Unmanned aerial vehicle GNSS dual-antenna anti-interference spacing optimization method and device

Abstract

The application relates to a UAV GNSS double-antenna anti-interference spacing optimization method and device. By establishing a received signal model and a covariance matrix, combining a power inversion criterion to solve weights and calculate spatial utilization, optimal spacing is obtained through iterative optimization, the influence of redundant nulls can be reduced, the spatial utilization of navigation signals is improved, and the anti-interference reliability of UAV GNSS navigation is ensured.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method and apparatus for optimizing the anti-interference spacing of dual GNSS antennas for unmanned aerial vehicles (UAVs). Background Technology

[0002] The grating lobe effect, a typical phenomenon in array signal processing, has been extensively studied in the field of radar target observation. To reduce the deployment cost of large-scale array antennas, radar arrays often employ a configuration scheme where the element or subarray spacing exceeds half a wavelength, forming a sparse array. In the synthesized beam pattern of a sparse array, insufficient spatial sampling leads to spatial aliasing, resulting in multiple grating lobes with amplitudes similar to the main lobe. This not only affects the main lobe width but also significantly reduces the effective scanning range of the radar. It is worth noting that even if a single subarray pattern has no grating lobes, the phenomenon can still occur in the combined pattern of all subarrays due to excessive subarray spacing. The similarity in amplitude between the main lobe and the grating lobes further leads to ambiguity in radar target direction estimation. Unlike radar systems, navigation spatial anti-jamming technology focuses on the formation of spatial nulls in array pattern analysis. Therefore, the grating lobe effect manifests as main lobe beam repetition in radar observation and as an increase in redundant nulls in navigation anti-jamming. Redundant nulls increase the number and width of nulls, causing spatial gain variations and discontinuous usable gain range, thus affecting the incident range of navigation signals. Current research on the grating lobe effect mainly focuses on sparse radar arrays, with a lack of research on anti-jamming aspects of satellite navigation arrays. Therefore, studying the impact of the grating lobe effect on the formation mechanism of array nulls and its effect on the spatial utilization of navigation signals is of great significance for achieving low-cost and efficient anti-jamming for UAVs with dual antennas and ensuring reliable reception of navigation signals. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and device for optimizing the anti-interference spacing of UAV GNSS dual antennas to address the above-mentioned technical problems.

[0004] A method for optimizing the anti-interference spacing of dual GNSS antennas for unmanned aerial vehicles (UAVs), the method comprising: Initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal; A dual-antenna signal receiving model is established, which includes GNSS navigation signals, interference signals and noise components, and the signal incident direction and array element spacing are correlated by spatial frequency. Calculate the signal covariance matrix based on the power and spatial characteristics of the GNSS navigation signal, interference signal, and noise components; Based on the signal covariance matrix, the adaptive zeroing weights are solved using the power inversion criterion to form null traps in the interference direction; The array factor response of the array to signals from different directions is calculated using the adaptive zeroing weights; the array factor response includes redundant null traps caused by grating lobe effects. Calculate the space utilization rate under the current array element spacing based on the array factor response; The optimal spacing is determined by iteratively adjusting the spacing between array elements to maximize space utilization, and the UAV GNSS dual antennas are configured based on the optimal spacing.

[0005] In one embodiment, the method further includes: setting the initial spacing between the dual antenna elements to a value greater than 0. ; Obtain the wavelength of the GNSS navigation signal; The wavelength is determined based on the satellite navigation frequency band; Determine the effective incident angle range of the satellite as θ∈[-π / 2,π / 2].

[0006] In one embodiment, the method further includes: establishing a dual-antenna received signal model as follows: ; in, s Indicates GNSS navigation signal, This represents the steering vector of the GNSS navigation signal. J Indicates interference signal. Represents the noise vector. The interference steering vector represents the interference signal. , , , d represents the element spacing. For wavelength, and These represent the incident angles of the interference signal and the GNSS navigation signal, respectively. and All of these are spatial frequencies.

