A method for unmanned aerial vehicle assisted maritime safety communication based on cooperative beamforming

Abstract

This invention discloses a UAV-assisted maritime security communication method based on cooperative beamforming. First, the positions of legitimate vessels, eavesdropping vessels, and dual UAV swarms are determined. A multi-objective optimization problem is designed. Then, using an improved mayfly optimization algorithm and cooperative beamforming, the optimal three-dimensional position and optimal excitation current weight of the UAV swarm transmitting data signals, and the optimal three-dimensional position and ideal excitation current weight of the UAV swarm transmitting interference signals are designed. Subsequently, a virtual antenna array of one UAV swarm acts as a maritime relay UAV, forwarding data signals from a shore-based base station to legitimate vessels at a long distance. A virtual antenna array of another UAV swarm acts as a maritime jamming UAV, transmitting interference signals to eavesdropping vessels, thus ensuring secure communication for legitimate vessels. This invention improves the security performance of maritime wireless communication.

Description

Technical Field

[0001] This invention relates to the field of technology, and more specifically, to a method for unmanned aerial vehicle-assisted maritime safety communication based on cooperative beamforming. Background Technology

[0002] Due to the difficulty of deploying basic equipment in the marine environment and the problem that large ships cannot communicate near the shore, it is difficult for shore-based base station data to be directly transmitted to legal ships at sea, and the open wireless communication environment may pose security risks to communication.

[0003] With the continuous development of maritime activities and the marine economy, the establishment of high-speed and reliable maritime communication has gradually attracted attention. However, the complex maritime environment makes the deployment of basic communication facilities difficult. At this time, drones act as air relays to forward base station data to assist maritime communication and provide reliable and efficient wireless communication services for ships at sea. However, the location of ships at sea is far from the base station. In the traditional mode, drones cannot directly send data signals. They need to receive data from the base station and then move to the vicinity of the ship at sea, which will also consume additional energy. In addition, in the scenario where drones act as air relays to forward data signals, there may be eavesdropping ships at sea that steal legitimate information.

[0004] While layer-by-layer encryption can achieve secure communication in wireless networks, frequent encryption and decryption require significant computing power, posing a serious challenge to ships at sea with limited hardware resources. One approach is to utilize drone swarms to establish connections and then send jamming signals to the eavesdropping vessel using single-hop or multi-hop methods. However, this method incurs additional energy consumption, which is undoubtedly a significant challenge for drones with limited transmission power. Furthermore, the eavesdropping vessel is often far from the drone swarm; in traditional methods, the drone swarm cannot directly send jamming signals but must move closer to the eavesdropping vessel, which also consumes additional energy. Summary of the Invention

[0005] The purpose of this invention is to design and develop a UAV-assisted maritime safety communication method based on cooperative beamforming, which improves the security and reliability of maritime wireless network communication through an improved mayfly optimization algorithm.

[0006] The technical solution provided by this invention is as follows: A UAV-assisted maritime safety communication method based on cooperative beamforming includes the following steps: Step 1: Collect the locations of legally operating communication vessels and maritime eavesdropping vessels; Step 2: Determine the initial position of the dual UAV group based on the positions of the legitimate maritime communication vessels and the maritime eavesdropping vessels, and the dual UAV group uses cooperative beamforming to form a virtual antenna array for a maritime relay UAV and a virtual antenna array for a maritime jamming UAV, respectively. Step 3: Construct the overall objective function for the multi-objective optimization problem; Step 4: Update the dual-UAV swarm using the improved mayfly optimization algorithm to obtain the optimal three-dimensional position and optimal excitation current weight for all UAVs in the dual-UAV swarm. Step 5: The dual UAV swarm is updated to the optimal three-dimensional position and optimal excitation current weight to complete maritime safety communication.

[0007] Preferably, the dual drone swarm includes a relay drone swarm and a jamming drone swarm; The relay drone swarm consists of 16 patrol drones selected from closest to furthest from the legally authorized communication vessels at sea; The jamming drone swarm consists of eight patrol drones selected from closest to furthest from the eavesdropping vessel at sea.

[0008] Preferably, the overall objective function is: In the formula, Let be the overall objective function. Let the first objective function be... The second objective function is... This is the third objective function.

[0009] Preferably, the first objective function satisfies: In the formula, This indicates the transmission power of a single drone in a relay drone swarm. This indicates the total number of drones in the relay drone swarm. This indicates the antenna gain of the relay drone antenna array in the direction of legal communication vessels at sea. This indicates the path loss between the relay drone antenna array and legally operating communication vessels at sea. This indicates the transmission power of a single drone within a swarm of interfering drones. This indicates the total number of drones in the swarm of interfering drones. Indicates noise power. This indicates the antenna gain of the jamming drone antenna array in the direction it faces legitimate communication vessels at sea. This indicates the path loss between the jamming drone antenna array and legitimate communication vessels at sea.

