A testing method and system for phased array antenna of satellite portable station

Abstract

This application relates to the field of phased array antenna testing, and more particularly to a method and system for testing phased array antennas for portable satellite stations. The system establishes a historical particle library containing all initial particles, adjusts the velocity update formula in a particle swarm optimization algorithm, iteratively updates the historical particle library, and updates the positions of newly added particles to the historical particle library during iteration. This application reduces the occurrence of unreasonable array element structures during particle search, thereby reducing experimental costs.

Description

Technical Field

[0001] The present application relates to the field of phased array antenna testing, and in particular to a method and system for testing a phased array antenna of a portable satellite station. Background Art

[0002] The portable satellite station is a mobile satellite communication terminal used in emergency communications, field operations and other fields. The phased array antenna of the portable satellite station can quickly align with the satellite by electronically controlling the beam direction, enhancing signal stability, and can effectively adapt to complex environments. To ensure the reliable performance of the phased array antenna and guarantee the communication quality meets the standards in extreme environments, the phased array antenna needs to be tested. Among them, the darkroom test can shield electromagnetic interference and reflected signals, providing a pure environment for accurately measuring the antenna radiation characteristics and parameters.

[0003] In existing technologies, particle swarm optimization (PSO) can be used to find phased array configurations that maximize antenna gain. A well-designed array element structure can significantly improve antenna gain, which is crucial for enhancing the communication performance of portable satellite stations in complex environments. PSO, an optimization algorithm based on swarm intelligence, simulates the social behavior of flocks of birds or schools of fish to find the optimal solution. This algorithm can quickly identify the optimal parameter combination. Its powerful nonlinear optimization capabilities ensure that the optimal solution is found among a large number of possible parameter configurations, effectively shortening product development cycles.

[0004] The traditional particle swarm algorithm updates the particle positions based on the optimal solution, which can easily lead to unreasonable array element structures during the iteration process. This is because the particle position update is overly dependent on the global optimal solution, which may cause particles to gather in the local optimal area too early, thus falling into the local optimal solution and being difficult to escape, resulting in low particle convergence efficiency. The experimental cost of adjusting the array element structure and retesting is high, which will cause unnecessary overhead. Summary of the Invention

[0005] In order to solve the technical problem of how to reduce repeated phased array antenna testing caused by unreasonable array element structure, the present application provides a testing method and system for a phased array antenna of a satellite portable station.

[0006] In a first aspect, the present application provides a method for testing a phased array antenna of a portable satellite station, which adopts the following technical solution:

[0007] A method for testing a phased array antenna of a portable satellite station comprises the following steps: establishing a historical particle library containing all initial particles; adjusting a velocity update formula in a particle swarm optimization algorithm; iteratively updating the historical particle library; updating the positions of newly added particles to the historical particle library during iteration; adjusting the velocity update formula by: setting an objective function and constraint conditions according to a directional pattern of a phased array antenna after testing, wherein a solution space of the objective function contains the velocity and position of historical particles, and the objective function value is the fitness of the historical particles; calculating a similarity index between a position in the solution space and the historical particles; and calculating the similarity index and the fitness of a position in the solution space. The repulsion coefficient component under the influence of historical particles; the mean of the repulsion coefficient components of each historical particle is used as the repulsion coefficient; all solution space positions whose Manhattan distance to the historical particle in the solution space is less than or equal to the preset radius are used as similar positions, the direction of all similar positions of the historical particle and the historical particle is used as the vector direction, the repulsion coefficient of all similar positions of the historical particle is used as the modulus of the vector, and the sum of the repulsion vectors of all similar positions of the historical particle on the historical particle is used as the change in the velocity repulsion force; the change in the velocity repulsion force is used as a factor and accumulated with the velocity update formula of the particles to be iterated in the particle swarm algorithm as the final velocity update formula.