[0007] In one embodiment, the method further includes: calculating the signal covariance matrix based on the power and spatial characteristic information of the GNSS navigation signal, interference signal, and noise components. ; in, Represents the signal covariance matrix. , and These represent the power of the GNSS navigation signal, interference signal, and noise component, respectively.

[0008] In one embodiment, the method further includes: setting the weighting coefficient of the first array element in the dual-antenna received signal model. ; and based on the signal covariance matrix, the weighting coefficients of the second array element are solved using the power inversion criterion. .

[0009] In one embodiment, the method further includes: using the adaptive zeroing weights to calculate the array factor response of the array to signals in different directions as follows: ; in, , and The amplitudes of the first and second array elements are respectively. and These represent the phases of the first and second array elements, respectively.

[0010] In one embodiment, the method further includes: calculating the average space utilization rate under the current element spacing based on the array factor response. ; in, For interference Space utilization rate during the time For frequency threshold, Represents the physical incident angular frequency. This represents the average space utilization rate.

[0011] In one embodiment, the method further includes: determining the optimal spacing by iteratively adjusting the element spacing to maximize space utilization. ; in, This represents the location where the poles of the space utilization function occur.

[0012] A device for optimizing the anti-interference spacing of dual-antenna GNSS antennas for unmanned aerial vehicles, the device comprising: The initialization module is used to initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal. The model building module is used to build a dual-antenna received signal model. This model includes GNSS navigation signals, interference signals and noise components, and correlates the signal incident direction with the array element spacing through spatial frequency. The covariance calculation module is used to calculate the signal covariance matrix based on the power and spatial characteristic information of the GNSS navigation signal, interference signal, and noise components. The weight calculation module is used to solve the adaptive zeroing weights based on the signal covariance matrix using the power inversion criterion to form nulls in the interference direction; and to calculate the array factor response of the array to signals in different directions using the adaptive zeroing weights; the array factor response includes redundant nulls caused by grating lobe effects. The optimization module is used to calculate the space utilization rate under the current array element spacing based on the array factor response; determine the optimal spacing by iteratively adjusting the array element spacing with the goal of maximizing space utilization; and configure the UAV GNSS dual antennas based on the optimal spacing.

[0013] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps: Initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal; A dual-antenna signal receiving model is established, which includes GNSS navigation signals, interference signals and noise components, and the signal incident direction and array element spacing are correlated by spatial frequency. Calculate the signal covariance matrix based on the power and spatial characteristics of the GNSS navigation signal, interference signal, and noise components; Based on the signal covariance matrix, the adaptive zeroing weights are solved using the power inversion criterion to form null traps in the interference direction; The array factor response of the array to signals from different directions is calculated using the adaptive zeroing weights; the array factor response includes redundant null traps caused by grating lobe effects. Calculate the space utilization rate under the current array element spacing based on the array factor response; The optimal spacing is determined by iteratively adjusting the spacing between array elements to maximize space utilization, and the UAV GNSS dual antennas are configured based on the optimal spacing.

[0014] The aforementioned method and device for optimizing the anti-interference spacing of UAV GNSS dual antennas, by establishing a received signal model and covariance matrix, solving for weights and calculating space utilization using the power inversion criterion, and iteratively optimizing to obtain the optimal spacing, can reduce the influence of redundancy nulls, improve the space utilization of navigation signals, and ensure the anti-interference reliability of UAV GNSS navigation. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for optimizing the anti-interference spacing of a UAV's GNSS dual antennas in one embodiment. Figure 2 This is a structural block diagram of a UAV GNSS dual-antenna anti-interference spacing optimization device in one embodiment; Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] In one embodiment, such as Figure 1 As shown, a method for optimizing the anti-interference spacing of dual GNSS antennas for UAVs is provided, including the following steps: Step 102: Initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal.

[0018] Step 104: Establish a dual-antenna signal receiving model. This model includes GNSS navigation signals, interference signals, and noise components, and correlates the signal incident direction with the array element spacing through spatial frequency.

