Antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm

Abstract

The invention relates to the technical field of wireless communication, in particular to an antenna radiation and mutual coupling collaborative optimization system based on a time domain hybrid fast algorithm. The antenna radiation and mutual coupling collaborative optimization system based on the time domain hybrid fast algorithm is applied to an unmanned aerial vehicle group communication system and comprises an antenna array unit, a data acquisition unit, a time domain hybrid fast algorithm unit, a mutual coupling calculation unit, an optimization control unit and a communication coordination unit. According to the invention, the three-dimensional geographic coordinates, attitude angle data and signal intensity of the unmanned aerial vehicle group are acquired in real time through the data acquisition unit, and real-time mutual coupling coefficient analysis of the mutual coupling calculation unit is combined, so that the system can dynamically sense the formation form change and trigger the optimization instruction, and the optimization control unit performs comparison through a preset threshold value. The node with the maximum deviation degree is adjusted preferentially, it is ensured that the antenna spacing and the radiation pattern are always adaptive to the dynamic environment, the mutual coupling effect is reduced, and the communication stability is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to an antenna radiation and mutual coupling collaborative optimization system based on a time-domain hybrid fast algorithm. Background Art

[0002] In drone group communications, multiple drones must maintain dynamic formation flight to perform collaborative missions. However, due to the real-time changes in the formation's configuration, the relative positions of the antennas are constantly adjusted, significantly increasing the mutual coupling effect between the antennas. This mutual coupling effect can cause signal interference, reduced radiation efficiency, and communication delays, severely restricting the stability of group communications.

[0003] Existing antenna optimization techniques often rely on static parameter design or offline simulation, which is incapable of adapting to dynamic environments. For example, while traditional genetic algorithms can globally search for optimal solutions, their computational time is prohibitively long, making them inadequate for real-time UAV communication. Simple adjustment strategies based on fixed thresholds ignore the nonlinear relationship between formation configuration and mutual coupling effects. Furthermore, existing systems lack support for multi-objective collaborative optimization, resulting in a sharp decline in performance in complex interference environments.

[0004] Therefore, there is an urgent need for a collaborative optimization system that can analyze antenna mutual coupling in real time, quickly adjust radiation parameters, and adapt to dynamic formation environments to improve the reliability and efficiency of UAV group communications. Summary of the Invention

[0005] The purpose of the present invention is to provide an antenna radiation and mutual coupling collaborative optimization system based on a time-domain hybrid fast algorithm to solve the problems that the traditional genetic algorithm proposed in the above background technology can globally search for the optimal solution, but the calculation time is too long, the simple adjustment strategy based on a fixed threshold ignores the nonlinear relationship between the formation morphology and the mutual coupling effect, and the existing system lacks support for multi-objective collaborative optimization.

[0006] To achieve the above objectives, the present invention provides an antenna radiation and mutual coupling collaborative optimization system based on a time-domain hybrid fast algorithm, the system being applied to a UAV group communication system. The system comprises an antenna array unit, a data acquisition unit, a time-domain hybrid fast algorithm unit, a mutual coupling calculation unit, an optimization control unit, and a communication coordination unit.

[0007] The antenna array unit includes multiple groups of reconfigurable antenna units for dynamically adjusting the radiation pattern;

[0008] The data acquisition unit is used to collect the three-dimensional geographic coordinates, attitude angle data, antenna spacing D, antenna signal strength S and environmental interference intensity I of each drone in the drone group in real time;

[0009] The mutual coupling calculation unit calculates the antenna spacing D according to the three-dimensional geographic coordinates, and calculates the antenna azimuth angle θ of the current UAV according to the attitude angle data, and combines other data uploaded by the data acquisition unit through the formula Calculate the real-time mutual coupling coefficient C between antennas, where S i is the signal strength of the ith antenna, D i is the distance between the ith antenna and other antennas, θ i is the azimuth angle between the i-th antenna and other antennas;

[0010] The optimization control unit is used to preset the threshold C th and compare the real-time mutual coupling coefficient C with the preset threshold C th For comparison, if C>C th , then generates an optimization instruction and sends it to the time domain hybrid fast algorithm unit through the communication coordination unit;

[0011] The time domain hybrid fast algorithm unit recalculates the optimal antenna parameters using a hybrid fast iterative algorithm based on the optimization instruction and the current radiation pattern, and adjusts the radiation pattern through the antenna array unit to reduce the mutual coupling effect.

