Antenna excitation reconstruction method and device

Abstract

The invention provides an antenna excitation reconstruction method and device, and the method comprises the steps: adjusting the phase of a to-be-measured antenna for multiple times, and constructing a transmission channel vector; under the condition that the phase of the to-be-measured antenna is changed every time, amplitude data received by the receiving antenna are obtained; constructing a measurement space matrix according to the transmission channel vector and the amplitude data; determining an excitation distribution initial vector according to the measurement space matrix; determining a truncation generalized gradient of a loss function according to the transmission channel vector and the amplitude data; and according to the truncated generalized gradient and the excitation distribution initial vector, determining a final estimation value of the excitation distribution of the to-be-measured antenna, and completing excitation reconstruction of the to-be-measured antenna. According to the method, the excitation vector is continuously iteratively updated, so that the excitation vector gradually approaches the optimal solution, rapid excitation reconstruction of the phased array is realized, and the excitation reconstruction accuracy is improved. By introducing gradient truncation, the abnormal error gradient can be limited, and the robustness of the algorithm is improved.

Description

Technical Field

[0001] The present application relates to the technical field of antennas, and in particular, to an antenna excitation reconstruction method and apparatus. Background Art

[0002] In wireless communication and radar systems, as a key component for signal transmission and reception, the performance of an antenna directly determines the communication quality and efficiency of the entire system. Antenna excitation reconstruction technology, as a method for optimizing antenna performance, aims to dynamically adjust the excitation parameters of the antenna to adapt to different communication environments and requirements, thereby achieving more efficient signal transmission and reception.

[0003] However, currently in realizing antenna excitation reconstruction, balancing computational efficiency and reconstruction accuracy remains a key problem. Summary of the Invention

[0004] In view of this, the purpose of the embodiments of the present application is to provide an antenna excitation reconstruction method and apparatus, which can improve the reconstruction efficiency and accuracy.

[0005] In a first aspect, the embodiments of the present application provide an antenna excitation reconstruction method, including: adjusting the phase of the antenna to be measured multiple times, and constructing a transmission channel vector; obtaining the amplitude data received by the receiving antenna each time the phase of the antenna to be measured changes; constructing a measurement space matrix according to the transmission channel vector and the amplitude data; determining an initial vector of the excitation distribution according to the measurement space matrix; determining a truncated generalized gradient of a loss function according to the transmission channel vector and the amplitude data; and determining a final estimated value of the excitation distribution of the antenna to be measured according to the truncated generalized gradient and the initial vector of the excitation distribution, thereby completing the excitation reconstruction of the antenna to be measured.

[0006] In the above implementation process, by only performing amplitude measurement and applying the generalized gradient descent method to antenna excitation reconstruction, the excitation vector can be continuously iteratively updated, and the value of the excitation vector can be adjusted according to certain criteria (such as reducing errors) to gradually approach the optimal solution, thereby realizing the rapid excitation reconstruction of the phased array and improving the excitation reconstruction accuracy. In addition, by introducing gradient truncation, abnormal error gradients can be restricted to ensure the normal operation of the algorithm and further improve the algorithm robustness of the excitation reconstruction process.

[0007] In an embodiment, the determining a final estimated value of the excitation distribution of the antenna to be measured according to the truncated generalized gradient and the initial vector of the excitation distribution includes: iteratively calculating a current estimated value of the excitation distribution vector of the antenna to be measured according to the truncated generalized gradient; and determining the current estimated value obtained in the last iteration as the final estimated value when the iteration reaches the iteration condition.

[0008] In one embodiment, the formula for the truncated generalized gradient is as follows: where x t is the excitation vector calculated at the t-th iteration, m represents the m-th measurement, M is the number of measurements, a m is the transmission channel vector, ψ m is the amplitude data at the m-th measurement, and Q t+1 is the truncation set at the (t + 1)-th iteration.

[0009] In the above implementation process, by using the generalized gradient method to iteratively solve the current estimated value, the excitation retrieval can be quickly completed, the non-convex optimization method can be extended to the scenario of phase-free excitation retrieval, and the application scenarios can be increased. In addition, the gradient truncation operation effectively suppresses the noise interference and the risk of gradient explosion by constraining the update step size, ensuring the iteration stability. By adopting the truncated generalized gradient, the efficiency and accuracy of antenna measurement and optimization can be significantly improved, the computational overhead can be reduced, the algorithm convergence can be accelerated, the algorithm stability can be enhanced, the antenna excitation distribution can be optimized, and the resource utilization rate can be improved.