[0019] Step 106: Calculate the signal covariance matrix based on the power and spatial characteristics of the GNSS navigation signal, interference signal, and noise components.

[0020] Step 108: Based on the signal covariance matrix, the adaptive nulling weights are solved using the power inversion criterion to form null traps in the interference direction.

[0021] Step 110: Calculate the array factor response of the array to signals in different directions using adaptive zeroing weights.

[0022] The array factor response contains redundant null traps caused by grating lobe effects.

[0023] Step 112: Calculate the space utilization rate under the current array element spacing based on the array factor response; determine the optimal spacing by iteratively adjusting the array element spacing with the goal of maximizing space utilization, and configure the UAV GNSS dual antennas based on the optimal spacing.

[0024] In the above-mentioned UAV GNSS dual-antenna anti-interference spacing optimization method, by establishing a received signal model and covariance matrix, combining the power inversion criterion to solve the weights and calculate the space utilization rate, the optimal spacing is obtained through iterative optimization. This can reduce the influence of redundancy null traps, improve the space utilization rate of navigation signals, and ensure the anti-interference reliability of UAV GNSS navigation.

[0025] In one embodiment, the array factor obtained by adaptive nulling under dual-antenna conditions is first analyzed. To obtain the array factor, the excitation of each array element, i.e., the amplitude and phase of the array element coefficients, must first be obtained. To obtain the coefficients, the signal covariance matrix must first be obtained. Under single-interference conditions, the received signal of each array element can be expressed as: ; in, sIndicates GNSS navigation signal, This represents the steering vector of the GNSS navigation signal. J Indicates interference signal. Represents the noise vector. The interference steering vector represents the interference signal. , , , d represents the element spacing. For wavelength, and These represent the incident angles of the interference signal and the GNSS navigation signal, respectively. and All of these are spatial frequencies.

[0026] In another embodiment, when beamforming is formed, the element weights are related to the covariance matrix of the array's received signals, and the covariance of the received signals between two elements is: ; Represents the signal covariance matrix. , and Let H represent the power of the GNSS navigation signal, interference signal, and noise component, respectively, and let I be the identity matrix. Substituting the signal steering vector, we get: ; The signal covariance matrix is ​​a Hermitian matrix. According to the property of inverting Hermitian matrices, its inverse matrix can be calculated from the adjoint matrix. , yes The adjoint matrix, yes The determinant of .

[0027] In one embodiment, the power inversion criterion is used to solve the calculation. The inverse matrix is: ; in, .

[0028] Therefore, the weight vector is calculated as follows: ; Weight coefficient of array element 1 The weight coefficient of array element 2 The amplitude and phase of the element excitation for the two array elements are obtained as follows: ; Therefore, we can see that the amplitudes of each array element are similar, the difference lies in the phase, and the phase difference between array elements reflects the spatial angular information of the interference. The array factor of the two array elements can be calculated, and thus the array's response to different spatial signals can be obtained as follows: ; It can be seen that the spatial frequency is A time-array factor of 0 indicates suppression of signals in that direction. The dual-antenna system utilizes an adaptive nulling algorithm to calculate array weights containing spatial information of the interfering signal based on the received signal covariance matrix. This allows for control of the array pattern to form nulls in the interference direction, while still maintaining the anti-interference capability of the two array elements. Furthermore, the response is a cosine function, exhibiting periodicity, which also satisfies the grating lobe effect, leading to redundant nulls in the array pattern.