[0008] The beneficial effects are: by calculating the similarity between the position in the solution space and the historical particles, the speed of the particles is dynamically adjusted to avoid areas similar to the historical particles with low fitness. By adjusting the speed update formula, introducing the historical particle library and the exclusion coefficient mechanism, the particles are prevented from searching for unreasonable array element structures, reducing the risk of particles converging to the local optimal solution, improving the global search capability and optimization efficiency of the algorithm, reducing repeated tests caused by unreasonable array element structures, avoiding unnecessary experimental overhead, and thus effectively reducing experimental costs.

[0009] Optionally, the rejection coefficient is calculated as: Where, is the repulsion coefficient of the ith position in the solution space; is the similarity index between the i-th position in the solution space and the j-th historical particle; is the fitness value of the jth historical particle; represents the total number of historical particles; is an exponential function; Represents the repulsion coefficient component of the i-th position in the solution space under the influence of the j-th historical particle.

[0010] The beneficial effects are: it provides a method to quantify the repulsion coefficient. The higher the fitness of the historical particle, the smaller its repulsion effect on the solution space position. On the contrary, historical particles with low fitness will significantly increase the repulsion coefficient, causing the particles to stay away from these unreasonable areas. The higher the similarity between a certain position in the solution space and the historical particles, the stronger the repulsion effect it will receive. As the number of particles recorded in the historical particle library increases, although the repulsion contribution of each historical particle to the solution space position will accumulate, the final result will be divided by the total number of historical particles, so the repulsion coefficient will tend to be stable. This means that even if the historical particle library continues to expand, the repulsion coefficient of the solution space position will not increase indefinitely, thereby ensuring the stability and reliability of the algorithm.

[0011] Optionally, the rejection coefficient is calculated as: Where, is the repulsion coefficient of the i-th position in the solution space; is the similarity index between the i-th position in the solution space and the j-th historical particle; is the fitness value of the jth historical particle; represents the total number of historical particles; Represents the repulsion coefficient component of the i-th position in the solution space under the influence of the j-th historical particle.

[0012] The beneficial effect is: providing another method for quantifying the rejection coefficient, introducing The purpose is to make the formula remain numerically stable when the fitness value is close to zero, avoiding division by zero errors. The growth rate of is slower, and the algorithm will not overly exclude certain areas, thus reducing the risk of falling into a local optimal solution.

[0013] Optionally, the similarity index calculation formula is:

[0014] Where, is the similarity index between the i-th position in the solution space and the j-th historical particle; is the mth value of the lth characteristic sequence of the kth array element at the ith position in the solution space; is the jth historical particle The mth value of the lth characteristic sequence of the array element; is the length of the characteristic sequence; is the total number of preset array elements; The function is to take the maximum value; is an exponential function; wherein, the vector angle between any array element and its adjacent array element is calculated, and the sequence formed by arranging the angles is used as the first characteristic sequence; the vector modulus length ratio between any array element and its adjacent array element is calculated, and the ratio is arranged as the second characteristic sequence.

[0015] The beneficial effects are: Indicates the relationship between the kth array element at the i-th position in the solution space and the j-th historical particle The similarity of the array elements is that when the kth array element at the i-th position in the solution space is similar to the j-th historical particle The closer the characteristic sequences of the elements are in each sequence number, the closer the kth element in the i-th position of the solution space is to the jth historical particle. The higher the similarity of the elements in the solution space, the higher the similarity of the elements in the jth historical particle; when there are more elements with high similarity among all the elements in the i-th position of the solution space in all the elements in the j-th historical particle, the higher the similarity index between the i-th position of the solution space and the j-th historical particle.

[0016] Optionally, the similarity index is calculated as:

[0017] Where, is the similarity index between the i-th position in the solution space and the j-th historical particle; is the mth value of the lth characteristic sequence of the kth array element at the ith position in the solution space; is the jth historical particle The mth value of the lth characteristic sequence of the array element; is the length of the characteristic sequence; is the total number of preset array elements; The function is to take the maximum value; is an exponential function, and a represents a hyperparameter. The vector angle between any array element and its adjacent elements is calculated, and the sequence formed by arranging the angles is used as the first feature sequence. The vector modulus length ratio between any array element and its adjacent elements is calculated, and the ratio is arranged as the second feature sequence.