[0010] In one embodiment, the receiving antenna is arranged at a set position in the far field of the antenna to be measured, and there is one receiving antenna; the amplitude data is phase-free data.

[0011] In the above implementation process, the measurement of the antenna to be measured can be completed only by one receiving antenna at the set position in the far field, which can reduce the hardware requirements for excitation detection, reduce the hardware cost and operation difficulty, and at the same time increase the application scenarios. In addition, by adopting phase-free data, the measurement process can be simplified, the measurement difficulty can be reduced, and at the same time the result stability and accuracy can be improved.

[0012] In one embodiment, the phase of the antenna to be measured is adjusted multiple times, including: adjusting the phase of the antenna to be measured multiple times through a random matrix; where the random matrix is a Gaussian distribution random matrix; and in each phase adjustment of the antenna to be measured, the phase of the antenna to be measured is adjusted by the value corresponding to the antenna position and the corresponding number of phase adjustment times in the random matrix.

[0013] In the above implementation process, since the elements of the random matrix are independently and randomly generated, by using the random matrix to adjust the phase of the antenna to be measured, it helps to break the inherent pattern of the data, reduce the information redundancy, and thus improve the accuracy and robustness of the excitation reconstruction.

[0014] In one embodiment, constructing a transmission channel vector includes: constructing the transmission channel vector according to the first position parameter of the antenna element of the antenna to be measured in each phase adjustment, the second position parameter of the receiving antenna, the active element pattern, and the elements in the random matrix.

[0015] In one embodiment, the formula of the transmission channel vector is:

[0016] ; where N is the number of antennas, is the active element pattern of the nth unit, θ is the elevation angle corresponding to the receiving antenna, is the azimuth angle corresponding to the receiving antenna, m represents the mth measurement, T represents the transpose operation of the vector, R n is the position vector of the nth unit in the Cartesian coordinate system, r is the direction vector of the receiving antenna of n units in the Cartesian coordinate system, Δα nm = G nm is the phase adjusted by the nth unit at the mth measurement, G nm is the element in the mth row and nth column of the matrix G, j is the imaginary unit, and k is the wave number.

[0017] In the above implementation process, by constructing the transmission channel vector, complex channel characteristics (such as delay, attenuation, multipath effect) can be abstracted into vector form, which is beneficial to data analysis. In addition, using the transmission channel vector, data can be divided into multiple sub-streams and transmitted through different channels. In this way, the resources of multiple channels can be utilized to achieve parallel transmission and improve the data transmission rate.

[0018] In one embodiment, the formula of the measurement space matrix is: where, is the measurement space matrix, a m is the transmission channel vector, and Q0 is the index set.

[0019] In one embodiment, the formula of the initial excitation distribution vector is: where x0 is the initial excitation distribution vector, M is the number of measurements, ψ m is the amplitude data at the mth measurement, is the normalized eigenvector corresponding to the maximum eigenvalue of the measurement space matrix.

[0020] To make the above objects, features, and advantages of the present application more obvious and understandable, specific embodiments are hereinafter given, and in conjunction with the accompanying drawings, the detailed description is as follows. Brief Description of the Drawings

[0021] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0022] Figure 1 Schematic diagram of the interaction of each device in the operating environment of the antenna excitation reconstruction method provided by the embodiments of the present application;

[0023] Figure 2 Flowchart of the antenna excitation reconstruction method provided by the embodiments of the present application;

[0024] Figure 3 Schematic diagram of the change of the reconstruction error with the ratio of the number of samples to the number of elements for the 10×10 unit half-wave spacing ideal antenna planar array provided by the embodiments of the present application at signal-to-noise ratios of 10 dB, 15 dB, 20 dB, 25 dB, and 30 dB;

[0025] Figure 4 Change of the reconstruction error with the signal-to-noise ratio for the 10×10 unit half-wave spacing ideal antenna planar array provided by the embodiments of the present application when the ratio of the number of measurement times to the number of elements M / N = 4, 5, 6, 7, 8;

[0026] Figure 5 Excitation reconstruction result of the large phased array of the 50×50 unit half-wave spacing ideal antenna planar array provided by the embodiments of the present application;

[0027] Figure 6 Change of the excitation reconstruction success rate with the elevation angle when the receiving antenna of the 11×11 unit patch cylindrical conformal array provided by the embodiments of the present application is placed at different elevation angle positions in the far field;

[0028] Figure 7 Excitation reconstruction result of the 11×11 unit patch cylindrical conformal array provided by the embodiments of the present application when the receiving antenna is in the 0° direction. Detailed implementation manners

[0029] The following will describe the technical solutions in the embodiments of the present application in combination with the drawings in the embodiments of the present application.