[0029] In one embodiment, the dual antennas have limited degrees of freedom, all of which are used to create nulls for interference suppression. This makes it difficult to control the beam gain for navigation signals; the radiation pattern does not change with the navigation signal. A fixed radiation pattern will cause gain differences for signals from different directions, affecting navigation signal reception. Therefore, in addition to analyzing the impact on interference suppression, it is also necessary to analyze the range of receivable signal angles in the radiation pattern formed by anti-interference, i.e., to analyze the space utilization rate after adaptive nulling of the array. Therefore, the following derivation of the space utilization rate of the dual-antenna array for navigation signal reception after adaptive beamforming is presented. The dual-element array obtains element weights after beamforming. Then, at this time, the spatial frequency is µ Under the condition that the interference remains constant, the output signal-to-interference-plus-noise ratio of the navigation signal is: ; Since the signal-to-interference-plus-noise ratio (SIR) of the array output is independent of the weight vector coefficients, for ease of calculation, let the weight vector be: ; For the numerator and denominator of the weight vector, the power gains of the array factor for the signal term, interference term, and noise term are respectively: ; ; ; Because the power of interference suppression is generally much higher than the signal power, while the power of the navigation signal when it reaches the receiver is extremely low, much lower than the noise power, that is... Therefore, the signal power term can be further omitted, and at the same time, let, , ,get: ; From the above derivation, it can be seen that the array output signal-to-interference-plus-noise ratio (SIR) is only related to the denominator term, which is related to the navigation signal term. The power gain of the interference and noise terms in the denominator term is independent of the incident angle of the navigation signal. Therefore, the condition for space utilization can be transformed into: ; in, The effective incident angle range for signal reception at this time is obtained as follows: ; Therefore, the effective incident spatial frequency range is: ; in, .

[0030] Converted to physical incident angular frequency, assuming uniform signal incidence, effective incident angle range and at the angle of incidence Under these circumstances, space utilization rate for: ; ; in, This is an indicator function, indicating that the indicator function structure is 1 when the condition is met.

[0031] In one embodiment, to reflect the array's signal reception capability after interference suppression, the statistically significant space utilization rate is further calculated. The direction of interference is treated as a random variable, considered as a uniform distribution. At this point, the average space utilization rate is: ; Average space utilization is a function of the ratio of element spacing to wavelength. Because the integrand contains a cosine term, the average space utilization rate exhibits periodic changes, and the integrand oscillates rapidly as the element spacing increases.

[0032] Spatial aliasing results in identical array factor responses from different physical incident directions. The array factor is controlled by the phase difference between array elements. Therefore, based on the element weights obtained through beamforming adaptive nulling, all null positions generated by spatial aliasing under dual-antenna adaptive nulling anti-interference conditions can be determined, i.e.: ; in, The null positions of the array factors directly obtained from the weighting coefficients after adaptive zeroing correspond to the spatial frequencies of the interference signal. To satisfy the redundant null positions caused by spatial aliasing under certain conditions.

[0033] To analyze the impact of spatial aliasing on space utilization, the physical incident angle is converted into a sinusoidal spatial frequency, and the Heaviside step function is used instead of the indicator function, resulting in: ; The above formula is about The function, when differentiated, yields: ; because The function property is that the extreme points appear in The location of the extreme points is symmetrical. Based on the conclusions drawn earlier, if at a certain point... If a peak value is obtained at a certain location, then under the aliasing formula conditions, other peak values ​​can be obtained. Furthermore, the larger the element spacing, the more likely the aliasing position is to be satisfied. k As the value increases, the number of peaks also increases. However, the amplitude of the peaks decreases, and the weighting function... The peak value decreases as the spacing increases, and the pre-coefficient also directly leads to a decrease in the overall integral result.

[0034] As can be seen from the space utilization rate, after conditionally integrating the signal angle and interference angle, the average utilization rate is only related to the relative relationship between the array element spacing and the wavelength, i.e. To facilitate the analysis of the impact of element spacing on average space utilization, a step function is also used instead of the indicator function, i.e.: ; make Rewriting the above equation as a function of n gives: ; It can be seen that the frequency of the cosine term of the integral kernel function is proportional to n. As n increases, the integrand oscillates rapidly within the integration region, making... The periodic fluctuations reflect that as the array spacing increases, the number of grating lobes increases, leading to enhanced inter-beam interference and resulting in periodic changes in space utilization.

