Antenna pattern integrated platform and method based on adaptive training

Abstract

The invention discloses an antenna pattern comprehensive platform and method based on adaptive training. The platform comprises an antenna array plane, a display control module and a signal acquisition and processing module. Wherein the display control module can control the darkroom turntable, the display control module is integrated with an optimization algorithm module, the signal processing module transmits a test result to the display control module in real time, and the optimization algorithm module optimizes and generates a new feed amplitude phase weight according to the test result. The new feed amplitude and phase weight is issued to the signal processing module through the display control module for beam forming of antenna array surface testing, one-time optimization iteration is completed, the optimized testing result is fed back to the optimization algorithm module, and a new generation of feed weight is obtained through optimization for testing until the optimization target or the upper limit of the number of iterations is achieved. According to the method, the antenna pattern data is acquired online in real time, the technology is optimized in real time, the links of calibration, manual parameter adjustment and the like are avoided, and high-performance and high-efficiency antenna array synthesis is realized.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and particularly relates to an antenna pattern synthesis platform based on adaptive training. Background Technology

[0002] In modern wireless communication, radar, and radio frequency systems, antenna array technology is widely used due to its excellent performance. However, the design and optimization of antenna arrays is a complex systems engineering project involving multiple disciplines. Traditional antenna array testing and optimization processes typically employ manual intervention, using optimization algorithms to simulate the feed weights of each element in the antenna array, and then testing with these weights to achieve the expected performance indicators. This approach is not only inefficient, but also prone to amplitude and phase errors due to complex electromagnetic and hardware environments, such as channels, cables, and interfaces. This results in significant discrepancies between measured and simulated results, making it difficult to meet the high-precision requirements of array performance indicators. The main problems are as follows: Low system integration: The existing antenna integrated system is isolated in simulation and testing. The simulation system and the testing system operate independently and have poor collaborative capabilities, making it difficult to form a closed-loop optimization system for simulation and testing. High channel amplitude and phase accuracy requirements: Existing optimization methods use simulation optimization to obtain channel weights and then calibrate the channel amplitude and phase to achieve accurate weight configuration and achieve the positive design of the desired radiation pattern. However, due to the radiation pattern error of the test unit involved in the optimization and the channel amplitude and phase error, it is difficult to meet the low sidelobe index requirements. The optimization algorithms used in existing systems are mostly based on fixed patterns, lacking adaptability to complex scenarios and easily getting trapped in local optima. Insufficient real-time performance: There is a significant time delay between data acquisition, processing, and optimization adjustments in the existing system, which affects optimization efficiency; High demand for manual intervention: Existing systems still require a significant amount of manual intervention during testing, making it difficult to achieve truly unattended operation; Poor environmental adaptability: The signal flow from the antenna array to the computer that displays the test data has amplitude and phase errors caused by channels, cables, turntables and environment, which will result in a large gap between the simulation optimization and the actual measurement results. The applicant found that the root cause of the above problems lies in the fact that the existing system fails to achieve efficient collaboration between the antenna array, test equipment, optimization algorithm and control system, and lacks an intelligent real-time feedback optimization mechanism for test results. As a result, when faced with complex electromagnetic environments and high precision requirements, the existing system often exhibits defects such as slow optimization speed, low accuracy and poor adaptability, making it difficult to meet the high standards of modern communication systems for antenna array performance. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies by providing an antenna pattern synthesis platform and method based on adaptive training. The platform and method synchronously call up the optimization algorithm module, signal acquisition and processing module, antenna array and anechoic chamber turntable through the display control module. By acquiring antenna pattern data online in real time and optimizing it in real time, the platform avoids calibration and manual parameter adjustment, thereby achieving high-performance and high-efficiency antenna array synthesis.

[0004] The objective of this invention is achieved through the following technical solution: An antenna pattern synthesis platform based on adaptive training includes: The antenna array is mounted on a turntable in an anechoic chamber. The display control module is used to display the test process and test results. The signal acquisition and processing module acquires and processes the radiation characteristics data of the antenna array in real time. The display control module controls the anechoic chamber turntable and integrates an optimization algorithm module. The signal processing module transmits the test results to the display control module in real time. The optimization algorithm module generates new feed amplitude and phase weights based on the test results. The new feed amplitude and phase weights are sent to the signal processing module through the display control module for beamforming of the antenna array test, completing one optimization iteration. The optimized test results are then fed back to the optimization algorithm module to optimize a new generation of feed weights for testing, until the optimization target or the upper limit of the number of iterations is reached.