[0012] As a further improvement of this technical solution, the data acquisition unit is internally provided with a GPS positioning module, a posture sensor, a signal strength sensor and an environmental interference detection module;

[0013] The GPS positioning module is used to collect the three-dimensional geographic coordinates of each drone in the drone group in real time, and the three-dimensional geographic coordinates include longitude x, latitude y and altitude z;

[0014] The attitude sensor is used to obtain the attitude angle data of the drone, including yaw angle, pitch angle and roll angle;

[0015] The signal strength sensor is used to measure the received signal strength R and the transmitted signal strength S of the antenna;

[0016] The environmental interference detection module is used to collect the electromagnetic interference intensity I in the environment.

[0017] As a further improvement of the present technical solution, the specific operation steps of the mutual coupling calculation unit for calculating the antenna spacing D according to the three-dimensional geographic coordinates are as follows:

[0018] Collect the three-dimensional geographic coordinates (x1, y1, z1) of drone A and the three-dimensional geographic coordinates (x2, y2, z2) of drone B;

[0019] By formula and Convert latitude and longitude to radians;

[0020] By formula and Calculate the horizontal ground distance D h , where R is the radius of the earth, the average value is 6371km, and the result is D h The unit is meter;

[0021] By formula D v =|z2-z1| Calculate the vertical height difference D v ;

[0022] By formula Calculate the antenna spacing D;

[0023] The specific operation steps of the mutual coupling calculation unit to calculate the antenna azimuth angle θ of the current UAV according to the attitude angle data are as follows:

[0024] The horizontal coordinates (x1, y1) and (x2, y2) of the adjacent drones are collected through the GPS positioning module, and the yaw angle yaw of the current drone is collected through the attitude sensor;

[0025] According to the formula Calculate the current UAV's antenna azimuth angle θ.

[0026] As a further improvement of this technical solution, the optimization control unit is further used to dynamically adjust the optimization priority according to the UAV formation, specifically:

[0027] The signal strength sensor collects the received signal strength R of each drone, and the environmental interference detection module collects the electromagnetic interference strength I in the current environment;

[0028] According to the formula Calculate the communication quality score of each drone antenna;

[0029] According to the formula Priority=C×Q, the optimization priority of each antenna is calculated, and the optimization instructions are executed in order from high to low priority.

[0030] As a further improvement of this technical solution, the specific optimization steps of the time domain hybrid fast algorithm unit include:

[0031] Step 1: Construct an electromagnetic field distribution matrix based on the current antenna spacing and signal strength;

[0032] Step 2: Calculate the initial radiation efficiency using the finite-difference time-domain method;

[0033] Step 3: Combine genetic algorithm to perform global search on antenna parameters to obtain the optimal pattern parameters;

[0034] Step 4: Send the optimized parameters to the antenna array unit for adjustment.

[0035] As a further improvement of this technical solution, the communication coordination unit is also used to synchronize the location information of the drone group in real time, specifically:

[0036] Obtain the latitude and longitude coordinates of each drone through the GPS module;

[0037] By formula Calculate the real-time distance ΔD between adjacent drones;

[0038] Preset safety threshold D th , when ΔD<D th When the formation reorganization instruction is triggered, the formation reorganization instruction includes adjusting the flight altitude and azimuth of the UAV to ensure that the antenna spacing meets the mutual coupling optimization condition.

