Spatial non-uniform null depth control method for satellite anti-interference scene

Abstract

The invention discloses a space non-uniform null depth control method for a satellite anti-interference scene, solves the problems that the null depth is difficult to control and the null is offset and too narrow due to the fact that a traditional uniform array model cannot process phase nonlinearity, and belongs to the technical field of communication and radar signal processing. The method comprises the following steps: acquiring aperiodic two-dimensional coordinates of each array element in a spatial non-uniform array, calculating an array steering vector, defining a main lobe angular domain by taking an expected signal direction as a center based on the steering vector, defining an area except the main lobe angular domain in a scanning range as a side lobe angular domain, and carrying out dense discrete sampling in an interference source direction, a null angle domain is formed; taking the complex weight vector of the array element as an optimization variable, constructing a target function, introducing a constraint condition, and determining an optimization problem; and solving the optimization problem to obtain an optimal complex weight vector, and loading the optimal complex weight vector to array processing hardware.

Description

Technical Field

[0001] This application relates to a spatial non-uniform null depth control method for satellite anti-interference scenarios, belonging to the field of communication and radar signal processing technology. Background Technology

[0002] With the increasing complexity of the electromagnetic environment, the anti-interference capability of array antennas has become a core competitive advantage in wireless communication, radar, and satellite navigation systems. Spatial non-uniform arrays (aperiodic arrays) break the half-wavelength periodic arrangement limitation of traditional uniform arrays. By utilizing irregular element position distribution, they can achieve a larger physical aperture with the same number of elements, thus significantly improving the system's angular resolution. Furthermore, the non-uniform layout can suppress grating lobe effects through positional freedom, providing ample optimization space for low sidelobe design and refined spatial filtering.

[0003] In practical engineering applications, null depth control of spatially non-uniform arrays is an extremely challenging task, far exceeding the complexity of traditional uniform periodic arrays. Firstly, the most significant characteristic of non-uniform arrays lies in their strong phase nonlinearity. Since the element spacing no longer follows the half-wavelength equal spacing rule, the array vector exhibits a highly nonlinear mapping relationship with spatial angles. This physical characteristic makes traditional beam pointing algorithms based on analytical solutions or linear searches perform poorly in aperiodic scenarios, often failing to establish nulls with precise positioning and sufficient depth in the direction of interference sources.

[0004] Secondly, while non-uniform layouts break the grating lobe limitation and increase the physical aperture through sparse arrangement, this large aperture characteristic also leads to a double-edged sword effect of extremely narrow beams. The null regions formed under this layout are usually too narrow. In high-dynamic environments such as satellite communications, when the position of the interference source shifts slightly, there are physical position errors in the array element installation, or the system experiences frequency drift due to environmental changes, the narrow nulls are prone to shifting, causing the interference signal to quickly leave the suppression area, resulting in the instantaneous failure of the entire anti-interference system.

[0005] Furthermore, from an algorithmic robustness perspective, the classic Minimum Variance Distortionless Response (MVDR) algorithm exhibits limitations in aperiodic scenarios. Particularly in environments with highly complex interference source distributions, the covariance matrix of non-uniform arrays is prone to numerical ill-conditioning, leading to solution failures or spurious responses. More critically, traditional statistical processing methods cannot quantitatively control the specific decibel suppression value of nulls, which is particularly insufficient for demanding engineering tasks requiring precise resource allocation.

[0006] Finally, hardware implementation constraints are also a significant issue in the engineering process of zero-dimple depth control. When the ideal weights output by traditional optimization algorithms cannot be accurately mapped in hardware, it not only causes signal distortion but also leads to a significant reduction in the actual generated zero-dimple depth, severely restricting the large-scale application of complex aperiodic layouts in practical radio frequency systems. Summary of the Invention

[0007] To address the problems of uncontrollable null depth, null offset, and excessive narrowness caused by phase nonlinearity in traditional uniform array models, this application provides a spatial non-uniform null depth control method for satellite anti-interference scenarios.

[0008] This application discloses a method for controlling the depth of a non-uniform null depression in a space-based environment for satellite anti-jamming scenarios, comprising:

[0009] Obtain the aperiodic two-dimensional coordinates of each element in a spatially non-uniform array, and calculate the array steering vector based on the aperiodic two-dimensional coordinates;

[0010] Based on the calculated array steering vector, the main lobe angular domain is defined with the desired signal direction as the center, and the area outside the main lobe angular domain within the scanning range is defined as the side lobe angular domain. Dense discrete sampling is performed in the direction of the interference source to form a null corner domain.