[0010] Preferably, the second objective function satisfies: In the formula, This indicates the path loss between the relay drone array and the maritime eavesdropping vessel. This indicates the path loss between the jamming drone antenna array and the eavesdropping vessel at sea. This indicates the antenna gain of the relay drone antenna array in the direction it points towards the ship being eavesdropped on at sea. This indicates the antenna gain of the jamming drone antenna array in the direction it is pointing towards ships at sea that are eavesdropping on it.

[0011] Preferably, the third objective function satisfies: In the formula, Indicates the first Energy consumption when relaying drones for communication. Indicates the first Energy consumption when jamming drone communications.

[0012] Preferably, step four specifically includes the following steps: Step 1: Initialize the number of drone groups, the maximum number of iterations, and empty archives; Step 2: Use the Tent mapping method to perform chaotic initialization and obtain an optimized initial solution; Step 3: Based on the optimized initial solution, and by applying the upper and lower bounds of the corresponding variables, obtain the initial values ​​of the three-dimensional positions and excitation current weights of all UAVs in the dual UAV group, and store them in the archive. Step 4: Calculate the first objective function, the second objective function, and the third objective function based on the three-dimensional position of the dual UAV group and the initial value of the excitation current weight. Select the non-dominated solutions from the UAV solutions corresponding to the first objective function, the second objective function, and the third objective function and replace the solutions in the archive. Randomly select a set of solutions in the archive as the current optimal solution. Step 5: Use a hybrid solution update strategy to update the solutions of the dual UAV group in different dimensions to obtain the optimal three-dimensional position and optimal excitation current weight of all UAVs in the dual UAV group.

[0013] Preferably, step 5 specifically includes: Step a: Update the drone swarm in the interference set using the whale optimization algorithm, and update the drone swarm in the relay set using the arithmetic optimization algorithm; Step b: Compare the solution of the dual-drone group generated in each iteration with the current optimal solution, and store the better one in the archive; Step c: End the current loop when the number of iterations reaches the maximum number of iterations, and obtain the optimal solution for the interfering drone and the relay drone.

[0014] Preferably, the whale optimization algorithm updates the drone swarm in the interference set specifically including: The solutions for interfering with the drones are obtained to form an interference set, and the elite solutions in the current interference set are selected using a roulette wheel algorithm. Update the vertical position solution of the UAV in the interference set: In the formula, For the updated set of disturbances, the first The vertical position of the drone group The first in the current set of interference The vertical position of the drone group The vertical position for the elite solution. The first parameter of the whale optimization algorithm, This is the second parameter of the whale optimization algorithm. , All numbers are random numbers between 0 and 1; like and Then update the horizontal position solution of the UAV in the interference set: In the formula, For the updated set of disturbances, the first The x-axis and y-axis positions of the drone group This represents the current iteration number. The maximum number of iterations, A random number between 0 and 1 To determine the conditional parameters, Let i be the horizontal position of the i-th group of drones in the current set of interfering drones. The average value at horizontal positions; like and Then, the elite solution is used to replace the horizontal and vertical positions of the current set of drones in the interference set, and the excitation current weights of the drones in the interference set are updated: ; In the formula, The excitation current weights of the UAVs in the updated disturbance set, Let i be the excitation current weight of the i-th group of drones in the current set of interfering drones. The excitation current weights are for the elite solution.

[0015] Preferably, the drone swarm in the relay set updated by the arithmetic optimization algorithm specifically includes: The solutions obtained from the relay drones are formed into a relay set, and the elite solutions of the current relay set are selected using a roulette wheel algorithm. ,like Then, the exploration phase begins, and the update of the vertical position of the UAV in the relay set satisfies: In the formula, For the relay drone ensemble The vertical position of the drone group The vertical position for the elite solution. The coefficients of the arithmetic optimization algorithm are the highest. All of them are the second coefficients of the arithmetic optimization algorithm. These represent the control parameters used to adjust the search process. , All are random numbers between 0 and 1. For the first Mathematical optimization acceleration value at the next iteration; like Then, the development phase begins, and the update of the vertical position of the UAV in the relay set satisfies: In the formula, A random number between 0 and 1; like , and Then the update of the horizontal position of the UAV in the relay set satisfies: In the formula, Let i be the horizontal position of the i-th group of drones in the relay drone set. Indicates the average value at horizontal positions; like , and Then the update of the horizontal position of the UAV in the relay set satisfies: ;like and Then, the elite relay solution is used to replace the horizontal and vertical positions of the UAVs in the current relay set. If at this point... Then the update of the excitation current weights of the UAVs in the relay set satisfies: In the formula, A random number between 0 and 1 Let be the value of the excitation current of the i-th group of drones in the current relay drone set. The excitation current weight for the elite solution; if , and Then the update of the excitation current weights of the UAVs in the relay set satisfies: .

[0016] The beneficial effects of this invention are as follows: (1) The present invention designs and develops a UAV-assisted maritime safety communication method based on cooperative beamforming. The maritime relay virtual array antenna utilizes cooperative beamforming to form a beam with high antenna gain and high directivity, and directly sends data signals to a distant legal vessel at sea without multiple hops. The main lobe of the data beam points in the direction of the legal vessel at sea.