[0030] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0031] In recent years, active phased array antennas have been developed towards larger apertures, higher carrier frequencies, and broader frequency bands. However, in large phased arrays, manufacturing and assembly tolerances of elements and channels, as well as the effects of long-term operation and harsh environmental conditions, can lead to inevitable errors in the amplitude and phase of each channel. Therefore, accurately and efficiently reconstructing the excitation parameters is crucial for ensuring the stability and reliability of the phased array.

[0032] In practical near-field or far-field excitation reconstruction, most methods rely on measuring the amplitude and phase information of the field. Phase-free measurement techniques eliminate the need for phase information and achieve amplitude-phase recovery of the excitation or calibration field of the antenna array. This method can greatly simplify the measurement process of the antenna array under test in applications such as source reconstruction or array diagnosis. At the same time, since it does not need to consider the phase interference caused by multipath effects, it provides enhanced robustness in anti-noise.

[0033] The rotating unit electric vector method is an early method for excitation reconstruction using phase-free measurement. It determines the electric field contribution of each element by adjusting the phase of the array elements one by one and observing the resulting amplitude changes. However, each element needs to be phase-scanned from 0° to 360°, making this process time-consuming, especially for large phased arrays. In addition, in a noisy environment, the small field contributions of individual elements will reduce the reconstruction accuracy. Compressive sensing technology performs well in reducing measurement requirements and anti-noise. It can solve the approximate convex optimization problem through phase-free measurement and discard some non-convex terms introduced by phase-free measurement, or use an iterative scheme for solution.

[0034] However, this transformation may come at the cost of reducing the solution accuracy and computational efficiency. In addition, these methods rely on precise knowledge of the reference complex excitation of the original array to construct the differential (virtual) array, which may have limitations in practical applications. Additionally, methods similar to Phaselift and Phasecut solve the non-convexity of phase-free measurement by lifting the solution space to a higher dimension. However, for convex optimization-based methods, with the increase in the scale of the phased array, the rapid growth of variables poses a major challenge to large-scale excitation reconstruction.

[0035] In view of this, the present application proposes an antenna excitation reconstruction method. By only performing amplitude measurements and applying the generalized gradient descent method to antenna excitation reconstruction, the excitation vector can be continuously iteratively updated, and the value of the excitation vector can be adjusted according to certain criteria (such as reducing errors) to gradually approach the optimal solution, thereby realizing the rapid excitation reconstruction of the phased array and improving the excitation reconstruction accuracy. In addition, by introducing gradient truncation, abnormal error gradients can be restricted to ensure the normal operation of the algorithm and further improve the algorithm robustness in the excitation reconstruction process.

[0036] To facilitate the understanding of this embodiment, first, a detailed introduction is given to the operating environment for implementing an antenna excitation reconstruction method disclosed in the embodiments of this application.

[0037] As Figure 1 shown, it is a schematic diagram of the interaction among various devices in the operating environment of the antenna excitation reconstruction method provided by the embodiments of this application, including: an electronic device 100, a to-be-measured antenna 200, a receiving antenna 300, and a phase shifter 400.

[0038] Among them, the electronic device 100 is connected to the to-be-measured antenna 200 and the phase shifter 400 through a transmission line, and the phase shifter 400 is connected to the to-be-measured antenna 200. The phase shifter 400 is used to receive information such as a random matrix and a transmission channel vector sent by the electronic device 100, and perform phase regulation on the to-be-measured antenna 200. The receiving antenna 300 is used to receive the amplitude information fed back by the to-be-measured antenna 200, and transmit the received amplitude information to the electronic device 100. The electronic device 100 is used to determine the real-time situation of the reconstruction of the to-be-measured antenna 200 according to the amplitude information received by the receiving antenna.

[0039] Here, the receiving antenna 300 is a probe, and the to-be-measured antenna 200 is a phased array antenna.

[0040] Optionally, the electronic device 100 can be a network server, a database server, a personal computer (PC), a tablet computer, a smart phone, a personal digital assistant (PDA), etc., and the electronic device 100 can be selected according to the actual situation.

[0041] Among them, the electronic device 100 is used to execute the antenna excitation reconstruction method proposed in the embodiments of this application.

[0042] The above phase shifter 400 can be set independently of the to-be-measured antenna 200, or can be integrally set with the to-be-measured antenna 200, and the setting method of the phase shifter 400 can be selected according to the actual situation.

[0043] The implementation process of the antenna excitation reconstruction method is described in detail below through several embodiments.