[0018] The beneficial effect is: by introducing configurable constants , users can adjust the accuracy of similarity calculation according to specific application scenarios and optimization problems, thereby improving the adaptability and optimization efficiency of the algorithm.

[0019] Optionally, the objective function is set by: obtaining a radiation pattern of the phased antenna after testing; using the inverse of the sum of the sidelobe level and the beamwidth in the radiation pattern of the phased antenna as the objective function; and maximizing the objective function as the optimization target.

[0020] Optionally, the testing process of the phased array antenna is as follows: a darkroom test is performed on the phased array antenna, the darkroom is provided with a signal source and a turntable, the distance between the signal source and the turntable is set to meet the preset far-field conditions, the phased array antenna is installed on the turntable, the rotation of the antenna under test is controlled by controlling the turntable, the received signal strength of the antenna under test at each rotation angle is recorded, and the radiation pattern of the phased array antenna is obtained after the test is completed.

[0021] Optionally, the constraint condition is: the array element position is less than or equal to the minimum fit distance of a preset number of unit array elements in space; the preset range is used as the array position range, and the array position range is the area where all array elements are distributed in space.

[0022] The beneficial effect is: by setting the minimum fitting distance of the array element position and the array position range, the electromagnetic coupling problem between the array elements is avoided, while meeting the portability requirements, ensuring the feasibility and practicality of the optimized phased array antenna in actual applications.

[0023] Optionally, the array position range is a circular area with a preset radius.

[0024] The beneficial effects are: setting the array position range to a circular area with a preset radius simplifies the setting of constraint conditions, facilitates calculation and implementation, and meets the layout requirements of phased array antennas in practical applications.

[0025] In a second aspect, the present application provides a test system for a phased array antenna of a satellite portable station, which adopts the following technical solution:

[0026] A test system for a phased array antenna of a portable satellite station includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the test method for the phased array antenna of the portable satellite station is implemented.

[0027] The beneficial effect is that the test method of the phased array antenna of the satellite portable station is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0028] The present application has the following technical effects: by adjusting the speed update formula, introducing a historical particle library and a rejection coefficient mechanism, particles are prevented from searching for unreasonable array element structures, the risk of particles converging to local optimal solutions is reduced, the global search capability and optimization efficiency of the algorithm are improved, repeated tests caused by unreasonable array element structures are reduced, unnecessary experimental expenses are avoided, and thus experimental costs are effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a method for adjusting a speed update formula in a method for testing a phased array antenna of a satellite portable station according to an embodiment of the present application.

[0030] Figure 2 This is a flow chart of a method for setting an objective function in a method for testing a phased array antenna of a satellite portable station according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] The present application discloses a method for testing a phased array antenna of a portable satellite station, the method comprising the steps of establishing a historical particle library containing all initial particles, adjusting the speed update formula in the particle swarm optimization algorithm, iteratively updating the historical particle library, and updating the positions of newly added particles to the historical particle library during iteration. Figure 1 The speed update formula adjustment method includes steps S10 to S12, which are as follows:

[0032] S10: Setting the objective function and constraints according to the radiation pattern of the phased antenna after the test.

[0033] Specifically, refer to Figure 2 , the method for setting the objective function includes steps S100 and S101:

[0034] S100: Obtaining a directional pattern of the phased-control antenna after the test.

[0035] The phased array antenna was tested in a darkroom. Absorbent material was applied to the walls of the darkroom to prevent echo signals from interfering with the test results. The darkroom was equipped with a signal source and a turntable. The distance between the two was set to meet the preset far-field conditions. The phased array antenna was mounted on the turntable, and the turntable's rotation drove the phased array antenna. The turntable controlled the rotation of the antenna under test, and the received signal strength of the antenna under test was recorded at each rotation angle.

