to be performed when the credibility of the constraint condition is not less than the pre-given confidence level.

[0126] Assume that the spatial position of the randomly distributed array elements is (x(n,m,u),y(n,m,u),z(n,m,u)). One constraint of the pattern synthesis is that the expected value of the main lobe gain is G0. Use the function Indicates the direction in which the beam points The main lobe beam gain of the directional pattern synthesized above.

[0127] use It represents the deviation of the beam gain after beam synthesis from the expected value:

[0128]

[0129] An uncertainty constraint for beam synthesis optimization can be expressed as

[0130]

[0131] In the formula, is the number of sub-arrays participating in beam synthesis; Due to the randomness of the number and position of the units participating in the pattern synthesis each time, it is impossible to repeat the experiment in large numbers to obtain its random distribution function. At this time, fuzzy variables are used to describe the number of units participating in the pattern synthesis. Affected by the fuzziness, it is believed that in beam synthesis, the sum of the number of participating units satisfies the constraint condition to a certain extent. Then, for a given confidence level α, we hope that the number of units participating in the pattern synthesis will meet the constraint condition with a credibility of no less than α and a probability of no less than β.

[0132] Therefore, the fuzzy chance constrained programming model for multi-array pattern synthesis can be obtained as follows:

[0133]

[0134] Where (α, β) is the predetermined chance measure, α, β∈[0,1] represent the credibility and probability of satisfying the constraint conditions; ch{} is the chance measure; is the calculated main lobe beam gain; α is a pre-given confidence level, which is determined according to decision needs; and θ n (u) are the normal angles of the u-th sub-array (horizontal azimuth and elevation in the array coordinate system); and θ i are the azimuth and elevation angles of the spherical coordinate system required for beam synthesis of the randomly distributed array; and θ Be are the 3dB beam widths of the array element patterns in the sub-array planes. The last two constraints describe that the angle at which the normal line of the sub-array plane participating in the beam synthesis deviates from the random array beam synthesis direction should be less than half of the beam width.

[0135] In order to effectively use equation (7) to solve the uncertainty problem in the beam synthesis of randomly distributed array antennas, the number of sub-array surfaces (or array elements) participating in beam synthesis is considered as an interval random variable, which is located in the variation interval As the beam pointing angle changes, it can be determined (or inferred) based on pre-simulation calculations. The larger the value, the higher the confidence that the constraint is satisfied. In this way, by determining the value interval and probability statistics law of the number of sub-array planes (or array elements) ξ, the uncertainty of ξ (random fuzzy performance or fuzzy random performance) can be described more accurately, and the computational complexity brought by the complex hybrid intelligent algorithm can be reduced. Figure 5 Beam synthesis results for randomly distributed arrays.

[0136] The above is only a specific embodiment of the present invention, and the present invention is described in detail. The unexplained part is a conventional technology. However, the protection scope of the present invention is not limited to this. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for suppressing spatial variability of the comprehensive characteristics of a randomly distributed array beam, characterized by: Here are the steps: Step 1: Determine the beam scanning range of the mission airspace; Step 2: Establish a mapping relationship between each sub-array coordinate system and the random array polar coordinate system, and select the sub-array that meets the beam space coverage range according to the task spatial beam scanning range and the mapping relationship; Step 3: For each beam arranged in the beam scanning range, beam synthesis is performed according to the selected sub-array surface; Step 4: Determine whether the integrated beam meets the constraint conditions. If so, proceed to step 6; otherwise, proceed to step 5. Step 5: Adjust the number and distribution of sub-arrays, re-perform beam synthesis, and return to step 4; Step 6: Output the beam synthesis result corresponding to the current beam; Step 7: Determine whether there are other beams in the beam scanning range. If so, proceed to step 3; otherwise, proceed to step 8; Step 8: Count the sub-array resource allocation results corresponding to different beams in the task.

2. The method according to claim 1, characterized in that: In step 1, the azimuth range of the mission airspace beam scanning range is The pitch angle range is [θ min ,θ max ].