[0044] Please refer to Figure 2 , which is a flowchart of the antenna excitation reconstruction method provided by the embodiments of this application. The following will elaborate on the Figure 2 specific process shown in detail.

[0045] Step S201, adjust the phase of the to-be-measured antenna multiple times, and construct a transmission channel vector.

[0046] The phase of the antenna to be measured here can be adjusted by a phase shifter. The phase shifter adjusts the phase of the antenna to be measured according to the obtained adjustment parameters.

[0047] Among them, the adjustment parameters can be determined by a random matrix, and the parameters in the random matrix are used to characterize the phase values of the antennas set for each antenna element during each measurement.

[0048] It should be understood that multiple adjustment parameters can be determined, and the phase of the antenna to be measured can be adjusted multiple times by multiple adjustment parameters. After each adjustment of the antenna to be measured, the amplitude data sent by the antenna to be measured is obtained and preprocessed. At the same time, a transmission channel vector including all adjustment parameters is constructed.

[0049] The transmission channel is a physical or logical channel established between two communication parties. The transmission channel vector abstracts the influence of the transmission channel on the signal into a vector form, and each element corresponds to the channel response between different transmission paths or antennas.

[0050] Optionally, the transmission channel vector can be obtained by methods such as pilot-assisted estimation, blind estimation, and feedback mechanism. The acquisition method of the transmission channel vector can be selected according to the actual situation.

[0051] Step S202, in the case where the phase of the antenna to be measured changes each time, obtain the amplitude data received by the receiving antenna.

[0052] The amplitude data received by the receiving antenna is the data sent by the antenna to be measured after each phase adjustment of the antenna to be measured. The amplitude data refers to the quantization information describing the electromagnetic radiation or received signal intensity distribution of the antenna to be measured in a specific working state, usually presented as the variation data of the amplitude value (such as voltage, power, or field strength) with the spatial angle, frequency, or time.

[0053] Optionally, the amplitude data can be measured by methods such as far-field testing and simulation modeling.

[0054] Step S203, construct a measurement space matrix according to the transmission channel vector and the amplitude data.

[0055] Step S204, determine the initial vector of the excitation distribution according to the measurement space matrix.

[0056] Among them, the measurement space matrix is an intermediate value used to calculate the initial vector of the excitation distribution and has no specific physical meaning.

[0057] The initial vector of the excitation distribution is a mathematical vector representing the initial excitation parameters (such as amplitude, phase) of each antenna element in the design of the array antenna or beamforming optimization. It can be used as the starting point for algorithm iteration.

[0058] Step S205: Determine the truncated generalized gradient of the loss function based on the transmission channel vector and amplitude data.

[0059] Step S206: Determine the final estimated value of the antenna excitation distribution to be measured based on the truncated generalized gradient and the initial vector of the excitation distribution, and complete the excitation reconstruction of the antenna to be measured.

[0060] The loss function is used to measure the difference between the model's predicted value and the true value. To minimize this difference, it is usually necessary to calculate the gradient of the loss function with respect to the model parameters and update the parameters along the gradient direction. However, when the model complexity is high or the data volume is large, directly calculating and updating the gradient may lead to numerical instability or low training efficiency. The truncated generalized gradient controls its norm (i.e., the length or size of the vector) by clipping or scaling the gradient, thus avoiding the problems of gradient vanishing or gradient explosion.

[0061] The final estimated value of the antenna excitation distribution to be measured here can be determined using the gradient descent method.

[0062] The gradient descent method is an iterative optimization algorithm for finding the minimum value of a function, used to adjust the model parameters to minimize the loss function. Its core idea is to calculate the gradient of the objective function and gradually update the parameters along the negative gradient direction, approaching the minimum point of the function.

[0063] It can be understood that each time the phase of the antenna to be measured is adjusted, the amplitude data of the antenna to be measured can be obtained each time, and the transmission channel vector can be constructed. After all the phase adjustments are completed, the truncated generalized gradient and the initial vector of the excitation distribution are calculated based on the collected amplitude data and transmission channel vector, and the new truncated generalized gradient and the new estimated value of the excitation distribution are iteratively solved. After the currently calculated estimated value meets certain conditions, this estimated value is determined as the final estimated value.

[0064] For example, when the error between the current estimated value and the estimated value of the previous iteration reaches the threshold, the current estimated value is determined as the final estimated value. Another example is that after the iteration reaches the iteration condition, the estimated value of the last iteration is determined as the final estimated value.