[0036] In one embodiment, the test parameters are: far-field condition of 70m, signal source frequency of 10GHz, power of 0dBm, turntable rotation range of -90° to +90°, and turntable step angle of 1°. The test parameters can be adjusted according to the actual application scenario and are not detailed here.

[0037] In the phased array antenna test of the satellite portable station, the measurement results are given in the form of a radiation pattern. The radiation pattern specifically includes the radiation intensity of the antenna at different elevation angles and azimuth angles. The radiation intensity is usually expressed in dB (decibel).

[0038] S101: The inverse of the sum of the sidelobe level and the beamwidth in the phased antenna's pattern is used as the objective function, and maximizing the objective function is the optimization goal. The solution space of the objective function contains the velocity and position of the historical particles, and the objective function value is the fitness of the historical particles.

[0039] In one embodiment, the sidelobe level and beamwidth are extracted from the antenna pattern. The sidelobe level is specifically the ratio of the maximum sidelobe level to the mainlobe level in the antenna pattern, and the beamwidth is the angular range of the mainlobe between two specific directions, typically the half-power beamwidth. The inverse of the sum of the sidelobe level and the beamwidth is used as the objective function, and maximizing the objective function is the optimization goal.

[0040] Low sidelobe level can reduce interference and improve communication quality, so minimizing the sidelobe level can be used as an optimization goal. Since narrow beamwidth can improve the directivity and resolution of the antenna, minimizing the beamwidth can be used as an optimization goal.

[0041] To ensure the rationality and practicality of the test results, constraints are required, such as the minimum spacing between array elements (to avoid electromagnetic coupling) and the array position range (to meet portability requirements). Specifically, the constraints are: the array element position must be less than or equal to the minimum spatially adaptable distance between a preset number (e.g., two) of unit elements; the preset range is used as the array position range, which is the area in which all array elements are distributed in space. The array position range is a circular area with a preset radius (e.g., 1 meter), but other shapes are also possible and will not be detailed here. The calculation of the minimum adaptable distance is based on existing techniques and will not be described here.

[0042] The initial particle position can determine the search process. In order to ensure the diversity of particles and avoid falling into local optimality, the entire solution space should be covered as much as possible when generating initial particles, so particle initialization is required. Specifically, the array position range is used as the selection range of each array element position, and the minimum movable distance of the array element is 1cm. Under the condition of meeting the constraints, the position of each array element is randomly selected in the selection range to construct the initial particle, and the total number of array elements is preset. , randomly generated in the solution space Initial particles are generated, and the objective function value of each particle is recorded as the fitness value of each particle. For example, particle positions: (0.01, 0.06), (0.52, 0.22), ..., (0.00, 0.91), where (0.01, 0.06) represents the spatial position of the first array element, and so on. The position of a single particle includes the spatial positions of all array elements.

[0043] Since the existing particles in the iterative process represent the tested phased array arrangement schemes, in order to facilitate the subsequent guidance of particle velocity updates based on the tested schemes and their fitness, a historical particle library needs to be established.

[0044] Specifically, a historical particle library is initially established that contains all initial particles. During iteration, the positions of newly added particles are updated to the historical particle library, and the total number of historical particles in the current historical particle library is obtained.

[0045] For example, there are 50 particles initially, all of which are included in the historical particle library. After the first round of iteration, the positions of 2 particles remain unchanged, and 48 particles appear in new positions. The positions of these 48 particles are included in the historical particle library, and the fitness values ​​are also recorded.

[0046] At this point, the particle initialization is completed and the historical particle library is obtained.

[0047] S11: Calculate the similarity index between the position in the solution space and the historical particles, and calculate the repulsion coefficient component of the position in the solution space under the influence of the historical particles based on the similarity index and fitness; take the average of the repulsion coefficient components of each historical particle as the repulsion coefficient.

[0048] Since the array element structure and fitness value in the historical particle library are known, in order to avoid particles searching for unreasonable array element structures, the solution space positions with array element structures similar to the array element structures corresponding to historical particles can be identified. Based on the fitness values ​​of historical particles, the solution space areas that are unnecessary to search are identified, and higher repulsive forces are given to such areas to prevent particles from moving to such areas.