3. The method according to claim 2, characterized in that: In step 2, the process is as follows: The array distribution of a typical aircraft is modeled, including eight sub-arrays, and the beam space coverage corresponding to different arrays is fixed; Select sub-arrays or sub-array combinations that overlap with the mission airspace beam coverage requirements; The entire coverage of the beam space where the task is located is achieved by scanning I beams at different times, and the beam synthesis result of the i-th (1≤i≤I) beam is optimized; The mapping relationship establishment process is as follows: First, the azimuth-elevation angle of the i-th beam in the random array polar coordinate system is Mapped to the random array rectangular coordinate system (x i ,y i ,z i ), and then transform it to the rectangular coordinate system of the sub-array surface (x i (u),y i (u),z i (u)), and finally transformed to the sub-array polar coordinate system and θ i are the azimuth and elevation angles of the beam in the random array polar coordinate system, and satisfy θ imin ≤θ i ≤θ imax ;x i ,y i 、z i are the coordinate values ​​of the random array in the rectangular coordinate system; x i (u), y i (u), z i (u) is the coordinate value of the u-th sub-array in the rectangular coordinate system; θ i (u), The azimuth-elevation angle of the u-th sub-array in the polar coordinate system; set up and θ Be are the 3dB beamwidths in azimuth and elevation of the array element pattern in the sub-array plane, respectively. If the u-th sub-array plane meets the following conditions: Then it is judged that the sub-array surface can participate in beam synthesis; make Through statistics in In different variation ranges, the number of sub-array faces that meet the condition (1) on a certain array face. When the number of sub-array faces that meet the condition is greater than the minimum sub-array face number threshold required for beam synthesis, it is determined that the beam formed by the corresponding array face can cover the spatial angle 4. The method according to claim 3, characterized in that: In step 3, the beam synthesis process is as follows: Assume that the randomly distributed array has U planar sub-arrays with normals pointing in different directions, the position of each sub-array is randomly variable, the direction of the sub-array surface normal changes with the curvature of the aircraft surface, and each sub-array surface in the U sub-arrays is a regular array composed of N*M array elements; For the planar array with constant amplitude feeding, assuming that the radar transmits narrowband signals, the array antenna elements are arranged at intervals of d = λ0 / 2, and λ0 = c / f0; If we consider that the normal direction of the u-th sub-array is When the random array composed of U sub-arrays needs to be in the beam pointing direction To form a beam, let the amplitude weight coefficient of each subarray / element of the random array be Then the antenna beam gain of the randomly distributed array can be expressed as: In the formula, is the pointing angle of the formed beam, which is the angle in spherical coordinates; U is the number of sub-array planes; M and N are the number of rows and columns of array elements in each sub-array plane, respectively; For the beam forming direction The weighting coefficients related to the sub-array surface u, the array element numbers m and n in the sub-array; α(u) and β(u) are the normal directions of the u-th sub-array. and θ(u)), The two associated angle parameters represent the random array in the direction of beam formation. Transform to the azimuth and elevation angles in the u-th sub-array; For the array antenna pattern design implemented by beam synthesis method, the effect of side lobes on beam gain cannot be ignored; If the normalized amplitude of the main lobe of the beam is a0, the normalized amplitude of the P larger side lobe levels is a p (p=1,2,…,P); then the main lobe reception and radiation efficiency of the array antenna can be expressed as η p : In the same sub-array Since cos[α(u)] and cos[β(u)] do not change with n and m, equation (2) can be expressed as:

5. The method according to claim 4, characterized in that: In step 4, the constraints include: 1) The beam gain of the random array beam synthesis is not less than G0; 2) The maximum main-sidelobe ratio of random array beam synthesis is not less than a p0 .

