Antenna Radiation and Mutual Coupling Cooperative Optimization System Based on Time-Domain Hybrid Fast Algorithm

Abstract

The present application 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 a UAV 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. In the present application, the three-dimensional geographic coordinates, attitude angle data and signal strength of the UAV group are acquired in real time by the data acquisition unit, and the real-time mutual coupling coefficient analysis of the mutual coupling calculation unit is combined, so that the system can dynamically perceive the formation mode change and trigger the optimization instruction, and the optimization control unit compares the preset threshold value, preferentially adjusts the node with the largest deviation degree, ensures that the antenna spacing and the radiation pattern are always adapted to the dynamic environment, reduces the mutual coupling effect, and significantly improves the communication stability.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to an antenna radiation and mutual coupling cooperative optimization system based on a time-domain hybrid fast algorithm. Background Technology

[0002] In drone swarm communication, multiple drones need to maintain dynamic formation flight to perform collaborative tasks. However, due to the real-time changes in formation, the relative positions of the antennas are constantly adjusted, leading to a significant increase in antenna mutual coupling. This mutual coupling effect can cause problems such as signal interference, reduced radiation efficiency, and communication delays, severely restricting the stability of swarm communication.

[0003] In existing technologies, antenna optimization often employs static parameter design or offline simulation, which cannot adapt to dynamic environments. For example, while traditional genetic algorithms can globally search for the optimal solution, their computational time is too long to meet the real-time communication requirements of UAVs; and 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, leading to a sharp decline in performance under 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 swarm communication. Summary of the Invention

[0005] The purpose of this invention is to provide an antenna radiation and mutual coupling cooperative optimization system based on a time-domain hybrid fast algorithm, in order to solve the problems mentioned in the background art, such as the traditional genetic algorithm being able to search for the optimal solution globally but having too long a computation time, the simple adjustment strategy based on a fixed threshold ignoring the nonlinear relationship between formation shape and mutual coupling effect, and the lack of support for multi-objective cooperative optimization in existing systems.

[0006] To achieve the above objectives, the present invention aims to provide an antenna radiation and mutual coupling cooperative optimization system based on a time-domain hybrid fast algorithm. The system is applied to an unmanned aerial vehicle (UAV) swarm communication system and 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.

[0007] The antenna array unit includes multiple sets of reconfigurable antenna elements 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 swarm in real time.

[0009] The mutually coupled calculation unit calculates the antenna spacing D based on three-dimensional geographic coordinates, and simultaneously calculates the current UAV antenna azimuth angle θ based on attitude angle data. Combined with other data uploaded by the data acquisition unit, it calculates the final antenna spacing D using the formula... Calculate the real-time mutual coupling coefficient C between antennas, where S i Let D be the signal strength of the i-th antenna. i Let θ be the distance between the i-th antenna and the other antennas. i Let be 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 the real-time mutual coupling coefficient C is compared with the preset threshold C. th Comparing them, 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;

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

[0012] As a further improvement to this technical solution, the data acquisition unit is internally equipped with a GPS positioning module, an attitude 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 swarm in real time. The three-dimensional geographic coordinates include longitude x, latitude y and altitude z.

[0014] The attitude sensor is used to acquire the attitude angle data of the UAV, including yaw, pitch and roll angles.

[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 to this technical solution, the specific operation steps of the mutual coupling calculation unit in calculating the antenna spacing D based on three-dimensional geographic coordinates are as follows:

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

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

[0020] Through formula and Calculate the horizontal ground distance D h Where R is the Earth's radius, with an average value of 6371 km, the result is D. h The unit is meters;

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

[0022] Through formula Calculate the antenna spacing D;

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

[0024] The horizontal coordinates (x1, y1) and (x2, y2) of the adjacent UAVs are collected by the GPS positioning module, and the yaw angle of the current UAV is collected by the attitude sensor.

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

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

[0027] The signal strength R of each UAV is collected by a signal strength sensor, and the electromagnetic interference strength I in the current environment is collected by an environmental interference detection module.