[0005] In one embodiment, each antenna element in the antenna array has an independent radio frequency interface, and each antenna element is connected to the signal acquisition and processing module via a radio frequency cable.

[0006] In one embodiment, the signal acquisition and processing module performs filtering, analog-to-digital conversion, and beamforming on the acquired radiation characteristic data.

[0007] In one embodiment, the signal acquisition and processing module communicates with the display control module via a serial port and operates synchronously via the synchronization pulse of the anechoic chamber turntable.

[0008] In one embodiment, a DC regulated power supply for powering the signal acquisition and processing module is also included.

[0009] This invention also provides an antenna pattern synthesis method based on adaptive training, which, based on the aforementioned antenna pattern synthesis platform, includes the following steps: Set the population size, number of iterations, and rotation range of the darkroom turntable; The initial population is generated using an optimization algorithm; The display control module transmits the initial population weights to the signal processing module and controls the turntable to rotate to test and collect the orientation map corresponding to the number of population weights; The radiation pattern data of the population size is sent back to the display control module, and the optimization algorithm optimizes the next generation of population based on the main-to-secondary ratio of the radiation pattern and its corresponding amplitude and phase weights. The test pattern for the next generation of the population is then obtained, and this process is repeated until the ratio of primary to secondary patterns in the test pattern is better than 40 dB.

[0010] In one implementation, the population size is set to 100 and the number of iterations is set to 200.

[0011] In one implementation, the rotation range of the darkroom turntable is +90° to -90°.

[0012] In one implementation, the optimization algorithm used is the Harris Eagle improved algorithm, and the initial 100 populations are set with weights obtained from theoretical Chebyshev distribution, Taylor distribution and simulation optimization results based on unit measured data.

[0013] In one implementation, the signal acquisition and processing module utilizes time-division multiplexing and simultaneous multi-beaming to simultaneously call five RAM registers to process five radiation patterns as the test turntable rotates from angle... To angle The time interval is divided into 20 weight transformations to achieve 100 sets of orientation patterns tested in one turntable rotation.

[0014] The beneficial effects of this invention are as follows: (1) By directly using the actual test pattern results of the antenna as the data source for optimization, the intermediate process is avoided, the influence of various amplitude and phase errors on the results is overcome, and finally a solution that basically matches the optimal pattern obtained from the optimization simulation can be found in the test. Through the combination of real-time feedback mechanism and intelligent optimization algorithm, and the ability to test multiple patterns in parallel, the system can complete the rapid optimization iteration of the antenna array amplitude and phase weights for the optimization target in a short time, greatly shortening the optimization cycle and significantly improving the overall efficiency of testing and optimization.

[0015] (2) The system can complete the entire optimization process without human intervention by automatically collecting, analyzing and optimizing the test results in real time and retesting, which reduces human resource costs and improves the automation level of the system.

[0016] (3) It supports flexible configuration of various antenna array forms and optimization targets. Most importantly, even when the test environment and system errors have a significant impact on the test results, it can still find the amplitude and phase weights that match the simulation results through iteration. In other words, it can adapt to the needs of different application scenarios, which significantly enhances the versatility and applicability of the system.

[0017] (4) By dynamically adjusting the feeding parameters of the antenna unit, the system can make full use of the potential of the antenna array, reduce resource waste, and improve overall performance. The introduction of intelligent optimization algorithm avoids the defect of traditional optimization methods being prone to local optima. At the same time, the integrated design and real-time feedback mechanism ensure the stability and reliability of the optimization process.

[0018] (5) The host computer is equipped with a graphical interface, which allows users to easily set optimization goals, monitor the optimization process and view the results, significantly improving the ease of operation. Attached Figure Description

[0019] The invention will now be described in more detail with reference to embodiments and the accompanying drawings. Figure 1 This diagram illustrates the interconnection of the various modules of the present invention. Figure 2 This diagram illustrates the principle of the optimization iteration process of the present invention. Figure 3 This diagram illustrates the principle of simultaneous multi-beam and time-division multiplexing in the signal acquisition and processing module of the present invention. Figure 4 The normalized radiation pattern of a 1*10 linear array with equal amplitude and in-phase feeding test is shown. Figure 5 The normalized radiation pattern with a main-to-sub ratio of 40dB obtained from the simulation of the algorithm optimization of this invention is shown. Figure 6 This shows that the amplitude and phase weights corresponding to the algorithm-optimized 40dB main-to-sub ratio radiation pattern are directly applied to the normalized radiation pattern obtained from the test. Figure 7 The normalized direction plot shows the optimal result of the optimization iteration test of the present invention.