[0065] In the above implementation process, by only performing amplitude measurements and applying the generalized gradient descent method to antenna excitation reconstruction, the excitation vector can be continuously iteratively updated, and the value of the excitation vector can be adjusted according to certain criteria (such as reducing the error) to gradually approach the optimal solution, thus realizing the rapid excitation reconstruction of the phased array and improving the accuracy of excitation reconstruction. In addition, by introducing gradient truncation, abnormal error gradients can be restricted to ensure the normal operation of the algorithm and further improve the algorithm robustness of the excitation reconstruction process.

[0066] In a possible implementation, step S206 includes: iteratively calculating the current estimated value of the antenna excitation distribution vector to be measured according to the truncated generalized gradient; and determining the current estimated value obtained in the last iteration as the final estimated value when the iteration reaches the iteration condition.

[0067] The current estimated value here refers to the estimated value of the excitation distribution corresponding to each iteration of the algorithm.

[0068] It can be understood that since the antenna to be measured undergoes multiple phase adjustments, when each phase adjustment is performed on the antenna to be measured, iterative calculations can be performed according to the generalized gradient descent method to determine the current estimated value of the antenna to be measured each time.

[0069] The above iteration condition can be reaching the number of iterations, the current estimated value reaching the estimated value threshold, etc., and this iteration condition can be selected according to the actual situation.

[0070] In a possible implementation, the formula for the truncated generalized gradient is:

[0071]

[0072] where x t is the excitation vector calculated at the t-th iteration, m represents the m-th measurement, M is the number of measurements, a m is the transmission channel vector, ψ m is the amplitude data at the m-th measurement, and Q t+1 is the truncation set at the (t + 1)-th iteration.

[0073]

[0074] where γ is the truncation threshold.

[0075] The generalized gradient descent method here can be:

[0076]

[0077] where μ is the step size of the gradient descent.

[0078] In the above implementation process, by using the generalized gradient method to iteratively solve the current estimated value, the excitation retrieval can be quickly completed, the non-convex optimization method can be extended to the no-phase excitation retrieval scenario, and the application scenario can be increased. In addition, the gradient truncation operation effectively suppresses the noise interference and the risk of gradient explosion by constraining the update step size, ensuring the iteration stability. By using the truncated generalized gradient, the efficiency and accuracy of antenna measurement and optimization can be significantly improved, the computational overhead can be reduced, the algorithm convergence can be accelerated, the algorithm stability can be enhanced, the antenna excitation distribution can be optimized, and the resource utilization rate can be increased.

[0079] In a possible implementation, a receiving antenna is disposed at a set position in the far field of the antenna to be measured, and there is one receiving antenna; the amplitude data is non-phase data.

[0080] Understandably, when the receiving antenna is disposed at a set position in the far field of the antenna to be measured and there is one receiving antenna, a far-field single-probe measurement can be formed. The far-field single-probe measurement is a method for collecting and analyzing data on radiation characteristics by using a single probe in the far-field region.

[0081] The implementation principle of the far-field single-probe measurement is as follows: The measurement is carried out in the far-field region. At this time, the electromagnetic wave is approximately a plane wave, the directions of the electric field and the magnetic field are fixed, and the amplitude decays slowly with the distance, which is suitable for obtaining stable radiation pattern, gain, polarization and other parameters. In addition, a probe (i.e., the receiving antenna) is used as a receiver, and by adjusting the phases of the antennas of the phased array to be measured, the radiation field signals under different phase configurations are detected.

[0082] In this far-field single-probe measurement, key parameters such as the radiation pattern and gain of the antenna are directly obtained through the approximation of the plane wave that satisfies the far-field condition, avoiding complex near-field to far-field transformation algorithms, with high accuracy; the single-probe configuration does not require a multi-channel synchronization system, reducing equipment requirements, significantly reducing hardware costs and operation complexity, and at the same time being applicable to the test requirements of various antennas (such as dipoles, paraboloids, arrays), with a wide range of application scenarios.

[0083] The non-phase data here refers to a data type that only contains signal amplitude information and lacks phase information.

[0084] When using non-phase data for measurement, there is no need to equip a phase detection module, which can reduce the cost of the detector, simplify the measurement process, and reduce the measurement difficulty. Only the amplitude value is stored or transmitted, which can save storage and bandwidth resources compared with complex data (real part + imaginary part). In addition, in an environment where the phase is unstable due to vibration, temperature drift, etc., the non-phase data can avoid the interference of phase distortion on the result, improving the stability and accuracy of the result.

