GNSS compact antenna array layout method and device and storage medium

Abstract

The invention provides a GNSS (Global Navigation Satellite System) compact antenna array layout method and device and a storage medium, and the method comprises the steps: generating a plurality of GNSS antenna arrays according to the area of a carrier platform and the size range of antennas, generating interference by using simulation software, calculating the average availability of a receiver, selecting the GNSS antenna array with the large average availability of the receiver to enter a mating pool, the method has the beneficial effects that by fusing the electromagnetic characteristics of the antenna body and a multi-target game mechanism, the global optimal solution of the anti-interference performance is realized under the constraint of a limited carrier space; a hardware solution with strong interference suppression and high space efficiency is provided for a miniaturized remote sensing platform.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation, and in particular to a GNSS compact antenna array layout method, device and storage medium. Background Art

[0002] As remote sensing platforms continue to rely more and more on high-precision positioning and timing services, the Global Navigation Satellite System (GNSS) has become the core spatial and temporal reference infrastructure for ensuring the integrity of remote sensing data links. However, GNSS satellites orbit at high altitudes and transmit low signal power, with the typical received power reaching the ground being only around -160dBW, completely drowned out by background noise. This makes it highly susceptible to intentional or unintentional electromagnetic interference, exposing remote sensing data links to the potential risk of reduced availability or even failure.

[0003] In recent years, with the miniaturization and widespread availability of low-cost jamming equipment, malicious interference targeting GNSS has shifted from centralized, single-source interference to distributed, multi-source interference from space, air, and ground-based sources. The spatial distribution of interference is characterized by high density and multi-source characteristics, rendering the defense strategy of time-frequency filtering based on single GNSS antennas completely ineffective. Consequently, an increasing number of GNSS receivers are adopting array antenna structures, using algorithms such as minimum variance distortionless response (MVDR) and power inversion (PI) to construct spatial beamforming or nulling models to achieve adaptive spatial interference suppression against multiple interference sources. Generally speaking, an N-element array antenna provides N-1 degrees of freedom, meaning that N-1 nullings can be constructed to suppress interference signals from N-1 different directions. Therefore, there is a significant positive correlation between antenna array size and interference suppression capability, making the number of antennas a key parameter determining system performance.

[0004] Existing antenna arrays typically default to half a wavelength in spacing. Since GNSS signals received in practice are typically in the L-band, with wavelengths ranging from 15cm to 30cm, simply increasing the number of antennas in an array without changing the element spacing results in a quadratic increase in array size. However, the space available on remote sensing platforms makes it difficult to deploy large arrays. Therefore, finding the optimal antenna array deployment within these strict spatial constraints is a pressing issue. Summary of the Invention

[0005] The main purpose of the present invention is to provide a GNSS compact antenna array layout method, device and storage medium, aiming to solve the problem of how to deploy the antenna array with the greatest benefit under limited space constraints.

[0006] The present invention provides a GNSS compact antenna array layout method, comprising: S1. Perform data acquisition operations to obtain the carrier platform area and the size range of the antenna; S2. Execute a GNSS antenna array generation operation to randomly generate a plurality of GNSS antenna arrays according to the area of the carrier platform and the size range of the antenna; S3, executing a simulation array generation operation, using preset HFSS simulation software to generate a simulation array corresponding to the GNSS antenna array, and randomly generating interference; S4. Execute a receiver average availability calculation operation to simulate satellite signals according to a preset STK simulation software, calculate the elevation angle and pitch angle of each GNSS signal, and calculate the average receiver availability of each GNSS antenna array; S5. Execute a GNSS antenna array selection operation to select a preset number of GNSS antenna arrays as target GNSS antenna arrays based on the average availability of each receiver. S6. Performing a chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and inputting the chromosome into the mating pool; S7, performing an iterative genetic operation to iteratively select two chromosomes from the mating pool for genetic operation, and input the obtained chromosomes into the mating pool, repeating the iterative genetic operation, and obtaining a target mating pool after the iteration is completed; S8. Execute a target chromosome selection operation, perform a receiver average availability calculation operation on each chromosome in the target mating pool, and select the chromosome with the largest receiver average availability as the target chromosome based on the receiver average availability of each chromosome; S9. Execute an application operation to apply the GNSS antenna array corresponding to the target chromosome as a target GNSS antenna array.

[0007] Furthermore, the step S7 of performing iterative genetic operation to iteratively select two chromosomes from the mating pool for genetic operation, input the obtained chromosomes into the mating pool, repeat the iterative genetic operation, and obtain the target mating pool after the iteration is completed includes: S701, randomly selecting two chromosomes from the mating pool, and randomly selecting cutting points on the two selected chromosomes; S702, exchanging sub-segments according to the positions of the selected cutting points to generate new chromosomes; S703, repairing the new chromosome to obtain a repaired chromosome; S704: Input the repaired chromosome into the mating pool, repeat the iterative genetic operation, and obtain the target mating pool after the iteration is completed.

[0008] Furthermore, the step S703 of repairing the new chromosome to obtain a repaired chromosome includes: S7031. Obtain constraint conditions based on the carrier platform area and the size range of the antenna; wherein the constraint conditions include minimum spacing constraint and / or boundary constraint, and / or antenna size constraint; S7032: Delete duplicate points from the new chromosome, and repair the new chromosome based on the constraint condition to obtain a repaired chromosome.