[0039] As a further improvement of this technical solution, the specific operation steps of step 1 are:

[0040] The electromagnetic field distribution matrix E is constructed based on the antenna spacing D calculated by the mutual coupling calculation unit, the antenna signal strength S obtained by the data acquisition unit, and the attitude angle data of each drone. Specifically, it is:

[0041] According to the three-dimensional geographic coordinates of the UAV, a discretized three-dimensional grid model containing the positions of all antenna units is established;

[0042] The signal strength S of each antenna and the antenna azimuth angle θ are used as input parameters to calculate the electric field intensity component E at the grid node based on Maxwell's equations. x ,Ey,E z , forming an electromagnetic field distribution matrix Where m, n, and p are the number of segments of the grid in the X, Y, and Z axes, respectively.

[0043] As a further improvement of this technical solution, the specific operating steps of step 2 are:

[0044] The electromagnetic field distribution matrix E is subjected to time domain iterative calculation using the finite difference time domain method, specifically including:

[0045] Set the time step Δt and the spatial steps Δx, Δy, Δz to satisfy the Courant-Friedrichs-Lewy stability condition;

[0046] Initialize the electromagnetic field boundary conditions and use the perfectly matched layer absorbing boundary;

[0047] The electric and magnetic field components are updated time-step by time-step through the discretized Maxwell curl equation until a steady state is reached.

[0048] According to the steady-state field distribution, the formula Calculate the initial radiation efficiency η0 of the antenna, where P rad is the radiation power, P in is the input power.

[0049] As a further improvement of this technical solution, the specific operation steps of step three are as follows:

[0050] Population initialization: Randomly generate an initial population of N individuals, each of which represents a set of antenna parameter combinations, including phase offset φ k , amplitude weight A k and angular adjustment Δθ k (k=1,2,...,K), where K is the total number of antenna elements;

[0051] Fitness evaluation: Taking maximizing the radiation efficiency η and minimizing the mutual coupling coefficient C as the optimization goal, the fitness function is defined as Where α and β are weight coefficients, and α + β = 1;

[0052] Genetic operation: Use the roulette wheel method to select individuals with high fitness to enter the next generation, perform single-point crossover on the selected individuals, exchange some parameters, and m Randomly adjust the phase or amplitude parameters of some individuals;

[0053] Iteration termination: When the preset number of iterations T is reached max Or when the fitness function change rate is lower than the threshold ε, the optimal parameter combination is output.

[0054] As a further improvement of this technical solution, the specific operation steps of step 4 are as follows:

[0055] The optimal parameter combination (φ k ,A k ,Δθ k ) is converted into control instructions for antenna array units;

[0056] The phase and amplitude are adjusted by the phase shifter and attenuator of the reconfigurable antenna unit, while the servo mechanism is driven to adjust the antenna pointing direction so that the radiation pattern is consistent with the optimization result.

[0057] Real-time monitoring of the adjusted mutual coupling coefficient C′, if C′≤C th , the optimization is completed, otherwise return to step 1 and recalculate.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention uses a data acquisition unit to obtain the three-dimensional geographic coordinates, attitude angle data, and signal strength of the drone group in real time. Combined with the real-time mutual coupling coefficient analysis of the mutual coupling calculation unit, the system can dynamically perceive changes in the formation morphology and trigger optimization instructions. The optimization control unit compares the preset threshold and prioritizes the adjustment of nodes with the largest deviation, ensuring that the antenna spacing and radiation pattern always adapt to the dynamic environment, reducing the mutual coupling effect and significantly improving communication stability.

[0060] 2. The present invention rapidly constructs an electromagnetic field distribution matrix based on real-time collected antenna spacing, signal strength, and attitude data, and shortens the field distribution iteration time through parallel calculation. The genetic algorithm adopts an elite retention strategy and adaptive crossover mutation probability in the optimization process to reduce redundant searches. After the two work together, the optimization time in typical scenarios is shortened, and the efficiency is improved compared to traditional genetic algorithms. It can dynamically track the changes in the morphology of drone formations and adjust antenna parameters in real time to ensure low latency and high stability of the communication link.