[0017] (2) The UAV-assisted maritime security communication method based on cooperative beamforming designed and developed in this invention uses a virtual array antenna for maritime interference to directly send interference signals to the eavesdropping vessel at sea. The main lobe of the interference beam points in the direction of the eavesdropping vessel at sea. At this time, the eavesdropping vessel is unable to steal data due to the interference signal in the direction of the eavesdropping vessel, thereby improving the security performance of the maritime wireless network.

[0018] (3) The present invention designs and develops a UAV-assisted maritime safety communication method based on cooperative beamforming. Through the improved mayfly optimization algorithm, the initial solution is optimized by using the Tent chaotic initialization method to avoid the problem of the initial solution getting trapped in local optima. In addition, a hybrid solution update strategy is used, and different optimization schemes are adopted for relay UAVs and jamming UAVs. At the same time, different update strategies are adopted for the upper and lower bounds of different dimensions of the UAV solution (horizontal position, vertical position, excitation current) during the solution update process, so that the performance of the obtained UAV solution is better, and thus the value of the objective function is more ideal. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the maritime physical layer security communication system assisted by a dual UAV swarm based on cooperative beamforming, as described in this invention. Figure 2 This is a flowchart illustrating the UAV-assisted maritime safety communication method based on cooperative beamforming as described in this invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0021] like Figure 1 As shown, the UAV-assisted maritime safety communication method based on cooperative beamforming provided by the present invention includes: Step 1: Collect the locations of the legally authorized maritime communication vessel B and the maritime eavesdropping vessel W; In this embodiment, the location of the legitimate maritime communication vessel B is determined by GPS positioning equipment and sensors carried by the maritime patrol drone, and the location of the maritime eavesdropping vessel W is detected in the same way. Step Two, as follows Figure 2 As shown, the initial positions of the relay drone group and the jamming drone group are determined based on the positions of the legitimate maritime communication vessel B and the maritime eavesdropping vessel W. The relay drone swarm consists of 16 cruise drones selected from the nearest to the farthest distance from the legitimate communication vessel B at sea. Using cooperative beamforming, the relay drone swarm forms a virtual antenna array, with the main lobe of the array facing towards the legitimate communication vessel B, transmitting data signals to it. The jamming drone swarm consists of 8 cruise drones selected from the nearest to the farthest distance from the eavesdropping vessel W at sea. The jamming drone swarm interferes with the eavesdropping vessel's signal transmission. Using cooperative beamforming, the jamming drone swarm forms a virtual antenna array, with the main lobe of the array facing towards the eavesdropping vessel, transmitting jamming signals to it. Step 3: Design three objective functions based on the requirements for maritime safety communication, and formulate a multi-objective optimization problem based on multi-objective optimization theory; Among them, the three objective functions include a first objective function representing the signal-to-interference-plus-noise ratio of a legally communicating maritime vessel B with dual UAV antenna arrays. The second objective function represents the signal-to-interference-plus-noise ratio of a maritime eavesdropping vessel W communicating with dual UAV antenna arrays. The third objective function represents the total energy consumption of the two drone swarms during flight. ; The signal-to-interference-plus-noise ratio (SNR) is used to detect signal quality; it refers to the ratio of the power of the received useful signal to the power of the interfering signal.

[0022] First objective function In the context of the first objective function, the useful signal power is the total power of the legitimate signals received by the legitimate communication vessel B from the relay drone swarm. The interference signal power is the partial interference signal power received by the legitimate communication vessel B when the interference drone swarm sends interference signals to the eavesdropping vessel W, along with the noise power in the environment. Specifically, it satisfies: In the formula, This represents the set of relay drone swarms, including the locations of all drones within the swarm. , and These represent the x-axis and y-axis positions of the UAV swarm in the geodetic coordinate system. The vertical position of the relay drone. This represents the set of excitation current weights for a relay drone swarm. This represents the set of jamming drone swarms, including the locations of all drones within the swarm. , and These represent the x-axis and y-axis positions of the UAV swarm in the geodetic coordinate system. To disrupt the vertical position of the drone swarm, This represents the set of excitation current weights for interfering with the drone swarm. The signal-to-noise ratio of interference received by legally communicating vessels at sea, and satisfying the following:

[0023] In the formula, This indicates the transmission power of a single drone in a relay drone swarm. This indicates the total number of drones in the relay drone swarm. This indicates the antenna gain of the relay drone antenna array in the direction of the legally communicating vessel B at sea. This represents the path loss between the relay drone antenna array and the legally operating maritime communication vessel B. This indicates the transmission power of a single drone within a swarm of interfering drones. This indicates the total number of drones in the swarm of interfering drones. Indicates noise power. This indicates the antenna gain of the interfering drone's antenna array in the direction of the legitimate communication vessel B at sea. This represents the path loss between the jamming drone antenna array and the legitimate maritime communication vessel B. Second objective function In this context, since the maritime eavesdropping vessel W wants to eavesdrop on the information transmitted by the relay drone swarm, the useful signal power is the legitimate power received by the maritime eavesdropping vessel W from the relay drone swarm. At this time, the interference signal power is the interference signal power transmitted by the interference drone antenna array to the maritime eavesdropping vessel W plus the noise power in the environment. Therefore, the second objective function... satisfy:

[0024] In the formula, The signal-to-noise ratio (SNR) of the interference received by the vessel being eavesdropped on at sea, and satisfying the following: In the formula, This represents the path loss between the relay drone array and the maritime eavesdropping vessel W. This represents the path loss between the jamming drone antenna array and the maritime eavesdropping vessel W. This indicates the antenna gain of the relay drone antenna array in the W direction towards the eavesdropping ship at sea. This indicates the antenna gain of the jamming drone antenna array pointing towards the W-direction of the eavesdropping ship at sea; The antenna gain of the relay UAV antenna array towards the legitimate communication vessel B at sea and the antenna gain of the relay UAV antenna array towards the eavesdropping vessel W at sea both satisfy the following:

[0025] In the formula, For relaying UAV antenna arrays to the first Antenna gain in the direction of ships at sea, and , For relay UAV antenna array factor, The direction of the corresponding ship served by the relay drone antenna array. Indicates the first Angle of elevation of ships at sea Indicates the first A type of azimuth angle for ships at sea. This represents the size of the far-field beam pattern of a relay UAV, typically taken as 1. The denominator represents the antenna array efficiency, and the elevation angle under legal link conditions is represented by the denominator. and azimuth Integral calculation; The intermediate-level UAV antenna array factor satisfies:

[0026] In the formula, Indicating the first [unit / type] in the relay UAV antenna array The excitation current weight of the drone. Represents the imaginary unit. Denotes the phase constant, and , Indicates wavelength; The path loss between the relay UAV antenna array and the legitimate maritime communication vessel B, and the path loss between the relay UAV array and the maritime eavesdropping vessel W, satisfy the following:

[0027] In the formula, For relaying UAV antenna arrays to the first Antenna gain in the direction of a ship at sea, in units of , For calculating parameters, These are the calculation parameters for a relay drone swarm. For non-line-of-sight links, the attenuation factor is... This is the first intermediate parameter, and its value is 5.0188 dB. The second intermediate parameter has a value of 0.3511 dB. The distance between the relay drone center and the corresponding vessel. This indicates the carrier frequency in MHz. The calculation parameters satisfy: In the formula, This is the attenuation factor for line-of-sight links; The calculation parameters under the relay UAV cluster satisfy: In the formula, The height of the center of the virtual antenna array for the relay drone; The antenna gain of the jamming drone antenna array towards the legitimate communication vessel B at sea and the antenna gain of the jamming drone antenna array towards the eavesdropping vessel W at sea satisfy the following: In the formula, To interfere with the UAV antenna array towards the first Antenna gain in the direction of ships at sea The first representing the interference of drone antenna array services The direction of ships at sea Indicates the first Angle of elevation of ships at sea Indicates the first A type of azimuth angle for ships at sea. This represents the magnitude of the far-field beam pattern of the jamming UAV, typically taken as 1, with the denominator representing the elevation angle under the jamming link. and azimuth Integral calculation, To interfere with the antenna array factor of the UAV; The interference drone antenna array factor satisfies: In the formula, Indicating the interference of the UAV antenna array, the first The excitation current weight of the drone; The path loss between the jamming drone antenna array and the legitimate maritime communication vessel B, and the path loss between the jamming drone antenna array and the maritime eavesdropping vessel W, satisfy the following:

[0028] In the formula, To interfere with the UAV antenna array towards the first Antenna gain in the direction of ships at sea. This indicates the distance from the center of the virtual antenna array of the interfering drone to the corresponding ship. To interfere with the calculation parameters of the drone swarm; The computational parameters under the interference drone swarm satisfy: In the formula, To interfere with the height of the center of the drone's virtual antenna array; The third objective function satisfy: In the formula, Indicates the first Energy consumption when relaying drones for communication. Indicates the first Energy consumption when jamming drone communications; The first Energy consumption and the first relay drone communication Energy consumption when jamming UAV communications meets the following requirements: In the formula, For the first Energy consumption when drones communicate, and , Indicates the end time of the flight. Indicates the drone at a certain time speed, For time k, the first The propulsion power consumption of the drone The drone's speed at the final moment. The initial speed of the drone. Indicates the weight of the drone. Represents gravitational acceleration. Indicates the drone's altitude at the end of the flight. This indicates the initial altitude of the drone. No. The propulsion power consumption of the drone meets the following requirements:

[0029] In the formula, Indicates the first The propulsion power consumption of a drone flying in two-dimensional horizontal space. This represents the blade outline in a hovering state. This represents the induced power during hovering, and and They are two constants. Indicates the speed of the drone. This represents the tip velocity of the rotor blades. This represents the average rotor blade induced velocity during hovering. Indicates the fuselage drag ratio. Indicates air density, Indicates the rigidity of the rotor blades. This indicates the area of ​​the rotor disk.