[0028] According to the formula Calculate the communication quality score for each UAV antenna;

[0029] Calculate the optimization priority of each antenna according to the formula Priority = C × Q, and execute the optimization instructions in descending order of priority.

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

[0031] Step 1: Construct the 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: Use a genetic algorithm to perform a global search for antenna parameters to obtain the optimal radiation pattern parameters;

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

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

[0036] The latitude and longitude coordinates of each drone are obtained through the GPS module;

[0037] Through formula Calculate the real-time distance ΔD between adjacent UAVs;

[0038] Preset safety threshold D th When ΔD < D th When the formation is reorganized, a formation reorganization command is triggered. The formation reorganization command includes adjusting the UAV's flight altitude and azimuth angle to ensure that the antenna spacing meets the mutual coupling optimization conditions.

[0039] As a further improvement to this technical solution, the specific operation steps of step one are as follows:

[0040] The electromagnetic field distribution matrix E is constructed based on the antenna spacing D calculated by the mutually coupled computing unit, the antenna signal strength S acquired by the data acquisition unit, and the attitude angle data of each UAV. Specifically:

[0041] Based on the UAV's three-dimensional geographic coordinates, a discretized three-dimensional mesh model containing the locations of all antenna units is established;

[0042] Using the signal strength S and antenna azimuth angle θ of each antenna as input parameters, the electric field intensity component E at the grid nodes is calculated based on Maxwell's equations. x Ey, E z To form an electromagnetic field distribution matrix Where m, n, and p are the number of grid segments along the X, Y, and Z axes, respectively.

[0043] As a further improvement to this technical solution, the specific operation steps of step two are as follows:

[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 space steps Δx, Δy, Δz to satisfy the Courant-Friedrichs-Lewy stability condition;

[0046] Initialize the electromagnetic field boundary conditions using a perfectly matched layer absorbing boundary.

[0047] By using the discretized Maxwell's curl equations, the electric and magnetic field components are updated step by step over time until a steady state is reached.

[0048] Based on the steady-state field distribution, using the formula Calculate the initial radiation efficiency η0 of the antenna, where P rad For radiated power, P in This refers to the input power.

[0049] As a further improvement to 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 representing a set of antenna parameter combinations, including phase offset φ. k Amplitude weight A k and the direction angle adjustment amount Δθ k (k = 1, 2, ..., K), where K is the total number of antenna elements;

[0051] Fitness evaluation: The fitness function is defined as follows: Maximizing radiation efficiency η and minimizing the mutual coupling coefficient C are the optimization objectives. Where α and β are weighting coefficients, and α + β = 1;

[0052] Genetic operations: A roulette wheel selection method is used to choose individuals with high fitness to enter the next generation. Single-point crossover is then performed on the selected individuals, exchanging some parameters with probability p. m Randomly adjust the phase or amplitude parameters of certain individuals;

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

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

[0055] The optimal parameter combination (φ) obtained by the genetic algorithm k A k ,Δθ k This is converted into control commands for the antenna array unit;

[0056] The phase and amplitude are adjusted by the phase shifter and attenuator of the reconfigurable antenna element, while the servo mechanism is driven to adjust the antenna pointing, 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 If the result is satisfactory, the optimization is complete; otherwise, return to step one and recalculate.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. This invention acquires the three-dimensional geographic coordinates, attitude angle data and signal strength of the UAV swarm in real time through the data acquisition unit. Combined with the real-time mutual coupling coefficient analysis of the mutual coupling calculation unit, the system can dynamically perceive changes in formation and trigger optimization commands. Through the optimization control unit, the nodes with the largest deviation are prioritized for adjustment by comparing preset thresholds, ensuring that the antenna spacing and radiation pattern are always adapted to the dynamic environment, reducing the mutual coupling effect and significantly improving communication stability.

