CN113630197B - Antenna phase adjustment method and device, storage medium and electronic equipment - Google Patents
Antenna phase adjustment method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure provides a phase adjustment method and device of an antenna, a storage medium and electronic equipment, wherein the method comprises the following steps: obtaining actual measurement phase characterization information of an antenna; inputting the actually measured phase characterization information into a preset adjustment model to obtain a control voltage adjustment matrix which is output by the preset adjustment model according to the actually measured phase characterization information; based on the control voltage adjustment matrix, a control voltage is applied to each phased element of the phased array in the antenna. According to the method and the device, the operation amount and operation time are saved by training the preset adjusting model, the corresponding control voltage can be determined directly based on the obtained actually measured phase characterization information through the nonlinear operation capability of the deep learning model, so that the phase change realized by each phase control unit of the antenna LCPA after the corresponding control voltage is input can exactly offset the initial phase error caused by the manufacturing process, the working performance of the antenna is further improved, and the better antenna optimizing effect is achieved.
Description
Technical Field
The disclosure relates to the technical field of antennas, and in particular relates to a phase adjustment method and device for an antenna, a storage medium and electronic equipment.
Background
The antenna adjusts the wave beam phase through a liquid crystal phased array voltage control system (LCPA, liquid crystal phased array), so that the performance adjustment of the antenna is realized. Fig. 1 is a schematic diagram of a usage principle of an LCPA voltage control system, in which a liquid crystal layer is sandwiched between an upper substrate and a lower substrate of a phase shifter, and a bias voltage is applied between a printed circuit of an upper substrate unit and a metal ground of the lower substrate, so that the magnitude of an electric field between the upper substrate and the lower substrate can be changed, the arrangement direction of liquid crystal molecules in a corresponding area of each unit patch is changed, the dielectric constant of the liquid crystal layer is changed, and the phase delay of beam propagation is changed, so that the modulation of the beam phase is realized, and each unit patch is equivalent to a phase control unit. Fig. 2 shows a fitted curve (also referred to as V-phi curve) between control voltage (V) and phase (phi) of the LCPA voltage controlled system, which is a nonlinear curve and cannot be completely matched with an actual device due to factors such as electric field edge effects, process manufacturing errors, etc.
In practical engineering application, due to the problems of manufacturing technology and the like, the inconsistency among all phase control units in the phased array can lead to a random initial phase of the antenna when the antenna is not regulated and controlled, and the performance of the antenna is affected. In this case, under the condition that the fitted V-phi curve cannot be completely matched with the actual device, how to apply the voltages of each phase control unit in the LCPA to enable the antenna to achieve the optimal performance becomes a problem to be solved. In the prior related art, the calculation can be performed through an algorithm based on iterative solution such as a mode search algorithm or a gradient descent algorithm, but the algorithm has a large number of iterations and low convergence speed, and the convergence result depends on the control of the environmental noise of the test site, so that the accuracy of the convergence result is low, and the optimization efficiency and the optimization effect of the antenna are seriously affected.
Disclosure of Invention
An embodiment of the present disclosure is directed to providing a method and an apparatus for adjusting a phase of an antenna, a storage medium, and an electronic device, so as to solve the problems of low optimization efficiency and poor optimization effect of the antenna in an antenna optimization process in the prior art.
The embodiment of the disclosure adopts the following technical scheme: a phase adjustment method of an antenna, comprising: obtaining actual measurement phase characterization information of an antenna, wherein the actual measurement phase characterization information is phase characterization information of the antenna under the condition that no control voltage is applied; inputting the actually measured phase characterization information into a preset adjustment model to obtain a control voltage adjustment matrix which is output by the preset adjustment model according to the actually measured phase characterization information; and applying control voltages to each phased array unit in the antenna based on the control voltage adjustment matrix, wherein one control voltage in the control voltage adjustment matrix is used for controlling one phased array unit in the phased array.
In some embodiments, the measured phase characterization information includes at least: the actual far field pattern of the antenna or a phase function representing the actual far field pattern.