[0030] Therefore, the overall objective function is designed as follows:

[0031] Step 4: Due to the large number of decision variables in the objective function, multi-objective optimization problems are prone to getting trapped in local optima. Therefore, an improved mayfly optimization algorithm is proposed. By adopting the Tent chaotic initialization method, the performance of the initial solution is improved. A hybrid solution update strategy is adopted, namely, the whale optimization algorithm updates the interference drone swarm and the arithmetic optimization algorithm updates the relay drone swarm. Moreover, the solutions of different dimensions in the decision variables of each optimization algorithm have different update strategies according to the different upper and lower bounds, so that the performance of the iterative solution is better. The optimal three-dimensional position and optimal excitation current weight of all drones in the dual drone swarm are obtained, ensuring that the interference signal strength received by the eavesdropping ship through the main lobe of the virtual antenna array is maximized, while ensuring that the interference signal received by our ship through the side lobe of the virtual antenna array is minimized. The specific steps include the following: Step 1: Initialize the number of drone groups (N) pop ), Maximum number of iterations ( ) and empty archive; Step 2: Use the Tent mapping method to perform chaotic initialization and obtain the optimized initial solution:

[0032] In the formula, The first character represents the Tent chaotic map. initial values, of which For Tent mapping parameters; Step 3: Based on the optimized initial solution, and by applying the upper and lower bounds to the corresponding variables, obtain the initial values ​​of the three-dimensional positions and excitation current weights of the relay UAV and the jamming UAV, and store them in the archive. In the formula, Let be the initial value set for the relay UAV solution. Each element in the set includes the three-dimensional position of the corresponding UAV and the excitation current weight. The initial value set is defined for the solution of the disturbing UAV. Each element in the set includes the three-dimensional position of the corresponding UAV and the excitation current weight. and Let represent the lower and upper bounds of the solutions for different dimensions of the relay UAV (horizontal position, vertical position, and excitation current), respectively. and These represent the lower and upper bounds of the solutions for different dimensions of the interfering UAV (horizontal position, vertical position, and excitation current), respectively; Step 4: Start the iteration process, and calculate the first objective function using the three-dimensional positions and excitation current weights of the dual UAV group. ), second objective function ( ) and the third objective function ( The value of ) is due to the process of multi-objective optimization problems. and , The optimization results are contradictory (it needs to maximize) And minimize , Therefore, when a solution is in At its best, , The performance of the solution might be relatively poor. To achieve the optimal overall performance of the multi-objective optimization problem, we select solutions from those obtained by the objective function that are not all superior to the others (non-dominated solutions). , and The non-dominated solution in the corresponding UAV solution is used to replace the previously archived solution for the dual UAV group (the UAV's 3D position and excitation current weight values), and a set of solutions is randomly selected from the archive as the current optimal solution (denoted as...). ); Step 5: Traditional mayfly optimization algorithms use a uniform formula to calculate the optimization position. However, since the number of drones in the two drone groups may differ, and using the same optimization scheme may result in monotonous performance, this invention adopts a hybrid solution update strategy. Specifically, the whale optimization algorithm is used to update the drone groups in the interference set, and an arithmetic optimization algorithm is used to update the drone groups in the relay set. Specifically, since the boundary value ranges of the solutions (horizontal position, vertical position, excitation current) for each drone group are different (horizontal movement range: 0-100m, vertical movement range: 60-120m, excitation current weight range: 0-1), using uniform changes for optimization may affect the performance of the objective function. Therefore, based on optimizing different drone groups using different algorithms, different update schemes are adopted for each drone group according to the boundary differences of the solutions. This is the improved mayfly optimization algorithm, which includes the following steps: Step a: Update the drone swarm in the interference set using the whale optimization algorithm, and update the drone swarm in the relay set using the arithmetic optimization algorithm: 1) In the whale optimization algorithm: Obtain solutions for interfering with drones (the first iteration uses the initial solution, and each subsequent iteration uses the updated solution from the previous iteration), forming an interference set. Then, use a roulette wheel algorithm to select the elite solutions from the current interference set. Update the vertical position solution of the interfering UAV in the interference set: In the formula, For the updated set of disturbances, the first The vertical position of the drone group The first in the current set of interference The vertical position of the drone group The vertical position for the elite solution. The first parameter of the whale optimization algorithm, The second parameter of the whale optimization algorithm, and , , A random number between 0 and 1 A random number between -1 and 1. , All are random numbers between 0 and 1; if and Then update the horizontal position solution of the UAV in the interference set: In the formula, For the updated set of disturbances, the first The x-axis and y-axis positions of the drone group This represents the current iteration number. The maximum number of iterations, A random number between 0 and 1 To determine the conditional parameters, The first in the current set of interference The horizontal position of the drone group For the first Average horizontal position of the group of drones; like and Then the elite solution is adopted ( ) to replace the current interference set of the drone's horizontal and vertical positions ( ), and update the excitation current weights of the UAVs in the disturbance set: In the formula, The excitation current weights of the UAVs in the updated disturbance set, The first in the current set of jamming drones The excitation current weight of the drone group The excitation current weights for the elite solution; 2) In arithmetic optimization algorithms: Obtain the solution for the relay drone (the initial solution in the first iteration, and the solution updated in each subsequent iteration), form a relay set, and select the elite solution of the current relay set using a roulette wheel algorithm. ,like Then proceed to the exploration phase, and update the vertical position of the drones in the relay set using the following formula:

[0033] In the formula, and Let represent the minimum and maximum values ​​of the acceleration function, respectively. For the relay drone ensemble The vertical position of the drone group The vertical position for the elite solution. and All of these are coefficients of arithmetic optimization algorithms, and , To adjust the parameters, , These represent the control parameters used to adjust the search process. A random number between 0 and 1; In this embodiment, , , .

[0034] like Then proceed to the development phase and update the vertical position of the drones in the relay set using the following formula:

[0035] In the formula, A random number between 0 and 1; like , and Then update the horizontal position of the drone in the relay set using the following formula:

[0036] In the formula, For the relay drone ensemble The horizontal position of the drone group Indicates the average value at horizontal positions; like , and Then update the horizontal position of the drone in the relay set using the following formula:

[0037] like and Using relay elite solution ( ) replace the horizontal and vertical positions of the UAVs in the current relay set ( If at this time Then, update the excitation current weights of the UAVs in the relay set according to the following formula:

[0038] In the formula, A random number between 0 and 1 Let be the value of the excitation current of the i-th group of drones in the current relay drone set. The excitation current weights for the elite solution; like , and Then, update the excitation current weights of the UAVs in the relay set according to the following formula:

[0039] Step b: The solution for the dual-UAV swarm generated in each iteration and the current optimal solution. Compare the results and save the better one to the archive; Step c: End the current loop when the number of iterations reaches the maximum number of iterations, and obtain the optimal solutions for the interfering UAV and the relay UAV (two sets of optimal UAV three-dimensional positions and excitation current weight values). In this embodiment, the maximum number of iterations is 500.

[0040] Step 5: Initialize the positions of the relay UAV and the jamming UAV within the defined area. Based on the optimal three-dimensional position of the UAVs and the optimal excitation current weight of the designed dual UAV group, fly into the air to form the maritime relay virtual array antenna and the maritime jamming virtual array antenna, respectively. The relay UAV group sends data signals to the legitimate ship B at sea, and the jamming UAV group sends jamming signals to the eavesdropping ship W to complete the maritime security communication.

[0041] The specific process of the improved mayfly optimization algorithm in this invention is as follows: Algorithm 1: Improved Ephemeral Optimization Algorithm 1. Setting parameters: Number of drone groups (N) pop ), Maximum number of iterations (t) max ) and empty archive; 2. for i=1 to N pop do / / i indicates that the current group of drones is the i-th group. 3. Calculate the initial value of the relay UAV solution according to formula (19); / / Improvement point: Tent mapping method 4. Calculate the initial value of the solution for the interfering UAV according to formula (20); / / Improvement point: Tent mapping method 5.end 6. for t=1 to t max do / / t represents the current iteration number 7. Calculate the solutions to the first, second, and third objective functions in a multi-objective optimization problem; 8. Select the non-dominated solution; 9. Replace the original solutions in the archive with non-dominated solutions, and randomly select a set of solutions from the archive as the current optimal solution; 10. for i=1 to N pop do 11. Calculate the parameter values ​​of the improved mayfly optimization algorithm according to formula (22). and other relevant parameters; 12. Update random numbers , ; 13. Update the solution for the interfering drone swarm according to Algorithm 2; / / Improvement point: Hybrid solution update strategy 14. Update the relay drone swarm solution according to Algorithm 3; / / Improvement point: Hybrid solution update strategy 15. end 16. After one iteration using the improved mayfly optimization algorithm, the optimized UAV solution is obtained; 17.end 18. Obtain the final optimal UAV solution.

[0042] Algorithm 2: Solution Update Algorithm Based on Whale Optimization Algorithm (Interference UAV Solution) 1. Update the vertical position value of the UAV according to formula (21); 2. if t < (t max / 2) then 3. if

[0043] 4. Calculate the horizontal position value of the drones in the interfering drone swarm according to formula (23); 5. end 6.else 7. if

[0044] 8. Use the selected elite solution Iteratively update the current position value ; 9. Update the excitation current weight according to formula (24). ; 10. end 11.end 12. Return the solution to the set of interfering drones. ; Algorithm 3: Solution Update Algorithm Based on Arithmetic Optimization (Relay UAV Solution) 1. Calculate the value of MOA according to formula (25); 2.if

[0045] 3. Exploration phase: Apply multiplication / division mathematical operators to update the vertical position value of the UAV using formula (26). ; 4. else 5. Development phase: Apply addition / subtraction mathematical operators to update the vertical position value of the UAV using formula (27). ; 6.end 7. if t < (t max / 2) then 8. if

[0046] 9. if

[0047] 10. Calculate the horizontal position value of the UAV according to formula (28). ; 11. else 12. Calculate the horizontal position value of the UAV according to formula (29). ; 13. end 14.end 15.else 16. if

[0048] 17. Use the selected elite solution Iteratively update the current position value ; 18. if

[0049] 19. Update the excitation current weight according to formula (30). ; 20. else 21. Update the excitation current weight according to formula (31) ; 22. end 23. end 24.end 25. Return to the relay drone ensemble solution .

