A sparse array for three-dimensional unmanned aerial vehicles and its beamforming method

Abstract

This invention discloses a three-dimensional sparse array of unmanned aerial vehicles (UAVs) and its beamforming method. The array mainly includes UAVs and antennas, with communication between the UAVs via wireless signals. The method includes: mounting an antenna on each UAV; forming an array from multiple UAVs; optimizing the UAV array distribution to control beam pointing; optimizing the array structure through UAV movement while keeping the observation baseline constant; and optimizing beamforming after each change in array structure. This invention can better complete various tasks in complex environments, reducing the influence of grating lobes while exhibiting strong adaptability, which is beneficial for application implementation.

Description

Technical Field

[0001] This invention belongs to the field of collaborative work of unmanned aerial vehicle (UAV) swarms and the field of narrowband beamforming in array signal processing, and particularly relates to a three-dimensional UAV sparse array and its beamforming method. Background Technology

[0002] In recent years, as unmanned aerial vehicles (UAVs) have become an important component of the integrated space-ground information network, public interest in UAVs has grown significantly. Due to their high mobility, low cost, and ease of deployment, they are widely used in various fields, including military, commercial, and civilian applications, such as radar imaging, environmental monitoring, pollution control, and reducing drag through close-range formation flight. However, as the demands for adaptability and multi-task collaboration in UAV applications increase, the efficiency and intelligence of individual UAV operations can no longer meet these needs. This has led to the concept of UAV swarm collaboration, which expands the operational capabilities of UAVs through formations of multiple UAVs.

[0003] Conventional phased array antennas, and even most array antennas, are uniform arrays, meaning the distance between adjacent elements is equal. In contrast, unequal-spaced arrays, with varying distances between adjacent elements, can significantly reduce the number of elements. Furthermore, optimization can avoid grating lobes and suppress sidelobe levels, offering great flexibility and allowing for the synthesis of desired radiation patterns based on design objectives. Additionally, the significant reduction in the number of elements reduces the overall complexity and failure rate of the antenna system. Although drastically reducing the number of elements inevitably leads to a decrease in array gain compared to a uniform full array, the larger aperture, higher resolution, and the ability to maintain low sidelobes without amplitude-phase weighting make the research on aperiodic arrays highly promising for both military and civilian applications. Over decades of development, aperiodic arrays can be broadly categorized into three types: thinned arrays, sparse arrays, and clustered arrays. Sparse arrays distribute all elements arbitrarily within a fixed array aperture, without grating constraints, offering greater freedom.

[0004] Beamforming is a combination of antenna technology and digital signal processing technology. It is a technique that generates a beam with a specific direction by weighting different elements in an antenna array. Beamforming concentrates energy into a specific beam instead of propagating it in all directions, which greatly improves the signal-to-noise ratio at the receiver and enhances system performance.

[0005] In recent years, beamforming in conjunction with UAV arrays has attracted widespread attention from researchers both domestically and internationally. Current products have high computational complexity. For example, CN113777572A (published on 2021-12-10), entitled "A Three-Dimensional Ultra-Sparse Array Static Pattern Synthesis Method," divides the spatial domain into multiple regions for optimization, increasing optimization time and lacking good real-time performance. CN114639972A (published on 2022-06-17), entitled "Method and Apparatus for Optimizing Array Element Positions of UAV Array Antennas," places the UAV array on a single plane, failing to achieve a three-dimensional array. Therefore, the realization of sparsely distributed UAV arrays in three-dimensional space and its beamforming problem still holds significant research value. Summary of the Invention

[0006] The purpose of this invention is to provide a three-dimensional unmanned aerial vehicle (UAV) sparse array and its beamforming method to solve the technical problem of UAV beamforming in a three-dimensional space.

[0007] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0008] A three-dimensional sparse array of unmanned aerial vehicles (UAVs) and its beamforming method are disclosed. The UAV sparse array includes UAVs and antennas, and the UAVs communicate with each other via wireless signals. The antennas are mounted on each UAV and can be single antenna elements or uniform matrix antenna arrays. The beamforming method for the three-dimensional UAV sparse array includes the following steps:

[0009] Step 1: Equip each drone with an antenna, and form an array by assembling multiple drones to perform beamforming;

[0010] Step 2: Use optimization algorithms to optimize the distribution of the UAV array in order to control the beam pointing.

