A method for secure maritime communication based on drone swarms

Abstract

The present invention discloses a method for maritime confidential communication based on a drone swarm, comprising: first, obtaining the positions of two ships in legal communication at sea and the position of a ship eavesdropping at sea, and determining the number of drones that can perform communication tasks; second, determining the optimal position and optimal excitation current weight of each drone in a virtual antenna array by taking the maximum first objective function, the minimum second objective function and the minimum third objective function as optimization targets; the first objective function is: the signal to interference plus noise ratio of a ship that legally receives signals at sea in the communication of the drone antenna array; the second objective function is: the signal to interference plus noise ratio of a ship eavesdropping at sea in the communication of the drone antenna array; the third objective function is: the energy consumption of the drone during flight; and third, after the drone in the virtual antenna array moves to the optimal position, it sends interference signals to the eavesdropping ship at sea according to the optimal excitation current weight to achieve confidential communication at sea.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine physical layer secure communication, and in particular relates to a marine secure communication method based on drone swarms. Background Art

[0002] With the continuous development of maritime activities and marine economy, the establishment of a high-speed and reliable maritime communication has gradually attracted attention. UAVs can assist in maritime communications as aerial base stations and aerial relays, providing reliable and efficient wireless communication services for ships at sea. In the scenario where drones are used as aerial relays / base stations, legitimate ships at sea want to communicate, but there may be unknown eavesdropping ships at sea to steal legitimate information. Although confidential communications in wireless networks can be achieved by using upper-layer encryption, frequent encryption and decryption require high computing power, which poses a severe challenge to ships at sea with limited hardware resources. At this time, a drone swarm can be used to establish a connection, and then a single-hop or multi-hop method can be used to send interference information to the eavesdropping ship, but this method will bring additional energy consumption, which undoubtedly brings great challenges to drones with limited transmission power. In addition, the location of the eavesdropping ship is far away from the drone swarm. In the traditional mode, the drone swarm cannot directly send interference signals and needs to move to the vicinity of the eavesdropping ship, which will also consume additional energy. Summary of the invention

[0003] The main purpose of the present invention is to provide a method for maritime secure communication based on drone swarms, which can determine the optimal position of drones in a virtual antenna array and the optimal excitation current weight, and reduce the energy consumption of drone swarm movement while ensuring safe communication of ships at sea.

[0004] Another object of the present invention is to determine the optimal position and optimal excitation current weight of the UAV through an improved locust optimization algorithm, so as to obtain better optimization effect, further improve the security of maritime communications, and further reduce the energy consumption of UAV swarm movement.

[0005] The technical solution provided by the present invention is:

[0006] A method for secure communication at sea based on drone swarms, comprising the following steps:

[0007] Step 1: Obtain the locations of the two ships that are legally communicating at sea and the location of the eavesdropping ship at sea, and determine the number of drones that can perform communication tasks;

[0008] Wherein, the drones capable of performing communication tasks form a virtual antenna array;

[0009] Step 2: Determine three objective functions, take the maximum of the first objective function, the minimum of the second objective function and the minimum of the third objective function as the optimization goals, and determine the optimal position and optimal excitation current weight of each drone in the virtual antenna array;

[0010] Wherein, the first objective function is: the signal to interference plus noise ratio of the vessel legally receiving the signal at sea of ​​the UAV antenna array communication;

[0011] The second objective function is: the signal to interference plus noise ratio of the maritime eavesdropping vessel of the drone antenna array communication;

[0012] The third objective function is: energy consumption of the drone during flight;

[0013] Step 3: After the UAV in the virtual antenna array moves to the optimal position, it sends an interference signal to the maritime eavesdropping ship according to the optimal excitation current weight to achieve confidential communication at sea.

[0014] Preferably, the expression of the first objective function is:

[0015]

[0016] in, represents the three-dimensional position coordinates of the drone in the virtual antenna array, represents the excitation current weight of the UAV; P A Indicates the signal transmission power of the ship that legally sends the signal; β A2B represents the path loss coefficient between two ships that are legally communicating at sea; P TU represents the total transmission power of the virtual antenna array; G B represents the antenna gain of the drone antenna array to the vessel that legally receives the signal; β U2B represents the path loss coefficient between the drone antenna array and the ship that legally receives the signal; σ 2 Represents the noise power.