[0061] 3. The present invention calculates the mutual coupling coefficient and communication quality score in real time through the optimization control unit, and dynamically generates an optimization priority list, giving priority to processing nodes with significant mutual coupling effects and poor communication quality. At the same time, by introducing formation reorganization instructions, it can actively avoid the nonlinear mutation area of ​​mutual coupling. This mechanism enables the system to adaptively adjust the optimization strategy in dynamic formations, thereby improving the efficiency of mutual coupling suppression.

[0062] 4. The present invention adopts a multi-objective fitness function to integrate the goals of maximizing radiation efficiency and minimizing mutual coupling coefficient, and achieves a dynamic balance between the goals through weight coefficients. In a complex electromagnetic interference environment, the system uses a genetic algorithm to globally search for the Pareto optimal solution, ensuring that the radiation efficiency drops at a low level while the mutual coupling coefficient can be stabilized below the threshold, significantly improving the robustness in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a block diagram of the overall principle of the antenna radiation and mutual coupling collaborative optimization system based on the time domain hybrid fast algorithm of the present invention.

[0064] Figure 2 The figure is a schematic diagram of the operation flow of the antenna radiation and mutual coupling collaborative optimization system based on the time domain hybrid fast algorithm of the present invention.

[0065] Figure 3 The figure is a schematic diagram of the steps of executing the optimization instruction by the time-domain hybrid fast algorithm unit of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] In a specific embodiment, Figure 1-3 As shown, an antenna radiation and mutual coupling collaborative optimization system based on a time-domain hybrid fast algorithm is applied to a UAV group communication system. The system includes an antenna array unit, a data acquisition unit, a time-domain hybrid fast algorithm unit, a mutual coupling calculation unit, an optimization control unit, and a communication coordination unit.

[0068] The antenna array unit includes multiple groups of reconfigurable antenna units, which are used to dynamically adjust the radiation pattern to adapt to different communication requirements and mutual coupling environments.

[0069] The data acquisition unit is used to collect the three-dimensional geographic coordinates, attitude angle data, antenna spacing D, antenna signal strength S, and environmental interference intensity I of each drone in the drone group in real time. The data acquisition unit is equipped with a GPS positioning module, attitude sensor, signal strength sensor, and environmental interference detection module.

[0070] The GPS positioning module is used to collect the three-dimensional geographic coordinates (including longitude x, latitude y and altitude z) of each drone in the drone group in real time, providing accurate basic data for mutual coupling calculation and position synchronization.

[0071] The attitude sensor is used to obtain the attitude angle data of the drone (yaw angle, pitch angle and roll angle) to accurately calculate the antenna azimuth angle and improve the accuracy of mutual coupling calculation.

[0072] The signal strength sensor is used to measure the antenna's received signal strength R and transmitted signal strength S, providing a basis for communication quality evaluation for the optimization control module.

[0073] The environmental interference detection module is used to collect the electromagnetic interference intensity I in the environment, helping the optimization control module to adjust the optimization priority and ensure the stability of key communication links.

[0074] The mutual coupling calculation unit calculates the antenna spacing D based on the three-dimensional geographic coordinates, providing key parameters for the calculation of the mutual coupling coefficient, specifically:

[0075] The first step is to collect the three-dimensional geographic coordinates (x1, y1, z1) of drone A and the three-dimensional geographic coordinates (x2, y2, z2) of drone B.

[0076] Step 2: Use the formula and Convert latitude and longitude to radians;

[0077] Step 3: Use the formula and Calculate the horizontal ground distance D h , where R is the radius of the earth, the average value is 6371km, and the result is D h The unit is meter;

[0078] Step 4: Use formula D v =|z2-z1| Calculate the vertical height difference D v ;

[0079] Step 5: Use the formula Calculate the antenna spacing D.