[0035] Replace the above expression. ,but: ; The original integral can then be transformed into: ; To obtain the corresponding oscillation period, let ,but It can be rewritten as: ; in, , The oscillation is caused by cause, The weighting function is defined as follows: ; yes by The weighted average is calculated based on the weights, and the oscillation period is determined by... The weight distribution determines this. Specifically, exist The value is large when it is close to 2, therefore the integral kernel of the weighting function diverges near the boundary. Therefore, consider The oscillatory behavior at time can be approximated. The oscillation period at this time for: ; It can be known at this time exist n The oscillation frequency is 2, and the oscillation period is 0.5. Because... The oscillation period is determined by It dominates near the region, and at this time The oscillation period is 0.5, therefore exist n The oscillation period is 0.5, that is: ; The heavyened function is bounded and its integration interval is finite, therefore It converges gradually as n increases, and the convergent value can be obtained. Let Heaviside function in the integrand The surrounding area is 2 π According to the Riemann-Lebesguelemma lemma, for a periodic function that oscillates rapidly, the integral converges to the integral of the Heaviside function over one period multiplied by the weighting function: ; in, , is a periodic function with a period of 2π, and its average value over one period is . for: ; because ,therefore The solution is , Therefore, the average value is: ; The integral result of the weighting function is: ; The final convergence value is: .

[0036] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0037] In one embodiment, such as Figure 2 As shown, a device for optimizing the anti-interference spacing of dual-antenna GNSS arrays for unmanned aerial vehicles (UAVs) is provided, comprising: an initialization module 202, a model building module 204, a covariance calculation module 206, a weight calculation module 208, and an optimization module 210, wherein: Initialization module 202 is used to initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal; The model building module 204 is used to build a dual-antenna received signal model, which includes GNSS navigation signals, interference signals and noise components, and correlates the signal incident direction with the array element spacing through spatial frequency. The covariance calculation module 206 is used to calculate the signal covariance matrix based on the power and spatial characteristic information of the GNSS navigation signal, interference signal and noise components; The weight calculation module 208 is used to solve the adaptive zeroing weights according to the signal covariance matrix and the power inversion criterion to form nulls in the interference direction; and to calculate the array factor response of the array to signals in different directions using the adaptive zeroing weights; the array factor response includes redundant nulls caused by grating lobe effect. The optimization module 210 is used to calculate the space utilization rate under the current array element spacing based on the array factor response; determine the optimal spacing by iteratively adjusting the array element spacing with the goal of maximizing space utilization; and configure the UAV GNSS dual antennas based on the optimal spacing.

[0038] Specific limitations regarding the UAV GNSS dual-antenna anti-jamming spacing optimization device can be found in the limitations of the UAV GNSS dual-antenna anti-jamming spacing optimization method described above, and will not be repeated here. Each module in the aforementioned UAV GNSS dual-antenna anti-jamming spacing optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0039] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores the wavelengths of GNSS navigation signals and the effective incident angle range of satellite signals. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for optimizing the anti-interference spacing of a UAV's GNSS dual-antenna system.

[0040] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0041] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0042] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0043] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can 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), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0044] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0045] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing the anti-interference spacing of dual GNSS antennas for unmanned aerial vehicles (UAVs), characterized in that, The method includes: Initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal; A dual-antenna signal receiving model is established, which includes GNSS navigation signals, interference signals and noise components, and the signal incident direction and array element spacing are correlated by spatial frequency. Calculate the signal covariance matrix based on the power and spatial characteristics of the GNSS navigation signal, interference signal, and noise components; Based on the signal covariance matrix, the adaptive zeroing weights are solved using the power inversion criterion to form null traps in the interference direction; The array factor response of the array to signals from different directions is calculated using the adaptive zero-adjustment weights; the array factor response includes redundant null traps caused by grating lobe effects; Calculate the space utilization rate under the current array element spacing based on the array factor response; The optimal spacing is determined by iteratively adjusting the spacing between array elements to maximize space utilization, and the UAV GNSS dual antennas are configured based on the optimal spacing.

