CN-121997779-A - Multi-beam synthesis method and device based on cooperative null generation strategy

CN121997779ACN 121997779 ACN121997779 ACN 121997779ACN-121997779-A

Abstract

The application provides a multi-beam synthesis method and device based on a cooperative null generation strategy, and belongs to the technical field of antenna design. The method comprises the steps of randomly generating design indexes in a beam direction range, constructing a mask matrix based on the design indexes, constructing a multi-beam comprehensive model, inputting the mask matrix into the multi-beam comprehensive model, training the multi-beam comprehensive model to output amplitude excitation and phase excitation of each beam, constructing a target mask matrix according to beam performance indexes in an actual application scene, inputting the target mask matrix into the trained multi-beam comprehensive model, outputting amplitude excitation and phase excitation of each array unit for different beams, loading the amplitude excitation and the phase excitation into a multi-beam phased array, and generating a multi-beam far field pattern. The multi-beam synthesis method and device based on the cooperative null generation strategy provided by the application are used for reducing the loss of equivalent omni-directional radiation power on the premise of effectively inhibiting multi-beam interference.

Inventors

SONG CHUNYI
Chen haotian
Ma Zixian
XIE XINHONG
LI NAYU
XU ZHIWEI

Assignees

浙江大学

Dates

Publication Date: 20260508
Application Date: 20260409

Claims (10)