[0080] At the same time, the current UAV antenna azimuth angle θ is calculated based on the attitude angle data to improve the accuracy and real-time performance of the mutual coupling calculation. Specifically:

[0081] The first step is to collect the horizontal coordinates (x1, y1) and (x2, y2) of the adjacent drones through the GPS positioning module, and collect the yaw angle yaw of the current drone through the attitude sensor;

[0082] Step 2: According to the formula Calculate the current UAV's antenna azimuth angle θ.

[0083] Combined with other data uploaded by the data acquisition unit, the formula Calculate the real-time mutual coupling coefficient C between antennas to provide a decision basis for the optimization control module, where S i is the signal strength of the ith antenna, D i is the distance between the ith antenna and other antennas, θ i is the azimuth angle between the i-th antenna and other antennas.

[0084] Optimized control unit for preset threshold C th The real-time mutual coupling coefficient C is compared with the preset threshold C th For comparison, if C>C th , then the optimization instructions are generated and sent to the time domain hybrid fast algorithm unit through the communication coordination unit to ensure timely response to changes in mutual coupling effects.

[0085] At the same time, the optimization control unit is also used to dynamically adjust the optimization priority based on the UAV formation to ensure that the optimization needs of key UAVs and communication links are met first. Specifically:

[0086] The first step is to collect the received signal strength R of each drone through the signal strength sensor, and at the same time collect the electromagnetic interference strength I in the current environment through the environmental interference detection module;

[0087] Step 2: According to the formula Calculate the communication quality score of each drone antenna;

[0088] Step 3: Calculate the optimization priority of each antenna according to the formula Priority = C × Q, and execute the optimization instructions in descending order of priority.

[0089] The communication coordination unit is also used to synchronize the location information of the drone group in real time, ensuring that the location information of the drone group is updated in a timely manner and providing basic data for formation reorganization and optimization. Specifically:

[0090] The first step is to obtain the latitude and longitude coordinates of each drone through the GPS module;

[0091] Step 2: Use the formula Calculate the real-time distance ΔD between adjacent drones;

[0092] Step 3: Preset safety threshold D th , when ΔD<D th When the formation reorganization instruction is triggered, the antenna spacing is guaranteed to meet the mutual coupling optimization conditions and reduce the impact of the mutual coupling effect on communication. The formation reorganization instruction includes adjusting the flight altitude and azimuth of the UAV to ensure that the antenna spacing meets the mutual coupling optimization conditions.

[0093] like Figure 3 As shown, the time domain hybrid fast algorithm unit uses a hybrid fast iterative algorithm to recalculate the optimal antenna parameters based on the optimization instruction and the current radiation pattern, and adjusts the radiation pattern through the antenna array unit to reduce the mutual coupling effect. Specifically:

[0094] The first step is to construct an electromagnetic field distribution matrix based on the current antenna spacing and signal strength, providing a basic model for subsequent radiation efficiency calculation and parameter optimization.

[0095] 1. Based on the antenna spacing D calculated by the mutual coupling calculation unit, the antenna signal strength S obtained by the data acquisition unit, and the attitude angle data of each drone, the electromagnetic field distribution matrix E is constructed. Specifically,

[0096] 2. Based on the 3D geographic coordinates of the UAV, a discretized 3D grid model containing the locations of all antenna units is established;

[0097] 3. Take the signal strength S and antenna azimuth angle θ of each antenna as input parameters and calculate the electric field intensity component E at the grid node based on Maxwell's equations x ,Ey,E z , forming an electromagnetic field distribution matrix Where m, n, and p are the number of segments of the grid in the X, Y, and Z axes, respectively.

[0098] Step 2: Use the finite-difference time-domain method to calculate the initial radiation efficiency to provide an evaluation benchmark for parameter optimization.

[0099] 1. Apply the finite-difference time-domain method to perform time-domain iterative calculations on the electromagnetic field distribution matrix E, including:

[0100] 2. Set the time step Δt and the spatial step Δx, Δy, Δz to satisfy the Courant-Friedrichs-Lewy stability condition;

[0101] 3. Initialize the electromagnetic field boundary conditions and use the perfectly matched layer absorbing boundary;

[0102] 4. Update the electric and magnetic field components time-step by time-step through the discretized Maxwell curl equation until a steady state is reached.