2. The method according to claim 1, characterized in that, The initialization of the dual-antenna array element spacing, determining the GNSS signal wavelength and the effective incident angle range of the satellite, includes: Set the initial spacing between the dual antenna elements to a value greater than 0. ; The wavelength of the GNSS navigation signal is obtained; the wavelength is determined based on the satellite navigation frequency band. The effective incident angle range of the satellite is determined to be θ∈[-π / 2,π / 2].

3. The method according to claim 1, characterized in that, Establish a dual-antenna received signal model, including: The dual-antenna signal receiving model is established as follows: in, s Indicates GNSS navigation signal, The signal steering vector representing the GNSS navigation signal. J Indicates interference signal. Represents the noise vector. The interference steering vector represents the interference signal. , , , d represents the element spacing. For wavelength, and These represent the incident angles of the interference signal and the GNSS navigation signal, respectively. and All of these are spatial frequencies.

4. The method according to claim 3, characterized in that, Based on the power and spatial characteristic information of the GNSS navigation signal, interference signal, and noise components, the signal covariance matrix is ​​calculated, including: Based on the power and spatial characteristics of the GNSS navigation signal, interference signal, and noise components, the signal covariance matrix is ​​calculated as follows: in, Represents the signal covariance matrix. , and Let H represent the power of the GNSS navigation signal, interference signal, and noise component, respectively, where H represents the conjugate transpose, and I is the identity matrix.

5. The method according to claim 4, characterized in that, Based on the signal covariance matrix, the adaptive nulling weights are solved using the power inversion criterion to form null traps in the interference direction, including: Set the weight coefficient of the first element in the dual-antenna signal receiving model. ; and based on the signal covariance matrix, the weighting coefficients of the second array element are solved using the power inversion criterion. .

6. The method according to claim 5, characterized in that, The array factor response of the array to signals from different directions is calculated using the adaptive zero-adjustment weights, including: The array factor response of the array to signals from different directions is calculated using the adaptive zero-adjustment weights: in, , and The amplitudes of the first and second array elements are respectively. and These represent the phases of the first and second array elements, respectively.

7. The method according to claim 6, characterized in that, The spatial utilization rate under the current element spacing is calculated based on the array factor response, including: The average space utilization rate under the current element spacing is calculated based on the array factor response: in, For interference Space utilization rate during the time For frequency threshold, Represents the physical incident angular frequency. This represents the average space utilization rate.

8. The method according to claim 7, wherein the optimal spacing is determined by iteratively adjusting the element spacing to maximize space utilization, comprising: The optimal spacing is determined by iteratively adjusting the element spacing to maximize space utilization: in, This represents the location where the poles of the space utilization function occur.

9. A device for optimizing the anti-interference spacing of dual-antenna GNSS antennas for unmanned aerial vehicles, characterized in that, The device includes: The initialization module is used to initialize the spacing between the dual antenna array elements and determine the wavelength of the GNSS navigation signal and the effective incident angle range of the satellite signal. The model building module is used to build a dual-antenna received signal model. This model includes GNSS navigation signals, interference signals and noise components, and correlates the signal incident direction with the array element spacing through spatial frequency. The covariance calculation module is used to calculate the signal covariance matrix based on the power and spatial characteristic information of the GNSS navigation signal, interference signal, and noise components. The weight calculation module is used to solve the adaptive zeroing weights based on the signal covariance matrix using the power inversion criterion to form nulls in the interference direction; and to calculate the array factor response of the array to signals in different directions using the adaptive zeroing weights; the array factor response includes redundant nulls caused by grating lobe effects. The optimization module is used to calculate the space utilization rate under the current array element spacing based on the array factor response; determine the optimal spacing by iteratively adjusting the array element spacing with the goal of maximizing space utilization; and configure the UAV GNSS dual antennas based on the optimal spacing.

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