An unmanned aerial vehicle jamming method based on distributed beam synthesis

Abstract

The application discloses a kind of unmanned aerial vehicle interference methods based on distributed beam synthesis, first, with the weakest interference power in the whole no-fly zone as the goal, establish the mathematical model of node deployment and beam synthesis joint optimization, construct a maximum-minimum optimization problem;Second, to improve the calculation efficiency, the non-convex optimization problem is decomposed into node deployment subproblem and beam synthesis subproblem to support distributed solution;Finally, through the low complexity iterative calculation of alternating optimization framework, the node deployment strategy and beam synthesis scheme with interference robustness are obtained, the unmanned aerial vehicle interference method based on distributed beam synthesis described in the application can effectively suppress all potential unmanned aerial vehicle targets in the region by maximizing the weakest interference power in the no-fly zone, overcome the shortcomings of traditional centralized interference method, such as poor robustness and high computational complexity, thereby improving the interference performance and practical value of anti-unmanned aerial vehicle system.

Description

Technical Field

[0001] This invention relates to the field of wireless communication jamming technology and anti-drone systems, specifically targeting scenarios such as urban no-fly zones. It proposes a drone jamming method based on distributed beamforming, which achieves robust jamming of all potential drone targets within the no-fly zone by jointly optimizing the deployment of jamming nodes and beamforming strategies. Background Technology

[0002] Unauthorized drone activity can disrupt critical infrastructure and even endanger public safety. To mitigate these risks, regulatory agencies typically establish no-fly zones by imposing flight restrictions during natural disasters, large public gatherings, or sudden emergencies. Existing counter-drone systems generally interfere with the downlink control links of drones operating within these no-fly zones by radiating noise signals, forcing them to land.

[0003] Existing anti-drone jamming technologies mainly employ a centralized, single-point jamming architecture. This involves radiating noise signals into the no-fly zone through a single jamming node, attempting to disrupt the drone's downlink control link and force it to land. For example:

[0004] Shenzhen Anweipu Technology Co., Ltd. (Patent Publication No.: CN12032451A) proposed a directional jamming method based on the characteristics (frequency band, bandwidth) of UAV transmission signals;

[0005] Chaoyang Microelectronics Technology Co., Ltd. (Patent Publication No.: CN120263337A) adopts a similar single-point target interference strategy.

[0006] However, this type of method has the following key drawbacks:

[0007] 1. Insufficient robustness: There are a large number of drones in the no-fly zone, and their positions are dynamic and scattered. Single-point interference is difficult to achieve full coverage and is prone to interference "blind spots".

[0008] 2. High computational complexity: If centralized joint optimization is adopted, the coupling relationship between node deployment and beamforming will cause the problem to have non-concave and non-convex characteristics, making it extremely difficult to solve;

[0009] 3. Limited practicality: Centralized architecture has high requirements for communication bandwidth and computing resources, making it difficult to adapt to real-world scenarios of multi-node distributed deployment.

[0010] Therefore, there is an urgent need for a distributed UAV jamming method that combines full-area jamming coverage, strong robustness, and high computational efficiency to meet the actual control needs of urban no-fly zones. Summary of the Invention

[0011] The purpose of this invention is to provide a UAV jamming method based on distributed beamforming. By constructing a distributed multi-node collaborative jamming architecture and jointly optimizing the spatial deployment of jamming nodes and beamforming strategies, the weakest jamming power in the no-fly zone is maximized, thereby ensuring effective suppression of all potential UAVs in the area.

[0012] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0013] This invention discloses a UAV jamming method based on distributed beamforming, the method comprising the following steps:

[0014] Step S1: To meet the needs of drone management in no-fly zones, and with the goal of maximizing the weakest interference power in the entire area, establish a mathematical model for joint optimization of node deployment and beamforming, and construct a maximum-minimum optimization problem.

[0015] Step S2: Decompose the original non-concave and non-convex problem into a node deployment sub-problem and a beamforming sub-problem;

[0016] Step S3: Design a solution algorithm for the node deployment subproblem;

[0017] Step S4: Design a solution algorithm for the beam synthesizer problem;

[0018] Step S5: Execute step S3 at the fusion center and execute step S4 at the interference nodes in a distributed manner. In this way, the two types of sub-problems are optimized alternately, and finally a node deployment strategy and beamforming scheme with interference robustness are obtained.