In some embodiments, the preset adjustment model is trained based on the following steps: determining a default phase adjustment function based on at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage; randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix; training a preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
In some embodiments, the determining a default phase adjustment function based on the at least one sample antenna comprises: determining a voltage phase curve of each sample antenna; an average of all of the voltage phase curves is determined, and the default phase adjustment function is determined to characterize the curve of the average of all of the voltage phase curves.
In some embodiments, the loss function is used to characterize at least one of the following antenna performance parameters: the gain of the main beam in the far-field directional diagram of the antenna, the ratio of the main lobe and the side lobe in the far-field directional diagram of the antenna and the directivity coefficient.
In some embodiments, the preset conditions include at least one of: the value of the loss function is minimum; the value of the loss function is less than a preset threshold.
The embodiment of the disclosure also provides a phase adjustment device of an antenna, including: the acquisition module is used for acquiring actual measurement phase characterization information of the antenna, wherein the actual measurement phase characterization information is the phase characterization information of the antenna under the condition that no control voltage is applied; the processing module is used for inputting the actually measured phase characterization information into a preset regulation model to obtain a control voltage regulation matrix which is output by the preset regulation model according to the actually measured phase characterization information; and the adjusting module is used for applying control voltages to each phased array unit in the antenna based on the control voltage adjusting matrix, wherein one control voltage in the control voltage adjusting matrix is used for controlling one phased array unit in the phased array.
In some embodiments, further comprising: the training module is used for training the preset adjustment model based on the following steps: determining a default phase adjustment function based on at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage; randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix; training a preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
Embodiments of the present disclosure also provide a storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of the above-described phase adjustment method of an antenna.
The embodiment of the disclosure also provides an electronic device, at least comprising a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the phase adjustment method of the antenna when executing the computer program on the memory.
The beneficial effects of the embodiment of the disclosure are that: through training a preset adjusting model, the operation amount and operation time are saved, through the nonlinear operation capability of the deep learning model, corresponding control voltage determination can be directly carried out based on the obtained actually measured phase characterization information, so that the phase change realized by each phase control unit of the antenna LCPA after the corresponding control voltage is input can exactly offset the initial phase error caused by the manufacturing process, the working performance of the antenna is further improved, and a better antenna optimization effect is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a schematic diagram of a related art LCPA voltage control system;
FIG. 2 is a graph of a fit between control voltage and phase of an LCPA voltage control system of the related art;
fig. 3 is a flowchart of a phase adjustment method of an antenna according to a first embodiment of the present disclosure;
FIG. 4 is a schematic diagram of training steps of a preset adjustment model according to a first embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a phase adjustment device of an antenna according to a second embodiment of the present disclosure;
fig. 6 is a schematic diagram of another structure of a phase adjustment device of an antenna according to a second embodiment of the disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present disclosure.
Detailed Description
Various aspects and features of the disclosure are described herein with reference to the drawings.
It should be understood that various modifications may be made to the embodiments of the application herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of this disclosure will occur to persons of ordinary skill in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It is also to be understood that, although the disclosure has been described with reference to some specific examples, a person skilled in the art will certainly be able to achieve many other equivalent forms of the disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the disclosure in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
The antenna adjusts the wave beam phase through LCPA, thereby realizing the performance adjustment of the antenna. The LCPA voltage control system is a nonlinear system, and the V-phi curve corresponding to the phase adjustment function B (V) is nonlinear. In the related art, a V-phi curve is obtained mainly by actually measuring a V-phi scatter diagram of each unit in LCPA and performing function fitting, then the V-phi curve is written into a control MCU in a table form, and table lookup is performed according to phase shift requirements to configure control voltage, so that phase adjustment of antenna beams is realized. However, in practical engineering applications, the performance of the LCPA voltage control system may be interfered by various influencing factors, such as electric field edge effect, process manufacturing errors (uniformity errors of the glass substrate, electrode uniformity errors), and the like. The combined effect of these physical processes results in the V- Φ curve of the LCPA obtained not being fully compatible with the actual device.