[0017] Preferably, the path loss coefficient between the two ships in legal communication at sea is:

[0018]

[0019] Among them, h s is the sea level height, d A2B is the distance between two ships that can legally communicate at sea, C U These are environment-related constants.

[0020] Preferably, the path loss coefficient between the drone antenna array and the vessel that legally receives the signal is:

[0021]

[0022] Among them, η LOS is the attenuation factor of the line-of-sight link, η NLOS is the attenuation factor of the non-line-of-sight link, α a and α b represents the parameters of the sigmoid function in the marine environment; f c Indicates the carrier frequency of the drone; d U2B represents the distance between the center of the UAV virtual antenna array and the ship that legally receives the signal, h U is the height of the center of the UAV virtual antenna array.

[0023] Preferably, the expression of the second objective function is:

[0024]

[0025] in, represents the three-dimensional position coordinates of the drone in the virtual antenna array, represents the excitation current weight of the UAV; P A represents the transmission power of the ship that legally sends the signal; β A2E P represents the path loss coefficient between the legitimate signal sending ship and the eavesdropping ship; TU represents the total transmission power of the virtual antenna array; G E represents the antenna gain of the drone antenna array toward the eavesdropping ship; β U2E represents the path loss coefficient between the UAV antenna array and the eavesdropping ship; σ 2 Represents the noise power.

[0026] Preferably, the expression of the third objective function is:

[0027]

[0028] in, represents the three-dimensional position coordinates of the UAV in the virtual antenna array, E i represents the energy consumption of the i-th UAV in the virtual antenna array when communicating, N U represents the number of drones in the virtual antenna array;

[0029] The energy consumption of UAV communication in the virtual antenna array is calculated as:

[0030]

[0031]

[0032] In the formula, T represents the total flight time of the UAV, v(t) represents the speed of the UAV at time t, and m U represents the weight of the drone, g represents the acceleration of gravity, h(T) represents the altitude of the drone at the end of the flight, and h(0) represents the altitude of the drone at the beginning; P(v) represents the propulsion energy consumption of the drone when it flies in a two-dimensional horizontal space, P B and P I are two constants, representing the blade profile and induced power in the hovering state, v represents the speed of the drone, and v tip is the tip speed of the rotor blade, v m represents the average rotor blade induced speed in the hovering state, d0 represents the fuselage drag ratio, ρ a represents the air density, s r Indicates the hardness of the rotor blade, A r Represents the rotor disc area.

[0033] Preferably, in the step 2, determining the optimal position and optimal excitation current weight of each UAV in the virtual antenna array based on the improved locust optimization algorithm comprises the following steps:

[0034] Step 1: Use Logistic mapping to perform chaos initialization, initialize the positions of all drones and the excitation current weights, and use the combination of the initialized drone vertical position, horizontal position and excitation current weight as the initialization solution, and store it as the first solution set;

[0035] Step 2: Calculate the first objective function and the second objective function corresponding to the solutions in the first solution set to obtain multiple non-dominated solutions as the second solution set; and use the roulette algorithm to select a solution from the second solution set as the current optimal solution;

[0036] Step 3: Optimizing the first solution set using the locust optimization algorithm;

[0037] Step 4: Optimize and adjust the vertical position of the UAV corresponding to each solution in the first solution set optimized in step 3 using the current optimal solution; and update the first solution set using the result of the optimization adjustment;

[0038] Loop through steps 2 to 4 until the set number of iterations is reached;

[0039] Among them, the current optimal solution obtained in the last iteration is the global optimal solution, and the UAV position and excitation current weight corresponding to the global optimal solution are the optimal position and optimal excitation current weight of the UAV.

[0040] Preferably, in step 4, the vertical position of the drone is optimized and adjusted by the following formula:

[0041]

[0042] in, is the vertical position of the drone corresponding to the current optimal solution, is the vertical position of the current i-th group of drones, is the vertical position of the updated i-th group of drones; p is the random number for judgment, e represents the base of the exponential function, M2 represents the random optimal vertical position optimization parameter, M2′ represents the optimal vertical position optimization parameter, rand is a parameter random number, t represents the current number of iterations, t max Indicates the maximum number of iterations.