[0049] When measuring whether the array element structure is similar, it is only necessary to meet the requirements of scale invariance and rotation invariance, that is, to ensure that the positional relationship between each array element and other array elements in the array element structure corresponding to the solution space position is similar to that of the array element structure corresponding to the historical particle. Therefore, it is necessary to first obtain the distance between each array element and the adjacent array element in the array element structure.

[0050] Specifically, the Euclidean distance between any two different array elements in the array element structure corresponding to the solution space and the array element structure corresponding to the historical particles is obtained as the distance between the two array elements. The number of empirical neighbors is set to A = 5, and the A array elements with the smallest distance to each array element are selected as the neighboring array elements of each array element.

[0051] In order to meet the requirements of scale invariance and rotation invariance, when searching for similar array elements for a single array element in the solution space position among all array elements at another particle position, it should be ensured that the angles between any two vectors of all vectors of the array element and its adjacent array elements are similar and the ratios of the vector moduli are similar.

[0052] Specifically, the vectors of each array element and any adjacent array element in the array element structure corresponding to each position in the solution space and all historical particle positions are obtained as the adjacent vector set of each array element, the angle and module length ratio of any two vectors in the adjacent vector set of each array element are obtained, and the vectors are arranged from small to large according to the size of the angle, wherein the vector angle between any array element and the adjacent array element is calculated, and the sequence formed by the arrangement of the angles is used as the first characteristic sequence; the vector module length ratio between any array element and the adjacent array element is calculated, and the ratios are arranged as the second characteristic sequence.

[0053] In one embodiment, the similarity index calculation formula between each position in the solution space and each historical particle is:

[0054] Where, is the similarity index between the i-th position in the solution space and the j-th historical particle; is the mth value of the lth characteristic sequence of the kth array element at the ith position in the solution space; is the jth historical particle The mth value of the lth characteristic sequence of the array element; is the length of the characteristic sequence; is the total number of preset array elements; The function is to take the maximum value; is an exponential function.

[0055] Indicates the relationship between the kth array element at the i-th position in the solution space and the j-th historical particle The similarity of the array elements is that when the kth array element at the i-th position in the solution space is similar to the j-th historical particle The closer the characteristic sequences of the elements are in each sequence number, the closer the kth element in the i-th position of the solution space is to the jth historical particle. The higher the similarity of the elements in the solution space, the higher the similarity of the elements in the jth historical particle; when there are more elements with high similarity among all the elements in the i-th position of the solution space in all the elements in the j-th historical particle, the higher the similarity index between the i-th position of the solution space and the j-th historical particle.

[0056] When the similarity index between the position in the solution space and the historical particles with low fitness values ​​is high, the position in the solution space is more likely to be a position with unreasonable array structure, and it is more necessary to avoid particles searching these areas.

[0057] In one embodiment, in order to further improve the accuracy of similarity calculation and make the algorithm more flexible to adapt to different optimization problems, the factor Replace with , Is a positive real number used to control the sensitivity of similarity calculation. The value of can change the response degree of similarity calculation to the difference of feature sequences. When is larger, the similarity calculation is more sensitive to the slight differences in the feature sequence, thus different array element structures can be distinguished more accurately. When it is small, the similarity calculation is less sensitive to the difference in feature sequences, which helps the algorithm explore the solution space globally. , users can adjust the accuracy of similarity calculation according to specific application scenarios and optimization problems, thereby improving the adaptability and optimization efficiency of the algorithm.

[0058] In one embodiment, the calculation formula of the rejection coefficient is:

[0059] Where, is the repulsion coefficient of the i-th position in the solution space; is the similarity index between the i-th position in the solution space and the j-th historical particle; is the fitness value of the jth historical particle; represents the total number of historical particles; is an exponential function.