Because of the problems of manufacturing process and the like, the situation that the thickness of each phase control unit in the LCPA is uneven may exist, and at the moment, an initial phase error exists when the antenna is not regulated and controlled, and the antenna performance is affected, so that the LCPA needs to be optimized to offset the influence of the initial phase error on the antenna performance, and the antenna can exert optimal performance.
Because LCPA cannot build a mathematical analytical model of a voltage-controlled system, it is necessary to design and use a model-free optimization algorithm independent of the mathematical model of the system in an optimization system for processing. Common optimization algorithms for LCPA voltage controlled systems are: pattern search algorithm, gradient descent algorithm. Both algorithms are optimization algorithms based on iterative solution. (1) The pattern search method changes the value of each control signal variable during each iteration, so that the updated variable steps in a direction that increases the system performance. When changing the values of all variables does not result in an increase in system performance, the step size is halved and the process is repeated until the step size is small enough. The iteration times of the algorithm are generally 1000-2000 times, the algorithm convergence speed is low, and the antenna optimization time is seriously influenced. (2) The gradient descent method changes the values of all control signal variables simultaneously in each iteration process, so that the updated control signal steps along the original signal gradient descent direction. Until the phase shift performance reaches a set convergence value. The iteration times of the algorithm are more than 80 times; meanwhile, the nonlinear solution is not accurate enough, and the calculated amount can be multiplied when large-scale array calculation is performed.
In order to solve the above-mentioned problems in the prior art, a first embodiment of the present disclosure provides a phase adjustment method of an antenna, which is mainly used for a phase adjustment process of an antenna with LCPA, and a flowchart thereof is shown in fig. 3, and mainly includes steps S10 to S30:
s10, obtaining actual measurement phase characterization information of the antenna.
The thickness of the liquid crystal layer is uneven in each phase control unit in the LCPA of the antenna possibly caused by the manufacturing process, so that the dielectric constants of the liquid crystal layer parts corresponding to the phase control units are different, an initial phase is arranged between beams sent out by the antenna under the condition that no control voltage is applied, and data obtained by acquiring the actually measured phase characterization information of the antenna at the moment reflect the difference between the phase control units. The purpose of this embodiment is how to quickly determine the control voltage to each phase control unit, so that the phase adjustment performed by the phase control unit after the control voltage is applied can cancel out the initial phase error generated by the process problem.
In some embodiments, the measured phase characterization information may be an actual far-field pattern corresponding to the antenna or a phase function representing the actual far-field pattern
Other parameters for characterizing the phase situation, such as the field strength value in the normal direction, where θ and +.>
For representing the offset angle of the beam from the antenna in the horizontal and vertical directions, i.e. azimuth and elevation.
S20, inputting the actually measured phase characterization information into a preset adjustment model to obtain a control voltage adjustment matrix which is output by the preset adjustment model according to the actually measured phase characterization information.
The preset adjustment model in this embodiment is a deep learning model, which can be obtained by training using any neural network model, for example, a 16/19 layer VGG network, a 22 layer googlenet network, or the like, and can also perform design of network layer number and structure by itself, which is not limited in this embodiment. Specifically, the implementation of the preset adjustment model in this embodiment may be equivalently a solution of an optimization problem, and the core of the implementation is that a phase shift performance evaluation index J is defined, where J is a function related to the control voltage adjustment matrix, that is, a specific value of the control voltage adjustment matrix has an influence on J, and the purpose of the preset adjustment model is to solve the control voltage adjustment matrix that minimizes the value of J. At this time, when the control voltage adjustment matrix output by the preset adjustment model is applied to each phase control unit of the antenna LCPA, the phase adjustment performed by the phase control unit can offset the initial phase error generated by the process problem, so as to optimize the antenna performance.