[0043] Preferably, in said step 4, it further comprises:

[0044] If the current number of iterations is less than half of the maximum number of iterations and the parameter random number is less than the parameter value, the horizontal position of the drone is optimized using the following formula:

[0045]

[0046] in, is the horizontal position of the current i-th group of drones, is the average horizontal position of the current UAV, is the updated horizontal position of the i-th group of UAVs; M1 represents the random horizontal horizontal position optimization parameter, and M1′ represents the horizontal horizontal position optimization parameter.

[0047] Preferably, in said step 4, it further comprises:

[0048] If the current number of iterations is greater than or equal to half of the maximum number of iterations and the parameter random number is less than the parameter value, the drone excitation current weight is optimized and adjusted using the following formula:

[0049]

[0050] in, is the excitation current of the current i-th group of UAVs, is the UAV excitation current weight corresponding to the current optimal solution, is the updated excitation current of the i-th group of UAVs; M3 represents the random optimal excitation current optimization parameter, and M3′ represents the optimal excitation current optimization parameter.

[0051] The beneficial effects of the present invention are:

[0052] The maritime secure communication method based on drone swarms provided by the present invention can determine the optimal position of drones in a virtual antenna array and the optimal excitation current weight, thereby reducing the energy consumption of the drone swarm movement while ensuring safe communication of ships at sea.

[0053] The present invention determines the optimal position and optimal excitation current weight of the UAV through an improved locust optimization algorithm, which can improve the optimization effect of the solution, further improve the security of maritime communications, and further reduce the energy consumption of UAV swarm movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the UAV antenna array collaborative beam maritime secure communication described in the present invention.

[0055] Figure 2 This is a flow chart of the method for secure communication at sea based on drone swarms described in the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0057] During the wireless network communication process at sea, there are many eavesdropping ships at sea that attempt to eavesdrop on the communication data of legitimate ships, causing information to be leaked in advance, thus posing a threat to the secure communication process of wireless networks at sea. Figure 1-2 As shown, the present invention provides a method for maritime secure communication based on drone swarms, which utilizes drone swarms to form a virtual array antenna, determines the optimal position of the drone and the excitation current weight, and then utilizes collaborative beamforming to form a beam with high antenna gain and high directivity, and directly sends interference signals to eavesdropping ships at sea without multiple hops. The main lobe direction of the interference beam points to the direction of the eavesdropping ship at sea. At this time, since the direction of the eavesdropping ship is affected by the interference signal, the eavesdropping ship cannot steal data, thereby improving the confidentiality performance of the maritime wireless network. In the process of realizing maritime secure communication using drone swarms, aerial drones are regarded as communication nodes of the array antenna, and communication nodes that participate in collaborative beamforming to transmit data are defined as collaborative nodes. The specific process is as follows:

[0058] 1. Determine the positions of vessels A and B that are legally communicating at sea based on the maritime navigation schedule.

[0059] Second, determine the number of drones equipped on legal communication vessels, that is, the number of drones that can perform communication tasks.

[0060] 3. The drone detects the location of the eavesdropping ship E at sea based on the equipment it carries (such as radar).

[0061] 4. Design three objective functions based on the needs of confidential communications at sea, and plan the multi-objective optimization problem based on the multi-objective optimization theory.

[0062] Among them, the overall objective function is designed as follows:

[0063] min F={-f1,f2,f3}; (1)

[0064]

[0065]

[0066]

[0067] in, represents the location of the drone swarm, It indicates that the excitation current weights f1, f2, and f3 of the UAV correspond to three objective functions respectively.

[0068] The first objective function f1 represents the signal to interference plus noise ratio of the legitimate receiving ship B at sea in the communication of the drone antenna array. The signal to interference plus noise ratio is used to detect signal quality and refers to the ratio of the power of the received useful signal to the power of the interference signal. In the first objective, the useful signal power is the legitimate power sent by the legitimate ship A received by the legitimate ship B, and the interference signal power is the interference signal power received by the legitimate ship B when the drone swarm sends an interference signal to the eavesdropping ship E, as well as the noise power in the environment.

[0069] The second objective function f2 represents the signal to interference plus noise ratio of the maritime eavesdropping ship E in the communication of the drone antenna array. Since the eavesdropping ship E wants to eavesdrop on the information sent by the legitimate ship A, the useful signal power is the legitimate power sent by the legitimate ship A received by the eavesdropping ship E. At this time, the interference signal power is the interference signal power sent by the drone antenna array to the eavesdropping ship E and the noise power in the environment.