[0019] Preferably, step S1 specifically includes:

[0020] Consider a distributed anti-drone system consisting of a fusion center and It consists of several mobile interference nodes. The fusion center and each node are connected via a mobile ad hoc network. Each node is equipped with a uniform rectangular array deployed in the YZ plane, with the number of elements being [missing information]. ,in, and These represent the number of elements along the y-axis and z-axis, respectively. The spacing between adjacent array elements is half a wavelength. The location of the fusion center is... To ensure line-of-sight links and avoid occupying valuable urban land resources, the interference nodes were deployed on the rooftops of high-rise buildings. Specifically, different nodes were deployed on the rooftops of different buildings, with the first... The position of each node is denoted as . , Indicates the first The height of the building where each node is located is a fixed value. In densely populated urban areas, unauthorized drone activity can pose a serious risk to public safety. In this context, establishing a temporary no-fly zone to mitigate potential risks becomes particularly important. To effectively manage the no-fly zone, a distributed cooperative jamming strategy is adopted, enabling multiple jamming nodes to operate collaboratively, ensuring rapid deployment and effective jamming signal coverage of the entire area. Upon receiving jamming instructions from the fusion center, each node moves to its designated location and then transmits directional jamming signals. To enhance the spatial selectivity and energy efficiency of the jamming signal, the nodes utilize transmitted beamforming to concentrate the jamming power onto the no-fly zone. Specifically, in At this moment, the first Interference signals emitted by each node It can be represented as: ; For the first Narrowband beam combiner with nodes The noise signal generated by this node can be modeled as additive white Gaussian noise that is spatially and temporally independent and identically distributed, following a distribution. .remember If a point is located within a no-fly zone, then the signals received at that point from various interfering nodes... It can be represented as: ; For the first Each node and Gain coefficient of the propagation channel between them Interference signal The transmission delay experienced Characterizing the first The guidance vector from each node to that point is in the following form: ; , Direction angle and pitch angle The definitions are as follows: Since line-of-sight links dominate, channel gain can be modeled as path loss under a free-space propagation model, i.e. For the sake of brevity, this invention standardizes it as follows: Since the signals from each interfering node are statistically independent, the received signal... The power can be expressed in the following form: When a target location is given The above equation provides a closed-form expression for the jamming power at that location. However, the location of unauthorized drones is inherently uncertain, so concentrating the jamming power at just one specific location is insufficient. To suppress all potential drones within the no-fly zone, it is essential to ensure effective jamming coverage across the entire area. Without loss of generality, the no-fly zone is modeled as an ellipsoid, where any point can be represented as... ; The center of the ellipsoid is represented by the sphere. The distance representing the offset from the center of the sphere is affected by... Constraints , These are the semi-major axes of the x, y, and z axes, respectively, which together determine the scale of the ellipsoid. To ensure the robustness of cooperative jamming, this invention aims to design a transmit beamforming scheme to maximize the jamming effectiveness at the weakest power location within the no-fly zone. Therefore, the following optimization problem is constructed: ; The regulations stipulate the first Deployable range of each node This indicates the upper limit of the transmit power of each node.

[0021] Preferably, step S2 specifically involves: in the original non-concave and non-convex problem, the guiding vector... Inherently coupled node position and uncertain drone locations This led to and beam synthesizer The nonlinear interdependence between them. This interdependence makes simultaneous optimization... and Facing significant computational challenges, to reduce computational complexity and facilitate distributed solution, the original problem is decomposed into... The problem is divided into two blocks: beamforming and node deployment. The beamforming block aims to optimize... To improve beam gain in the worst-case scenario, which is achieved by The problem consists of several sub-problems, corresponding to the first... The question is: ;exist When fixed, the node deployment block optimizes the position of each interfering node. To ensure robustness to interference, the corresponding sub-problems are as follows: The solutions to the two subproblems in the above blocks are interdependent. Specifically, the solutions obtained by solving the node deployment subproblem are interdependent. This will directly affect the angle of the steering vector in the beam combiner problem. On the other hand, by optimizing variables in the beamforming subproblem... This also affects the objective function value of the node deployment sub-problem. This bidirectional coupling leads to an alternating optimization strategy in this invention, iteratively solving the node deployment and beamforming sub-problems sequentially to ultimately achieve robust cooperative interference against the target region. It is worth noting that in practical scenarios, to reduce the signaling overhead between the interfering nodes and the fusion center, the first... Each node can transmit only the corresponding first... The objective function value of a beam synthesizer problem (denoted as ) (dimension 1), rather than beam synthesizer (dimension is) This ensures robustness against interference while improving bandwidth efficiency. In this case, the node deployment subproblem can be simplified to: Preferably, step S3 specifically includes:

[0022] Step S31: To improve the actual solution efficiency, an optimization problem is constructed after approximating the original node deployment subproblem: The lower bound of the objective function of the original problem can be obtained by solving the above approximation problem; Step S32: Introduce auxiliary variables. : This transforms the max-min problem into a minimization problem under semi-infinite programming, eliminating non-smoothness.

[0023] Step S33: Using the S-lemma, the above infinite number of constraints are equivalent to a single convex constraint (transforming the above semi-infinite programming problem into a convex problem): This transforms the semi-infinite programming problem into a convex problem.

[0024] Step S34: Solve the convex problem using the convex optimization toolkit to obtain the optimized positions of the interference nodes. .

[0025] Preferably, step S4 specifically includes: Step S41: Positioning the interfering node Discretize the angle formed by the plane and any point within the no-fly zone to obtain the sets of heading and pitch angles, denoted as follows: and Step S42: For a beam combiner problem, introduce auxiliary variables. : This eliminates non-smoothness; Step S43: Introduce matrix variables and define The problem can be transformed into the following equivalent form: Subsequently, the optimization problem is solved using positive semidefinite relaxation to obtain the optimized beam combiner. .

[0026] Preferably, step S5 specifically includes: Step S51: Setting Number of iterations Step S52: Based on the fusion center end Solve the node deployment subproblem and optimize the node positions. Send to each interference node; Step S53: Each interference node based on Distributed parallel solution to the beam synthesizer problem yields And the optimized objective function value Send them separately to the fusion center; Step S54: Number of iteration rounds Step S55: Repeat steps S52 to S54 until the results converge. Finally, each interference node is determined according to... Adjust your position and through Beamforming is used to interfere with the no-fly zone.

[0027] Preferably, the guide vector The format is as follows: ;in, , .

[0028] Preferably, the channel gain can be modeled as the path loss under the free-space propagation model, i.e. and standardize it to .

[0029] Preferably, the convex optimization toolkit is the CVX toolkit. Preferably, semidefinite relaxation refers to ignoring... The constraints are solved to obtain the matrix. Then, the beam synthesizer is obtained through eigenvalue decomposition. .

[0030] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0031] High robustness: By maximizing the weakest jamming power within the no-fly zone, it ensures effective suppression of all potential UAV targets within the area, maintaining jamming effectiveness even if UAV positions change dynamically or are dispersed (e.g., Figure 4 As shown, the interference power range of the method of the present invention varies very little with the scale of the no-fly zone.

[0032] High computational efficiency: It decomposes the joint optimization problem of non-concave and non-convex into two low-complexity subproblems, and achieves efficient solution through alternating optimization, which is suitable for real-time deployment scenarios;

[0033] Flexible distributed architecture: The distributed collaborative mode of integrating the central and interference nodes reduces the performance dependence on a single node, and each node only needs to transmit one-dimensional objective function values ​​(rather than high-dimensional beam combiners), which greatly saves communication bandwidth;

[0034] Good scene adaptability: The beamforming pattern can be dynamically adjusted according to the shape of the no-fly zone and the location of the nodes (e.g., Figure 3 As shown, the optimized beam can accurately adapt to the outline of the no-fly zone, making it suitable for the management needs of no-fly zones of different sizes and shapes. Attached Figure Description

[0035] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of the present invention.

[0036] Figure 2 This is a flowchart illustrating the present invention.

[0037] Figure 3 These are the beam patterns before and after node beam optimization in the embodiments of the present invention.