[0103] 5. According to the steady-state field distribution, the formula Calculate the initial radiation efficiency η0 of the antenna, where P rad is the radiation power, P in is the input power.

[0104] Step 3: Combine genetic algorithms to perform a global search of antenna parameters to obtain the optimal pattern parameters, ensuring that the antenna parameters obtain the global optimal solution and improving communication performance and stability.

[0105] 1. Population initialization: Randomly generate an initial population of N individuals, each of which represents a set of antenna parameter combinations, including phase offset φ k , amplitude weight A k and angular adjustment Δθ k (k=1,2,...,K), where K is the total number of antenna elements;

[0106] 2. Fitness evaluation: Taking maximizing radiation efficiency η and minimizing mutual coupling coefficient C as the optimization goal, the fitness function is defined as Where α and β are weight coefficients, and α + β = 1;

[0107] 3. Genetic operation: Use the roulette wheel method to select individuals with high fitness to enter the next generation, perform single-point crossover on the selected individuals, exchange some parameters, and use the probability p to select individuals with high fitness to enter the next generation. m Randomly adjust the phase or amplitude parameters of some individuals;

[0108] 4. Iteration termination: When the preset number of iterations T is reached max Or when the fitness function change rate is lower than the threshold ε, the optimal parameter combination is output

[0109] Step 4: Send the optimized parameters to the antenna array unit for adjustment. By adjusting the antenna parameters in real time, the mutual coupling effect is reduced and the communication efficiency is improved.

[0110] 1. The optimal parameter combination (φ k ,A k ,Δθ k ) is converted into control instructions for antenna array units;

[0111] 2. Adjust the phase and amplitude through the phase shifter and attenuator of the reconfigurable antenna unit, and drive the servo mechanism to adjust the antenna pointing direction so that the radiation pattern is consistent with the optimization result;

[0112] 3. Real-time monitoring of the adjusted mutual coupling coefficient C′. If C′≤C th , the optimization is completed, otherwise return to step 1 and recalculate.

[0113] Working principle:

[0114] In a specific application, the GPS positioning module inside the data acquisition unit first collects three-dimensional geographic coordinates (including longitude x, latitude y and altitude z), the attitude sensor obtains the attitude angle data of the drone (including yaw, pitch and roll angles), the signal strength sensor measures the antenna's received signal strength R and transmitted signal strength S, and the environmental interference detection module collects the electromagnetic interference intensity I in the environment;

[0115] Then, the antenna spacing D is calculated according to the three-dimensional geographic coordinates by the mutual coupling calculation unit, and the antenna azimuth angle θ of the current drone is calculated according to the attitude angle data, and then the formula is used Calculate the real-time mutual coupling coefficient C between antennas;

[0116] At this time, the threshold value C is preset by optimizing the control unit. th , the real-time mutual coupling coefficient C is compared with the preset threshold C th For comparison, if C>C th , then an optimization instruction is generated and sent to the time domain hybrid fast algorithm unit through the communication coordination unit. At the same time, the optimization control unit dynamically adjusts the optimization priority according to the UAV formation and executes the optimization instructions in order from high to low priority;

[0117] While the communication coordination unit sends optimization instructions, it can also synchronize the location information of the drone group in real time;

[0118] When the time domain hybrid fast algorithm unit receives the optimization instruction, it combines the current radiation pattern and uses the hybrid fast iterative algorithm to recalculate the optimal antenna parameters, and adjusts the radiation pattern through the antenna array unit to reduce the mutual coupling effect.

[0119] In summary, the present invention first collects information such as the three-dimensional geographic coordinates, attitude angle data, signal strength, and environmental interference intensity of the drone through a data acquisition module. Next, the mutual coupling calculation module calculates the antenna spacing and antenna azimuth based on this information, and then calculates the real-time mutual coupling coefficient. The optimization control module compares the real-time mutual coupling coefficient with a preset threshold, generates an optimization instruction, and sends it to the time-domain hybrid fast algorithm module through the communication coordination module. The time-domain hybrid fast algorithm module recalculates the optimal antenna parameters based on the optimization instruction and the current radiation pattern using a hybrid fast iterative algorithm, and adjusts the radiation pattern through the antenna array module to reduce the mutual coupling effect.