[0011] Using the complex weight vector of the array elements as the optimization variable, an objective function is constructed, constraints are introduced, and the optimization problem is determined. The constraints include main lobe gain constraints, side lobe suppression constraints, null depth constraints, and amplitude constraints.

[0012] The optimization problem is solved to obtain the optimal complex weight vector, which is then loaded into the array processing hardware.

[0013] As a preferred option, in the transmission service mode, the objective function is to minimize the maximum response amplitude in the sidelobe angular domain, and the constraints include main lobe gain constraint, null depth constraint and amplitude constraint.

[0014] The main lobe gain constraint is used to constrain the lower limit of the main lobe gain.

[0015] The null depth constraint is used to constrain the array response amplitude in the null corner domain from falling below a set null suppression threshold.

[0016] The amplitude constraint is used to limit the amplitude of the complex weights of the array elements in the array to not exceed the peak value allowed by the physical hardware.

[0017] As a preferred option, the optimization problem under the launch service mode is:

[0018]

[0019] in, This represents the maximum response amplitude in the sidelobe corner domain;

[0020] The lower limit of main lobe gain;

[0021] Given the non-periodic two-dimensional coordinates of N array elements with respect to the cosine spatial coordinates of a given observation direction, the array steering vector is generated in the angular domain limit. The grid, after being flattened in one dimension, yields cosine space coordinates. , The value and range; This is the m-th guide vector after one-dimensional flattening. , , , ;

[0022] Indicates the side lobe angle region. It is a zero-concave-angle region;

[0023] For the zero-trap suppression threshold operator in the transmit service mode;

[0024] Complex weight vector , Let be the complex weight of the nth array element.

[0025] As a preferred option, in the receiving service mode, the objective function is to maximize the target gain, and the constraints include sidelobe suppression constraints, null depth constraints, and amplitude constraints.

[0026] The zero-depression depth constraint is used to constrain the array response amplitude in the zero-depression corner domain from falling below a set threshold value;

[0027] The sidelobe suppression constraint is used to constrain the array response amplitude in the sidelobe corner domain to be lower than the sidelobe suppression index.

[0028] The amplitude constraint is used to limit the amplitude of the complex weights of the array elements in the array to not exceed the peak value allowed by the physical hardware.

[0029] As a preferred option, the optimization problem under the receiving service mode is:

[0030]

[0031] in, For target gain;

[0032] Given the non-periodic two-dimensional coordinates of N array elements with respect to the cosine spatial coordinates of a given observation direction, the array steering vector is generated in the angular domain limit. The grid, after being flattened in one dimension, yields cosine space coordinates. , The value and range; This is the m-th guide vector after one-dimensional flattening. , , , ;

[0033] Indicates the coordinates of the center of the main lobe;

[0034] Indicates the side lobe angle region. It is a zero-concave-angle region;

[0035] Sidelobe suppression ratio, For the zero trap suppression threshold operator in the receive service mode;

[0036] Complex weight vector , Let be the complex weight of the nth array element.

[0037] As a preferred option, the zero-dimpled corner region for:

[0038]

[0039] in, With zero trap radius, These are the coordinates of the zero-point.

[0040] As a preferred option, the side lobe angle domain is:

[0041]

[0042] in, To the center of the main lobe distance , This is a parameter for the range of sidelobes.

[0043] The beneficial effects of this application are as follows: By reading the spatial coordinates of non-periodic array elements and dynamically constructing spatial constraints, this application achieves precise matching of the spatial phase characteristics of non-uniform arrays. To ensure the consistency of hardware mapping, this application applies a uniform amplitude hard constraint to the weight vector, ensuring that the solved complex weights can be directly mapped to the RF front-end hardware, avoiding the power amplifier from deviating from the linear dynamic range, thereby ensuring the robustness of nulling performance. This application designs different null control strategies for different service modes: In transmit mode, by fixing the lower limit of the main lobe gain and minimizing the global sidelobes, quantization-controlled nulls are generated while ensuring signal quality; in receive service mode, by fixing the sidelobe suppression ratio and setting the null depth as a hard constraint, an adaptive trade-off between suppression strength and radiation performance is achieved by maximizing the main lobe gain. In addition, the system has a built-in automatic scheduling mechanism for solving deep nulls, prioritizing the use of high-performance solvers, and automatically switching to a high-fault-tolerant alternative and increasing the iteration upper limit when numerical ill-conditioning is detected, ensuring that weight vectors that meet the -50dB depth index can still be stably generated under spatially non-uniform arrays. Experiments have shown that this application can effectively resist interference position shifts and non-periodic phase nonlinearity, forming a controllable deep null trap, and significantly improving the anti-interference robustness of the system under complex electromagnetic environments. Attached Figure Description