1. A multi-beam synthesis method based on a collaborative nulling strategy, the method comprising: Determining a beam direction range of a multi-beam phased array, randomly generating a design index in the beam direction range, defining a main lobe center area, a main lobe area, side lobe levels and null areas of each beam based on the design index, and constructing a mask matrix based on the main lobe center area, the main lobe area, the side lobe levels and the null areas of each beam by taking the side lobe levels as boundary constraints, wherein the null areas of each beam are a set of main lobe areas of other beams in the multi-beam; Constructing a multi-beam comprehensive model based on a residual network and a transducer module, inputting the mask matrix into the multi-beam comprehensive model as a training set, and training the multi-beam comprehensive model to output amplitude excitation and phase excitation of each beam, wherein the residual network learns local spatial features of the mask matrix and outputs a local feature map, and the transducer module learns global spatial features of the mask matrix based on the local feature map and outputs a global feature map; Constructing a target mask matrix according to the beam performance index in the actual application scene; Inputting the target mask matrix into a trained multi-beam comprehensive model, outputting amplitude excitation and phase excitation of each array unit in the target mask matrix for different beams, loading the amplitude excitation and the phase excitation into a multi-beam phased array, generating a multi-beam far-field pattern, and realizing beam synthesis based on the multi-beam far-field pattern.
2. The method of claim 1, wherein the defining the main lobe center region, main lobe region, side lobe level, and null region for each beam based on the design criteria comprises: the design index at least comprises beam direction and main lobe width, and the main lobe center area of each beam is determined based on the beam direction; Determining a main lobe region for each beam based on the main lobe width; Determining side lobe levels based on the main lobe regions; The null region for each beam is determined based on a set of main lobe regions in other beams in the multi-beam.
3. The method of claim 1, wherein the constructing a mask matrix based on the main lobe center region, the main lobe region, the side lobe levels, and the null region of each beam comprises: determining the row vector number of the mask matrix according to the array unit number of the multi-beam phased array in the horizontal direction, and determining the column vector number of the mask matrix based on the array unit number of the multi-beam phased array in the vertical direction; gray value distribution is respectively carried out on the main lobe center area, the main lobe area, the side lobe level and the null area of each wave beam according to the design index, so as to form a two-dimensional mask of each wave beam; And carrying out three-dimensional stacking on the two-dimensional mask of each beam according to the number of row vectors and the number of column vectors to obtain the mask matrix.
4. The method of claim 1, wherein the residual network learns local spatial features of the mask matrix, outputting a local feature map, comprising: The residual network comprises a plurality of residual blocks, wherein the plurality of residual blocks comprise N downsampled residual blocks and M basic residual blocks, and downsampling processing is carried out on the mask matrix based on the N downsampled residual blocks; And carrying out two-dimensional convolution operation on the mask matrix after the downsampling processing based on the M basic residual blocks, extracting local spatial features, and outputting the local feature map.
5. The method of claim 1, wherein the transform module learns global spatial features of the mask matrix based on the local feature map, outputting a global feature map, comprising: The transducer module comprises a multi-head self-attention layer and a position-wise feed-forward network, receives the local feature map, learns long-distance space dependency relationship in the mask matrix based on the multi-head self-attention layer, and outputs global features; And the position-wise feed forward network performs dimension expansion and compression conversion on the global feature and outputs a global feature map.
6. The method of claim 1, wherein said training the multi-beam synthesis model to output the amplitude and phase excitations of each beam comprises: Inputting the mask matrix into the multi-beam comprehensive model, extracting local spatial features of the mask matrix by the residual network, extracting global spatial features of the mask matrix by the transducer module, and outputting a global feature map; Splitting the global feature map according to the channel dimension of the global feature map, and performing mapping operation on the split global feature map to obtain an amplitude excitation initial value and a phase excitation initial value of each wave beam; And constructing a total loss function based on the side lobe level, the equivalent omnidirectional radiation power and the signal-to-interference ratio, and adjusting model parameters of the multi-beam comprehensive model through back propagation of the total loss function until the training of the multi-beam comprehensive model is completed after the termination condition is reached.
7. The method of claim 1, wherein loading the amplitude stimulus and the phase stimulus into a multi-beam phased array generates a multi-beam far field pattern, comprising: loading the amplitude excitation and the phase excitation to each array unit in the multi-beam phased array to form an excitation matrix; determining a calculation mode according to the application scene of the multi-beam phased array, and calculating the excitation matrix based on the calculation mode to obtain far-field radiation information of each beam; the far field radiation information for each beam is integrated to generate the multi-beam far field pattern.
8. The method of claim 6, wherein constructing the total loss function based on the sidelobe levels, the equivalent omni-directional radiation power, and the signal-to-interference ratio comprises: respectively calculating side lobe level loss, equivalent omnidirectional radiation power loss and signal-to-interference ratio loss of the multi-beam phased array; determining the weight of each loss according to the performance priority requirement of the multi-beam phased array; And carrying out weighted summation on the side lobe level loss, the equivalent omnidirectional radiation power loss and the signal-to-interference ratio loss based on the weight to obtain the total loss function.
9. The method of claim 7, wherein the determining a calculation mode according to the application scenario of the multi-beam phased array, and calculating the excitation matrix based on the calculation mode, to obtain far-field radiation information of each beam, includes: if the application scene is an ideal multi-beam phased array scene, calculating the excitation matrix based on two-dimensional inverse Fourier transform to obtain far-field radiation information of each beam; And if the application scene is an actual engineering scene, extracting far-field electric field components of each array unit from full-wave simulation, and obtaining far-field radiation information of each wave beam through electric field superposition and radiation intensity conversion calculation.
10. The multi-beam comprehensive device based on the cooperative nulling generation strategy is characterized by comprising a construction module, a training module and a processing module; the construction module is used for determining a beam direction range of the multi-beam phased array, randomly generating a design index in the beam direction range, defining a main lobe center area, a main lobe area, a side lobe level and a null area of each beam based on the design index, and constructing a mask matrix based on the main lobe center area, the main lobe area, the side lobe level and the null area of each beam by taking the side lobe level as boundary constraint, wherein the null area of each beam is a set of main lobe areas of other beams in the multi-beam; The training module is used for constructing a multi-beam comprehensive model based on a residual network and a transducer module, inputting the mask matrix into the multi-beam comprehensive model as a training set, and training the multi-beam comprehensive model to output amplitude excitation and phase excitation of each beam; the construction module is also used for constructing a target mask matrix according to the beam performance index in the actual application scene; The processing module is used for inputting the target mask matrix into a trained multi-beam comprehensive model, outputting amplitude excitation and phase excitation of each array unit in the target mask matrix for different beams, loading the amplitude excitation and the phase excitation into a multi-beam phased array, generating a multi-beam far-field directional diagram, and realizing beam synthesis based on the multi-beam far-field directional diagram.