[0009] Furthermore, after the step S6 of performing the chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and inputting the chromosome into the mating pool, the step further includes: S711. Perform a mutation operation on the chromosomes in the mating pool to obtain mutated chromosomes; wherein the mutation operation is any one or more of antenna number mutation, antenna size mutation, and antenna position mutation; S712: Perform repair processing on the mutated chromosome to obtain a repaired chromosome.

[0010] Furthermore, after the step S6 of performing the chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, the method further includes: S721. Process each chromosome using a preset dynamic mask matrix to keep the length of each chromosome consistent.

[0011] Furthermore, after the step S5 of performing the GNSS antenna array selection operation to select a preset number of GNSS antenna arrays as target GNSS antenna arrays according to the average availability of each receiver, the method further includes: S601, according to the formula Calculate the crowding degree between the selected target GNSS antenna arrays; where, represents the congestion degree of the i-th target GNSS antenna array, represents the value of the mth objective function of the i+1th solution in the sorted sequence, represents the value of the mth objective function of the i-1th solution in the sorted sequence, represents the maximum value of the mth objective function, represents the minimum value of the mth objective function, and m represents the mth objective function; S602: Determine whether all congestion levels are greater than a preset congestion level. S603: If both are greater than the preset congestion degree, it is determined that the selected target GNSS antenna array is reasonable.

[0012] Furthermore, in the satellite signal simulated according to the preset STK simulation software, the satellite signal is a Beidou satellite signal.

[0013] The present invention also provides a GNSS compact antenna array layout device, comprising: A data acquisition module, used to instruct the implementation of step S1, performing a data acquisition operation to obtain the carrier platform area and the size range of the antenna; an antenna array generation module, configured to instruct the implementation of step S2, executing a GNSS antenna array generation operation, so as to randomly generate a plurality of GNSS antenna arrays according to the area of the carrier platform and the size range of the antenna; a simulation array generation module, configured to instruct the implementation of step S3, perform a simulation array generation operation, generate a simulation array corresponding to the GNSS antenna array using preset HFSS simulation software, and randomly generate interference; a calculation module, configured to instruct the implementation of step S4, performing an average receiver availability calculation operation, simulating satellite signals according to a preset STK simulation software, calculating the elevation angle and pitch angle of each GNSS signal, and calculating the average receiver availability of each of the GNSS antenna arrays; an antenna array selection module, configured to instruct the implementation of step S5, executing a GNSS antenna array selection operation, so as to select a preset number of GNSS antenna arrays as target GNSS antenna arrays according to the average availability of each receiver; a chromosome generation module, configured to instruct the implementation of step S6, to perform a chromosome generation operation, to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and to input the chromosome into the mating pool; an iterative genetic module, configured to instruct the implementation of step S7, to perform an iterative genetic operation, to iteratively select two chromosomes from the mating pool for genetic operation, and to input the obtained chromosomes into the mating pool, to repeat the iterative genetic operation, and to obtain a target mating pool after the iteration is completed; a target chromosome selection module, configured to instruct the implementation of step S8, perform a target chromosome selection operation, perform a receiver average availability calculation operation on each chromosome in the target mating pool, and select the chromosome with the largest receiver average availability as the target chromosome based on the receiver average availability of each chromosome; The application module is used to instruct the implementation of step S9 and perform an application operation to apply the GNSS antenna array corresponding to the target chromosome as a target GNSS antenna array.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0016] The beneficial effects of the present invention are as follows: by integrating the electromagnetic characteristics of the antenna body with the multi-objective game mechanism, a global optimal solution for anti-interference performance is achieved under the constraints of limited carrier space, providing a hardware solution for miniaturized remote sensing platforms with both strong interference suppression and high space efficiency. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 1 is a schematic diagram of a two-dimensional relationship between array spacing, number of array elements, and SVCC according to an embodiment of the present invention; Figure 2 1 is a schematic diagram of a three-dimensional relationship between array spacing, number of array elements, and SVCC according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a relationship affecting the average availability of a receiver according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the relationship between antenna spacing, antenna size, and S12 parameters according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the relationship between the number of antennas and the average receiver availability according to an embodiment of the present invention; Figure 6 This is a flow chart of a GNSS compact antenna array layout method according to an embodiment of the present invention; Figure 7 This is a schematic block diagram of the structure of a GNSS compact antenna array layout device according to one embodiment of the present invention; Figure 8 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. The connection can be a direct connection or an indirect connection.

[0020] The term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] In addition, in the present invention, descriptions such as "first" and "second" are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] Assuming that there is a satellite navigation signal and K uncorrelated interference signals incident in the far field as plane waves, the process of the antenna array simultaneously receiving the satellite navigation signal and the interference signal can be expressed as: in, in, Indicates time, is the signal vector received by the satellite navigation antenna array, is the satellite navigation signal received at the coordinate origin, is the kth interference signal received at the coordinate origin, The mean is zero and the variance is N-dimensional additive Gaussian white noise, and are the elevation angle and azimuth angle of the incident signal, is the incident signal wavelength, For the The three-dimensional coordinates of the array elements, is the plane wave unit propagation vector, represents the signal steering vector, It represents the phase difference of the nth array element relative to the first array element. represents the exponential function, represents the imaginary unit, and T represents transpose.