[0044] Figure 1 This is a zero-depression cross-section diagram under the launch service mode;

[0045] Figure 2 A cross-sectional view of zero-depression mode in the receive service mode;

[0046] Figure 3 This is a scatter plot of zero-trap depth and zero-trap width. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0048] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0049] The present application will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the application.

[0050] In practical engineering applications, controlling the null depth of non-uniform arrays faces numerous severe challenges. First, there is the nonlinearity of the phase. Due to the varying element spacing, the array vector exhibits highly nonlinear changes with angle, making it difficult for traditional analytical solution-based algorithms to form sufficiently deep and precisely positioned nulls in the interference direction. Second, the large aperture characteristic of non-uniform arrays results in extremely thin beams, often leading to excessively narrow nulls. When the interference source position shifts slightly, there are physical positional deviations in element installation, or the system experiences frequency drift, the interference easily escapes the null region, causing suppression failure. Furthermore, the classic Minimum Variance Distortionless Response (MVDR) algorithm is prone to numerical ill-conditioning in complex aperiodic interference scenarios and cannot provide quantitative, fixed-point control of the null depth. Simultaneously, the weight vectors generated by traditional algorithms often exhibit drastic amplitude fluctuations, exceeding the linear dynamic range of power amplifiers in practical RF links, leading to difficulties in hardware implementation.

[0051] This application employs a fully digital beamforming architecture and achieves precise intervention in the spatial response of non-uniform arrays by introducing an adjustable null depth suppression operator. A spatial sampling model is established by reading the coordinates of specific aperiodic array elements. To address the phase nonlinearity introduced by the non-uniform layout, this application introduces a method for addressing the weight vector in the optimization problem. The uniformity of the modulus hard constraint ensures that the solved complex weights not only mathematically satisfy the null depth requirement, but also physically can be directly mapped to the RF front-end hardware. This avoids the power amplifier deviating from the linear dynamic range due to excessive amplitude fluctuations between array elements, thus guaranteeing the robustness of the measured nulling performance. The spatial non-uniform null depth control method for satellite anti-interference scenarios in this embodiment includes:

[0052] Step 1: Obtain the aperiodic two-dimensional coordinates of each element in the spatially non-uniform array, and calculate the array steering vector based on the aperiodic two-dimensional coordinates; specifically,

[0053] Array physical modeling and pre-computation: In the algorithm initiation phase, the physical configuration is first converted into a mathematical expression: Wavenumber calculation: the operating frequency is known. and the speed of light Calculate wavelength and wave number .

[0054] Read data from a spatially non-uniform array. This layout is aperiodic, meaning the aperiodic physical coordinates of each element are... , For a given observation direction Its coordinates in the direction cosine space are:

[0055] , .

[0056] Array guide vector for:

[0057]

[0058] Step 2: Based on the calculated array steering vector, define the main lobe angular domain with the desired signal direction as the center, define the area outside the main lobe angular domain within the scanning range as the side lobe angular domain, and delineate the null angular domain according to the direction of the interference source.

[0059] UV mesh discretization and full sampling matrix: Algorithm in the angular domain limit Intrinsic generation The grid, flatten and stretch these two-dimensional grids into One guide vector. Construct the full sampling matrix. :

[0060]

[0061] The m-th guide vector after flattening is , ,in , , ,

[0062] By calculating grid points To the center of the main lobe Distance:

[0063] ,

[0064] The full sampling matrix is ​​divided into three subsets:

[0065] In the direction of the desired signal Define the main lobe angular domain centered on the main lobe. Main lobe angular domain: a single vector. .

[0066] The region outside the main lobe angle region within the scan range is defined as the side lobe angle region. :

[0067] .

[0068] According to the direction of the interference source Dense discrete sampling is performed within a circular region surrounding it to form a null corner region. :

[0069]

[0070] in, For the sidelobe range parameter The coordinates of the zero-point are... The radius is zero.