[0020] In the accompanying drawings, the same parts use the same reference numerals. The drawings are not to scale. Detailed Implementation

[0021] The invention will now be further described with reference to the accompanying drawings.

[0022] This invention provides an antenna pattern synthesis platform based on adaptive training, such as... Figure 1 As shown, it includes: The antenna array is mounted on a turntable in an anechoic chamber. The display control module is used to display the test process and test results. The signal acquisition and processing module acquires and processes the radiation characteristics data of the antenna array in real time. The display control module controls the anechoic chamber turntable and integrates an optimization algorithm module. The signal processing module transmits the test results to the display control module in real time. The optimization algorithm module generates new feed amplitude and phase weights based on the test results. The new feed amplitude and phase weights are sent to the signal processing module through the display control module for beamforming of the antenna array test, completing one optimization iteration. The optimized test results are then fed back to the optimization algorithm module to optimize a new generation of feed weights for testing, until the optimization target or the upper limit of the number of iterations is reached. It should be noted that in this embodiment, the optimization of the 1*10 linear array at the 1.05GHz frequency point with a main-to-subsidiary ratio better than 40dB is performed. The display control module is written in VB.NET and runs on the host computer. It is used to control the actions of various modules of the synchronization platform and is the core of the entire platform, ensuring the smooth progress of optimization. At the same time, it displays the test process and results. The display control module architecture includes two functional modules: display and control. The display module mainly includes three functions: test status display, radiation pattern display, and optimization algorithm display. The control module includes a turntable control module, an antenna control module, and an optimization control module. Taking advantage of MATLAB's advantages in mathematical processing, the intelligent optimization algorithm is implemented in MATLAB code. In order to integrate it into the .NET program, the program is encapsulated as a dynamic link library (DLL) using MATLAB's deploy tool and then referenced in .NET. The intelligent optimization algorithm function is called through the display control module to realize parameter input and result output. Then, through the communication interface between the host computer and the turntable, the speed, rotation angle range, step size, etc. of the turntable can be set. Specifically, during adaptive training, the antenna array is mounted on the anechoic chamber turntable. The RF output port of each unit is connected to the signal processing module via an RF cable to directly sample 10 RF signals at a frequency of 1.05 GHz. This can also be any array configuration and any frequency supported by the signal processing module. A DC regulated power supply powers the signal processing module. The signal processing module communicates with the display control module via a serial port and achieves normal synchronization between hardware components through the turntable synchronization pulse. Furthermore, each antenna element in the antenna array has an independent radio frequency interface, and each antenna element is connected to the signal acquisition and processing module via a radio frequency cable. That is, the antenna array can be configured in various array forms (such as linear array, area array, etc.) and is suitable for a variety of different application scenarios. In this embodiment, the signal acquisition and processing module is used to acquire radiation characteristic data of the antenna array in real time, perform filtering, analog-to-digital conversion and beamforming on the acquired signal, and provide a high-speed communication interface with the display control module to ensure the real-time performance and integrity of data transmission. By increasing the communication rate with the display control module and using time-division multiplexing and simultaneous multi-beaming in the signal processing module, the function of testing multiple sets of weighted radiation patterns in parallel with one turntable rotation can be realized, which greatly improves resource utilization and testing efficiency. In one embodiment, such as Figure 1 As shown, the signal acquisition and processing module communicates with the display control module via a serial port and operates synchronously via the synchronization pulse of the anechoic chamber turntable. It also includes a DC regulated power supply that supplies power to the signal acquisition and processing module. It should be noted that in this embodiment, the test results are transmitted to the display control module in real time through the signal processing module. The optimization algorithm module generates new feed amplitude and phase weights based on the test results. The new weights are then sent to the signal processing module through the display control module for beamforming of the antenna array test, completing one optimization iteration. The optimized test results are then fed back to the optimization algorithm module to optimize a new generation of feed weights for testing, until the optimization target or the upper limit of the number of iterations is reached. The entire process requires no manual intervention, achieving unattended automated testing and optimization. Compared with existing technologies (where testing and optimization are usually performed in isolation, lacking a real-time feedback mechanism), this embodiment significantly improves optimization efficiency through a real-time feedback online optimization mechanism, while also greatly saving labor costs.