[0085] In one embodiment, the amplitude data can be represented by the following formula:

[0086] ψ = [ψ1, ψ2, …, ψ M T ;

[0087]

[0088] where x is an N-dimensional complex vector representing the excitation of the array element, and e m is the random noise at the m-th measurement.

[0089] ​In the above implementation process, the measurement of the antenna to be measured can be completed by only one receiving antenna set at a far-field position, which can reduce the hardware requirements for excitation detection, lower the hardware cost and operation difficulty, and at the same time increase the application scenarios. In addition, by using non-phase data, the measurement process can be simplified, the measurement difficulty can be reduced, and at the same time, the result stability and accuracy can be improved.

[0090] In a possible implementation manner, the phase of the antenna to be measured is adjusted multiple times, including: adjusting the phase of the antenna to be measured multiple times through a random matrix.

[0091] Among them, the random matrix is a Gaussian distribution random matrix. A Gaussian distribution random matrix means that each element in the matrix is an independent random variable that follows a standard normal distribution (that is, the mean is 0 and the variance is 1).

[0092] In each phase adjustment of the antenna to be measured, the phase of the antenna to be measured is adjusted by the value corresponding to the antenna position and the corresponding phase adjustment times in the random matrix.

[0093] In one embodiment, the dimension of the random matrix is M×N. Among them, N is the total number of antennas to be measured, and M is the number of measurements.

[0094] The above-mentioned antenna to be measured can be a phased array antenna. When adjusting the antenna to be measured, the phase of the phase shifter in the phased array component can be adjusted by the size of the random matrix.

[0095] In the above implementation process, since the elements of the random matrix are independent and randomly generated, by using the random matrix to adjust the phase of the antenna to be measured, it helps to break the inherent pattern of the data, reduce information redundancy, and thus improve the accuracy and robustness of excitation reconstruction.

[0096] In a possible implementation manner, constructing a transmission channel vector includes: constructing a transmission channel vector according to the first position parameter of the antenna element of the antenna to be measured, the second position parameter of the receiving antenna, the active element pattern, and the elements in the random matrix in each phase adjustment.

[0097] Among them, the second position parameter may include parameters such as the elevation angle and azimuth angle corresponding to the receiving antenna position of the receiving antenna. The first position parameter is the position vector of each unit of the antenna to be measured.

[0098] The active element pattern here refers to the radiation pattern in an array antenna when only one element is excited and the rest of all elements are terminated with matching loads.

[0099] When performing multiple phase adjustments on the antenna to be measured, after each adjustment, a transmission channel vector is constructed based on the first position parameter, the second position parameter, the active element pattern, and the elements in the random matrix obtained after the adjustment.

[0100] In one embodiment, the formula for the transmission channel vector is:

[0101]

[0102] where N is the number of antennas, is the active element pattern of the nth element, θ is the elevation angle corresponding to the receiving antenna, is the azimuth angle corresponding to the receiving antenna, m represents the mth measurement, T represents the transpose operation of the vector, and R n is the position vector of the nth element in the Cartesian coordinate system, r is the direction vector of the receiving antenna of the n elements in the Cartesian coordinate system, and Δα nm = G nm is the phase adjusted for the nth element at the mth measurement, and G nm is the element in the mth row and nth column of the matrix G, j is the imaginary unit, and k is the wave number.

[0103] Here

[0104] In the above implementation process, by constructing the transmission channel vector, complex channel characteristics (such as delay, attenuation, multipath effect) can be abstracted into vector form, which is beneficial to data analysis. In addition, using the transmission channel vector, data can be divided into multiple sub-streams and transmitted through different channels. In this way, the resources of multiple channels can be utilized to achieve parallel transmission and improve the data transmission rate.

[0105] In a possible implementation manner, where the formula for the measurement space matrix is:

[0106]

[0107] where is the measurement space matrix, a m is the transmission channel vector, and Q0 is the index set.

[0108] Here, the index set represents the indices of the first |Q0| largest values in ψ m / ‖a m ‖2 (m = 1, 2,..., M), and ‖·‖2 is the Euclidean norm calculation.

[0109] where the formula for the initial vector of the excitation distribution is:

[0110]

[0111] Among them, \(x_0\) is the initial vector of the excitation distribution, \(M\) is the number of measurements, and \(\psi\) m is the amplitude data at the \(m\)-th measurement, is the normalized eigenvector corresponding to the maximum eigenvalue of the measurement space matrix.

[0112] The following are several embodiments to further help understand the antenna excitation reconstruction scheme in this application:

[0113] For example, apply the antenna excitation reconstruction method in the embodiment of this application to an actual ideal omnidirectional antenna array and a patch conformal antenna array to evaluate the excitation reconstruction ability for different phased arrays. In the following examples, a 6-bit phase shifter is used for phase control, the phase step is 31.5326°, and the parameters \(\mu = 0.6\) and \(\gamma = 0.7\) are set.