[0060] Represents the repulsion coefficient component of the i-th position in the solution space under the influence of the j-th historical particle. When the fitness value of the j-th historical particle is low, and the similarity index between the i-th position in the solution space and the j-th historical particle is high, the repulsion coefficient component of the i-th position in the solution space under the influence of the j-th historical particle is higher.

[0061] exist When it is small (i.e., the fitness value is low), it will increase significantly, making Approaching 1. This means that when the fitness value of a historical particle is low, its contribution to the exclusion coefficient of the solution space position is greater. This property enables the algorithm to more effectively avoid areas similar to low-fitness particles, thereby reducing the occurrence of inappropriate solutions.

[0062] In one embodiment, the calculation formula of the rejection coefficient can also be:

[0063] Where, is the repulsion coefficient of the ith position in the solution space; is the similarity index between the i-th position in the solution space and the j-th historical particle; is the fitness value of the jth historical particle; Represents the total number of historical particles.

[0064] Represents the repulsion coefficient component of the i-th position in the solution space under the influence of the j-th historical particle. The purpose is to make the formula remain numerically stable when the fitness value is close to zero, avoiding division by zero errors. The growth rate of is slower, and the algorithm will not overly exclude certain areas, thus reducing the risk of falling into a local optimal solution.

[0065] S12: All solution space positions whose Manhattan distance to the historical particle in the solution space is less than or equal to the preset radius are considered as close positions. The direction of all close positions of the historical particle and the historical particle is used as the vector direction, and the repulsion coefficient of all close positions of the historical particle is used as the vector modulus. The sum of the repulsion vectors of all close positions of the historical particle on the historical particle is used as the change in the velocity repulsion force. The change in the velocity repulsion force is used as a factor and added to the particle swarm algorithm's velocity update formula for the next iteration to form the final velocity update formula.

[0066] In the traditional particle swarm algorithm, each particle represents a solution vector, and the velocity represents the direction and speed of the particle in the solution space. Each particle records its own optimal position, and the entire swarm records the global optimal position. This application improves the traditional particle swarm algorithm without changing the position update formula of the traditional particle swarm algorithm. The change in the velocity repulsion is added to the calculation of the velocity update formula. The change in the velocity repulsion is the overall repulsion of the target particle in the neighborhood solution space, which has direction and magnitude. The velocity is adjusted by component in each dimension. The position update formula and velocity update formula of the traditional particle swarm algorithm are both prior art and will not be described in detail here.

[0067] Set the radius R=0.2, obtain all solution space positions whose Manhattan distance to each particle in the solution space is less than or equal to the empirical radius as the close positions, take the direction of all close positions of each particle and each particle as the vector direction, take the repulsion coefficient of all close positions of each particle as the modulus of the vector, obtain the sum of the repulsion vectors of all close positions of each particle on each particle as the change in velocity due to repulsion, and add the change in velocity due to repulsion to the calculation of the traditional particle swarm algorithm velocity update formula to obtain the particle velocity update strategy with repulsion.

[0068] The particle velocity update strategy with repulsive force is applied to the particle swarm algorithm. In each round of iteration, the particle velocity update strategy with repulsive force is updated according to the particle velocity update strategy with repulsive force (position change is less than the position change threshold, aggregation is higher than the aggregation threshold, and rounds are higher than the round threshold. For example, the position change threshold is 10 -4 =m, the aggregation threshold is 0.95, and the round threshold is 500 times. The above thresholds can be customized by the user and are not described here. Obtain the phased array arrangement corresponding to the position of U=3 particles with the highest fitness value.

[0069] In one embodiment, the optimal phased array arrangement scheme adjusted through darkroom testing may be affected by factors such as geomagnetic noise and environmental interference during actual application, and may not necessarily represent the most suitable phased array arrangement scheme for the application. Therefore, R&D personnel are required to further conduct actual tests on the obtained scheme and further improve and optimize it according to application requirements. The phased array arrangement schemes are provided to R&D personnel for actual testing, and the phased array arrangement scheme that best suits the actual application is selected based on the test results. At the same time, R&D personnel can fine-tune the existing phased array arrangement scheme according to scenario requirements.