[0070] The third objective function f3 represents the energy consumption of the drone during flight.

[0071] The calculation method and parameter definition of each parameter in the above three objective functions are as follows:

[0072]

[0073]

[0074] A U =η LOS -η NLOS ; (7)

[0075] B U2B=20log 10 (d U2B )+20log 10 (4πf c / 300)+η NLOS ; (8)

[0076] ρ U2B =(180 / π)×arcsin(h U / d U2B ); (9)

[0077]

[0078]

[0079]

[0080]

[0081] B U2E =20log 10 (d U2E )+20log 10 (4πf c / 300)+η NLOS ; (14)

[0082] ρ U2E =(180 / π)×arcsin(h U / d U2E ); (15)

[0083]

[0084]

[0085]

[0086]

[0087] Among them, P A represents the transmission power of the signal sent by the legitimate vessel A, P TU represents the total transmission power of the virtual antenna array, σ 2 represents the noise power; β A2B represents the path loss coefficient between the legitimate ship A sending the signal and the legitimate ship B receiving the signal, where h s is the sea level height, d A2B is the distance between the legitimate ship A sending the signal and the legitimate ship B receiving the signal, f c Indicates the carrier frequency of the drone in MHz, C Uis a constant related to the environment. In the offshore environment, this constant is set to 1dB; β U2B represents the path loss coefficient between the drone antenna array and the legitimate ship B receiving the signal, where d U2B represents the distance between the center of the UAV virtual antenna array and the legal ship B receiving the signal, h U is the height of the center of the (UAV) virtual antenna array (the average height of the UAV group), η LOS is the attenuation factor of the line-of-sight link, η NLOS is the attenuation factor of the non-line-of-sight link, α a and α b represents the parameters of the sigmoid function in the marine environment, a is 5.0188 dB, b is 0.3511 dB; G B represents the antenna gain of the drone antenna array to the legitimate vessel B receiving the signal, represents the position of the i-th UAV, (θ B ,φ B ) represents the direction of the legitimate ship B receiving the signal, θ B represents the elevation angle, φ B represents the azimuth angle, ω(θ,φ) represents the size of the UAV unit far-field beam pattern, which is usually taken as 1, η∈[0,1] represents the UAV antenna array efficiency; AF is the array factor, represents the excitation current weight of the i-th UAV in the UAV antenna array, i ψ represents the imaginary unit, λ represents the wavelength, k=2π / λ represents the phase constant, N U Indicates the number of drones.

[0088] β A2E represents the path loss coefficient between the legitimate ship A sending the signal and the eavesdropping ship E, d A2E is the distance between the legitimate ship A sending the signal and the eavesdropping ship E; β U2E represents the path loss coefficient between the drone antenna array and the eavesdropping ship E, d U2E G represents the distance from the center of the UAV virtual antenna array to the eavesdropping ship E; E represents the antenna gain of the drone antenna array toward the eavesdropping ship E, (θ E ,φ E ) represents the direction of the eavesdropping ship, θ E represents the elevation angle, φ E Indicates the azimuth.

[0089] E i represents the energy consumption of the i-th UAV during communication, T represents the total flight time, v(t) represents the speed of the UAV at time t, and m Urepresents the weight of the drone, g represents the acceleration of gravity, h(T) represents the altitude of the drone at the end of the flight, and h(0) represents the altitude of the drone at the beginning; P(v) represents the propulsion energy consumption of the drone when it flies in a two-dimensional horizontal space, P B and P I are two constants, representing the blade profile and induced power in the hovering state, v represents the speed of the drone, and v tip is the tip speed of the rotor blade, v m represents the average rotor blade induced speed in the hovering state, d0 represents the fuselage drag ratio, ρ a represents the air density, s r Indicates the hardness of the rotor blade, A r Represents the rotor disc area.

[0090] 5. Perform optimization calculation according to the overall optimization goal (maximum first objective function, minimum second objective function and minimum third objective function) to determine the optimal position and optimal excitation current weight of each UAV.

[0091] As a preferred method, an improved locust optimization algorithm is used to optimize the optimal position and optimal excitation current weight of each UAV sending interference signals. The specific process is as follows:

[0092] S1. Set parameters, including the number of drone groups (N pop ), maximum number of iterations (t max ), empty archive, etc.