[0114] Example 1: The antenna to be measured is a 10×10 element half-wavelength spacing ideal antenna planar array;

[0115] The antenna to be measured is composed of \(N = 100\) unit ideal omnidirectional antennas, and the spacing of the antenna to be measured is \(\lambda / 2\), where \(\lambda\) is the free space wavelength. The excitation of the antenna elements to be measured adopts Chebyshev synthesis weighting with a sidelobe level \(SLL=-30dB\); a receiving antenna is placed at the position with an elevation angle of 0°, and Gaussian white noise with different signal-to-noise ratios (SNRs) is applied to the measurement values. Considering the excitation error reconstructed by the embodiment of this application under different sampling number to element number ratios (\(M / N\)) and different signal-to-noise ratios, the reconstructed excitation error is defined as: where \(x\) * is the original excitation value.

[0116] As Figure 3 the results shown, show the variation of the reconstruction error with the sampling number to element number ratio when the fixed signal-to-noise ratios are 10dB, 15dB, 20dB, 25dB, and 30dB; as Figure 4 the results shown, show the variation of the reconstruction error with the signal-to-noise ratio when \(M / N = 4, 5, 6, 7, 8\) is fixed. It can be seen from the results that the reconstruction error shows a monotonically decreasing relationship with the signal-to-noise ratio and the number of measurements, and when \(M / N\geq5\) and \(SNR\geq20dB\), the reconstruction error \(\epsilon\leq0.1\). The antenna excitation reconstruction method in the embodiment of this application has good reconstruction accuracy.

[0117] Example 2: The antenna to be measured is a 50×50 element half-wavelength spacing ideal antenna planar array;

[0118] The antenna to be measured consists of N = 2500 unit ideal omnidirectional antennas, with an antenna spacing of λ / 2. The excitation of the antenna elements uses Chebyshev synthesis weighting with SLL = -30dB, and the phase distribution at a certain scanning angle is considered. A receiving antenna is placed at the position of 0° pitch angle for measurement.

[0119] As Figure 5 The results shown in the figure display the excitation reconstruction results for a large phased array containing 2500 elements. In this example, M / N = 4, SNR = 20dB. The results indicate that the amplitude distribution and phase distribution of the excitation reconstruction structure by the antenna excitation reconstruction method in the embodiments of the present application are basically the same as those of the original array, and it only takes 13.85 seconds. The antenna excitation reconstruction method in the embodiments of the present application has high processing ability and calculation speed for large phased arrays.

[0120] Example 3: The antenna to be measured is an 11×11 element patch cylindrical conformal array;

[0121] The antenna to be measured consists of N = 121 patch antennas, integrated on a cylinder with a radius of 305.69mm. The radiation pattern is simulated using full-wave software and data is collected. The array operates at 5GHz, with a transverse element spacing of 30mm and a longitudinal spacing of 5.625°. The element excitation uses Chebyshev synthesis weighting with SLL = -30dB, and the longitudinal space phase difference caused by the conformal layout is compensated.

[0122] As Figure 6 The results shown in the figure display the influence curve of different pitch angles on the success rate of array excitation reconstruction when the receiving antenna is placed at different pitch angle positions in the far field and 100 random Monte Carlo trials are performed each time (if the excitation reconstruction error in a certain trial is less than -25dB, it is considered successful). The results show that in most pitch angle regions, especially in the regions of -60° to -30° and 30° to 60°, there is a high success rate, while the reconstruction success rate is relatively low in the large pitch angle regions close to ±90°.

[0123] As Figure 7 The results shown in the figure, based on Example 3, fix the far-field receiving antenna in the 0° direction and successfully achieve excitation recovery. Through the antenna excitation reconstruction method in the embodiments of the present application, considering the accuracy of phase shifters and noise interference, Figure 7 it is possible to accurately recover the original excitation amplitude and phase for the conformal phased array with a complex structure shown in the figure. The antenna excitation reconstruction method in the embodiments of the present application has good applicability to existing different types of conformal arrays.

[0124] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the antenna excitation reconstruction method described in the above method embodiment.

[0125] A computer program product of the antenna excitation reconstruction method provided by the embodiment of the present application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the steps of the antenna excitation reconstruction method described in the above method embodiment. For details, refer to the above method embodiment and will not be elaborated herein.