[0060] 2. This invention rapidly constructs an electromagnetic field distribution matrix based on real-time acquired antenna spacing, signal strength, and attitude data. It also shortens the field distribution iteration time through parallel computation. The genetic algorithm employs an elite retention strategy and adaptive crossover mutation probability during the optimization process to reduce redundant searches. With both working together, the optimization time in typical scenarios is shortened, and the efficiency is improved compared to traditional genetic algorithms. It can dynamically track changes in UAV formation and adjust antenna parameters in real time, ensuring low latency and high stability of the communication link.

[0061] 3. This invention optimizes the control unit to calculate the mutual coupling coefficient and communication quality score in real time, and dynamically generates an optimization priority list to prioritize the processing of nodes with significant mutual coupling effects and poor communication quality. At the same time, by introducing a formation reorganization instruction, it can actively avoid the nonlinear mutation region of mutual coupling. This mechanism enables the system to adaptively adjust the optimization strategy in dynamic formation, thereby improving the efficiency of mutual coupling suppression.

[0062] 4. This invention integrates the goals of maximizing radiation efficiency and minimizing the mutual coupling coefficient by adopting a multi-objective fitness function, and achieves dynamic balance between the goals through weighting coefficients. In complex electromagnetic interference environments, the system uses a genetic algorithm to globally search for Pareto optimal solutions, ensuring that the radiation efficiency decreases relatively little while the mutual coupling coefficient remains stable below the threshold, significantly improving robustness in complex scenarios. Attached Figure Description

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

[0064] Figure 2 This is a schematic diagram of the operation process of the antenna radiation and mutual coupling cooperative optimization system based on the time-domain hybrid fast algorithm of the present invention.

[0065] Figure 3 This is a schematic diagram illustrating the steps of executing optimization instructions by the temporal hybrid fast algorithm unit of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] In one specific embodiment, such as Figure 1-3 As shown, an antenna radiation and mutual coupling cooperative optimization system based on a time-domain hybrid fast algorithm is applied to an unmanned aerial vehicle (UAV) swarm 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 sets of reconfigurable antenna elements, 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 real-time data on the 3D geographic coordinates, attitude angles, antenna spacing D, antenna signal strength S, and environmental interference intensity I of each drone in the drone swarm. The data acquisition unit internally includes 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 swarm in real time, providing accurate basic data for mutual coupling calculation and position synchronization.

[0071] Attitude sensors are used to acquire attitude angle data (yaw, pitch, and roll) of the UAV, which is used 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 received signal strength R and the transmitted signal strength S of the antenna, providing a basis for communication quality assessment for the optimization control module.

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

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

[0075] Step 1: Collect the three-dimensional geographic coordinates (x1, y1, z1) of UAV A and the three-dimensional geographic coordinates (x2, y2, z2) of UAV B.

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

[0077] Step 3: Using the formula and Calculate the horizontal ground distance D h Where R is the Earth's radius, with an average value of 6371 km, the result is D. h The unit is meters;

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

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

[0080] Simultaneously, the antenna azimuth angle θ of the current UAV is calculated based on the attitude angle data, improving 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 UAVs through the GPS positioning module, and to collect the yaw angle of the current UAV through the attitude sensor.

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

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

[0084] The optimized control unit is used for preset threshold C th And the real-time mutual coupling coefficient C is compared with the preset threshold C. th Comparing them, if C > C th Then, 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 the mutual coupling effect.

[0085] Meanwhile, the optimization control unit is also used to dynamically adjust optimization priorities based on the drone formation, ensuring that the optimization needs of critical drones and communication links are met first. Specifically:

[0086] The first step is to collect the received signal strength R of each UAV 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 for each UAV antenna;

[0088] The third step is to calculate the optimization priority of each antenna according to the formula Priority = C × Q, and execute the optimization instructions in order of priority from high to low.