When the model training is actually performed, the training step of the preset adjustment model can be performed offline, so that the operation time is further saved, and the operation amount when the antenna optimization is actually performed is reduced. Specifically, the training steps of the preset adjustment model are shown in fig. 4, and mainly include steps S21 to S23:
s21, determining a default phase adjustment function based on at least one sample antenna.
The sample antenna can be an antenna to be optimized or other antennas produced in the same batch as the antenna to be optimized, and in order to improve universality of a preset adjustment model under normal conditions, a large number of antennas in different batches can be selected when the sample antenna is selected, so that the preset adjustment model obtained through final training can achieve a good optimization effect when any antenna is optimized. The phase adjustment function is used for representing the adjustment condition of the control voltage on the phase of the sample antenna, and the default phase adjustment function is the phase adjustment function B (V) which can meet the requirement of all sample antennas, namely, all sample antennas can use the same phase adjustment function B (V) to determine the control voltage when performing phase adjustment. It should be noted that the size and dimensions of the LCPA array are the same in all sample antennas.
Specifically, when determining a default phase adjustment function, firstly determining a voltage phase curve (i.e. a V-phi curve) of each sample antenna, obtaining a V-phi scatter diagram of each sample antenna through actual measurement in the process of determining the V-phi curve of each sample antenna, and then obtaining the V-phi curve through interpolation fitting; during actual measurement, the voltage value can be controlled to carry out gradient change in a 0.25V step length within a 0-18V interval in the definite process of the scatter diagram, a corresponding phase value is obtained, and during fitting, a pchip interpolation function can be selected as an interpolation function, and the interpolation interval is 10mV. After determining the V-phi curves for each sample antenna, all the obtained V-phi curves are averaged to obtain an average processing result, and a default phase adjustment function B (V) is determined to characterize the curves of the value processing results of all the voltage phase curves. Further, the specific manner of the mean processing may be mean processing, moving average processing, or the like, or may be other manners for performing the mean processing, which is not limited in this embodiment.
For example, let the control voltage adjustment matrix be
Then apply electricityThe ideal phase matrix generated after the pressure matrix V can be expressed as:
the actual phase matrix will be expressed as: phi B =b (V) +s, where S is an m×n-dimensional noise matrix whose specific error distribution characteristics can be adjusted according to different noise types, e.g. when S is additive white gaussian noise, there is
σ n 2 Is the noise variance.
S22, randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix.
Initial phase error matrix Φ rand The method is used for representing the non-uniformity among the phase control units caused by process non-uniformity, namely simulating initial phase errors among the phase control units of different antennas caused by manufacturing process problems, and can be used for carrying out random large-scale generation through simulation software such as MATLAB. After generating the initial phase error matrix with preset quantity, the initial phase error matrix can not be directly measured in actual operation, so that the initial phase error matrix can be sequentially used as initial phase conditions of each phase control unit in the antenna to determine corresponding theoretical phase characterization information such as a directional diagram, field intensity values in normal direction and the like
And pass through
Representing the initial phase error condition of the phase control unit.
In actually determining the phase characterization information corresponding to each initial phase error matrix, the receiving unit is typically placed in a fixed location near the antenna, but due to each of the LCPA phased arraysThe phase control units have the same position difference, so that the distances between the receiving units and the phase control units are different, and the inherent phase difference phi between the units is further caused 0 Is generated. Specifically, Φ 0 The representation can be made by the following matrix:
wherein,,
it should be noted that the inherent phase difference Φ involved in the present embodiment 0 The cancellation can be performed directly by other means that have been disclosed in the prior art and are dedicated to the cancellation of the inherent phase difference, and the present embodiment does not disclose a scheme how to cancel the inherent phase difference.
In determining the initial phase error matrix Φ rand Corresponding theoretical phase characterization information
In this embodiment, the theoretical phase characterization information corresponding to the initial phase matrix is calculated according to the following formula:
s23, training the preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
The theoretical phase characterization information obtained in the step S22
As a network input of the preset adjustment model, the form of the input data can be selected as follows:
i.e. the input data can be equivalent to a total of 2 pixels
To ensure that the input data of the neural network is real, wherein N is θ Is->
Sampling points in the θ direction, +.>
Is that
At->
The number of samples in the direction.