[0023] Since the satellite navigation signal, interference signal and noise are unrelated, the covariance matrix of the array received signal data is for: When the number of interferences is less than the number of array elements, that is, ,but: in is the eigenvalue corresponding to the interference subspace, is the eigenvector corresponding to the interference subspace, is the eigenvalue corresponding to the noise subspace, is the eigenvector corresponding to the noise subspace, represents the mean sign, represents the satellite navigation antenna array receiving signal vector, represents the conjugate transpose of the signal vector received by the satellite navigation antenna array, represents the satellite navigation signal defense difference matrix, represents the interference signal covariance matrix, represents the noise covariance matrix, Indicates the satellite navigation signal power, represents the satellite navigation signal steering vector, represents the conjugate transpose of the satellite navigation signal steering vector, represents the kth interference signal power, represents the kth interference signal steering vector, represents the conjugate transpose of the kth interference signal steering vector, represents the noise power, represents the N-dimensional unit matrix, represents the characteristic matrix composed of eigenvectors, Represents the conjugate transpose of the characteristic matrix composed of eigenvectors.

[0024] Since the satellite navigation signal power is much smaller than the noise power and the interference signal power is much larger than the noise power, the inverse of the covariance matrix of the received signal is It can be expressed as: At this time, the optimal filter weight calculated by the satellite navigation anti-interference criterion is: When the PI algorithm is used, It is usually expressed as a constraint vector, that is, one element is 1 and the other elements are 0, where the position of 1 depends on the selection of the reference array element. When the MVDR algorithm is used, then represents the desired signal steering vector, represents a constant, represents the optimal weight of the PI algorithm, represents the nth eigenvector, represents the conjugate transpose of the nth eigenvector, where N is the number of antennas.

[0025] Regardless of whether the PI algorithm or the MVDR algorithm is used, . It can be seen from this that It is completely orthogonal to the steering vector of any interference signal.

[0026] When the anti-interference weight is orthogonal to the interference steering vector, the deepest null is formed in the interference direction. The steering vectors around the interference steering vector, which we call the adjacent steering vectors, are not completely orthogonal to the anti-interference weight vector. The degree of orthogonality determines the null width. When the adjacent steering vector is highly correlated with the interference steering vector, the stronger the orthogonality with the anti-interference weight vector, the smaller the inner product of the adjacent steering vector and the anti-interference weight vector. At this time, the directional pattern corresponding to the adjacent steering vector is closer to the interference steering vector, and the null width is larger.

[0027] Therefore, this paper takes linear array and planar array as examples and uses the correlation of adjacent steering vectors to analogize the null width for demonstration.

[0028] The linear array has only elevation angle and no azimuth angle resolution. If the array spacing is d, then the kth interference signal comes from The steering vector is: Then define the interference direction and approaching directions The correlation coefficient of the steering vector is: Using Taylor's expansion of the first-order expression, we can get: because ,but: 1. Derivative with respect to N Then set , take the derivative with respect to N: because , , ,but but ,set up Then when hour, , but ,but It decreases with the increase of N. 2. Derivative with respect to d set up because ,set up because ,right , , , Carry out Taylor expansion respectively, then: Ignoring higher-order terms above the fifth power, then: but ,but As d increases, it decreases, and the null width widens as the array element spacing narrows.

[0029] Calculate the SVCC of the linear array under different conditions of array element number and array spacing (ratio to wavelength), such as Figure 1 As shown in Figure 2, it can be seen that the derivation of the formula is completely correct, and SVCC decreases with the increase of array spacing and the number of array elements.

[0030] Assume that the rectangular array is composed of antennas, and the element spacing is and . Then the kth interference signal comes from The steering vector of the direction is: Direction and approaching directions The correlation coefficient of the steering vector is: because Small, perform Taylor expansion on the trigonometric functions and retain the first-order terms: Use the geometric progression sum formula: It can be seen that the SCVV has the same combination form as the linear array, so it can also be explained that the null width decreases with the increase of the number of array elements and increases with the decrease of the array element spacing.

[0031] Calculate the SVCC of the rectangular array under different conditions of number of array elements and array spacing (ratio to wavelength), such as Figure 2 The conclusion is exactly the same as that of the linear array case, and SVCC decreases with the increase of array spacing and the number of array elements.

[0032] One of the main ways to quantify the theoretical performance of an antenna is through the quality factor Q value. For circularly polarized antennas, McLean proposed an expression for the Q value of a circularly polarized antenna, which can be expressed as: in, is the wave number, is the radius of the smallest sphere that encloses the antenna.

[0033] The antenna directivity coefficient , antenna gain and antenna reflectivity The relationship between them is: in, in, is the load impedance, is the characteristic impedance of the transmission line.

[0034] and: because is a decreasing function, This is also a decreasing function. Therefore, while the specific gain of an antenna depends on many factors, from a macroscopic perspective, it is inevitable that antenna gain will increase with increasing antenna size. Furthermore, increasing the gain of a passive antenna will inevitably improve the received signal-to-noise ratio.

[0035] When the array spacing is large, the coupling between arrays decreases. Because the influence of coupling is generally not considered in conventional satellite navigation anti-interference research, when designing compact antenna arrays, the reduction of array spacing will cause coupling to become a non-negligible influencing factor. Assuming that the characteristics of each array element are the same and the array elements are non-directional, the array signal reception model considering mutual coupling is: Where M is the mutual coupling matrix.