[0071] The zero-dead corner region needs to be redefined for different business scenarios. .

[0072] Step 2, which extends the point constraints to region constraints, is the key to solving the problem of excessively narrow null traps in non-uniform arrays.

[0073] Step 3: Using the complex weight vector of the array elements To optimize the variables, an objective function is constructed, constraints are introduced, and the optimization problem is defined. The constraints include main lobe gain constraints, side lobe suppression constraints, null depth constraints, and amplitude constraints.

[0074] This application designs two zero-depth control paths for different service modes: In the transmit service mode, while ensuring the main lobe pointing accuracy is better than 1 / 10 of the beamwidth and the gain loss is minimized. Under the premise of fixing the lower limit of the main lobe gain, the global sidelobe level is minimized, and a beam depth deeper than the main lobe level is generated at the same frequency beam position. Deep controlled nulls. In receive mode, sidelobe suppression is enhanced, supporting simultaneous suppression of multiple additional interference sources with depth greater than [previous level]. Ultra-deep zero-traps suppression. This mode achieves an adaptive balance between suppression intensity and system radiation performance by maximizing the main lobe gain. This control strategy directly affects the spatial allocation of array degrees of freedom. In complex business scenarios, by sacrificing a very small proportion of main lobe gain and sidelobe level suppression, it achieves a zero-traps depth of up to -50dB in the direction of co-channel interference and malicious interference, aiming to solve the technical pain point of traditional algorithms being unable to suppress zero traps deeply or control them under non-uniform layouts. Specifically, it includes:

[0075] Complex weight vector , Let be the complex weight of the nth array element. Indicates the number of array elements.

[0076] Define the main lobe region according to the business scenario. Side lobe angle region and zero-concave corner region In the transmission service mode, the main lobe performance is treated as a hard constraint to ensure that it meets the lower limit of the main lobe gain. ;Null trap suppression threshold operator As a reinforcing constraint applied to a specific disturbance direction, by changing The algorithm forcibly adjusts the aperiodic weight vector. This allows for quantitative depth suppression of co-channel interference while maintaining coverage strength. The optimization problem under the transmission service mode is:

[0077]

[0078] in, This represents the maximum response amplitude in the sidelobe corner domain;

[0079] To ensure the consistency of hardware mapping, a uniform magnitude constraint is applied to all weights to limit the magnitude of the complex weights of array elements in the array from not exceeding the peak value allowed by the physical hardware.

[0080] This mode uses a fixed lower limit for the main lobe gain to primarily reduce the global sidelobes and ensure a preset zero-dip depth.

[0081] In the receiving service mode, under strong interference, the sidelobe level and null depth are directly given by the service requirements and are considered hard constraints. The core objective of the system then becomes maximizing the target gain while meeting the suppression requirements. The model suppresses the scaling factor by locking the null trap. The optimization problem in the receiver service mode is to find the optimal cancellation point in the extremely complex aperiodic phase layout, ensuring that the best main lobe gain is adaptively obtained while forming an extremely deep null.

[0082]

[0083] Sidelobe suppression ratio, For the zero trap suppression threshold operator in the receive service mode;

[0084] This mode achieves an adaptive trade-off between anti-interference strength and system radiation performance in a non-periodic layout by fixing the null depth index and maximizing the main lobe gain.

[0085] The depth of the zero-dive is specifically affected by the following parameters:

[0086] 1) Zero-deepness index operator ( This parameter directly determines the zero-dimpled corner region. The upper limit coefficient of the response. Increasing this parameter will force the output power in the direction of interference to be compressed, and increase the null depth.

[0087] 2) In transmit service mode, the lower limit of main lobe gain The minimum amplitude that the main beam direction must maintain is defined. Higher gain requirements limit the adjustment space of the weight vector. When the gain requirement is too high, the algorithm may not be able to find enough phase cancellation points in aperiodic layouts, resulting in the null depth failing to meet the preset target or a significant deterioration in the sidelobe level.

[0088] 3) Sidelobe suppression ratio In the receiving service mode, this parameter is obtained through... The maximum level of the global sidelobes is limited. Increasing... The goal (requiring lower sidelobes) helps improve beam focusing and reduce background noise interference. However, as the sidelobe constraint tightens, the degree of freedom decreases, which may lead to "competition" with the null depth, resulting in a narrower null width or depth bounce in a specific aperiodic layout. At the same time, it may cause the main lobe to widen, reducing the accuracy of beam pointing.