When training is actually performed, the model training termination condition is obtained by presetting a loss function of the adjustment model. Specifically, it may be set that model training is deemed complete when the loss function meets a preset condition. When the loss function meets the preset condition, the parameter values in the current model network can be saved to form a trained preset adjustment model, and the trained parameter values can be directly used for controlling the output of the voltage adjustment matrix when the preset adjustment model is used subsequently. It should be noted that, the parameter values in the model network may include various model network parameters such as network layer number, node number, weight value, etc., and the type and number of the parameters specifically included in the model network may be adjusted according to different neural network models used, which is not limited in this embodiment; the specific training optimization mode of the parameter values of each parameter is related to training logic in the network model, and the specific training mode is not limited in this embodiment, so long as the expected training effect can be achieved.
In this embodiment, when the loss function is selected, the antenna performance is mainly used as an index, for example, the loss function is at least used to characterize one of the following antenna performance parameters: the gain of the main beam in the far-field pattern of the antenna, the ratio of the main lobe and the side lobe in the far-field pattern of the antenna, and the directivity coefficient may also be other parameters for characterizing the performance of the antenna, and the embodiment is not particularly limited. The control voltage regulation matrix output by the preset regulation model can meet the requirement of the antenna performance parameter by limiting the antenna performance parameter as a loss function.
For example, in some embodiments, where the final optimization is aimed at maximizing the directivity coefficient D, the loss function may be designed by reference to the calculation formula for D for the main beam
In terms of:
at this time, the formula for setting the loss function can be:
at this time, if the maximum D value is required to be ensured, a preset condition is set to make the loss function
The minimum value is required, wherein,
for a control voltage regulation matrix which requires a preset regulation model output, i.e. in the control voltage regulation matrix +.>
Loss function->
Minimum. Will->
The measured phase characterization information formed by the antenna after being applied to each phase control unit is as follows:
at this time, the liquid crystal display device,
the calculated phase situation should be exactly equal to +.>
Cancel each other out, or the sum of the two approaches zero indefinitely, so that the corresponding calculated +.>
The directivity coefficient of (2) is the largest.
It should be appreciated that when the antenna performance parameter as the optimization objective changes, the corresponding loss function formula also changes, e.g., when the antenna performance parameter is selected to maximize the gain of the main beam in the far field pattern
Or->
Wherein,,
for a calibrated far field pattern, which can be determined by simulation, e.g.
Or can be obtained by means of actual measurement.
Because the selected optimization targets are different, the corresponding loss functions are different, when the preset conditions are set, a threshold value can be preset besides the value of the loss function is set to be minimum, and when the value of the loss function is smaller than the preset threshold value, the loss function is determined to accord with the preset conditions, so that the adaptability of the termination conditions is improved, and the training times and training time are reduced to a certain extent.
In addition, in some embodiments, for the trained preset adjustment model, the trained preset adjustment model may be tested by test data, and whether the preset adjustment model meets the actual requirement is determined according to the test result, and if the preset adjustment model cannot meet the requirement, the preset adjustment model may be adjusted by modifying the model structure, adjusting the number of model layers, the number of nodes or other model parameters, and retraining the model based on steps S21 to S23 is performed. The test data can be obtained through simulation, the actual requirement can be a specific requirement on the antenna performance, and the embodiment is not limited to the specific content, so long as the control voltage adjustment matrix output by the preset adjustment model is ensured
After application to the phased elements of the antenna, the antenna actually exhibits performance that meets the requirements.
Through the steps, training of the preset adjusting model is achieved, the control voltage adjusting matrix output by the preset adjusting model meets the preset condition of minimum loss function through continuous optimization of model parameters, and therefore initial phase errors caused by a manufacturing process of the phase control units can be eliminated after voltages corresponding to the control voltage adjusting matrix are applied to the phase control units of the antenna, and the antenna has better working performance.