6. The method according to claim 5, characterized in that: In step 5, the process of adjusting the number of sub-array surfaces is as follows: When determining the number of sub-arrays participating in the synthesis, the constraint condition of the allowable value of the variation range of beam gain is used as the verification and judgment condition, and the search interval of variables such as the number of sub-arrays participating in the beam synthesis is reduced by combining the dichotomy method to reduce the amount of algorithm calculation; Assume that the beam gain is a fixed value G0, as follows: in, is the beam pointing angle The number of sub-arrays participating in beam synthesis varies; Solve equation (4) by bisection method so that The process of obtaining the beam synthesis result with beam gain G0 can be described as follows: (1) Step 1: Verify the calculation by dividing the interval into half Assume that at the beginning, the number of sub-arrays participating in beam synthesis is U∈[U1,U2]. To further narrow the value range of U, divide [U1,U2] in half, calculate the midpoint of the number of sub-arrays x1=(U1+U2) / 2 and set U=x1 to perform beam synthesis, or look up the beam synthesis result table of the relevant arrays, and then calculate the beam gain value G according to the beam main lobe gain value obtained by beam synthesis or table lookup results. i ; (2) Step 2: Verify the calculation by dividing the interval into half If G i <G0, then take x1 = U1, and let x2 = (x1 + U2) / 2; If G i >G0, then take x1=U2, let x2=(x1+U1) / 2; Let U = x2 again to perform beam synthesis, and then the beam main lobe gain value G obtained by beam synthesis is i Numerical value; iterate in sequence; (k) Step k: Half-interval verification calculation If G i < G0, then take x k-1 = U1, let x k =(x k-1 + U2) / 2; If G i >G0, then take x k-1 =U2, let x k =(x k-1 +U1) / 2; Let U = x k Perform beam synthesis, and then obtain the beam main lobe gain value G according to the beam synthesis i ; Until the condition is met: |G i -G0|≤ε, or when the number of iterations K exceeds the set value, the iterative calculation ends; Through the above iterative process based on the dichotomy method, the maximum possible variation range of the number of sub-array surfaces that meets the main lobe beam gain under different scanning angle conditions can be obtained, and the sub-array surface search interval can be effectively reduced without reducing the credibility of the sub-array surface number distribution; Due to the randomness of the working state of the sub-array surface position and the normal direction of the array surface of the randomly distributed array, the beam synthesis result sometimes cannot completely achieve the optimization goal. Therefore, the beam synthesis result is optimized when the credibility of the constraint condition is not less than the pre-given confidence level; the problem of the beam forming a specified gain changing with the beam pointing angle in this step is regarded as a problem of dynamic optimization allocation of sub-array surface resources of a randomly distributed array and solved; use It represents the deviation of the beam gain after beam synthesis from the expected value: An uncertainty constraint for beam synthesis optimization can be expressed as In the formula, is the number of sub-arrays involved in beam synthesis; Fuzzy variables are used to describe the number of units involved in pattern synthesis; For a pre-given confidence level α, it is hoped that the number of sub-array planes / array elements participating in the pattern synthesis meets the constraint condition with a reliability of not less than α and a probability of not less than β; Therefore, the fuzzy chance constrained programming model for multi-array pattern synthesis is obtained as follows: Where α, β∈[0,1] are predetermined chance measures, representing the credibility and probability of satisfying the constraints; ch{} is the chance measure; In order to effectively use equation (7) to solve the uncertainty problem in the beam synthesis of randomly distributed array antennas, the number of sub-array planes or array elements involved in beam synthesis ξ is taken as an interval random variable, and its variation interval is Changes with the beam pointing angle, determined according to the pre-simulation calculation; interval The larger it is, the higher the confidence that the constraints are met; exist In the interval, ξ is a random variable that satisfies a certain probability distribution; in this way, by determining the value interval and probability statistical law of the number of sub-array surfaces ξ, the uncertainty of ξ is described, and the computational complexity brought by the complex hybrid intelligent algorithm is reduced.

7. The method according to claim 6, characterized in that: In step 6, the parameters included in the output result are as follows: 1) Number of sub-arrays involved in beam synthesis 2) Beam main lobe gain value G i ; 3) The credibility α and probability β of the beam synthesis result.

8. The method according to claim 7, characterized in that: In step seven, the constraints include: the beam number i is equal to the total number of beams I in the mission airspace coverage range, i=I.

9. The method according to claim 8, characterized in that: In step eight, the output is as follows: 1) Beam pointing of different beams in the mission airspace coverage; 2) beam gain for each beam pointing; 3) The number and distribution of required sub-array surfaces corresponding to each beam; 4) The credibility and probability of each beam.

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