[0089] 4) Null sampling radius (or angular domain range) This parameter defines the size of the range for discrete sampling near the center point of the interference source. Increasing this range... This creates a wider null in the target direction, which helps control the total suppression power and improves tolerance to interference source position shifts. However, the expansion of the null region means that more array degrees of freedom are needed for cancellation, which usually leads to more severe main lobe gain loss.

[0090] Step 4: Solve the optimization problem to obtain the optimal complex weight vector, and load it into the array processing hardware.

[0091] The method of this application can be applied to both the transmitting and receiving ends. At the transmitting end, the data is modulated to generate a beamforming domain signal, and the array element complex weight vector is adjusted using the method of this application. The signal is loaded into the array processing hardware; at the receiving end, the received signal is processed by the method of this application to obtain a beamforming domain signal; by implementing controlled-depth null mapping on the interference components in the non-uniform sampling domain, the signal can more accurately separate the useful signal components and the strong interference components in a specific direction from the aliasing field received by the non-periodic array, thereby significantly improving the output signal-to-noise ratio of the non-uniform array system in multi-interference scenarios.

[0092] The method proposed in this application aims to solve the problem of null depth control for spatially non-uniform arrays under complex interference environments. First, the array layout is read. Unlike traditional uniform arrays, the element distribution of non-uniform arrays is irregular. While this improves spatial resolution, it also introduces the challenge of phase nonlinearity. This application introduces aperiodic coordinates to establish a mathematical model that conforms to the actual hardware, ensuring that the commands generated by this application can be accurately applied to each irregularly distributed element.

[0093] This application employs a dynamic balancing algorithm combining hard and soft constraints, using different algorithmic logics to address different service requirements: In transmit mode, this application prioritizes ensuring signal coverage stability, setting main lobe gain loss as a hard constraint, while using sidelobe suppression as a soft constraint to minimize background noise while ensuring signal quality. In receive service mode, sidelobe suppression is elevated to a mandatory hard performance indicator. To ensure the signal's anti-interference capability in the face of interference, the system sacrifices a certain proportion of signal gain, trading local performance for overall anti-interference security.

[0094] The most significant feature of this application lies in its controllability. Instead of blindly suppressing noise, it sets the null depth as a quantified control target. By adjusting the suppression operator in the algorithm, it forces the radiation field generated by the driving array to form a deep null in the direction of interference. This control directly redistributes the array's degrees of freedom; the deeper the null, the more thoroughly the system shields against interference, but it may also lead to a reduction in main lobe energy or slight fluctuations in side lobes. Through this quantified trade-off, the algorithm can quickly find the optimal weight allocation scheme, achieving a non-periodic layout even in the complex environment of 32 concurrent beams. This strategy effectively suppresses extreme null directions. It transforms the previously unpredictable spatial response of a non-uniform array into a computationally quantifiable and highly robust engineering control process.

[0095] This application has the following characteristics:

[0096] High-precision aperiodic adaptation: By directly using the physical coordinates of array elements for modeling, the pointing deviation problem caused by phase nonlinearity under non-uniform layout is solved, achieving pointing accuracy better than 1 / 10 of the beamwidth.

[0097] Quantitatively controllable deep zero trap: Combining Figure 1 and Figure 2 Achieving deeper penetration through inhibitory factors The dB quantization null depth control allows for controllable null number and direction, achieving precise suppression of co-frequency and malicious interference.

[0098] Dynamic service trade-off mechanism: Supports switching between transmit and receive service modes, and balances main lobe gain loss and side lobe suppression ratio while ensuring a deep null by adjusting constraint priorities.

[0099] Hardware compatibility optimization: Model built-in Amplitude constraints ensure that the weights generated by the algorithm are fully adapted to the linear dynamic range of the non-uniform array RF power amplifier.

[0100] Zero-trapped space stability: combined Figure 3By utilizing spatial sampling broadening technology, the null width is controlled to about 1 / 30 of the beamwidth when the null depth is controlled to be deeper than -50dB, which significantly enhances the fault tolerance capability of the non-uniform array to the positional shift of the interference source.