S30, applying control voltages to each phased array unit in the antenna based on the control voltage adjustment matrix.
Based on the actually measured phase characterization information obtained in the step S10, the actually measured phase characterization information is input into a preset adjustment model as input data, so that a control voltage adjustment matrix can be obtained, and when each control voltage in the control voltage adjustment matrix is applied to a corresponding phase control unit, the initial phase error caused by the manufacturing process of the phase control unit can be eliminated, and the antenna has better working performance. It should be noted that the size of the control voltage adjustment matrix is the same as the size of the array formed by the phase control units in the LCPA, each element in the control voltage adjustment matrix corresponds to only one phase control unit in the LCPA array, the position of the phase control unit in the array is the same as the position of the corresponding element in the control voltage adjustment matrix, and the value of the element is the control voltage to be applied to the corresponding phase control unit.
According to the method, the device and the system, the operation amount and operation time are saved by training the preset adjusting model, the corresponding control voltage can be determined directly based on the obtained actually measured phase characterization information through the nonlinear operation capability of the deep learning model, so that the phase change realized by each phase control unit of the antenna LCPA after the corresponding control voltage is input can exactly offset the initial phase error caused by the manufacturing process, the working performance of the antenna is further improved, and the better antenna optimizing effect is achieved.
A second embodiment of the present disclosure provides a phase adjustment device for an antenna that may be installed in an antenna having an LCPA for implementing control voltage adjustment of individual phased elements in the LCPA. Specifically, fig. 5 shows a schematic structural diagram of a phase adjustment device of an antenna, which mainly includes an acquisition module 10, a processing module 20, and an adjustment module 30, wherein the acquisition module 10 is configured to acquire actual measurement phase characterization information of the antenna, where the actual measurement phase characterization information is phase characterization information of the antenna under a condition that no control voltage is applied to the antenna; the processing module 20 is configured to input the measured phase characterization information into a preset adjustment model, so as to obtain a control voltage adjustment matrix output by the preset adjustment model according to the measured phase characterization information; the adjustment module 30 is configured to apply control voltages to respective phased elements of the phased array in the antenna based on a control voltage adjustment matrix, wherein one control voltage in the control voltage adjustment matrix is configured to control one phased element in the phased array. Specifically, the measured phase characterization information includes at least: the actual far field pattern of the antenna or a phase function representing the actual far field pattern.
In some embodiments, as shown in fig. 6, it further includes a training module 40 on the basis of fig. 5, which is mainly used for training the preset adjustment model based on the following steps: determining a default phase adjustment function based on the at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage; randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix; training the preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
In some embodiments, training module 40 is specifically configured to: determining a voltage phase curve for each sample antenna; and determining the average processing result of all the voltage phase curves, and determining a default phase adjustment function to represent the curve of the average processing result of all the voltage phase curves.
In particular, the loss function is used to characterize at least one of the following antenna performance parameters: gain of main beam in far field pattern of antenna, ratio of main lobe and side lobe in far field pattern of antenna, and directivity coefficient. In addition, the preset condition includes at least one of: the value of the loss function is minimal; the value of the loss function is smaller than a preset threshold, and what condition is specifically selected as the preset condition can be set according to actual requirements, which is not limited in this embodiment.
According to the method, the device and the system, the operation amount and operation time are saved by training the preset adjusting model, the corresponding control voltage can be determined directly based on the obtained actually measured phase characterization information through the nonlinear operation capability of the deep learning model, so that the phase change realized by each phase control unit of the antenna LCPA after the corresponding control voltage is input can exactly offset the initial phase error caused by the manufacturing process, the working performance of the antenna is further improved, and the better antenna optimizing effect is achieved.