Description

Multi-beam synthesis method and device based on cooperative null generation strategy Technical Field The application relates to the technical field of antenna design, in particular to a multi-beam synthesis method and device based on a collaborative null generation strategy. Background The multi-beam phased array has the advantages of high flexibility and wide scanning angle by virtue of the characteristic of being capable of simultaneously generating a plurality of independent high-gain directional beams, and is widely applied to a plurality of key fields such as point-to-multipoint broadcasting, satellite communication, 5G wireless communication and the like. In the scenes of low earth orbit satellite communication user terminals and the like, the multi-beam phased array can meet the requirements of broadband transmission, access point communication and multipoint emergency communication, provides an efficient solution for a dynamic communication environment, and becomes one of core technologies for supporting multiple users and simultaneously transmitting data. For a general single-beam phased array system, the existing algorithm can generate nulls at interference positions of the beams, so that the anti-interference capability of the beams is improved. However, for a multi-beam phased array system, it is necessary to have each beam independently generate nulls in the main lobe directions of the other beams to achieve high signal-to-interference ratios to reduce interference in the main lobe region. Applying multiple null constraints on each beam simultaneously inevitably destroys its radiation characteristics, resulting in problems of reduced main lobe gain, increased beam width, and reduced main lobe gain, especially when the number of beams is large. Furthermore, when nulls are generated by a plurality of beams respectively in the same direction, these nulls are not simply probabilistic superimposed in space, and their phase inconsistency may cause an undesired coherent superimposition of electromagnetic fields in that direction, thereby amplifying interference energy in a local area, but rather causing deterioration in the inter-beam interference resistance. This results in the existing algorithms being difficult to achieve higher main lobe gain and anti-beam-to-beam interference effects when dealing with multi-beam shaping. On the other hand, in the existing phased array synthesis method based on deep learning, part of models need to be retrained when optimizing target changes, applicability is limited, the other part is limited by the performance of the traditional algorithm on which a training data set depends, performance requirements and real-time operation requirements of multi-beam formation are difficult to be met at the same time, and the balance problem of interference suppression and radiation efficiency between beams cannot be effectively solved. Disclosure of Invention In view of the above, the present application provides a multi-beam synthesis method and apparatus based on a collaborative nulling strategy, which are used to reduce the loss of equivalent omni-directional radiation power on the premise of effectively suppressing multi-beam interference. Specifically, the application is realized by the following technical scheme: the first aspect of the present application provides a multi-beam synthesis method based on a collaborative null generation strategy, the method comprising: Determining a beam direction range of a multi-beam phased array, randomly generating a design index in the beam direction range, defining a main lobe center area, a main lobe area, side lobe levels and null areas of each beam based on the design index, and constructing a mask matrix based on the main lobe center area, the main lobe area, the side lobe levels and the null areas of each beam by taking the side lobe levels as boundary constraints, wherein the null areas of each beam are a set of main lobe areas of other beams in the multi-beam; Constructing a multi-beam comprehensive model based on a residual network and a transducer module, inputting the mask matrix into the multi-beam comprehensive model as a training set, and training the multi-beam comprehensive model to output amplitude excitation and phase excitation of each beam, wherein the residual network learns local spatial features of the mask matrix and outputs a local feature map, and the transducer module learns global spatial features of the mask matrix based on the local feature map and outputs a global feature map; Constructing a target mask matrix according to the beam performance index in the actual application scene; Inputting the target mask matrix into a trained multi-beam comprehensive model, outputting amplitude excitation and phase excitation of each array unit in the target mask matrix for different beams, loading the amplitude excitation and the phase excitation into a multi-beam phased array, generating a multi-beam far-field patter