[0089] The communication coordination unit is also used to synchronize the location information of the drone swarm in real time, ensuring that the location information of the drone swarm 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 using a GPS module;

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

[0092] Step 3: Preset the security threshold D th When ΔD < D th At that time, a formation reorganization command is triggered to ensure that the antenna spacing meets the mutual coupling optimization conditions and reduce the impact of mutual coupling effect on communication. The formation reorganization command includes adjusting the UAV's flight altitude and azimuth angle 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, based on the optimization instructions and combined with the current radiation pattern, recalculates the optimal antenna parameters using a hybrid fast iterative algorithm, and adjusts the radiation pattern through the antenna array unit to reduce mutual coupling effects. 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 mutually coupled computing unit, the antenna signal strength S acquired by the data acquisition unit, and the attitude angle data of each UAV, an electromagnetic field distribution matrix E is constructed, specifically as follows:

[0096] 2. Based on the UAV's three-dimensional geographic coordinates, establish a discretized three-dimensional mesh model containing the locations of all antenna units;

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

[0098] The second step is to calculate the initial radiation efficiency using the finite-difference time-domain method, providing an evaluation benchmark for parameter optimization.

[0099] 1. For the electromagnetic field distribution matrix E, perform time-domain iterative calculations using the finite-difference time-domain method, specifically including:

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

[0101] 3. Initialize the electromagnetic field boundary conditions using a perfectly matched layer absorbing boundary.

[0102] 4. By using the discretized Maxwell's curl equations, the electric and magnetic field components are updated step by step over time until a steady state is reached;

[0103] 5. Based on the steady-state field distribution, using the formula... Calculate the initial radiation efficiency η0 of the antenna, where P rad For radiated power, P in This refers to the input power.

[0104] The third step is to combine a genetic algorithm to perform a global search for antenna parameters to obtain the optimal radiation pattern parameters, ensuring that the antenna parameters obtain the globally optimal solution and improving communication performance and stability.

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

[0106] 2. Fitness Evaluation: With the optimization objectives of maximizing radiation efficiency η and minimizing the mutual coupling coefficient C, the fitness function is defined as follows: Where α and β are weighting coefficients, and α + β = 1;

[0107] 3. Genetic Operations: A roulette wheel selection method is used to choose individuals with high fitness to enter the next generation. Single-point crossover is then performed on the selected individuals, exchanging some parameters with probability p. m Randomly adjust the phase or amplitude parameters of certain individuals;

[0108] 4. Iteration Termination: When the preset number of iterations T is reached... max Alternatively, when the rate of change of the fitness function 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) obtained by the genetic algorithm k A k ,Δθ k This is converted into control commands for the antenna array unit;

[0111] 2. The phase and amplitude are adjusted by the phase shifter and attenuator of the reconfigurable antenna element, and the servo mechanism is driven to adjust the antenna pointing so that the radiation pattern is consistent with the optimization result.

[0112] 3. Monitor the adjusted mutual coupling coefficient C′ in real time. If C′≤C th If the result is satisfactory, the optimization is complete; otherwise, return to step one and recalculate.

[0113] Working principle:

[0114] In specific applications, the three-dimensional geographic coordinates (including longitude x, latitude y, and altitude z) are first collected through the GPS positioning module inside the data acquisition unit. The attitude angle data of the UAV (including yaw, pitch, and roll angles) are obtained through the attitude sensor. The received signal strength R and transmitted signal strength S of the antenna are measured through the signal strength sensor. The electromagnetic interference intensity I in the environment is collected through the environmental interference detection module.

[0115] Next, the antenna spacing D is calculated based on the three-dimensional geographic coordinates using the mutually coupled computing unit, and the antenna azimuth angle θ of the current UAV is calculated based on the attitude angle data. Then, the result is obtained using the formula... Calculate the real-time mutual coupling coefficient C between antennas;

[0116] At this point, by optimizing the preset threshold C of the control unit th The real-time mutual coupling coefficient C is compared with the preset threshold C. th Comparing them, if C > C th Then, optimization instructions are generated and sent to the temporal 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 of priority from high to low.

