CN114221694B - Unmanned aerial vehicle swarm security communication method - Google Patents
Unmanned aerial vehicle swarm security communication method Download PDFInfo
- Publication number
- CN114221694B CN114221694B CN202210003107.6A CN202210003107A CN114221694B CN 114221694 B CN114221694 B CN 114221694B CN 202210003107 A CN202210003107 A CN 202210003107A CN 114221694 B CN114221694 B CN 114221694B
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- communication
- representing
- array antenna
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18502—Airborne stations
- H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/022—Site diversity; Macro-diversity
- H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/04—Large scale networks; Deep hierarchical networks
- H04W84/06—Airborne or Satellite Networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Astronomy & Astrophysics (AREA)
- Aviation & Aerospace Engineering (AREA)
- General Physics & Mathematics (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses an unmanned aerial vehicle swarm security communication method, which comprises the following steps: acquiring the number and the positions of standby unmanned aerial vehicles above a monitoring area, the number and the positions of remote base stations needing communication and the position of an eavesdropper; determining the number of unmanned aerial vehicles forming the virtual array antenna for transmitting information; the ground user selects an unmanned aerial vehicle from the unmanned aerial vehicle swarm as a first communication unmanned aerial vehicle; the first communication unmanned aerial vehicle selects a plurality of second communication unmanned aerial vehicles; the method comprises the steps that the sum of signal-to-noise ratios of base stations in virtual array antenna communication is minimum, the sum of the signal-to-noise ratios of eavesdroppers in communication with the base stations is minimum, and the minimum energy consumption of the unmanned aerial vehicle in the flight process is taken as an optimization target, and the optimal communication sequence of the remote base stations, the optimal position of each unmanned aerial vehicle in the virtual array antenna and the optimal excitation current weight are determined; and after the unmanned aerial vehicle in the virtual array antenna moves to the optimal position, transmitting communication data to the remote base station in the form of the virtual array antenna according to the optimal communication sequence and the optimal excitation current weight.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle swarm security communication method.
Background
In the field of wireless networks, unmanned aerial vehicles can serve as aerial base stations and aerial relays to assist ground networks, and provide reliable and efficient wireless communication services for ground users. Carry on miniature communication equipment through unmanned aerial vehicle and regard as low latitude communication platform, with the help of advantages such as its flexibility is high, easily arrange, can cover the region that has the needs in the very first time to for the user provides high probability stadia communication, make wireless communication network's deployment more nimble. Although a single drone is prominent in enhancing wireless network performance, due to the limitations of the drone in terms of physical size, weight, power, and the like, the overall capability of the drone is limited, and the availability of the drone during the whole mission cannot be guaranteed, which prompts the deployment of multiple drone clusters that collectively serve a communication system to achieve more efficient communication.
In a scenario that the unmanned aerial vehicle is used as an aerial relay/base station, a ground user located in a certain specific area needs to communicate with a remote base station, the communication between the ground user and the remote base station can establish connection through an unmanned aerial vehicle swarm, and then the communication is realized by using a single-hop or multi-hop method, but the method may cause link failure and bring extra energy consumption, and meanwhile, due to the openness of a channel, an eavesdropper does not have to attempt to intercept data transmitted between the ground user and the remote base station and maliciously tamper or destroy the data, so that a serious communication safety problem is caused. Confidential communication in a wireless network can be achieved by adopting an upper-layer encryption mode, but frequent encryption and decryption require high computing power, which brings serious challenges to a unmanned aerial vehicle with limited hardware resources.
Disclosure of Invention
The invention provides an unmanned aerial vehicle swarm security communication method, which designs an optimal base station communication sequence, an unmanned aerial vehicle node participating in cooperative beam forming data transmission and an optimal unmanned aerial vehicle position when communicating with each base station, and further solves a multi-target joint optimization model so as to improve the signal-to-noise ratio of a receiving base station.