[0093] S2. Since the decision variables of the designed multi-objective optimization problem are relatively complex, the traditional initialization method is likely to cause the initial solution to fall into the local optimal problem, and the subsequent updated iterative values ​​will be unsatisfactory. Therefore, in the improved locust optimization algorithm, we use the Logistic mapping method (Formula (25)) for chaos initialization. According to Formula (24), the initial values ​​of the UAV position and excitation current weights are obtained, where LB and UB represent the lower and upper bounds of the UAV solutions in different dimensions (the horizontal position, vertical position, and excitation current of the UAV), respectively. m i′ is the updated m i The obtained initialization solution is stored as the first solution set.

[0094] X i =LB+m i ×(UB-LB)(24)

[0095] m i′ =4m i ×(1-m i )(25)

[0096] S3. According to formulas (2) and (3), the values ​​of the first objective function (f1) and the second objective function (f2) are calculated through the position of the drone and the excitation current value.

[0097] S4. Select non-dominated solutions from the first solution set according to the values ​​of the first objective function and the second objective function. Since the optimization results of f1 and f2 in the multi-objective optimization problem are opposite (f1 needs to be maximized and f2 needs to be minimized), when a solution is the best in f1, it may be relatively poor in f2. In order to achieve the overall optimal performance of the multi-objective optimization problem, we do not select non-dominated solutions from the solutions obtained by the objective function (non-dominated solutions: for f1 and f2, assuming that solution S1 is better than S2, then S1 is the dominated solution. On the contrary, solution S1 is not always better than S2, and S1 is a non-dominated solution).

[0098] S5. The non-dominated solution obtained in S4 replaces the solution of the drone previously saved (the position of the drone and the excitation current weight value).

[0099] S6. Use the roulette algorithm from the archive (the larger the proportion, the greater the possibility of selection) to select a set of solutions as the current optimal solution (denoted as X a ).

[0100] S7. Use the locust optimization algorithm (traditional locust optimization algorithm) to optimize the first solution set, and calculate (update) the solution of the UAV (the position and excitation current of the UAV) according to formula (21).

[0101] A group of drones is represented as:

[0102]

[0103] Then X i For the i-th group of drones:

[0104]

[0105]

[0106]

[0107] Among them, X i and X j For the i-th and j-th group of drones, X i′ is the updated solution of the i-th group of drones, T * is the optimal solution currently obtained, d i,j represents the distance between the solutions of the i-th group and the j-th group of drones, γ is the reduction coefficient to reduce the attraction area of ​​locust activity, calculated by formula (22), where t represents the current iteration number, q is the function calculated by formula (23), f represents the attraction strength, and len represents the attraction length.

[0108] S8. Since the ranges of the solutions (horizontal position, vertical position, excitation current) of the drone are different (the horizontal movement range is 0-100 meters, the vertical movement range is 5-120 meters, and the excitation current range is 0-1 ampere), if the same method is used for optimization, it may affect the performance of the objective function. Therefore, the present invention adopts different solution update strategies for different dimensions, that is, the improved locust optimization algorithm is used for optimization. The parameters of the algorithm are first set (Formulas (30)-(34)), including the current average position of the drone and the parameter random number (rand), etc., where the parameter random number (rand) ranges from 0 to 1 and is automatically generated during each iteration; and the parameter value ζ is calculated according to formula (26).

[0109] S9, the solution (vertical position) of the UAV calculated according to formula (35) and replacing the vertical position of the existing UAV;

[0110] If the current number of iterations (t) is less than the maximum number of iterations (t max ) and the parameter random number (rand) is less than the parameter value ζ, then formula (27) is used to replace the solution (horizontal position) of the existing UAV;

[0111] If the current number of iterations (t) is not less than the maximum number of iterations (t max ) and the parameter random number is less than the parameter value ζ, then the X of the drone in the archive selected in S6 is used first. a The horizontal position and vertical position of the drone are used to replace the current horizontal position and vertical position of the drone; and the solution (excitation current) of the drone is updated according to formula (38);

[0112]

[0113]

[0114]

[0115]

[0116] Q=2a1×rand-a1 (30)

[0117]

[0118] R = 2 × rand (32)

[0119] l=(a2-1)×rand+1 (33)

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] in, is the vertical position of the current i-th group of drones, is the updated vertical position of the i-th group of UAVs, is the current optimal solution X in S6 a The vertical position value of the drone, p is a random number for judgment, which is automatically generated during each iteration and has a value range of 0-1; is the excitation current of the i-th group of UAVs, is the current optimal solution X in S6 a The excitation current value, is the excitation current of the i-th group of UAVs after update; is the horizontal position of the current i-th group of drones, is the average horizontal position of the current UAV, is the updated horizontal position of the i-th group of UAVs.