[0036] At this time, in order to eliminate the influence of the mutual coupling matrix, the array data containing the mutual coupling matrix should be subjected to the mutual coupling matrix inversion operation to compensate for the influence of the mutual coupling effect. Then, To simplify the analysis, assume that there is only a single interference, then: According to the matrix inversion lemma, we can get: Then the noise power after mutual coupling compensation is: because and Approximately orthogonal, then: Then the output SNR after mutual coupling compensation is: When there is no mutual coupling and no mutual coupling compensation is required, , then the output SNR without mutual coupling compensation is: Compared with the case without mutual coupling compensation, the SNR loss after mutual coupling compensation is: It can be seen that due to the influence of coupling, even if mutual coupling compensation is performed, the noise power will still be amplified to reduce the signal-to-noise ratio.

[0037] While increasing antenna size can improve gain and thus the signal-to-noise ratio (SNR), it also reduces the edge-to-edge distance for the same array spacing. The smaller the edge-to-edge distance, the greater the overlap in the near-field regions, leading to more significant energy exchange and shorter surface wave or current coupling paths. This in turn strengthens the electromagnetic interaction and mutual coupling between antenna elements. The stronger the mutual coupling effect, the more pronounced the decrease in SNR after mutual coupling compensation. These two factors also create a significant conflict in their impact on SNR.

[0038] In satellite navigation countermeasure scenarios, the most direct and mainstream evaluation indicator for the anti-interference performance of array receivers is the average receiver availability, which is defined as: in, is the average receiver availability, is an available function, when When , it means the receiver can work normally. When , it means that the receiver cannot work properly. The available function definitions are as follows: in, is the total number of visible satellites captured and tracked by the receiver after interference suppression, is the minimum number of visible satellites required for the receiver to meet positioning requirements, sgn is the sign function, is the carrier-to-noise ratio of the i-th satellite signal after interference suppression, To meet the carrier-to-noise ratio threshold for receiver acquisition / tracking sensitivity.

[0039] The number of array elements, array element spacing, antenna size, and coupling effect can all affect the average receiver availability, and there are many contradictions. The specific analysis is as follows: Figure 3 As shown: Increasing the number of antennas within a fixed-area carrier platform can reduce the null width, increase the available airspace area, and thus improve receiver availability. Furthermore, increasing the number of antennas can increase interference suppression, improve anti-interference performance, and thus improve the signal-to-noise ratio (SNR), thereby increasing the available airspace area and, in turn, increasing receiver availability. However, increasing the number of antennas inevitably reduces the array spacing, which widens the nulls, reducing the available airspace area and lowering receiver availability. Furthermore, reducing the array spacing increases coupling between antennas, and coupling suppression amplifies noise power, thereby reducing the SNR after mutual coupling compensation, further reducing the available airspace area and, consequently, decreasing receiver availability.

[0040] Furthermore, increasing antenna size can increase antenna gain and signal-to-noise ratio, thereby increasing the available airspace area and improving receiver availability. However, increasing antenna size also reduces the edge-to-edge distance between antennas, increasing coupling and reducing the signal-to-noise ratio after mutual coupling compensation. This, in turn, reduces the available airspace area and reduces receiver availability. Reducing the number of antennas and reducing antenna size yields the opposite conclusions, but also presents numerous contradictions.

[0041] In summary, the above factors should be comprehensively considered when designing the layout of a compact antenna array.

[0042] Since the compact GNSS antenna array layout design is a multi-objective optimization problem, the NSGA-II swarm intelligent optimization algorithm is used to solve it.

[0043] Antenna location: , represents the two-dimensional coordinates of N antennas.

[0044] Antenna size: , represents the equivalent radius of each antenna. Since circularly polarized antennas generally satisfy central symmetry, they are generally square. To simplify the analysis and keep the antenna structure basically consistent, we set .

[0045] Although coupling effects and antenna gain variations are also of concern in compact GNSS antenna array layout design, they are still intermediate indicators. The ultimate goal is still to care about receiver availability and the number of antennas within a limited carrier platform. The objective function is: Maximize the number of antennas: , Maximize the average receiver availability: , Minimum spacing constraint: , Boundary Constraints: , which stipulates that all antennas must be located within this rectangular area.

[0046] Antenna size constraints: .

[0047] Reference Figure 6 The present invention proposes a GNSS compact antenna array layout method, comprising: S1. Perform data acquisition operations to obtain the carrier platform area and the size range of the antenna; S2. Execute a GNSS antenna array generation operation to randomly generate a plurality of GNSS antenna arrays according to the area of the carrier platform and the size range of the antenna; S3, executing a simulation array generation operation, using preset HFSS simulation software to generate a simulation array corresponding to the GNSS antenna array, and randomly generating interference; S4. Execute a receiver average availability calculation operation to simulate satellite signals according to a preset STK simulation software, calculate the elevation angle and pitch angle of each GNSS signal, and calculate the average receiver availability of each GNSS antenna array; S5. Execute a GNSS antenna array selection operation to select a preset number of GNSS antenna arrays as target GNSS antenna arrays based on the average availability of each receiver. S6. Performing a chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and inputting the chromosome into the mating pool; S7, performing an iterative genetic operation to iteratively select two chromosomes from the mating pool for genetic operation, and input the obtained chromosomes into the mating pool, repeating the iterative genetic operation, and obtaining a target mating pool after the iteration is completed; S8. Execute a target chromosome selection operation, perform a receiver average availability calculation operation on each chromosome in the target mating pool, and select the chromosome with the largest receiver average availability as the target chromosome based on the receiver average availability of each chromosome; S9. Execute an application operation to apply the GNSS antenna array corresponding to the target chromosome as a target GNSS antenna array.