[0011] The optimization algorithm includes the following steps:

[0012] Step 2.1: Obtain the real-time three-dimensional coordinates of the UAV array elements;

[0013] Step 2.2: Determine the spatial range parameters and perform initialization settings; set the minimum distance, maximum number of iterations, and beam pitch angle direction parameters of the UAV, and determine the fitness function;

[0014] Step 2.3: Before reaching the maximum number of iterations, calculate the fitness function and perform data transformation operations;

[0015] Step 2.4: Stop iterating when the maximum number of iterations is reached or the termination condition is met, and obtain the optimal UAV node distribution information that is close to the objective function, as well as the amplitude and phase information of the excitation signal of each UAV antenna;

[0016] Step 3: With the observation baseline unchanged, optimize the array structure by maneuvering the UAV, and optimize beamforming after each change in array structure.

[0017] Furthermore, the drone is located at any accessible position in three-dimensional space. If there are buildings blocking the view, the spatial range is discontinuous. The sparse array refers to the non-uniform spacing between each pair of drones, and the spacing is limited by the spatial range and the minimum distance between the drones.

[0018] Furthermore, a drone is an unmanned aircraft operated by radio remote control equipment and its own program control device, or an unmanned aircraft operated completely or intermittently autonomously by an onboard computer.

[0019] Furthermore, in step 2.1, before acquiring the three-dimensional coordinates of the UAVs, a suitable origin is selected within the spatial range to establish the three-dimensional coordinates. Assuming there are N UAVs in the UAV swarm, the real-time three-dimensional coordinates (x, y, z) of each UAV are acquired. n ,y n ,z n ), n=1,2,…,N, and the real-time three-dimensional coordinates of the UAV array elements are used as the initialization settings in step 2.2.

[0020] Furthermore, step 2.2 specifically includes the following steps: determining the spatial range parameter (L). x ,L y ,L z Set the minimum spacing d between drones. min Maximum number of iterations k, main beam pointing Parameters, let

[0021]

[0022] Where θ is the pitch angle. If the azimuth is given, then the three-dimensional pattern function is... for:

[0023]

[0024] in This refers to the antenna pattern function on a single UAV. Let A be the antenna array pattern function. n Let λ be the excitation amplitude of the nth UAV antenna, λ be the wavelength, and j be the imaginary unit. The main beam direction is indicated by θ0, and the elevation angle direction is indicated by θ0. This indicates the azimuth direction.

[0025] The fitness function is the highest sidelobe level of the array factor pattern of the UAV array antenna; the optimization objective is to find the optimal UAV position and excitation values ​​under given constraints, so as to minimize the highest sidelobe level in the required region; the fitness function is defined as:

[0026]

[0027] Among them FF max The peak value is the main lobe.

[0028] Furthermore, in step 3, the process of adjusting the spatial position of each UAV involves dynamic path flight issues. It is necessary to avoid collisions between UAVs and to keep the position adjustment time as short as possible to ensure the real-time formation of the UAV swarm beam.

[0029] The present invention provides a three-dimensional unmanned aerial vehicle (UAV) sparse array and its beamforming method, which has the following characteristics:

[0030] 1. This invention enables a drone cluster array composed of multiple drones by equipping each drone with an antenna; by changing the position of each drone, the positions of the array elements are rearranged to achieve the effect of a sparse array.

[0031] 2. This invention can better complete various tasks in complex environments. While reducing the influence of radiation pattern grating lobes, it also has strong adaptability, which is beneficial to application implementation.

[0032] 3. This invention breaks through the limitations of traditional planar arrays and conformal arrays, and can obtain the beam pointing in the required direction in three-dimensional space, which is suitable for engineering applications. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the beamforming method of the sparse array of unmanned aerial vehicles (UAVs) according to the present invention.

[0034] Figure 2 This is a schematic diagram of the sparse array optimization algorithm of the present invention;

[0035] Figure 3 This is a schematic diagram of the coordinate system of the three-dimensional space UAV of the present invention;

[0036] Figure 4(a) shows the simulation verification of the beam pointing result when the elevation angle is 60° and the azimuth angle is 0°.