[0128] S10, return to S3 until the number of iterations reaches the maximum number of iterations and the current loop ends; during the iteration process, the calculation process in S3 is performed according to the first solution set after each update;

[0129] Among them, the current optimal solution obtained after the last cycle is the global optimal solution, and the drone position and excitation current weight corresponding to the global optimal solution are the optimal position and optimal excitation current weight of the drone.

[0130] The improved locust optimization algorithm improves the performance of the initial solution by integrating the Logistic chaos initialization method and the solution update strategy, and through the solution update strategy, the solutions of different dimensions have different update strategies according to the different upper and lower bounds, so that the performance of the iterative solution is better. The improved locust optimization algorithm solves the problem that multi-objective optimization problems are prone to fall into local optimal solutions due to the large number of decision variables in the optimization function.

[0131] 6. The drones fly into the air and form a virtual antenna array based on the designed optimal drone position. The main lobe of the array is directed toward the eavesdropping ship at sea, and interference signals are sent to the eavesdropping ship E at sea according to the optimal excitation current weight. This ensures that the interference signal strength received by the eavesdropping ship E through the main lobe of the virtual antenna array is the maximum, while ensuring that the interference signal received by our ship through the side lobe of the virtual antenna array is the minimum, thereby achieving confidential communication at sea. In addition, the energy consumption of the drone swarm is reduced.

[0132] Test example

[0133] The upper and lower limits of the drone were set (horizontal moving range 0-100 meters, vertical moving range 5-120 meters, excitation current range 0-1 ampere), the sea level height was 5 meters, the maximum number of iterations was 500 times, the number of drone groups was 30, and the number of drones in a group was 16. The optimization was performed using the improved multi-objective locust optimization algorithm and the traditional multi-objective locust optimization algorithm respectively.

[0134] The calculation process of the improved locust optimization algorithm is as follows:

[0135]

[0136] The specific optimization process of the traditional locust optimization algorithm is as follows:

[0137] S1. Set parameters, including the number of drone groups (N pop ), maximum number of iterations (t max ), empty archive, etc.;

[0138] S2. Use the random initialization method to obtain the initial values ​​of the drone position and excitation current weights

[0139] S3, start the iteration process, and calculate the values ​​of the first objective function (f1) and the second objective function (f2) according to the position of the UAV and the excitation current value according to formulas (2) and (3);

[0140] S4, select the non-dominated solutions among the solutions of the UAVs corresponding to the objective functions f1 and f2;

[0141] S5, the non-dominated solution obtained in S4 replaces the solution of the drone in the previous archive (the position of the drone and the excitation current weight value), and uses the roulette algorithm to select a set of solutions from the archive as the current optimal solution;

[0142] S6. Use the locust optimization algorithm to optimize and calculate the solution of the drone (the position of the drone and the excitation current) according to formula (21), where X i and X j is the UAV solution for the i-th and j-th groups, γ is the reduction coefficient to reduce the attraction area of ​​locust activity, q is the function calculated by formula (23), T* is the optimal solution currently obtained, where t represents the current iteration number, f represents the attraction strength, and len represents the attraction length;

[0143] S7, return to step S3 until the number of iterations reaches the maximum number of iterations (t=t max =500) when the current loop ends;

[0144] S8, taking out the final UAV solution (optimal UAV position and excitation current weight value);

[0145] S9. Using the obtained optimal UAV position, calculate the value of the third objective function (f3), i.e., the energy consumed by the UAV, according to formula (4).