A third embodiment of the present disclosure provides a storage medium, which may be installed in any antenna with an LCPA, and which is specifically a computer readable medium, storing a computer program, which when executed by a processor, implements the method provided by any embodiment of the present disclosure, including steps S31 to S33 as follows:
s31, obtaining actual measurement phase characterization information of the antenna, wherein the actual measurement phase characterization information is the phase characterization information of the antenna under the condition that no control voltage is applied;
s32, inputting the actually measured phase characterization information into a preset adjustment model to obtain a control voltage adjustment matrix which is output by the preset adjustment model according to the actually measured phase characterization information;
and S33, applying control voltages to each phased array unit in the antenna based on the control voltage adjustment matrix, wherein one control voltage in the control voltage adjustment matrix is used for controlling one phased array unit in the phased array.
Specifically, the measured phase characterization information includes at least: the actual far field pattern of the antenna or a phase function representing the actual far field pattern.
The computer program is further executed by the processor to train a preset model by: determining a default phase adjustment function based on the at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage; randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix; training the preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
The computer program is executed by the processor to determine a default phase adjustment function based on the at least one sample antenna, and specifically the processor performs the steps of: determining a voltage phase curve for each sample antenna; and determining the average processing result of all the voltage phase curves, and determining a default phase adjustment function to represent the curve of the average processing result of all the voltage phase curves.
In particular, the loss function is used to characterize at least one of the following antenna performance parameters: gain of main beam in far field pattern of antenna, ratio of main lobe and side lobe in far field pattern of antenna, and directivity coefficient. In addition, the preset condition includes at least one of: the value of the loss function is minimal; the value of the loss function is smaller than a preset threshold, and what condition is specifically selected as the preset condition can be set according to actual requirements, which is not limited in this embodiment.
According to the method, the device and the system, the operation amount and operation time are saved by training the preset adjusting model, the corresponding control voltage can be determined directly based on the obtained actually measured phase characterization information through the nonlinear operation capability of the deep learning model, so that the phase change realized by each phase control unit of the antenna LCPA after the corresponding control voltage is input can exactly offset the initial phase error caused by the manufacturing process, the working performance of the antenna is further improved, and the better antenna optimizing effect is achieved.
A fourth embodiment of the present disclosure provides an electronic device, which may be any antenna with an LCPA, and its schematic structure is shown in fig. 7, and includes at least a memory 100 and a processor 200, and further includes other functional modules or structural devices for implementing the original functions of the antenna. The memory 100 has stored thereon a computer program, and the processor 200 implements the method provided by any of the embodiments of the present disclosure when executing the computer program on the memory 100. Exemplary, the electronic device computer program steps are as follows S41 to S43:
s41, obtaining actual measurement phase characterization information of the antenna, wherein the actual measurement phase characterization information is the phase characterization information of the antenna under the condition that no control voltage is applied;
s42, inputting the actually measured phase characterization information into a preset adjustment model to obtain a control voltage adjustment matrix which is output by the preset adjustment model according to the actually measured phase characterization information;
and S43, applying control voltages to each phased array unit in the antenna based on the control voltage adjustment matrix, wherein one control voltage in the control voltage adjustment matrix is used for controlling one phased array unit in the phased array.
Specifically, the measured phase characterization information includes at least: the actual far field pattern of the antenna or a phase function representing the actual far field pattern.
The processor also executes a computer program to train the preset model as follows: determining a default phase adjustment function based on the at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage; randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix; training the preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
The processor, when executing the default phase adjustment function stored on the memory based on the at least one sample antenna, specifically executes the following computer program: determining a voltage phase curve for each sample antenna; and determining the average processing result of all the voltage phase curves, and determining a default phase adjustment function to represent the curve of the average processing result of all the voltage phase curves.
In particular, the loss function is used to characterize at least one of the following antenna performance parameters: gain of main beam in far field pattern of antenna, ratio of main lobe and side lobe in far field pattern of antenna, and directivity coefficient. In addition, the preset condition includes at least one of: the value of the loss function is minimal; the value of the loss function is smaller than a preset threshold, and what condition is specifically selected as the preset condition can be set according to actual requirements, which is not limited in this embodiment.