[0120] Through the above-mentioned implementation methods, the present invention can analyze antenna mutual coupling in real time, quickly adjust radiation parameters, and adapt to dynamic formation environments, significantly improving the reliability and efficiency of drone group communications.

[0121] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. Antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm, characterized by: The system is applied to a UAV group communication system, and the system includes an antenna array unit, a data acquisition unit, a time domain hybrid fast algorithm unit, a mutual coupling calculation unit, an optimization control unit, and a communication coordination unit; The antenna array unit includes multiple groups of reconfigurable antenna units for dynamically adjusting the radiation pattern; The data acquisition unit is used to collect the three-dimensional geographic coordinates, attitude angle data, antenna spacing D, antenna signal strength S and environmental interference intensity I of each drone in the drone group in real time; The mutual coupling calculation unit calculates the antenna spacing D according to the three-dimensional geographic coordinates, and calculates the antenna azimuth angle θ of the current UAV according to the attitude angle data, and combines other data uploaded by the data acquisition unit through the formula Calculate the real-time mutual coupling coefficient C between antennas, where S i is the signal strength of the ith antenna, D i is the distance between the ith antenna and other antennas, θ i is the azimuth angle between the i-th antenna and other antennas; The optimization control unit is used to preset the threshold C th and compare the real-time mutual coupling coefficient C with the preset threshold C th For comparison, if C>C th , then generates an optimization instruction and sends it to the time domain hybrid fast algorithm unit through the communication coordination unit; The time domain hybrid fast algorithm unit recalculates the optimal antenna parameters using a hybrid fast iterative algorithm based on the optimization instruction and the current radiation pattern, and adjusts the radiation pattern through the antenna array unit to reduce the mutual coupling effect.

2. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 1 is characterized in that: The data acquisition unit is internally provided with a GPS positioning module, a posture sensor, a signal strength sensor and an environmental interference detection module; The GPS positioning module is used to collect the three-dimensional geographic coordinates of each drone in the drone group in real time, and the three-dimensional geographic coordinates include longitude x, latitude y and altitude z; The attitude sensor is used to obtain the attitude angle data of the drone, including yaw angle, pitch angle and roll angle; The signal strength sensor is used to measure the received signal strength R and the transmitted signal strength S of the antenna; The environmental interference detection module is used to collect the electromagnetic interference intensity I in the environment.

3. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 2 is characterized in that: The specific operation steps of the mutual coupling calculation unit for calculating the antenna distance D according to the three-dimensional geographic coordinates are as follows: Collect the three-dimensional geographic coordinates (x1, y1, z1) of drone A and the three-dimensional geographic coordinates (x2, y2, z2) of drone B; By formula and Convert latitude and longitude to radians; By formula and Calculate the horizontal ground distance D h , where R is the radius of the earth, the average value is 6371km, and the result is D h The unit is meter; By formula D v =|z2-z1| Calculate the vertical height difference D v ; By formula Calculate the antenna spacing D; The specific operation steps of the mutual coupling calculation unit to calculate the antenna azimuth angle θ of the current UAV according to the attitude angle data are as follows: The horizontal coordinates (x1, y1) and (x2, y2) of the adjacent drones are collected through the GPS positioning module, and the yaw angle yaw of the current drone is collected through the attitude sensor; According to the formula Calculate the current UAV's antenna azimuth angle θ.

4. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 2 is characterized in that: The optimization control unit is also used to dynamically adjust the optimization priority according to the UAV formation, specifically: The signal strength sensor collects the received signal strength R of each drone, and the environmental interference detection module collects the electromagnetic interference strength I in the current environment; According to the formula Calculate the communication quality score of each drone antenna; According to the formula Priority=C×Q, the optimization priority of each antenna is calculated, and the optimization instructions are executed in order from high to low priority.

5. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 1 is characterized in that: The specific optimization steps of the time domain hybrid fast algorithm unit include: Step 1: Construct an electromagnetic field distribution matrix based on the current antenna spacing and signal strength; Step 2: Calculate the initial radiation efficiency using the finite-difference time-domain method; Step 3: Combine genetic algorithm to perform global search on antenna parameters to obtain the optimal pattern parameters; Step 4: Send the optimized parameters to the antenna array unit for adjustment.

6. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 2 is characterized in that: The communication coordination unit is also used to synchronize the location information of the drone group in real time, specifically: Obtain the latitude and longitude coordinates of each drone through the GPS module; By formula Calculate the real-time distance ΔD between adjacent drones; Preset safety threshold D th , when ΔD<D th When the formation reorganization instruction is triggered, the formation reorganization instruction includes adjusting the flight altitude and azimuth of the UAV to ensure that the antenna spacing meets the mutual coupling optimization condition.

7. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 5 is characterized in that: The specific operation steps of step one are: The electromagnetic field distribution matrix E is constructed based on the antenna spacing D calculated by the mutual coupling calculation unit, the antenna signal strength S obtained by the data acquisition unit, and the attitude angle data of each drone. Specifically, it is: According to the three-dimensional geographic coordinates of the UAV, a discretized three-dimensional grid model containing the positions of all antenna units is established; The signal strength S of each antenna and the antenna azimuth angle θ are used as input parameters to calculate the electric field intensity component E at the grid node based on Maxwell's equations. x , E y , E z , forming an electromagnetic field distribution matrix Where m, n, and p are the number of segments of the grid in the X, Y, and Z axes, respectively.

8. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 5 is characterized in that: The specific steps of step 2 are: The electromagnetic field distribution matrix E is subjected to time domain iterative calculation using the finite difference time domain method, specifically including: Set the time step Δt and the spatial steps Δx, Δy, Δz to satisfy the Courant-Friedrichs-Lewy stability condition; Initialize the electromagnetic field boundary conditions and use the perfectly matched layer absorbing boundary; The electric and magnetic field components are updated time-step by time-step through the discretized Maxwell curl equation until a steady state is reached. According to the steady-state field distribution, the formula Calculate the initial radiation efficiency η0 of the antenna, where P rad is the radiation power, P in is the input power.

9. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 5 is characterized in that: The specific steps of step three are: Population initialization: Randomly generate an initial population of N individuals, each of which represents a set of antenna parameter combinations, including phase offset φk, amplitude weight A k and direction angle adjustment Δθ k (k=1,2,...,K), where K is the total number of antenna elements; Fitness evaluation: Taking maximizing the radiation efficiency η and minimizing the mutual coupling coefficient C as the optimization goal, the fitness function is defined as Where α and β are weight coefficients, and α + β = 1; Genetic operation: Use the roulette wheel method to select individuals with high fitness to enter the next generation, perform single-point crossover on the selected individuals, exchange some parameters, and m Randomly adjust the phase or amplitude parameters of some individuals; Iteration termination: When the preset number of iterations T is reached max Or when the fitness function change rate is lower than the threshold ε, the optimal parameter combination is output.

10. The antenna radiation and mutual coupling collaborative optimization system based on time domain hybrid fast algorithm according to claim 5, characterized in that: The specific steps of step 4 are: The optimal parameter combination (φ k ,A k ,Δθ k ) is converted into control instructions for antenna array units; The phase and amplitude are adjusted by the phase shifter and attenuator of the reconfigurable antenna unit, while the servo mechanism is driven to adjust the antenna pointing direction so that the radiation pattern is consistent with the optimization result. Real-time monitoring of the adjusted mutual coupling coefficient C′, if C′≤C th , the optimization is completed, otherwise return to step 1 and recalculate.

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