[0048] As described in step S1 above, a data acquisition operation is performed to obtain the carrier platform area and the size range of the antenna, wherein the carrier platform area and the size range of the antenna are both obtained by manual uploading and input.

[0049] As described in step S2 above, a GNSS antenna array generation operation is performed to randomly generate multiple GNSS antenna arrays based on the carrier platform area and the antenna size range. The random generation method is that the theoretical maximum number of antennas is , and set (At least two antennas are required to form an array) For each antenna array, The number of antennas N is randomly selected in is the length of the platform area, is the width of the platform area, r is the spacing between antennas, and Poisson disk sampling is used to generate an initial uniformly distributed point set within the carrier platform, ensuring the spacing constraint between any two points. The initial point set is fine-tuned using the maximum minimum spacing potential function to improve uniformity. For each antenna array, all antenna sizes are uniformly distributed from Select from them to obtain multiple antenna arrays.

[0050] As described in steps S3-S4 above, a simulation array generation operation is performed, using the preset HFSS simulation software to generate a simulation array corresponding to the GNSS antenna array, randomly generating interference, and performing a receiver average availability calculation operation to simulate satellite signals according to the preset STK simulation software, calculate the elevation angle and pitch angle of each GNSS signal, and calculate the average receiver availability of each GNSS antenna array. Microstrip antennas have become the most widely used satellite navigation anti-interference antennas due to their thin profile, small size, and ease of use. Therefore, this article uses the Beidou B3I signal as an example, using HFSS simulation software to adjust the dielectric constant of the dielectric substrate to generate antennas of different sizes at the same B3I frequency, and simulates the antenna gains corresponding to the different sizes. Then, based on the antenna array position, the corresponding antenna array is simulated and generated to obtain the corresponding mutual coupling matrix, and the signal-to-noise ratio loss under this array layout is calculated. Then, N-1 interferences from different directions are randomly generated, where N is the number of antennas in the antenna array. The GNSS antenna array reception model is used in combination with the antenna gain to generate the corresponding interference suppression pattern. Finally, the Beidou constellation is simulated using the STK simulation software, and the elevation and pitch angles of each GNSS signal in different regions are calculated. Finally, the average receiver availability is calculated.

[0051] As described in step S5 above, a GNSS antenna array selection operation is performed to select a preset number of GNSS antenna arrays as target GNSS antenna arrays based on the average availability of each receiver. Since the randomly generated GNSS antenna arrays are uneven, a preset number of GNSS antenna arrays can be selected as backup antenna arrays based on the average availability of the receivers.

[0052] As described in steps S6-S7 above, a chromosome generation operation is performed to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array. The chromosome is then input into the mating pool. An iterative genetic operation is performed to iteratively select two chromosomes from the mating pool for genetic operation, and the resulting chromosome is input into the mating pool. The iterative genetic operation is repeated, and after the iteration, the target mating pool is obtained. Although the selected target GNSS antenna array may not be optimal, it can be used as a parent for chromosome inheritance. That is, it is encoded to generate a chromosome corresponding to each target GNSS antenna array. A genetic operation is then performed, which randomly selects two parents from the mating pool, randomly selects a cut point, and exchanges sub-segments to generate offspring.

[0053] As described in the above steps S8-S9, the target chromosome selection operation is performed, and the receiver average availability calculation operation is performed on each chromosome in the target mating pool, and according to the size of the receiver average availability of each chromosome, the chromosome with the largest receiver average availability is selected as the target chromosome, and the application operation is performed to apply the GNSS antenna array corresponding to the target chromosome as the target GNSS antenna array. Among them, the selection method is to select according to the receiver average availability, and the method of calculating the receiver average availability has been explained above and will not be repeated here. The chromosome with the largest receiver average availability is selected as the target chromosome, and then the GNSS antenna array corresponding to the target chromosome is applied as the target GNSS antenna array. Therefore, by integrating the electromagnetic characteristics of the antenna body and the multi-objective game mechanism, the global optimal solution for anti-interference performance is achieved under the constraints of limited carrier space, providing a hardware solution for miniaturized remote sensing platforms with both strong interference suppression and high spatial efficiency.

[0054] In one embodiment, the step S7 of performing iterative genetic operations to iteratively select two chromosomes from the mating pool for genetic operations, input the obtained chromosomes into the mating pool, and repeat the iterative genetic operations to obtain the target mating pool after the iteration is completed includes: S701, randomly selecting two chromosomes from the mating pool, and randomly selecting cutting points on the two selected chromosomes; S702, exchanging sub-segments according to the positions of the selected cutting points to generate new chromosomes; S703, repairing the new chromosome to obtain a repaired chromosome; S704: Input the repaired chromosome into the mating pool, repeat the iterative genetic operation, and obtain the target mating pool after the iteration is completed.

[0055] As described in the above steps S701-S704, since the parent is the chromosome with better performance, there is a chance that its offspring will obtain a chromosome with better performance than the parent. Therefore, the cutting point is randomly selected to exchange the sub-segments. Then, since the new chromosome obtained may not meet the previously set constraints, it is necessary to process it according to the previous constraints and re-input it into the mating pool, and repeat the iterative genetic operation. After the iteration is completed, the target mating pool is obtained.