[0023] In one embodiment, the present invention also provides an antenna pattern synthesis method based on adaptive training, such as... Figure 2 As shown, it includes the following steps: Set the population size, number of iterations, and rotation range of the darkroom turntable; The initial population is generated using an optimization algorithm; The display control module transmits the initial population weights to the signal processing module and controls the turntable to rotate to test and collect the orientation map corresponding to the number of population weights; The radiation pattern data of the population size is sent back to the display control module, and the optimization algorithm optimizes the next generation of population based on the main-to-secondary ratio of the radiation pattern and its corresponding amplitude and phase weights. Then test the corresponding pattern of the new generation of population, and continue the cycle until the ratio of primary to secondary patterns of the test pattern is better than 40dB. In one embodiment, the number of populations is set to 100, the number of iterations is set to 200, and the rotation range of the darkroom turntable is +90° to -90°. It should be noted that, as Figure 2As shown, the turntable rotation angle needs to be set first. According to the antenna pattern optimization requirements, the turntable rotation range is set to +90° to -90°. Then, the number of populations is set to 100 and the number of iterations is set to 200 (one population corresponds to one set of weighted feed amplitudes of the pattern). The initial population is generated according to the optimization algorithm. Different optimization algorithms can be selected as needed. In this embodiment, the Harris Eagle improved algorithm is used for verification. The 100 initial populations are set with weights obtained from theoretical Chebyshev distribution, Taylor distribution and simulation optimization results based on the measured data of the unit. Furthermore, the initial population weights are transmitted to the signal processing module via the display control module, which then controls the turntable to rotate and collect 100 sets of radiation patterns corresponding to the weights. The turntable rotation achieves the testing of 100 radiation patterns in one pass by the signal processing module using time-division multiplexing and simultaneous multi-beaming. During radiation pattern testing, the angle range is -90° to +90°, in 1° increments, totaling 181 points. The simultaneous multi-beaming principle involves simultaneously calling five RAM registers to process five radiation patterns. The time-division multiplexing principle involves rotating the test turntable from the angle range... To angle The time interval between the time-division weight transformations are performed 20 times, enabling 5*20=100 sets of orientation patterns to be tested in one turntable rotation. The time for turntable rotation and data communication transmission, etc., is about 110 seconds for testing 100 sets of orientation patterns in one iteration. Compared with the traditional anechoic chamber process test, which takes 30 seconds to test 1 set of orientation patterns, the test efficiency is greatly improved, which is nearly 30 times that of the traditional test. Next, 100 sets of radiation pattern data are sent back to the display control module. The intelligent optimization algorithm optimizes the new generation of population based on the main-to-sub ratio of the radiation pattern and its corresponding amplitude and phase weights. The new 100 sets of radiation patterns correspond to the power supply amplitude and phase weights. The radiation patterns corresponding to the new generation of population are then tested. This process is repeated until the main-to-sub ratio of the tested radiation pattern is better than 40dB. The amplitude and phase weights corresponding to this radiation pattern are the qualified weights that can achieve the optimization target main-to-sub ratio. After the system is set up and installed, no human intervention is required from the start of the first generation optimization test to the end. The entire process is automated, which greatly reduces the consumption of human resources. like Figure 4 As shown in the figure, the test results show that for the 1*10 linear array with equal amplitude and in-phase feeding, the main-to-subsidiary ratio is only 11.5dB, and the 3dB beamwidth is 19°, which is far from the optimization target of 40dB main-to-subsidiary ratio. The simulation pattern obtained after substituting the element radiation pattern into the algorithm for optimization iteration is shown in the figure. Figure 5 As shown, the primary-to-secondary ratio is 40dB, and the 3dB beamwidth is 27°, which meets the optimization target requirements. However, directly sending the amplitude and phase weights corresponding to the algorithm-optimized primary-to-secondary ratio of 40dB to the signal processing module to collect test results results is problematic. Figure 6As shown, the primary-to-secondary ratio deteriorates to 26.3dB, the 3dB beamwidth is 28°, and the simulation and test results differ significantly for the same amplitude and phase weights. This phenomenon is due to uncontrollable amplitude and phase errors caused by factors such as signal processing module channels, RF cables, and unit coupling. Therefore, our automated optimization and testing system is used. Each algorithm optimization is based on the actual test results, and the continuously iteratively optimized results are as follows: Figure 7 As shown, the main-to-sub ratio is 40dB and the 3dB beamwidth is 26.5°, which basically meets the optimal solution of the simulation optimization. That is, the present invention can eliminate the influence of various errors in the test process to the greatest extent and find the solution that meets the optimal result of the simulation optimization in the amplitude and phase weights of the actual test. The method can achieve the following: as long as the result can be achieved by the optimization simulation, the corresponding solution can also be found in the optimization test. It should be noted that this invention uses a display control module to simultaneously call up the optimization algorithm module, signal processing module, antenna array and anechoic chamber turntable, etc., and uses online real-time acquisition of antenna pattern data and real-time optimization technology to avoid calibration and manual parameter adjustment, thereby achieving high-performance and high-efficiency antenna array integration.