[0038] Figure 4 This is a graph showing the variation of received interference power with the no-fly zone scale coefficient in the embodiments of the present invention. Detailed Implementation

[0039] The following will refer to the accompanying drawings in the embodiments of the present invention. Figures 1 to 4 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0040] This invention addresses the practical needs of drone control within no-fly zones by proposing a drone jamming method based on distributed beamforming. This method aims to maximize the weakest jamming power within the no-fly zone, enabling collaborative work among multiple jamming nodes to effectively suppress all potential drone targets within the area. Simultaneously, this method overcomes the shortcomings of traditional centralized jamming schemes in terms of robustness and computational complexity, thereby improving the overall jamming performance and application value of anti-drone systems. First, with the goal of maximizing the weakest jamming power throughout the no-fly zone, a mathematical model for the joint optimization of node deployment and beamforming is established, constructing a max-min optimization problem. Second, to improve computational efficiency, this non-concave and non-convex problem is decomposed into node deployment sub-problems and beamforming sub-problems to support distributed solution. Finally, a low-complexity iterative calculation is performed using an alternating optimization framework to obtain a node deployment strategy and beamforming scheme with robust jamming capabilities. The UAV jamming method based on distributed beamforming described in this invention can effectively suppress all potential UAV targets in a no-fly zone by maximizing the weakest jamming power within the zone. This overcomes the shortcomings of traditional centralized jamming methods, such as poor robustness and high computational complexity, thereby improving the jamming performance and practical value of anti-UAV systems.

[0041] The method of the present invention includes the following steps:

[0042] Step S1: Establish a joint optimization model: With the goal of "maximizing the weakest interference power in the no-fly zone", integrate node positions, beam synthesizers, signal propagation models and no-fly zone geometric models to construct a maximum-minimum optimization problem;

[0043] Step S2: Decompose the coupled subproblems: Decompose the original non-concave and non-convex problem into node deployment subproblems and beamforming subproblems, decouple the strong coupling between the two, and support distributed parallel solution;

[0044] Step S3: Design the algorithm for the node deployment subproblem: By using methods such as convex approximation and S-lemma, the non-convex problem is transformed into a convex optimization problem, which efficiently solves for the optimal position of the interfering nodes;

[0045] Step S4: Design the beamforming subproblem algorithm: By using techniques such as angle discretization and semi-definite relaxation, the beamforming problem is transformed into a solvable semi-definite programming problem;

[0046] Step S5: Alternating optimization iteration: Sub-problem solutions are performed in a distributed manner between the fusion center and the interference nodes, and a robust final solution is obtained through iterative convergence.

[0047] II. Technical Content

[0048] (2.1) System and signal model

[0049] Consider a distributed system consisting of one fusion center and M mobile interference nodes:

[0050] Each interference node is equipped with a YZ-plane uniform rectangular array (number of array elements) (Adjacent array elements are spaced at half a wavelength); the fusion center and nodes communicate via a mobile ad hoc network, with nodes deployed on the rooftops of high-rise buildings (fixed height); the interference signal is represented as... , For beam synthesizer, It is Gaussian white noise;

[0051] The no-fly zone is modeled as an ellipsoid (center of the sphere). semi-major shaft ), any point satisfies , Q is the shape matrix of the ellipsoid.

[0052] (2.2) The objective of the joint optimization model is to maximize the weakest interference power within the no-fly zone, i.e.: Constraints include node deployment range and upper limit of transmission power Among them, the guide vector is the guide vector. By direction angle and pitch angle The channel gain is determined to follow a free-space path loss model. ; It represents the y-axis coordinates of a point within the no-fly zone and the m-th interfering node; It represents the x-axis coordinates of a point within the no-fly zone and the m-th interfering node; It is the m-th interference node and the point within the no-fly zone Channel gain coefficients between;

[0053] (2.3) Subproblem decomposition and solution

[0054] Beam synthesizer problem: fixed node position ,optimization To improve the worst-case beam gain, i.e.: ; through angle discretization and positive semidefinite relaxation (introducing a matrix) The problem is transformed into a semidefinite programming problem. The offset vector of a point within a no-fly zone relative to the center of the ellipsoid; The conjugate transpose of the steering vector 'a' is used to calculate the gain of beamforming. To achieve coherent signal superposition. Node deployment subproblem: Fixed beam combiner. ,optimization To ensure robustness against interference, i.e.: , Let m be the objective function value of the m-th beam combiner problem; Let be the set of deployable ranges for the m-th interference node. The semi-infinite programming problem is transformed into a convex problem using convex approximation and the S-lemma, and then solved using the CVX toolkit.

[0055] (2.4) Alternating optimization iteration

[0056] The fusion center and interference nodes achieve distributed collaboration through a process of node deployment, beamforming, and feedback iteration.