[0056] In one embodiment, the step S703 of repairing the new chromosome to obtain a repaired chromosome includes: S7031. Obtain constraint conditions based on the carrier platform area and the size range of the antenna; wherein the constraint conditions include minimum spacing constraint and / or boundary constraint, and / or antenna size constraint; S7032: Delete duplicate points from the new chromosome, and repair the new chromosome based on the constraint condition to obtain a repaired chromosome.

[0057] As described in the above steps S7031-S7032, that is, based on the area of the carrier platform and the size range of the antenna, the constraints are obtained. The constraints have been discussed in detail in the previous article and will not be repeated here. Then, since the generated chromosome has repeated points, they can be deleted and then repaired to obtain the repaired chromosome.

[0058] In one embodiment, after performing the chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array and inputting the chromosome into the mating pool in step S6, the method further includes: S711. Perform a mutation operation on the chromosomes in the mating pool to obtain mutated chromosomes; wherein the mutation operation is any one or more of antenna number mutation, antenna size mutation, and antenna position mutation; S712: Perform repair processing on the mutated chromosome to obtain a repaired chromosome.

[0059] As described in steps S711-S712 above, the mutation operation can be divided into antenna number mutation, antenna size mutation, and position mutation. Antenna number mutation can be divided into adding antennas and removing antennas. When increasing the number of antennas, new positions are randomly generated within the carrier platform area and repaired to meet physical requirements. When reducing the number of antennas, the antenna coupling contribution can be calculated. , and delete the antenna with the largest antenna coupling contribution. Position variation applies Cauchy perturbation to the selected antenna coordinates, namely: in, If the constraints are violated after the disturbance, the repair process is triggered.

[0060] Antenna size variation follows: .

[0061] In one embodiment, the conflict repair process specifically includes: Spacing conflict fix: calculate all antenna spacings first , identify the conflicting antenna pairs and move both in opposite directions by the same distance to satisfy the minimum separation constraint.

[0062] Out-of-bounds conflict repair: For out-of-bounds coordinates , which was projected to the region boundary: After repairing the cross-border conflict, re-detect the local spacing conflict. If a conflict occurs, repair the spacing conflict: Reset and repair: If the conditions cannot be met through spacing conflict correction and out-of-bounds conflict correction, reset and repair are performed to meet the constraints.

[0063] In one embodiment, after step S6 of performing the chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, the method further includes: S721. Process each chromosome using a preset dynamic mask matrix to keep the length of each chromosome consistent.

[0064] As described in step S721 above, since the number and size of antennas in each target GNSS antenna array are different, the lengths of the generated chromosomes will vary. Therefore, each chromosome can be processed using a preset dynamic mask matrix to ensure that the length of each chromosome remains consistent.

[0065] In one embodiment, after step S5 of performing the GNSS antenna array selection operation to select a preset number of GNSS antenna arrays as target GNSS antenna arrays based on the average availability of each receiver, the method further includes: S601, according to the formula Calculate the crowding degree between the selected target GNSS antenna arrays; where, represents the congestion degree of the i-th target GNSS antenna array, where represents the congestion degree of the i-th target GNSS antenna array, represents the value of the mth objective function of the i+1th solution in the sorted sequence, represents the value of the mth objective function of the i-1th solution in the sorted sequence, represents the maximum value of the mth objective function, represents the minimum value of the mth objective function, and m represents the mth objective function; S602: Determine whether all congestion levels are greater than a preset congestion level. S603: If both are greater than the preset congestion degree, it is determined that the selected target GNSS antenna array is reasonable.

[0066] As described in steps S601-S603 above, in order to ensure the diversity of each parent in the mating pool, the crowding between each antenna array can be calculated. Specifically, based on the average receiver availability of the target GNSS antenna array, it can be divided into multiple frontier layers, that is, divided into multiple gradients, and then the crowding of adjacent solutions in each frontier layer is calculated to ensure diversity, and the crowding of the boundary solution is set to infinity for forced retention, and the individuals with the highest non-dominated level and the largest crowding are retained.

[0067] In one embodiment, in the satellite signal simulated according to the preset STK simulation software, the satellite signal is a Beidou satellite signal.

[0068] In a specific embodiment, by adjusting the dielectric value of the antenna medium substrate, GNSS patch antennas of different sizes are generated and the corresponding directional patterns are simulated. Figure 4 It can be seen that the antenna gain increases with the increase of the antenna size, and the SNR of the received signal will also increase with the increase of the antenna size.

[0069] The same GNSS patch antenna is replicated to form a dual antenna array, and HFSS is used to simulate the S12 parameters under different antenna spacing and antenna size conditions, such as Figure 4As shown in the figure, it can be seen that, given the same antenna size, the S12 parameter decreases significantly as the antenna spacing increases. Given the same antenna spacing, the S12 parameter increases as the antenna size increases. Therefore, antenna coupling generally increases with increasing antenna size and decreasing antenna spacing.

[0070] The coupling corresponding to the center frequency in the nine cases above was recorded, and the signal-to-noise ratio loss after different antenna coupling compensations was calculated according to the formula, as shown in Table 1. It can be seen that the SNR loss increases with the increase of antenna coupling.