[0050] The results obtained by this invention are compared with the original mayfly optimization algorithm, as shown in Table 1. Table 1 shows the objective function values ​​obtained by the existing mayfly optimization algorithm and the improved mayfly optimization algorithm described in this invention. The proposed improved mayfly optimization algorithm achieves the best performance in three aspects: maximizing the signal-to-interference-plus-noise ratio of the legitimate vessel B at sea, minimizing the signal-to-interference-plus-noise ratio of the eavesdropping vessel W at sea, and minimizing the total flight energy consumption of the UAV. In particular, it performs better in minimizing the signal-to-interference-plus-noise ratio of the eavesdropping vessel W at sea, which demonstrates the superiority of the proposed improved mayfly optimization algorithm. This shows that the maritime physical layer security communication method based on cooperative beamforming dual UAV swarm assistance proposed in this invention can achieve the purpose of maritime wireless network communication security and reliability when adopting the improved mayfly optimization algorithm.

[0051] Table 1

[0052] This invention designs and develops a UAV-assisted maritime safety communication method based on cooperative beamforming. It establishes a dual-UAV virtual antenna array and then uses cooperative beamforming technology to transmit data signals and jamming signals to legitimate vessels and eavesdropping vessels respectively. This aims to improve the signal-to-interference-plus-noise ratio (SNR) for legitimate vessels and reduce the SNR for eavesdropping vessels, as well as reduce the total mobile energy consumption of the dual-UAV swarm. A joint optimization model addressing this multi-objective problem employs an improved mayfly optimization algorithm to calculate the optimal three-dimensional positions of all UAVs in both the relay and jamming UAV swarms, and to determine the ideal excitation current weights. The objective function is then solved based on these ideal excitation current weights. The dual-UAV swarm is then configured according to the designed optimal three-dimensional positions and excitation current weights. The excitation current weight is adjusted to achieve its optimal state, shortening the UAV's travel distance and thus reducing energy consumption. This enables the relay UAV virtual antenna array to achieve a high-gain and high-directivity beam through cooperative beamforming, with the main lobe pointing towards legitimate vessels, enhancing data transmission to legitimate vessels. Conversely, the jamming UAV virtual antenna array also achieves a high-gain and high-directivity beam through cooperative beamforming, with the main lobe pointing towards eavesdropping vessels, enhancing interference with eavesdropping vessels. Furthermore, it minimizes the impact of sidelobes on non-target vessels, maximizing the security performance of maritime wireless communication and ensuring the security and reliability of maritime wireless network communication.

[0053] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A UAV-assisted maritime safety communication method based on cooperative beamforming, characterized in that, Includes the following steps: Step 1: Collect the locations of legally operating communication vessels and maritime eavesdropping vessels; Step 2: Determine the initial position of the dual UAV group based on the positions of the legitimate maritime communication vessels and the maritime eavesdropping vessels, and the dual UAV group uses cooperative beamforming to form a virtual antenna array for a maritime relay UAV and a virtual antenna array for a maritime jamming UAV, respectively. Step 3: Construct the overall objective function for the multi-objective optimization problem; Step 4: Update the dual-UAV swarm using the improved mayfly optimization algorithm to obtain the optimal three-dimensional position and optimal excitation current weight for all UAVs in the dual-UAV swarm. Step 5: The dual UAV swarm is updated to the optimal three-dimensional position and optimal excitation current weight to complete maritime safety communication.

2. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 1, characterized in that, The dual drone swarm includes a relay drone swarm and a jamming drone swarm; The relay drone swarm consists of 16 patrol drones selected from closest to furthest from the legally authorized communication vessels at sea; The jamming drone swarm consists of eight patrol drones selected from closest to furthest from the eavesdropping vessel at sea.

3. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 2, characterized in that, The overall objective function is: In the formula, Let be the overall objective function. Let the first objective function be... The second objective function is... This is the third objective function.

4. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 3, characterized in that, The first objective function satisfies: In the formula, This indicates the transmission power of a single drone in a relay drone swarm. This indicates the total number of drones in the relay drone swarm. This indicates the antenna gain of the relay drone antenna array in the direction of legal communication vessels at sea. This indicates the path loss between the relay drone antenna array and legally operating communication vessels at sea. This indicates the transmission power of a single drone within a swarm of interfering drones. This indicates the total number of drones in the swarm of interfering drones. Indicates noise power. This indicates the antenna gain of the jamming drone antenna array in the direction it faces legitimate communication vessels at sea. This indicates the path loss between the jamming drone antenna array and legitimate communication vessels at sea.

5. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 4, characterized in that, The second objective function satisfies: In the formula, This indicates the path loss between the relay drone array and the maritime eavesdropping vessel. This indicates the path loss between the jamming drone antenna array and the eavesdropping vessel at sea. This indicates the antenna gain of the relay drone antenna array in the direction it points towards the ship being eavesdropped on at sea. This indicates the antenna gain of the jamming drone antenna array in the direction it is pointing towards ships at sea that are eavesdropping on it.

6. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 5, characterized in that, The third objective function satisfies: In the formula, Indicates the first Energy consumption when relaying drones for communication. Indicates the first Energy consumption when jamming drone communications.

7. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 6, characterized in that, Step four specifically includes the following steps: Step 1: Initialize the number of drone groups, the maximum number of iterations, and empty archives; Step 2: Use the Tent mapping method to perform chaotic initialization and obtain an optimized initial solution; Step 3: Based on the optimized initial solution, and by applying the upper and lower bounds of the corresponding variables, obtain the initial values ​​of the three-dimensional positions and excitation current weights of all UAVs in the dual UAV group, and store them in the archive. Step 4: Calculate the first objective function, the second objective function, and the third objective function based on the three-dimensional position of the dual UAV group and the initial value of the excitation current weight. Select the non-dominated solutions from the UAV solutions corresponding to the first objective function, the second objective function, and the third objective function and replace the solutions in the archive. Randomly select a set of solutions in the archive as the current optimal solution. Step 5: Use a hybrid solution update strategy to update the solutions of the dual UAV group in different dimensions to obtain the optimal three-dimensional position and optimal excitation current weight of all UAVs in the dual UAV group.

8. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 7, characterized in that, Step 5 specifically includes: Step a: Update the drone swarm in the interference set using the whale optimization algorithm, and update the drone swarm in the relay set using the arithmetic optimization algorithm; Step b: Compare the solution of the dual-drone group generated in each iteration with the current optimal solution, and store the better one in the archive; Step c: End the current loop when the number of iterations reaches the maximum number of iterations, and obtain the optimal solution for the interfering drone and the relay drone.

9. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 8, characterized in that, The whale optimization algorithm updates the drone swarm in the interference set, specifically including: The solutions for interfering with the drones are obtained to form an interference set, and the elite solutions in the current interference set are selected using a roulette wheel algorithm. Update the vertical position solution of the UAV in the interference set: In the formula, For the updated set of disturbances, the first The vertical position of the drone group The first in the current set of interference The vertical position of the drone group The vertical position for the elite solution. The first parameter of the whale optimization algorithm, This is the second parameter of the whale optimization algorithm. , All are random numbers between 0 and 1; if and Then update the horizontal position solution of the UAV in the interference set: In the formula, For the updated set of disturbances, the first The x-axis and y-axis positions of the drone group This represents the current iteration number. The maximum number of iterations, A random number between 0 and 1 To determine the conditional parameters, The first in the current set of jamming drones The horizontal position of the drone group The mean value at horizontal positions; if and Then, the elite solution is used to replace the horizontal and vertical positions of the current set of drones in the interference set, and the excitation current weights of the drones in the interference set are updated: In the formula, The excitation current weights of the UAVs in the updated disturbance set, The first in the current set of jamming drones The excitation current weight of the drone group The excitation current weights are for the elite solution.

10. The UAV-assisted maritime safety communication method based on cooperative beamforming according to claim 9, characterized in that, The arithmetic optimization algorithm updates the drone swarms in the relay set, specifically including: The solutions obtained from the relay drones are formed into a relay set, and the elite solutions of the current relay set are selected using a roulette wheel algorithm. ,like Then, the exploration phase begins, and the update of the vertical position of the UAV in the relay set satisfies: In the formula, For the relay drone ensemble The vertical position of the drone group The vertical position for the elite solution. The coefficients of the arithmetic optimization algorithm are the highest. All of them are the second coefficients of the arithmetic optimization algorithm. These represent the control parameters used to adjust the search process. , All are random numbers between 0 and 1. For the first The mathematical optimization speedup value at the next iteration; if Then, the development phase begins, and the update of the vertical position of the UAV in the relay set satisfies: In the formula, A random number between 0 and 1; if , and Then the update of the horizontal position of the UAV in the relay set satisfies: In the formula, For the relay drone ensemble The horizontal position of the drone group Indicates the average value at horizontal positions; if , and Then the update of the horizontal position of the UAV in the relay set satisfies: like and Then, the elite relay solution is used to replace the horizontal and vertical positions of the UAVs in the current relay set. If at this point... Then the update of the excitation current weights of the UAVs in the relay set satisfies: In the formula, A random number between 0 and 1 The first in the current relay drone ensemble The value of the excitation current of the drone group. The excitation current weight for the elite solution; if , and Then the update of the excitation current weights of the UAVs in the relay set satisfies: .

Images