An unmanned aerial vehicle swarm security communication method comprises the following steps:
step 1, acquiring the number and the position of standby unmanned aerial vehicles above a monitoring area, the number and the position of remote base stations needing communication and the position of an eavesdropper; determining the number of unmanned aerial vehicles forming the virtual array antenna for transmitting information;
step 2, the ground user selects an unmanned aerial vehicle from the unmanned aerial vehicle swarm as a first communication unmanned aerial vehicle;
step 3, selecting a plurality of second communication unmanned aerial vehicles by the first communication unmanned aerial vehicle;
the first communication unmanned aerial vehicle and the plurality of second communication unmanned aerial vehicles jointly form a virtual array antenna;
step 4, determining the optimal communication sequence of the remote base station, the optimal position of each unmanned aerial vehicle in the virtual array antenna and the optimal excitation current weight by taking the minimum sum of the signal-to-noise ratios of the base stations communicated by the virtual array antenna, the minimum sum of the signal-to-noise ratios of an eavesdropper when the virtual array antenna is communicated with each base station and the minimum energy consumption of the unmanned aerial vehicle in the flight process as optimization targets;
and 5, after the unmanned aerial vehicle in the virtual array antenna moves to the optimal position, the ground user transmits communication data to the first communication unmanned aerial vehicle, the first communication unmanned aerial vehicle transmits the communication data to the plurality of second communication unmanned aerial vehicles respectively, and the communication data are transmitted to the remote base station in a virtual array antenna mode according to the optimal communication sequence and the optimal excitation current weight.
Preferably, in step 1, a calculation formula of the number of drones forming the virtual array antenna to transmit information is as follows:
wherein, P T Representing the total power, P, required for transmitting the current communication data UAV_max Representing the maximum power of a single drone.
Preferably, the method is as followsIn step 2, the ground user calculates the k distance d between the ground user and each unmanned aerial vehicle above the monitoring area k And selecting the minimum d i The unmanned aerial vehicle corresponding to the value is used as the first communication unmanned aerial vehicle; wherein:
wherein (x) G ,y G ,z G ) And
and respectively representing the three-dimensional coordinates of the ground user needing to transmit information to the remote base station and each unmanned aerial vehicle k above the monitoring area.
Preferably, in step 5, before the first communication drone transmits the communication data to the plurality of second communication drones, the method further includes:
the first communication unmanned aerial vehicle initiates a communication request to the second communication unmanned aerial vehicle;
and the second communication unmanned aerial vehicle returns a message for confirming the establishment of the connection after receiving the communication request.
Preferably, in the step 4, the method further includes:
the optimization objective function is established as follows: minF = { -f 1 ,f 2 ,f 3 }；
Wherein:
in the formula (I), the compound is shown in the specification,
indicating the position of the drone or drone,
representing the excitation current weight of the drone,
representing the order of the communication base stations, N BS Representing the number of base stations, f 1 Representing the sum of the signal-to-noise ratios of the base stations, f 2 Sum of signal-to-noise ratios, f, representing an eavesdropper 3 The energy consumption of the unmanned aerial vehicle is shown,
representing the signal-to-noise ratio, SNR, of the jth base station in communication with the virtual array antenna KE-j Representing the signal-to-noise ratio of an eavesdropper while communicating at the jth base station, E i,j And the energy consumption of the ith unmanned aerial vehicle in communication with the jth base station is shown.
Preferably, the model for calculating the signal-to-noise ratio of the base station is as follows:
wherein, the first and the second end of the pipe are connected with each other,
denotes a distance between the virtual array antenna and the jth base station, alpha denotes a path loss exponent, P CB Represents the total transmission power of the virtual array antenna,
representing the constant path loss coefficient, σ, of the virtual array antenna and the jth base station 2 Representing the noise power;
represents the antenna gain of the jth base station; (x) U ,y U ,z U ) A set of three-dimensional position coordinates representing a drone in a virtual array antenna,
indicating the direction of the jth base station,
which represents the elevation angle of the vehicle,
indicating azimuth angle, and omega (theta, phi) indicating the size of a unit far-field beam pattern of the unmanned aerial vehicle; eta ∈ [0,1]Representing virtual array antenna efficiency;
as a function of array factor for the base station direction of the j-th station, I i Represents the excitation current weight of the ith drone in the virtual array antenna, λ represents the wavelength, k =2 π/λ represents the phase constant,
and the three-dimensional coordinates of the ith unmanned aerial vehicle in the virtual array antenna are represented.
Preferably, when the virtual array antenna communicates with the base station, the calculation model of the signal-to-noise ratio of the eavesdropper is as follows:
wherein d is KE Representing the distance, P, between the virtual array antenna center and a known eavesdropper CB Representing the total transmission power of the virtual array antenna; k KE Constant path loss coefficient representing the direction of an eavesdropper; g KE Antenna gain (θ) representing eavesdropper direction KE ,φ KE ) Representing the direction of an eavesdropper, theta KE Indicates the elevation angle phi KE Denotes the azimuth angle, AF (θ) KE ,φ KE ) An array factor function representing the direction of a known eavesdropper.