[0126] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0127] In addition, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0128] When the above-mentioned functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes. It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, elements defined by the statement "comprising..." do not exclude the existence of additional identical elements in the process, method, article or device comprising the said elements.

[0129] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0130] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or replacements, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An antenna excitation reconstruction method, characterized in that, Including: Adjust the phase of the antenna to be measured multiple times, and construct a transmission channel vector; Under the condition that the phase of the antenna to be measured changes each time, obtain the amplitude data received by the receiving antenna; Construct a measurement space matrix according to the transmission channel vector and the amplitude data; Determine the initial vector of the excitation distribution according to the measurement space matrix; Determine the truncated generalized gradient of the loss function according to the transmission channel vector and the amplitude data; Determine the final estimated value of the excitation distribution of the antenna to be measured according to the truncated generalized gradient and the initial vector of the excitation distribution, and complete the excitation reconstruction of the antenna to be measured.

2. The method according to claim 1, wherein The determining the final estimated value of the excitation distribution of the antenna to be measured according to the truncated generalized gradient and the initial vector of the excitation distribution includes: Iteratively calculate the current estimated value of the excitation distribution vector of the antenna to be measured according to the truncated generalized gradient; When the iteration reaches the iteration condition, determine the current estimated value obtained from the last iteration as the final estimated value.

3. The method according to claim 1, characterized in that, Wherein, The formula of the truncated generalized gradient is: where x t is the excitation vector calculated at the t-th iteration, m represents the m-th measurement, M is the number of measurements, a m is the transmission channel vector, ψ m is the amplitude data at the m-th measurement, Q t+1 is the truncation set at the (t + 1)-th iteration.

4. The method according to claim 1, characterized in that, Wherein, The receiving antenna is arranged at a set position in the far field of the antenna to be measured, and there is one receiving antenna; the amplitude data is non-phase data.

5. The method according to any one of claims 1-4, characterized in that Wherein, Adjusting the phase of the antenna to be measured multiple times includes: Adjust the phase of the antenna to be measured multiple times through a random matrix; wherein, the random matrix is a Gaussian distribution random matrix; Wherein, in each phase adjustment of the antenna to be measured, adjust the phase of the antenna to be measured through the value corresponding to the antenna position and the corresponding phase adjustment times in the random matrix.

6. The method according to any one of claims 1-4, characterized in that Wherein, Constructing a transmission channel vector includes: Construct the transmission channel vector according to the first position parameter of the antenna element of the antenna to be measured, the second position parameter of the receiving antenna, the active element pattern and the elements in the random matrix in each phase adjustment.

7. The method according to any one of claims 1-4, characterized in that Wherein, The formula of the transmission channel vector is: where N is the number of antennas, is the active element pattern of the nth element, θ is the elevation angle corresponding to the receiving antenna, is the azimuth angle corresponding to the receiving antenna, m represents the mth measurement, T represents the transpose operation of a vector, and R n is the position vector of the nth element in the Cartesian coordinate system, r is the direction vector of the receiving antennas of the n elements in the Cartesian coordinate system, and Δα nm = G nm is the phase adjusted for the nth element at the mth measurement, and G nm is the element in the mth row and nth column of the matrix G, j is the imaginary unit, and k is the wavenumber.

8. The method according to any one of claims 1 to 4, characterized in that Wherein, The formula of the measurement space matrix is: Among them, is the measurement space matrix, a m is the transmission channel vector, and Q0 is the index set.

9. The method according to any one of claims 1-4, characterized in that, Wherein, The formula of the initial vector of the excitation distribution is: where \(x_0\) is the initial vector of the excitation distribution, \(M\) is the number of measurements, \(\psi\) m is the amplitude data at the \(m\)-th measurement, is the normalized eigenvector corresponding to the maximum eigenvalue of the measurement space matrix.

10. An antenna excitation reconstruction device, characterized in that, Including: An adjustment module, configured to adjust the phase of the antenna to be measured multiple times and construct a transmission channel vector; An acquisition module, configured to obtain the amplitude data received by the receiving antenna under the condition that the phase of the antenna to be measured changes each time; A construction module, configured to construct a measurement space matrix according to the transmission channel vector and the amplitude data; A first determination module, configured to determine the initial vector of the excitation distribution according to the measurement space matrix; A second determination module, configured to determine the truncated generalized gradient of the loss function according to the transmission channel vector and the amplitude data; A third determination module, configured to determine the final estimated value of the excitation distribution of the antenna to be measured according to the truncated generalized gradient and the initial vector of the excitation distribution, and complete the excitation reconstruction of the antenna to be measured.

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