[0037] Figure 4(b) shows the simulation verification of the beam pointing result when the elevation angle is 120° and the azimuth angle is 10°.

[0038] Figure 5(a) is a schematic diagram of the UAV's position under the beam pointing condition corresponding to Figure 4(a);

[0039] Figure 5(b) is a schematic diagram of the UAV's position under the beam pointing condition corresponding to Figure 4(b). Detailed Implementation

[0040] To better understand the purpose, structure, and function of this invention, the following detailed description of a three-dimensional unmanned aerial vehicle (UAV) sparse array and its beamforming method, in conjunction with the accompanying drawings, is provided.

[0041] This invention provides a three-dimensional sparse array of unmanned aerial vehicles (UAVs). The array mainly comprises UAVs and antennas, with communication between the UAVs via wireless signals. An UAV refers to an unmanned aircraft controlled by radio remote control equipment and its own program control device, or an unmanned aircraft operated autonomously, either completely or intermittently, by an onboard computer. The antenna is mounted on each UAV and can be a single antenna element or a uniform matrix antenna array. The UAVs can be located at any accessible location in three-dimensional space; however, this spatial range may be discontinuous if there are complex conditions such as building obstructions.

[0042] The beamforming method for sparse arrays provided by this invention is as follows: Figure 1 As shown, the main processes include the following:

[0043] Step 1: Equip each drone with an antenna, and form an array of drones to perform beamforming.

[0044] Step 2: Optimize the UAV array distribution to achieve beam pointing control. The flowchart of this optimization method is shown below. Figure 2 As shown, the main steps include:

[0045] 1) Select a suitable origin within the spatial range to establish a three-dimensional coordinate system. Assume there are N drones in the drone swarm. Obtain the real-time three-dimensional coordinates (x, y, y) of each drone. n ,y n ,z n ), n=1,2,…,N.

[0046] 2) Determine the spatial range parameter (L) x ,L y ,L z Set the minimum spacing d between drones. min Maximum number of iterations k, main beam pointing Parameters, etc. A coordinate diagram is shown below. Figure 3 As shown. Let

[0047]

[0048] Where θ is the pitch angle. It is the azimuth angle. The formulas used for convenience have no actual meaning. Therefore, the three-dimensional pattern function... for:

[0049]

[0050] in This refers to the antenna pattern function on a single UAV. Let A be the antenna array pattern function. n Let λ be the excitation amplitude of the nth UAV antenna, λ be the wavelength, and j be the imaginary unit. The main beam direction is indicated by θ0, and the elevation angle direction is indicated by θ0. This indicates the azimuth direction.

[0051] The fitness function is the highest sidelobe level of the array factor pattern of the UAV array antenna. The optimization objective is to find the optimal UAV position and excitation values ​​under given constraints, such that the highest sidelobe level in the desired region is minimized. The fitness function is defined as:

[0052]

[0053] Among them FF max The peak value is the main lobe.

[0054] 3) Before reaching the maximum number of iterations k, calculate the fitness function and perform data transformation operations in each iteration.

[0055] 4) If the maximum number of iterations k is reached or the required radiation pattern function is obtained, the iteration is stopped. The optimal UAV node distribution information that is close to the required radiation pattern function, as well as the excitation signal amplitude and phase information of each UAV are obtained.

[0056] Step 3: While keeping the observation baseline unchanged, optimize the array structure by maneuvering the UAV and perform beamforming after each change in the array structure.

[0057] All steps and conclusions of this invention are verified and presented using the scientific computing software Matlab R2021b. In the simulation, the number of UAVs N = 22, and the minimum spacing d between UAV array elements is... min =λ, azimuth angle of the main beam Pitch angle θ 01 =60, azimuth angle Pitch angle θ 02 =120, maximum number of iterations k=500.

[0058] To demonstrate the effectiveness of this method, Figure 1 A flowchart illustrating the implementation of sparse arrays for unmanned aerial vehicles and their beamforming methods; Figure 2 This is a schematic diagram of the sparse array optimization algorithm of the present invention; Figure 3Figure 4 shows a schematic diagram of the coordinate system of the three-dimensional UAV of the present invention; Figures 4 and 5 show the simulation verification results of the beam pointing under two beam pointing conditions of the present invention and the corresponding UAV position schematic diagrams. As can be seen from Figures 4 and 5, the present invention can achieve different beam pointing results by changing the position of the UAV, which has practical application value.