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

[0118] When the time-domain hybrid fast algorithm unit receives the optimization instruction, it combines the current radiation pattern and uses a 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, this invention first acquires information such as the UAV's three-dimensional geographic coordinates, attitude angles, signal strength, and environmental interference intensity through a data acquisition module. Next, the mutual coupling calculation module calculates the antenna spacing and azimuth angle 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 optimization instructions, and sends them to the time-domain hybrid fast algorithm module via the communication coordination module. The time-domain hybrid fast algorithm module, based on the optimization instructions and the current radiation pattern, recalculates the optimal antenna parameters using a hybrid fast iterative algorithm, and adjusts the radiation pattern through the antenna array module to reduce mutual coupling effects.

[0120] Through the above 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 UAV swarm communication.

[0121] The foregoing has shown and described 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 embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An antenna radiation and mutual coupling co-optimization system based on time-domain hybrid fast algorithm, characterized in that, The system is applied to a UAV group communication system, and 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; The antenna array unit comprises multiple groups of reconfigurable antenna units, which are used for dynamically adjusting a radiation pattern; The data acquisition unit is used for acquiring three-dimensional geographic coordinates, attitude angle data, signal strength S of the antenna and environmental interference intensity I of each UAV in the UAV 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 of the current unmanned aerial vehicle according to the attitude angle data , and combines other data uploaded by the data acquisition unit to calculate the real-time mutual coupling coefficient C between the antennas through the formula , wherein S i is the signal strength of the i th antenna, D i is the spacing between the antenna of the current unmanned aerial vehicle and the i th antenna of the other unmanned aerial vehicle, is the azimuth angle between the antenna of the current unmanned aerial vehicle and the i th antenna of the other unmanned aerial vehicle. The optimization control unit is used for presetting a threshold C th , and comparing the real-time mutual coupling coefficient C with the preset threshold C th , if C>C th , an optimization instruction is generated and sent to the time-domain hybrid fast algorithm unit through the communication coordination unit. The time-domain hybrid fast algorithm unit re-computes optimal antenna parameters by using a hybrid fast iterative algorithm according to the optimization instruction and the current radiation pattern, and adjusts the radiation pattern through the antenna array unit, so as to reduce the mutual coupling effect; The specific optimization steps of the time-domain hybrid fast algorithm unit comprise: Step one: constructing an electromagnetic field distribution matrix according to the current antenna spacing and signal strength; Step two: calculating an initial radiation efficiency by using a time-domain finite difference method; Step three: performing global search on the antenna parameters by using a genetic algorithm to obtain optimal pattern parameters; Step four: sending the optimized parameters to the antenna array unit for adjustment; The specific operation steps of step one are: The electromagnetic field distribution matrix E is constructed according to the antenna spacing D calculated by the mutual coupling calculation unit, the signal strength S of the antenna acquired by the data acquisition unit and the attitude angle data of each UAV, and specifically: A discretized three-dimensional grid model containing positions of all antenna units is established according to the three-dimensional geographic coordinates of the UAVs; The signal strength S and the antenna azimuth angle As input parameters, the electric field strength components at the grid nodes are calculated based on the Maxwell equations , , , the electromagnetic field distribution matrix is formed where m, n, p are the number of segments of the grid in the X, Y, Z axes, respectively; The specific operation steps of step two are: The electromagnetic field distribution matrix E is calculated by using a time-domain finite difference method, and specifically includes: Setting time steps and spatial steps , , satisfying the Courant-Friedrichs-Lewy stability condition; Initializing electromagnetic field boundary conditions, and adopting a perfect matched layer absorbing boundary; Updating electric field and magnetic field components step by step through a discretized Maxwell curl equation until a steady state is reached; According to the steady-state field distribution, the initial radiation efficiency of the antenna is calculated by the formula wherein, is the radiated power, is the input power;​ The specific operation steps of step three are: Population initialization: an initial population containing N individuals is randomly generated, each individual representing a set of antenna parameter combinations, including phase offset , amplitude weight , and directional angle adjustment amount , where K is the total number of antenna units; Fitness evaluation: to maximize the radiation efficiency and to minimize the mutual coupling coefficient C, the fitness function is defined as wherein, , is a weight coefficient, and ; Genetic operation: select individuals with high fitness into next generation by roulette method, and perform single-point crossover on selected individuals to exchange part of parameters with probability Randomly adjust phase or amplitude parameters of some individuals; Iteration termination: when a preset iteration number is reached or a fitness function change rate is below a threshold , output the optimal parameter combination; The specific operation steps of step four are: combining the optimal parameters obtained by the genetic algorithm converting into control instructions for the antenna array elements The phase and amplitude are adjusted through the phase shifter and attenuator of the reconfigurable antenna unit, and the antenna pointing is adjusted through the servo mechanism at the same time, so that the radiation pattern is consistent with the optimization result; Real-time monitoring of adjusted mutual coupling coefficients If ≤ then optimization is complete, otherwise return to step one to recalculate.