[0024] In the description of this invention, it should be understood that the terms "upper", "lower", "bottom", "top", "front", "rear", "inner", "outer", "left", "right", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0025] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other embodiments.

Claims

1. An antenna pattern synthesis platform based on adaptive training, characterized in that, include: The antenna array is mounted on a turntable in an anechoic chamber. The display control module is used to display the test process and test results. The signal acquisition and processing module acquires and processes the radiation characteristics data of the antenna array in real time. The display control module controls the anechoic chamber turntable and integrates an optimization algorithm module. The signal processing module transmits the test results to the display control module in real time. The optimization algorithm module generates new feed amplitude and phase weights based on the test results. The new feed amplitude and phase weights are sent to the signal processing module through the display control module for beamforming of the antenna array test, completing one optimization iteration. The optimized test results are then fed back to the optimization algorithm module to optimize a new generation of feed weights for testing, until the optimization target or the upper limit of the number of iterations is reached.

2. The antenna pattern synthesis platform based on adaptive training according to claim 1, characterized in that, Each antenna element in the antenna array has an independent radio frequency interface, and each antenna element is connected to the signal acquisition and processing module via a radio frequency cable.

3. The antenna pattern synthesis platform based on adaptive training according to claim 2, characterized in that, The signal acquisition and processing module performs filtering, analog-to-digital conversion, and beamforming on the acquired radiation characteristic data.

4. The antenna pattern synthesis platform based on adaptive training according to claim 3, characterized in that, The signal acquisition and processing module communicates with the display control module via a serial port and operates synchronously via the synchronization pulse of the anechoic chamber turntable.

5. The antenna pattern synthesis platform based on adaptive training according to claim 4, characterized in that, It also includes a DC regulated power supply that powers the signal acquisition and processing module.

6. An antenna pattern synthesis method based on adaptive training, based on the antenna pattern synthesis platform according to any one of claims 1 to 5, characterized in that, Includes the following steps: Set the population size, number of iterations, and rotation range of the darkroom turntable; The initial population is generated using an optimization algorithm; The display control module transmits the initial population weights to the signal processing module and controls the turntable to rotate to test and collect the orientation map corresponding to the number of population weights; The radiation pattern data of the population size is sent back to the display control module, and the optimization algorithm optimizes the next generation of population based on the main-to-secondary ratio of the radiation pattern and its corresponding amplitude and phase weights. The test pattern for the next generation of the population is then obtained, and this process is repeated until the ratio of primary to secondary patterns in the test pattern is better than 40 dB.

7. The antenna pattern synthesis method based on adaptive training according to claim 6, characterized in that, The population size is set to 100, and the number of iterations is set to 200.

8. The antenna pattern synthesis method based on adaptive training according to claim 6, characterized in that, The rotation range of the darkroom turntable is +90° to -90°.

9. The antenna pattern synthesis method based on adaptive training according to claim 7, characterized in that, The Harris Eagle improved algorithm was selected as the optimization algorithm. The initial 100 populations were set with weights based on theoretical Chebyshev distribution, Taylor distribution and simulation optimization results based on unit measured data.

10. The antenna pattern synthesis method based on adaptive training according to claim 9, characterized in that, The signal acquisition and processing module utilizes time-division multiplexing and simultaneous multi-beaming to simultaneously call five RAM registers to process five radiation patterns as the test turntable rotates from angle... To angle The time interval is divided into 20 weight transformations to achieve 100 sets of orientation patterns tested in one turntable rotation.

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