[0057] The fusion center collects the beamforming results from each node. Solve the node deployment subproblem and assign new locations; each node independently solves the beamforming subproblem based on its new location. Feedback is sent to the fusion center; iteration continues until the objective function converges (e.g., the change in interference power between two adjacent iterations is less than 1e-3).

[0058] Example 1 (Scenario Setting):

[0059] Example scenario setting: Distributed anti-drone system: 1 fusion center + 5 jamming nodes. The fusion center is located at coordinates (0,0,0) (unit: meters), and the nodes are deployed on the rooftops of 5 high-rise buildings with a height of 100 meters.

[0060] Interference node array: Each node is equipped with an 8×8 uniform rectangular array, with element spacing of half a wavelength (corresponding to a center frequency of 2.4 GHz, half a wavelength is approximately 6.25 cm); No-fly zone ellipsoid: Center =(250,500,200), the semi-major axes of the x and y axes. meters, z-axis semi-major axis Meters; Optimization objective: Maximize the weakest interference power within the no-fly zone, with a convergence threshold of 1e-5 (i.e., stop when the change in the objective function between two adjacent iterations is less than 1e-5).

[0061] Step 1: Solving the node deployment subproblem

[0062] 1.1 Approximate Optimization Problem Construction: Based on the current beamforming results Construct a lower bound approximation for the original node deployment problem: 1.2 Semi-infinite programming transformation: Introducing an auxiliary variable t, the maximum-minimum problem is transformed into: 1.3 S-Lemma Convexification: Using the S-lemma, infinite constraints are transformed into convex matrix inequalities: 1.4 Convex Optimization Solution: The above convex problem is solved using the CVX toolkit to obtain the optimized node positions. And distribute it to each interference node.

[0063] Step 2: Solving the beam synthesizer problem

[0064] 2.1 Angle Discretization Sampling: The orientation angle formed by each node and the possible UAV positions within the no-fly zone. and pitch angle Discretize the samples at 5° intervals to obtain the angle set. 2.2 Semidefinite relaxation transformation: Introducing a matrix , The matrix variables of the beam synthesizer are derived from the beam synthesis vector. The outer product is obtained; the problem is transformed into: ;in The cross product matrix of direction vectors; It is an auxiliary variable; It is a guide vector; It is the trace of the matrix; This is the upper limit of the maximum transmit power for each interfering node; It is a matrix Semidefinite; 2.3 Solving semidefinite programming problems: neglecting Given the constraints, solve the semidefinite programming problem to obtain the matrix. Beam synthesizer obtained through feature decomposition and the objective function value Feedback was sent to the Integration Center.

[0065] Step 3: Alternating Iteration and Convergence Judgment

[0066] Initialization: Settings Number of iterations ;No. Round Iteration: Fusion Center Based on Solving the node deployment subproblem yields the following results: And distribute; each node based on Solving the beam synthesizer problem yields the following results: And provide feedback; Is the m-th interfering node at the th node? The optimized position vector after round of iterations; The problem of m beam synthesizers in the th... The objective function value of each iteration. Convergence criterion: If... If the iteration stops, then stop; otherwise... Repeat the above process.

[0067] III. Effect Verification and Analysis

[0068] 1. Beam pattern verification ( Figure 3Comparing the beam patterns before and after optimization, before optimization, the beams of each node were scattered and had high sidelobes; after optimization, the beams were precisely adapted to the location of the no-fly zone, the sidelobes were significantly reduced, and the beam width of different nodes was dynamically adjusted according to their distance from the no-fly zone (e.g., the beams of nodes closer to the no-fly zone were narrower, and the beams of nodes farther away were wider), ensuring full coverage of the no-fly zone.

[0069] Specifically: Figure 3 The diagrams show the beam patterns of each node before and after optimization, based on the embodiments of the present invention. Five interference nodes are deployed in each of two groups within the scene, with the beam center pointing towards the center of the no-fly zone. The upper group shows the optimized beam pattern in terms of azimuth angle, while the lower group shows the optimized beam pattern in terms of pitch angle. The beam direction and width in each node's beam pattern are adjusted according to its distance from the no-fly zone and the size of the area. Compared to the unoptimized result, the optimized beam can adapt to the shape of the no-fly zone, thus enabling the interference signal to cover the entire area and ensuring the effectiveness of the interference.