[0146] The objective function values ​​obtained after optimization of the two algorithms are shown in Table 1:

[0147] Table 1 Comparison of optimization results

[0148] Algorithm Name First goal Second goal Third goal Traditional multi-objective locust optimization algorithm 23.01 -18.17 <![CDATA[3.673×10 5 ]]> Improved multi-objective locust optimization algorithm 23.72 -19.80 <![CDATA[3.656×10 5 ]]>

[0149] The optimization results in Table 1 show that the optimization effects of the improved multi-objective locust optimization algorithm on improving the signal to interference plus noise ratio of legal maritime communication vessels, reducing the signal to interference plus noise ratio of illegal maritime eavesdropping vessels, and reducing the energy consumption of drone swarms are better than those of the traditional multi-objective locust optimization algorithm. That is, the improved locust optimization algorithm has achieved the best performance in maximizing the signal to interference plus noise ratio of legal maritime vessels B, minimizing the signal to interference plus noise ratio of maritime eavesdropping vessels E, and minimizing the total flight energy consumption of drones. In particular, the performance is better in minimizing the signal to interference plus noise ratio of maritime eavesdropping vessels E, which can fully illustrate the superiority of the improved locust optimization algorithm.

[0150] The present invention provides a method for maritime confidential communication based on drone swarms, and establishes a multi-objective joint optimization model for improving the signal to interference plus noise ratio of legal maritime communication vessels, reducing the signal to interference plus noise ratio of illegal maritime eavesdropping vessels, and reducing the energy consumption of drone swarms. The method first determines the position of legal maritime vessels and the position of maritime eavesdropping vessels, designs a multi-objective optimization problem, and then uses an improved locust optimization algorithm and collaborative beamforming to design the optimal position of the drone that sends the interference signal and the ideal excitation current weight of the drone, and then uses the virtual antenna array formed by the drone swarm to send interference signals to the maritime eavesdropping vessels to ensure secure communication between legal maritime communication vessels. The improved locust optimization algorithm introduces a Logistic chaos solution initialization method and a solution update strategy, and the solution update strategy gives different update methods through solutions in different regions to improve the superiority of the solution.

[0151] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for secure communication at sea based on drone swarms, characterized in that: The steps include: Step 1: Obtain the locations of the two ships that are legally communicating at sea and the location of the eavesdropping ship at sea, and determine the number of drones that can perform communication tasks; Wherein, the drones capable of performing communication tasks form a virtual antenna array; Step 2: Determine three objective functions, take the maximum of the first objective function, the minimum of the second objective function and the minimum of the third objective function as the optimization goals, and determine the optimal position and optimal excitation current weight of each drone in the virtual antenna array; Wherein, the first objective function is: the signal to interference plus noise ratio of the vessel legally receiving the signal at sea of ​​the UAV antenna array communication; The expression of the first objective function is: in, represents the three-dimensional position coordinates of the drone in the virtual antenna array, represents the excitation current weight of the UAV; P A Indicates the signal transmission power of the ship that legally sends the signal; β A2B represents the path loss coefficient between two ships that are legally communicating at sea; P TU represents the total transmission power of the virtual antenna array; G B represents the antenna gain of the drone antenna array to the vessel that legally receives the signal; β U2B represents the path loss coefficient between the drone antenna array and the ship that legally receives the signal; σ 2 represents the noise power; The second objective function is: the signal to interference plus noise ratio of the maritime eavesdropping vessel of the drone antenna array communication; The expression of the second objective function is: in, represents the three-dimensional position coordinates of the drone in the virtual antenna array, Represents the excitation current weight of the UAV; P A represents the transmission power of the ship that legally sends the signal; β A2E P represents the path loss coefficient between the legitimate signal sending ship and the eavesdropping ship; TU represents the total transmission power of the virtual antenna array; G E represents the antenna gain of the drone antenna array toward the eavesdropping ship; β U2E represents the path loss coefficient between the UAV antenna array and the eavesdropping ship; σ 2 represents the noise power; The third objective function is: energy consumption of the drone during flight; Step 3: After the UAV in the virtual antenna array moves to the optimal position, it sends an interference signal to the maritime eavesdropping ship according to the optimal excitation current weight to achieve confidential communication at sea.

2. The method for secure communication at sea based on drone swarm according to claim 1, characterized in that: The path loss coefficient between the two ships in legal communication at sea is: Among them, h s is the sea level height, d A2B is the distance between two ships that can legally communicate at sea, C U These are environment-related constants.