[0101] While this application has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of this application. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of this application as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for controlling the depth of a non-uniform null depression in space for satellite anti-interference scenarios, characterized in that, include: Obtain the aperiodic two-dimensional coordinates of each element in a spatially non-uniform array, and calculate the array steering vector based on the aperiodic two-dimensional coordinates; Based on the calculated array steering vector, the main lobe angular domain is defined with the desired signal direction as the center, and the area outside the main lobe angular domain within the scanning range is defined as the side lobe angular domain. Dense discrete sampling is performed in the direction of the interference source to form a null corner domain. Using the complex weight vector of the array elements as the optimization variable, an objective function is constructed, constraints are introduced, and the optimization problem is determined. The constraints include main lobe gain constraints, side lobe suppression constraints, null depth constraints, and amplitude constraints. The optimization problem is solved to obtain the optimal complex weight vector, which is then loaded into the array processing hardware.

2. The spatial non-uniform null depth control method for satellite anti-interference scenarios according to claim 1, characterized in that, In the transmission service mode, the objective function is to minimize the maximum response amplitude in the sidelobe angular domain, and the constraints include main lobe gain constraint, null depth constraint and amplitude constraint. The main lobe gain constraint is used to constrain the lower limit of the main lobe gain. The null depth constraint is used to constrain the array response amplitude in the null corner domain from falling below a set null suppression threshold. The amplitude constraint is used to limit the amplitude of the complex weights of the array elements in the array to not exceed the peak value allowed by the physical hardware.

3. The spatial non-uniform null depth control method for satellite anti-interference scenarios according to claim 2, characterized in that, The optimization problem under the transmission service model is: in, This represents the maximum response amplitude in the sidelobe corner domain; The lower limit of main lobe gain; Given the non-periodic two-dimensional coordinates of N array elements with respect to the cosine spatial coordinates of a given observation direction, the array steering vector is generated in the angular domain limit. The grid, after being flattened in one dimension, yields cosine space coordinates. , The value and range; This is the m-th guide vector after one-dimensional flattening. , , , ; Indicates the coordinates of the center of the main lobe; Indicates the side lobe angle region. It is a zero-concave-angle region; For the zero-trap suppression threshold operator in the transmit service mode; Complex weight vector , Let be the complex weight of the nth array element.

4. The spatial non-uniform null depth control method for satellite anti-interference scenarios according to claim 1, characterized in that, In the receiving service mode, the objective function is to maximize the target gain, and the constraints include sidelobe suppression constraints, null depth constraints, and amplitude constraints. The zero-depression depth constraint is used to constrain the array response amplitude in the zero-depression corner domain from falling below a set threshold value; The sidelobe suppression constraint is used to constrain the array response amplitude in the sidelobe corner domain to be lower than the sidelobe suppression index. The amplitude constraint is used to limit the amplitude of the complex weights of the array elements in the array to not exceed the peak value allowed by the physical hardware.

5. The spatial non-uniform null depth control method for satellite anti-interference scenarios according to claim 4, characterized in that, The optimization problem in the receiving service mode is: in, For target gain; Given the non-periodic two-dimensional coordinates of N array elements with respect to the cosine spatial coordinates of a given observation direction, the array steering vector is generated in the angular domain limit. The grid, after being flattened in one dimension, yields cosine space coordinates. , The value and range; This is the m-th guide vector after one-dimensional flattening. , , , ; Indicates the coordinates of the center of the main lobe; Indicates the side lobe angle region. It is a zero-concave-angle region; Sidelobe suppression ratio, For the zero trap suppression threshold operator in the receive service mode; Complex weight vector , Let be the complex weight of the nth array element.

6. The spatial non-uniform null depth control method for satellite anti-interference scenarios according to claim 3 or 5, characterized in that, Zero-trapped corner region for: in, With zero trap radius, These are the coordinates of the zero-point.

7. The spatial non-uniform null depth control method for satellite anti-interference scenarios according to claim 3 or 5, characterized in that, The side lobe angle domain is: in, To the center of the main lobe distance , This is a parameter for the range of sidelobes.

8. A computer-readable storage device storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the spatial non-uniform zero-dive depth control method for satellite anti-interference scenarios as described in any one of claims 1 to 7.

9. A space non-uniform null depth control device for satellite anti-jamming scenarios, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the spatial non-uniform zero-dive depth control method for satellite anti-interference scenarios as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the spatial non-uniform zero-dive depth control method for satellite anti-interference scenarios as described in any one of claims 1 to 7.

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