[0071] Table 1 Assume that the carrier platform length and width are 1 wavelength, the maximum and minimum antenna sizes are 0.1 wavelength and 0.2 wavelength, the population size is 30, the maximum evolutionary generations are 30, the mutation probability is 0.15, and the elite retention ratio is 0.1. The Pareto frontier of the number of antennas and the average receiver availability is as follows: Figure 5 shown.

[0072] according to Figure 5 As can be seen, within this carrier platform, when the number of antennas is 14 or less, the average receiver availability is essentially 1. However, when the number of antennas is between 15 and 18, the average receiver availability drops sharply, reaching 89%, 71%, 55%, and 35%, respectively. When the number of antennas reaches 19, the average receiver availability drops to 4%, essentially completely unavailable. This suggests that an antenna number between 15 and 17 is the optimal choice.

[0073] The optimal array layout when the number of antennas is 15-17 is as follows Figure 5 As shown in the figure, it can be seen that the antenna spacing in the three optimal array layouts is relatively uniform, and there is no situation where the spacing is too small or too large.

[0074] As remote sensing platforms continue to rely more and more on high-precision positioning and timing services, the high reliability of GNSS signals has become a core element in ensuring the integrity of remote sensing data links. However, complex electromagnetic interference environments pose a serious threat to GNSS signal availability. This study breaks through the traditional design paradigm and, through systematic analysis, reveals the inherent correlation and constraints between parameters such as the number, spacing, and size of antennas and null width, coupling, signal-to-noise ratio loss, and receiver availability. It also innovatively proposes a compact GNSS antenna array layout optimization design method based on the NSGA-II model. By integrating the electromagnetic characteristics of the antenna itself with a multi-objective game mechanism, this method achieves a global optimal solution for anti-interference performance within the constraints of limited carrier space, providing a hardware solution for miniaturized remote sensing platforms that combines strong interference suppression with high space efficiency.

[0075] Reference Figure 7 The present invention also provides a GNSS compact antenna array layout device, comprising: The data acquisition module 10 is used to instruct the implementation of step S1 and perform a data acquisition operation to obtain the carrier platform area and the size range of the antenna; The antenna array generation module 20 is configured to instruct the implementation of step S2, executing a GNSS antenna array generation operation to randomly generate a plurality of GNSS antenna arrays according to the area of the carrier platform and the size range of the antenna; The simulation array generation module 30 is used to instruct the implementation of step S3, perform the simulation array generation operation, generate a simulation array corresponding to the GNSS antenna array using the preset HFSS simulation software, and randomly generate interference; a calculation module 40 for instructing the implementation of step S4, performing an average receiver availability calculation operation, simulating satellite signals according to a preset STK simulation software, calculating the elevation angle and pitch angle of each GNSS signal, and calculating the average receiver availability of each GNSS antenna array; The antenna array selection module 50 is configured to instruct the implementation of step S5, performing a GNSS antenna array selection operation, and selecting a preset number of GNSS antenna arrays as target GNSS antenna arrays based on the average availability of each receiver; a chromosome generation module 60 for instructing to implement step S6, performing a chromosome generation operation, encoding and generating a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and inputting the chromosome into the mating pool; Iterative genetic module 70 is used to instruct the implementation of step S7, perform iterative genetic operation, iteratively select two chromosomes from the mating pool for genetic operation, input the obtained chromosomes into the mating pool, repeat the iterative genetic operation, and obtain the target mating pool after the iteration is completed; a target chromosome selection module 80, configured to instruct the implementation of step S8, perform a target chromosome selection operation, perform a receiver average availability calculation operation on each chromosome in the target mating pool, and select the chromosome with the largest receiver average availability as the target chromosome based on the receiver average availability of each chromosome; The application module 90 is used to instruct the implementation of step S9 and perform an application operation to apply the GNSS antenna array corresponding to the target chromosome as a target GNSS antenna array.

[0076] It should be noted that other embodiments of the GNSS compact antenna array layout device provided by the present invention are the same as the GNSS compact antenna array layout method and will not be described in detail here.

[0077] Reference Figure 8 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store various GNSS compact antenna arrays, etc. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it can implement the GNSS compact antenna array layout method described in any of the above embodiments.

[0078] Those skilled in the art will understand that Figure 8 The structure shown in is merely a block diagram of a portion of the structure related to the present application solution and does not constitute a limitation on the computer device to which the present application solution is applied.

[0079] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the GNSS compact antenna array layout method described in any of the above embodiments can be implemented.

[0080] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0081] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0082] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of the claims.

Claims

1. A GNSS compact antenna array layout method, characterized in that: include: S1. Perform data acquisition operations to obtain the carrier platform area and the size range of the antenna; S2. Execute a GNSS antenna array generation operation to randomly generate a plurality of GNSS antenna arrays according to the area of the carrier platform and the size range of the antenna; S3, executing a simulation array generation operation, using preset HFSS simulation software to generate a simulation array corresponding to the GNSS antenna array, and randomly generating interference; S4. Execute a receiver average availability calculation operation to simulate satellite signals according to a preset STK simulation software, calculate the elevation angle and pitch angle of each GNSS signal, and calculate the average receiver availability of each GNSS antenna array; S5. Execute a GNSS antenna array selection operation to select a preset number of GNSS antenna arrays as target GNSS antenna arrays based on the average availability of each receiver. S6. Performing a chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and inputting the chromosome into the mating pool; S7, performing an iterative genetic operation to iteratively select two chromosomes from the mating pool for genetic operation, and input the obtained chromosomes into the mating pool, repeating the iterative genetic operation, and obtaining a target mating pool after the iteration is completed; S8. Execute a target chromosome selection operation, perform a receiver average availability calculation operation on each chromosome in the target mating pool, and select the chromosome with the largest receiver average availability as the target chromosome based on the receiver average availability of each chromosome; S9. Execute an application operation to apply the GNSS antenna array corresponding to the target chromosome as a target GNSS antenna array.