2. The time-domain hybrid fast algorithm based antenna radiation and mutual coupling co-optimization system of claim 1, wherein, The data acquisition unit is internally provided with a GPS positioning module, an attitude sensor, a signal strength sensor and an environmental interference detection module; The GPS positioning module is used for acquiring three-dimensional geographic coordinates of each UAV in the UAV group in real time, and the three-dimensional geographic coordinates comprise longitude x, latitude y and altitude z; The attitude sensor is used to acquire attitude angle data of the unmanned aerial vehicle, including a yaw angle , a pitch angle and a roll angle; The signal strength sensor is used for measuring receiving signal strength R and transmitting signal strength S of the antenna; The environmental interference detection module is used for acquiring electromagnetic interference intensity I in the environment.

3. The antenna radiation and mutual coupling co-optimization system based on time-domain hybrid fast algorithm of claim 2, wherein, The specific operation steps of the mutual coupling calculation unit for calculating the antenna spacing D according to the three-dimensional geographic coordinates are: collecting three-dimensional geographic coordinates of the drone A collecting three-dimensional geographic coordinates of the drone B ; Convert latitude and longitude to radians by the formula and ​ Through formula and Calculate horizontal ground distance Where R is the Earth's radius, with an average value of 6371 km, the result is... The unit is meters; The vertical height difference is calculated by the formula ;​ The inter-antenna distance D is calculated by the formula D = 2 * d * cos(θ) The mutual coupling calculation unit calculates the antenna azimuth angle of the current UAV according to the attitude angle data The specific operation steps are as follows: Collect the horizontal coordinates of adjacent unmanned aerial vehicles through the GPS positioning module and Collect the yaw angle of the current unmanned aerial vehicle through the attitude sensor ; The antenna azimuth angle of the current UAV is calculated according to the formula . .

4. The antenna radiation and mutual coupling co-optimization system based on time-domain hybrid fast algorithm of claim 2, wherein, The optimization control unit is also used for dynamically adjusting the optimization priority according to the UAV formation, and specifically: The receiving signal strength R of each UAV is acquired through the signal strength sensor, and the electromagnetic interference intensity I in the current environment is acquired through the environmental interference detection module; According to the formula , the communication quality score of each unmanned aerial vehicle antenna is calculated; According to the formula , the optimization priority of each antenna is calculated , the optimization instruction is executed in order from high to low priority.

5. The antenna radiation and mutual coupling co-optimization system based on time-domain hybrid fast algorithm of claim 2, wherein, The communication coordination unit is also used for synchronizing position information of the UAV group in real time, and specifically: The longitude and latitude coordinates of each UAV are acquired through the GPS module; The real-time distance between adjacent drones is calculated by the formula , as follows ; Pre-set safety threshold When < a platoon reorganization instruction is triggered, which includes adjusting the flight height and azimuth angle of the UAVs to ensure that the antenna spacing meets the mutual coupling optimization condition.

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