According to the method, the device and the system, the operation amount and operation time are saved by training the preset adjusting model, the corresponding control voltage can be determined directly based on the obtained actually measured phase characterization information through the nonlinear operation capability of the deep learning model, so that the phase change realized by each phase control unit of the antenna LCPA after the corresponding control voltage is input can exactly offset the initial phase error caused by the manufacturing process, the working performance of the antenna is further improved, and the better antenna optimizing effect is achieved.
While various embodiments of the present disclosure have been described in detail, the present disclosure is not limited to these specific embodiments, and various modifications and embodiments can be made by those skilled in the art on the basis of the concepts of the present disclosure, and these modifications and modifications should be within the scope of the present disclosure as claimed.
Claims (10)
1. A phase adjustment method for an antenna, comprising:
obtaining actual measurement phase characterization information of an antenna, wherein the actual measurement phase characterization information is phase characterization information of the antenna under the condition that no control voltage is applied;
inputting the actually measured phase characterization information into a preset adjustment model to obtain a control voltage adjustment matrix which is output by the preset adjustment model according to the actually measured phase characterization information;
and applying control voltages to each phased array unit in the antenna based on the control voltage adjustment matrix to offset initial phase errors of each phased array unit, wherein one control voltage in the control voltage adjustment matrix is used for controlling one phased array unit in the phased array.
2. The phase adjustment method according to claim 1, wherein the measured phase characterization information includes at least: the actual far field pattern of the antenna or a phase function representing the actual far field pattern.
3. The phase adjustment method according to claim 1, characterized in that the preset adjustment model is trained based on the steps of:
determining a default phase adjustment function based on at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage;
randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix;
training a preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
4. A phase adjustment method according to claim 3, wherein said determining a default phase adjustment function based on at least one sample antenna comprises:
determining a voltage phase curve of each sample antenna;
determining the average processing results of all the voltage phase curves, and determining the default phase adjustment function to characterize the curves of the average processing results of all the voltage phase curves.
5. A phase adjustment method according to claim 3, characterized in that the loss function is used to characterize at least one of the following antenna performance parameters: the gain of the main beam in the far-field directional diagram of the antenna, the ratio of the main lobe and the side lobe in the far-field directional diagram of the antenna and the directivity coefficient.
6. The phase adjustment method according to claim 5, wherein the preset condition includes at least one of:
the value of the loss function is minimum;
the value of the loss function is less than a preset threshold.
7. A phase adjustment device for an antenna, comprising:
the acquisition module is used for acquiring actual measurement phase characterization information of the antenna, wherein the actual measurement phase characterization information is the phase characterization information of the antenna under the condition that no control voltage is applied;
the processing module is used for inputting the actually measured phase characterization information into a preset regulation model to obtain a control voltage regulation matrix which is output by the preset regulation model according to the actually measured phase characterization information;
and the adjusting module is used for applying control voltages to each phased array unit in the antenna based on the control voltage adjusting matrix so as to offset the initial phase errors of each phased array unit, wherein one control voltage in the control voltage adjusting matrix is used for controlling one phased array unit in the phased array.
8. The phase adjustment device according to claim 7, characterized by further comprising:
the training module is used for training the preset adjustment model based on the following steps:
determining a default phase adjustment function based on at least one sample antenna, wherein the phase adjustment function is used to characterize the adjustment of the phase of the sample antenna by the control voltage;
randomly generating a preset number of initial phase error matrixes, and determining theoretical phase characterization information corresponding to each initial phase error matrix;
training a preset adjustment model based on theoretical phase representation information corresponding to the initial phase error matrix until a loss function of the preset adjustment model determined based on the phase adjustment function meets preset conditions.
9. A storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the phase adjustment method of an antenna according to any one of claims 1 to 6.
10. An electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program on the memory, realizes the steps of the phase adjustment method of an antenna according to any of claims 1 to 6.
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