[0070] An embodiment of the present application also discloses a testing system for a phased array antenna of a satellite portable station, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a testing method for the phased array antenna of the satellite portable station according to the present application is implemented.

[0071] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0072] In this application, the aforementioned memory can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connected to the device.

[0073] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for testing a phased array antenna of a satellite portable station, characterized in that: Including steps: Establish a historical particle library containing all initial particles, adjust the speed update formula in the particle swarm optimization algorithm, iteratively update the historical particle library, and update the positions of newly added particles to the historical particle library during iteration; the adjustment method of the speed update formula is: The objective function and constraints are set based on the radiation pattern of the phased antenna after the test. The solution space of the objective function includes the velocity and position of the historical particles, and the objective function value is the fitness of the historical particles. Calculate the similarity index between the position in the solution space and the historical particles, and calculate the repulsion coefficient component of the position in the solution space under the influence of the historical particles based on the similarity index and fitness; take the average of the repulsion coefficient components of each historical particle as the repulsion coefficient; The calculation formula of the rejection coefficient is: Where, is the repulsion coefficient of the ith position in the solution space; is the similarity index between the i-th position in the solution space and the j-th historical particle; is the fitness value of the jth historical particle; represents the total number of historical particles; is an exponential function; Represents the repulsion coefficient component of the i-th position in the solution space under the influence of the j-th historical particle; The calculation formula of similarity index is: Where, is the mth value of the lth characteristic sequence of the kth array element at the ith position in the solution space; is the jth historical particle The mth value of the lth characteristic sequence of the array element; is the characteristic sequence length; is the total number of preset array elements; The function is to take the maximum value; is an exponential function, and a represents a hyperparameter. The vector angle between any array element and its adjacent elements is calculated, and the sequence formed by arranging the angles is used as the first feature sequence. The vector modulus length ratio between any array element and its adjacent elements is calculated, and the ratio is arranged as the second feature sequence. All solution space positions whose Manhattan distance to the historical particle in the solution space is less than or equal to the preset radius are regarded as close positions, the direction of all close positions of the historical particle and the historical particle is regarded as the vector direction, the repulsion coefficient of all close positions of the historical particle is regarded as the modulus of the vector, and the sum of the repulsion vectors of all close positions of the historical particle on the historical particle is regarded as the change in the velocity repulsion force; The change in velocity due to repulsive force is taken as a factor and added to the particle velocity update formula for the next iteration in the particle swarm algorithm to be used as the final velocity update formula.

2. The method for testing a phased array antenna of a portable satellite station according to claim 1, wherein: The objective function is set as follows: obtaining the radiation pattern of the phased antenna after the test; taking the inverse of the sum of the sidelobe level and the beam width in the radiation pattern of the phased antenna as the objective function; and maximizing the objective function as the optimization goal.

3. The method for testing a phased array antenna of a portable satellite station according to claim 2, wherein: The testing process of the phased array antenna is as follows: the phased array antenna is tested in a darkroom. The darkroom is equipped with a signal source and a turntable. The distance between the signal source and the turntable is set to meet the preset far-field conditions. The phased array antenna is installed on the turntable. The rotation of the antenna under test is controlled by controlling the turntable. The received signal strength of the antenna under test at each rotation angle is recorded. After the test is completed, the radiation pattern of the phased array antenna is obtained.

4. The method for testing a phased array antenna of a portable satellite station according to claim 1, wherein: The constraints are: the array element position is less than or equal to the preset minimum fit distance of unit array elements in space; the preset range is used as the array position range, and the array position range is the area where all array elements are distributed in space.

5. The method for testing a phased array antenna of a portable satellite station according to claim 4, wherein: The array position range is a circular area with a preset radius.

6. A test system for a phased array antenna of a portable satellite station, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for testing the phased array antenna of the satellite portable station according to any one of claims 1 to 5 is implemented.

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