[0059] This invention enables the formation of a drone swarm array by equipping each drone with an antenna; by changing the position of individual drones, the positions of the array elements are rearranged to achieve a sparse array effect; simulation verification shows that this invention is indeed feasible. This invention breaks through the limitations of traditional planar arrays and conformal arrays, and can obtain beam pointing in the desired direction in three-dimensional space, making it a suitable invention for engineering applications.

[0060] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A beamforming method for a sparsely distributed array of three-dimensional unmanned aerial vehicles (UAVs), characterized in that, The sparsely distributed array of drones includes drones and antennas, with communication between the drones via wireless signals; the antennas are mounted on each drone and can be single antenna elements or uniform matrix antenna arrays; the beamforming method for the three-dimensional sparsely distributed array of drones includes the following steps: Step 1: Equip each drone with an antenna, and form an array by assembling multiple drones to perform beamforming; Step 2: Use optimization algorithms to optimize the distribution of the UAV array in order to control the beam pointing. The optimization algorithm includes the following steps: Step 2.1: Obtain the real-time three-dimensional coordinates of the UAV array elements; Step 2.2: Determine the spatial range parameters and perform initialization settings; set the minimum distance, maximum number of iterations, and beam pitch angle direction parameters of the UAV, and determine the fitness function; Step 2.3: Before reaching the maximum number of iterations, calculate the fitness function and perform data transformation operations; Step 2.4: Stop iterating when the maximum number of iterations is reached or the termination condition is met, and obtain the optimal UAV node distribution information that is close to the objective function, as well as the amplitude and phase information of the excitation signal of each UAV antenna; Step 3: While keeping the observation baseline unchanged, optimize the array structure by maneuvering the UAV, and optimize beamforming after each change in array structure; In step 2.1, before obtaining the three-dimensional coordinates of the UAVs, a suitable origin is selected within the spatial range to establish the three-dimensional coordinates. Let the total number of UAVs in the swarm be... Dispatch drones and obtain the real-time 3D coordinates of each drone. The real-time three-dimensional coordinates of the UAV array elements are obtained and used as the initialization settings in step 2.2; Step 2.2 specifically includes the following steps: determining the spatial range parameters. Set the minimum spacing between drones Maximum number of iterations Main beam pointing Parameters, let ； in The pitch angle, Given the azimuth angle, the three-dimensional direction pattern function is... for: ； in This refers to the antenna pattern function on a single UAV. This is the antenna array radiation pattern function. For the first The excitation amplitude of the drone antenna, For wavelength, The imaginary unit, Main beam direction, The pitch angle is in the direction of, The azimuth direction; The fitness function is the highest sidelobe level of the array factor pattern of the UAV array antenna; the optimization objective is to find the optimal UAV position and excitation values ​​under given constraints, so as to minimize the highest sidelobe level in the required region; the fitness function is defined as: ； in The peak value is the main lobe.

2. The beamforming method for a sparsely distributed array of three-dimensional unmanned aerial vehicles according to claim 1, characterized in that, The drone is located at any accessible location in three-dimensional space. If there are buildings blocking the view, the space range is discontinuous. The sparse array refers to the non-uniform spacing between each pair of drones, and the spacing is limited by the spatial range and the minimum distance between the drones.

3. The beamforming method for a sparsely distributed array of three-dimensional UAVs according to claim 1, characterized in that, The unmanned aerial vehicle (UAV) is an unmanned aircraft operated by radio remote control equipment and its own program control device, or an unmanned aircraft operated autonomously, either completely or intermittently, by an onboard computer.

4. The beamforming method for a sparsely distributed array of three-dimensional unmanned aerial vehicles according to claim 1, characterized in that, In step 3, the process of adjusting the spatial position of each UAV involves dynamic path flight issues. It is necessary to avoid collisions between UAVs and to keep the position adjustment time as short as possible to ensure the real-time formation of UAV swarm beams.

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