3. The method for secure communication at sea based on drone swarm according to claim 2, characterized in that: The path loss coefficient between the drone antenna array and the ship that legally receives the signal is: Among them, η LOS is the attenuation factor of the line-of-sight link, η NLOS is the attenuation factor of the non-line-of-sight link, α a and α b represents the parameters of the sigmoid function in the marine environment; f c Indicates the carrier frequency of the drone; d U2B represents the distance between the center of the UAV virtual antenna array and the ship that legally receives the signal, h U is the height of the center of the UAV virtual antenna array.

4. The method for secure communication at sea based on drone swarm according to claim 3, characterized in that: The expression of the third objective function is: in, represents the three-dimensional position coordinates of the UAV in the virtual antenna array, E i represents the energy consumption of the i-th UAV in the virtual antenna array when communicating, N U represents the number of drones in the virtual antenna array; The energy consumption of UAV communication in the virtual antenna array is calculated as: In the formula, T represents the total flight time of the UAV, v(t) represents the speed of the UAV at time t, and m U represents the weight of the drone, g represents the acceleration of gravity, h(T) represents the altitude of the drone at the end of the flight, and h(0) represents the altitude of the drone at the beginning; P(v) represents the propulsion energy consumption of the drone when it flies in a two-dimensional horizontal space, P B and P I are two constants, representing the blade profile and induced power in the hovering state, v represents the speed of the drone, and v tip is the tip speed of the rotor blade, v m represents the average rotor blade induced speed in the hovering state, d0 represents the fuselage drag ratio, ρ a represents the air density, s r Indicates the hardness of the rotor blade, A r Represents the rotor disc area.

5. The method for secure communication at sea based on drone swarm according to claim 4, characterized in that: In the step 2, the optimal position and the optimal excitation current weight of each UAV in the virtual antenna array are determined based on the improved locust optimization algorithm, including the following steps: Step 1: Use Logistic mapping to perform chaos initialization, initialize the positions of all drones and the excitation current weights, and use the combination of the initialized drone vertical position, horizontal position and excitation current weight as the initialization solution, and store it as the first solution set; Step 2: Calculate the first objective function and the second objective function corresponding to the solutions in the first solution set to obtain multiple non-dominated solutions as the second solution set; and use the roulette algorithm to select a solution from the second solution set as the current optimal solution; Step 3: Optimizing the first solution set using the locust optimization algorithm; Step 4: Optimize and adjust the vertical position of the UAV corresponding to each solution in the first solution set optimized in step 3 using the current optimal solution; and update the first solution set using the result of the optimization adjustment; Loop through steps 2 to 4 until the set number of iterations is reached; Among them, the current optimal solution obtained in the last iteration is the global optimal solution, and the UAV position and excitation current weight corresponding to the global optimal solution are the optimal position and optimal excitation current weight of the UAV.

6. The method for secure communication at sea based on drone swarm according to claim 5, characterized in that: In step 4, the vertical position of the drone is optimized and adjusted using the following formula: in, is the vertical position of the drone corresponding to the current optimal solution, is the vertical position of the current i-th group of drones, is the vertical position of the updated i-th group of drones; p is the random number for judgment, e represents the base of the exponential function, M2 represents the random optimal vertical position optimization parameter, M2 ′ represents the optimal vertical position optimization parameter, rand is a parameter random number, t represents the current number of iterations, t max Indicates the maximum number of iterations.

7. The method for secure communication at sea based on drone swarm according to claim 6, characterized in that: In step 4, it also includes: If the current number of iterations is less than half of the maximum number of iterations and the parameter random number is less than the parameter value, the horizontal position of the drone is optimized using the following formula: in, is the horizontal position of the current i-th group of drones, is the average horizontal position of the current UAV, is the updated horizontal position of the i-th group of drones; M1 represents the random horizontal horizontal position optimization parameter, M1 ′ represents the average horizontal position optimization parameter.

8. The method for secure communication at sea based on drone swarm according to claim 7, characterized in that: In step 4, it also includes: If the current number of iterations is greater than or equal to half of the maximum number of iterations and the parameter random number is less than the parameter value, the drone excitation current weight is optimized and adjusted using the following formula: in, is the excitation current of the current i-th group of drones, is the UAV excitation current weight corresponding to the current optimal solution, is the updated excitation current of the i-th group of UAVs; M3 represents the random optimal excitation current optimization parameter, M3 ′ Represents the optimal excitation current optimization parameters.

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