[0070] 2. Interference power robustness verification ( Figure 4 When the scale factor γ of the no-fly zone (i.e., the semi-major axis of the ellipsoid scaled by γ) increases from 0.5 to 1.2, the maximum interference power of the baseline-centralized method (Non-Robust) decreases from -35dBm to -55dBm, and the minimum value decreases from -40dBm to -45dBm. The power range (maximum value - minimum value) increases from 5dB to 10dB, resulting in extremely poor robustness. In contrast, the maximum interference power of the method of this invention (Robust) remains stable at around -35dBm, and the minimum value remains stable at around -40dBm. The power range is always kept within 5dB, demonstrating a significant advantage in robustness.

[0071] Specifically: Figure 4 This demonstrates how the received interference power varies with the no-fly zone scale coefficient in the embodiments described in this invention. The variations are as follows: the (robust) scheme is the distributed robust interference method proposed in this invention, and the (non-robust) scheme is the baseline interference method. Figure 4 As can be seen from this, with the increase in the size coefficient of the no-fly zone As the interference power increases, the difference between the maximum and minimum values ​​of the baseline scheme increases significantly, indicating that the scheme has poor interference robustness. In contrast, the interference method described in this invention has strong robustness, and the difference between the maximum and minimum values ​​of the interference power does not change significantly. In practical applications, it can ensure that all potential UAVs in the no-fly zone are effectively interfered with.

[0072] In summary, this invention achieves robust global interference in no-fly zones through joint optimization of distributed beamforming and node deployment, effectively addressing the shortcomings of traditional centralized methods and possessing high engineering application value.

[0073] Finally, it should be noted that the same letters and characters in different formulas in this application have the same meaning, only their specific values ​​may differ. Furthermore, this invention is not limited to the above embodiments and can have many variations. All variations that can be directly derived or conceived by those skilled in the art from the disclosure of this invention should be considered within the scope of protection of this invention.

Claims

1. A UAV jamming method based on distributed beamforming, characterized in that... The method includes the following steps: Step S1: To meet the needs of drone management in no-fly zones, and with the goal of maximizing the weakest interference power in the entire area, establish a mathematical model for joint optimization of node deployment and beamforming, and construct a maximum-minimum optimization problem. Step S2: Decompose the original non-concave and non-convex problem into a node deployment sub-problem and a beamforming sub-problem; Step S3: Design a solution algorithm for the node deployment subproblem; Step S4: Design a solution algorithm for the beam synthesizer problem; Step S5: Execute step S3 at the fusion center and execute step S4 in a distributed manner at the interference nodes. In this way, the two types of sub-problems are optimized alternately, and finally a node deployment strategy and beamforming scheme with interference robustness are obtained. Step S2 specifically involves: the beamforming block aims to optimize... To improve beam gain in the worst-case scenario, which is achieved by The problem consists of several sub-problems, corresponding to the first... The question is: ;exist When fixed, the node deployment block optimizes the position of each interfering node. To ensure robustness to interference, the corresponding sub-problems are as follows: The node deployment subproblem can be simplified to: ;in, For the first Objective function values ​​for beam synthesizer problems; Characterizing the first The directional vector from each node to that point; For the first Narrowband beam synthesizer with 1 node; The center of the ellipsoid is represented by the sphere. The distance representing the offset from the center of the sphere. Indicates the first The position of each node; It is the shape matrix of the ellipsoid; It is stipulated that the first The deployable range of each node; Indicates the upper limit of the transmit power of each node; superscript Indicates conjugate transpose; superscript This is the transpose of a matrix / vector.

2. The UAV jamming method based on distributed beamforming according to claim 1, characterized in that, Step S1 specifically includes: considering a distributed anti-drone system, which consists of a fusion center and It consists of several mobile interference nodes. The fusion center and each node are connected via a mobile ad hoc network. Each node is equipped with a uniform rectangular array deployed in the YZ plane, with the number of elements being [missing information]. in, and These represent the number of elements along the y-axis and z-axis, respectively. The spacing between adjacent array elements is half a wavelength, and the location of the fusion center is... The interference nodes were all deployed on the rooftops of high-rise buildings, with different nodes deployed on different rooftops. The first node... The position of each node is denoted as . , Indicates the first The height of the building containing each node is a fixed value; superscript This is the transpose of a matrix / vector.