2. The GNSS compact antenna array layout method according to claim 1, wherein: The step S7 of performing iterative genetic operation to iteratively select two chromosomes from the mating pool for genetic operation, input the obtained chromosomes into the mating pool, repeat the iterative genetic operation, and obtain the target mating pool after the iteration is completed includes: S701, randomly selecting two chromosomes from the mating pool, and randomly selecting cutting points on the two selected chromosomes; S702, exchanging sub-segments according to the positions of the selected cutting points to generate new chromosomes; S703, repairing the new chromosome to obtain a repaired chromosome; S704: Input the repaired chromosome into the mating pool, repeat the iterative genetic operation, and obtain the target mating pool after the iteration is completed.

3. The GNSS compact antenna array layout method according to claim 2, wherein: The step S703 of repairing the new chromosome to obtain a repaired chromosome includes: S7031. Obtain constraint conditions based on the carrier platform area and the size range of the antenna; wherein the constraint conditions include minimum spacing constraint and / or boundary constraint, and / or antenna size constraint; S7032: Delete duplicate points from the new chromosome, and repair the new chromosome based on the constraint condition to obtain a repaired chromosome.

4. The GNSS compact antenna array layout method according to claim 1, wherein: After step S6 of performing the chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and inputting the chromosome into the mating pool, the method further includes: S711. Perform a mutation operation on the chromosomes in the mating pool to obtain mutated chromosomes; wherein the mutation operation is any one or more of antenna number mutation, antenna size mutation, and antenna position mutation; S712: Perform repair processing on the mutated chromosome to obtain a repaired chromosome.

5. The GNSS compact antenna array layout method according to claim 1, wherein: After step S6 of performing the chromosome generation operation to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, the method further includes: S721. Process each chromosome using a preset dynamic mask matrix to keep the length of each chromosome consistent.

6. The GNSS compact antenna array layout method according to claim 1, wherein: After step S5 of performing the GNSS antenna array selection operation to select a preset number of GNSS antenna arrays as target GNSS antenna arrays according to the average availability of each receiver, the method further includes: S601, according to the formula Calculate the crowding degree between the selected target GNSS antenna arrays; where, represents the congestion degree of the i-th target GNSS antenna array, represents the value of the mth objective function of the i+1th solution in the sorted sequence, represents the value of the mth objective function of the i-1th solution in the sorted sequence, represents the maximum value of the mth objective function, represents the minimum value of the mth objective function, and m represents the mth objective function; S602: Determine whether all congestion levels are greater than a preset congestion level. S603: If both are greater than the preset congestion degree, it is determined that the selected target GNSS antenna array is reasonable.

7. The GNSS compact antenna array layout method according to claim 1, wherein: In the satellite signal simulated according to the preset STK simulation software, the satellite signal is a Beidou satellite signal.

8. A GNSS compact antenna array layout device, characterized in that: include: A data acquisition module, used to instruct the implementation of step S1, performing a data acquisition operation to obtain the carrier platform area and the size range of the antenna; an antenna array generation module, configured to instruct the implementation of step S2, executing a GNSS antenna array generation operation, so as to randomly generate a plurality of GNSS antenna arrays according to the area of the carrier platform and the size range of the antenna; a simulation array generation module, configured to instruct the implementation of step S3, perform a simulation array generation operation, generate a simulation array corresponding to the GNSS antenna array using preset HFSS simulation software, and randomly generate interference; a calculation module, configured to instruct the implementation of step S4, performing an average receiver availability calculation operation, simulating satellite signals according to a preset STK simulation software, calculating the elevation angle and pitch angle of each GNSS signal, and calculating the average receiver availability of each of the GNSS antenna arrays; an antenna array selection module, configured to instruct the implementation of step S5, executing a GNSS antenna array selection operation, so as to select a preset number of GNSS antenna arrays as target GNSS antenna arrays according to the average availability of each receiver; a chromosome generation module, configured to instruct the implementation of step S6, to perform a chromosome generation operation, to encode and generate a chromosome corresponding to each target GNSS antenna array based on the antenna position and antenna size in each target GNSS antenna array, and to input the chromosome into the mating pool; an iterative genetic module, configured to instruct the implementation of step S7, to perform an iterative genetic operation, to iteratively select two chromosomes from the mating pool for genetic operation, and to input the obtained chromosomes into the mating pool, to repeat the iterative genetic operation, and to obtain a target mating pool after the iteration is completed; a target chromosome selection module, configured to instruct the implementation of step S8, perform a target chromosome selection operation, perform a receiver average availability calculation operation on each chromosome in the target mating pool, and select the chromosome with the largest receiver average availability as the target chromosome based on the receiver average availability of each chromosome; The application module is used to instruct the implementation of step S9 and perform an application operation to apply the GNSS antenna array corresponding to the target chromosome as a target GNSS antenna array.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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