3. The UAV jamming method based on distributed beamforming according to claim 2, characterized in that, Step S3 specifically includes the following sub-steps: Step S31: Construct an optimization problem approximating the original node deployment subproblem: Step S32: Introduce auxiliary variables : S33: Using the S-lemma, the above infinite number of constraints are equivalent to a single convex constraint: S34: Solve the convex problem using the convex optimization toolkit to obtain the optimized positions of the interference nodes. ; The center of the ellipsoid is represented by the sphere. The distance representing the offset from the center of the sphere. Indicates the first The position of each node; It is the shape matrix of the ellipsoid; It is stipulated that the first The deployable range of each node; For the first Objective function value for a beam synthesizer problem; superscript This is the transpose of a matrix / vector.

4. The UAV jamming method based on distributed beamforming according to claim 2, characterized in that, Step S4 specifically includes the following sub-steps: Step S41: Locate the interfering node Discretize the angle formed by the plane and any point within the no-fly zone to obtain the sets of heading and pitch angles, denoted as follows: and Step S42: For a beam combiner problem, introduce auxiliary variables. : Step S43: Introduce matrix variables and define The problem can be transformed into the following equivalent form: ; Subsequently, the above optimization problem is solved using positive semidefinite relaxation to obtain the optimized beam combiner. ;in The cross product matrix of direction vectors; It is an auxiliary variable; It is a guide vector; It is the trace of the matrix; This is the upper limit of the maximum transmit power for each interfering node; It is a matrix semidefinite; superscript This indicates the conjugate transpose.

5. The UAV jamming method based on distributed beamforming according to claim 1, characterized in that, Step S5 specifically includes the following sub-steps: Step S51: Setting Number of iterations Step S52: Based on the fusion center end Solve the node deployment subproblem and optimize the node positions. Send to each interference node; Step S53: Each interference node based on Distributed parallel solution to the beam synthesizer problem yields And the optimized objective function value Send them separately to the fusion center; Step S54: Number of iteration rounds Step S55: Repeat steps S52 to S54 until the results converge. Finally, each interference node is determined according to... Adjust your position and through Beamforming is used to interfere with the no-fly zone.

6. The UAV jamming method based on distributed beamforming according to claim 2, characterized in that, The guide vector The format is as follows: ;in, , ; and These represent the number of elements along the y-axis and z-axis, respectively.

7. A UAV jamming method based on distributed beamforming according to claim 2 or 6, characterized in that, Channel gain can be modeled as path loss under the free-space propagation model, i.e. and standardize it to ; Indicates the first The position of each node; The target location.

8. The UAV jamming method based on distributed beamforming according to claim 3, characterized in that, The convex optimization toolkit is the CVX toolkit; in At this moment, the first Interference signals emitted by each node It can be represented as: ;in, For the first Narrowband beam combiner with nodes This indicates that the noise signal generated by this node follows a distribution. ,remember This refers to a point within the no-fly zone, where signals received from various interfering nodes are observed. It can be represented as: In the formula, For the first Each node and target location Gain coefficient of the propagation channel between them Interference signal The transmission delay experienced Characterizing the first The guide vector from each node to that point; superscript Indicates conjugate transpose; Characterizing the first The guide vector from each node to that point.

9. A UAV jamming method based on distributed beamforming according to claim 4, characterized in that, The semidefinite relaxation refers to ignoring... The constraints are solved to obtain the matrix. Then, the beam synthesizer is obtained through eigenvalue decomposition. Direction angle and pitch angle The definitions are as follows: ; Receive signal The power can be expressed as: The no-fly zone is modeled as an ellipsoid, where any point can be represented as: ;in, The center of the ellipsoid is represented by the sphere. The distance representing the offset from the center of the sphere is affected by... Constraints , These are the semi-major axes of the x, y, and z axes, respectively; The following optimization problem is constructed: ;in, The regulations stipulate the first Deployable range of each node This indicates the upper limit of the transmit power for each node; Indicates the first The position of each node; For the target location; For the first Narrowband beam synthesizer with 1 node; superscript Indicates conjugate transpose; Characterizing the first The directional vector from each node to that point; It is the shape matrix of the ellipsoid; It represents the y-axis coordinates of a point within the no-fly zone and the m-th interfering node; It represents the x-axis coordinates of a point within the no-fly zone and the m-th interfering node; It is the m-th interference node and the point within the no-fly zone Channel gain coefficients between; It is Gaussian white noise.

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