Preferably, the calculation model of the energy consumption of the unmanned aerial vehicle is as follows:
where T represents the total flight time of the drone, v (T) represents the speed of the drone at time T, m D Representing the weight of the unmanned aerial vehicle, g representing the acceleration of gravity, h (T) representing the height of the unmanned aerial vehicle at the end of flight, and h (0) representing the height of the unmanned aerial vehicle at the beginning; p (v) represents the energy consumption of the unmanned plane in two-dimensional horizontal space flight, P B And P I Is two constants respectively representing the profile of the blade and the induced power in the hovering state, v represents the speed of the unmanned aerial vehicle tip Indicating the tip speed, v, of the rotor blade 0 Indicating average rotor blade induced velocity in hover, d 0 Denotes fuselage drag ratio, ρ denotes air density, s denotes rotor blade stiffness, and a denotes rotor disk area.
Preferably, in step 4, the optimal communication order of the remote base station, the optimal position of each drone in the virtual array antenna, and the optimal excitation current weight are determined based on the NSGA-ii algorithm, and the method includes the following steps:
step a, randomly generating a first generation population, wherein each element in the first generation population comprises a continuous connection solution and a discrete solution:
the continuous solution corresponds to the position and the excitation current weight of each unmanned aerial vehicle in the virtual array antenna, and the discrete solution corresponds to the communication sequence of the remote base station;
b, calculating an objective function value corresponding to each element in the first generation population;
c, selecting a plurality of elements from the previous generation population to cross each other by adopting a random method to generate a cross population, and selecting a plurality of elements to perform variation to generate a variation population;
d, combining the generated cross and variation populations with the previous generation population, carrying out non-dominated sorting on the combined population to obtain the level of a solution, calculating the congestion distance of each element, sorting the populations according to the sorting rule of ascending order of the level of the solution and descending order of the congestion distance, and reserving the top N POP An element; c-d is executed circularly, and the optimal solution set is obtained by knowing the specified circulation times;
and e, selecting the element which can realize the maximum total signal-to-noise ratio of the direction of the communication base station from the optimal solution set as a final solution.
The invention has the beneficial effects that:
aiming at the problem that an eavesdropper tries to intercept communication data and tamper the data to form threat in the wireless network security communication process in the wireless network communication process, based on an antenna array cooperation beam forming theory, a target joint optimization model formed by a base station signal-to-noise ratio sum function, an eavesdropper signal-to-noise ratio sum function and an unmanned aerial vehicle energy consumption function is utilized, an optimal base station communication sequence is designed by utilizing an evolutionary algorithm, unmanned aerial vehicle nodes participating in cooperation beam forming and data transmission and the optimal unmanned aerial vehicle positions in communication with all base stations are designed, the unmanned aerial vehicle ideal excitation current weight further solves the model, an unmanned aerial vehicle bee colony forms a virtual array antenna, further a beam with high antenna gain and high directionality is formed, the beam directly communicates with receivers such as the base stations without multi-hop, and the main lobe direction of the beam points to the direction of the communicated base stations, so that the high signal-to-noise ratio of the receiving base stations and the low signal-to-noise ratio of the eavesdropper are achieved, and the purpose of security and reliability of the wireless network communication is achieved.
Aiming at the problems of high working energy consumption, difficult cruising and the like of the unmanned aerial vehicle, the invention can shorten the moving distance of the unmanned aerial vehicle when the unmanned aerial vehicle is communicated with different base stations by designing the optimal communication sequence of the unmanned aerial vehicle cluster and the base stations, thereby effectively reducing the energy consumption.
Drawings
Fig. 1 is a schematic diagram of the secure communication of the drone swarm in the invention.
Fig. 2 is a flowchart of the unmanned aerial vehicle swarm security communication method of the invention.
Detailed Description
As shown in fig. 1-2, the present invention provides an unmanned aerial vehicle swarm security communication method based on an antenna array cooperative beam forming technology, which comprises the following specific processes:
1. and determining the number and the positions of communication nodes in the unmanned aerial vehicle swarm above the monitoring area.
2. The number and location of remote base stations that need to communicate is determined.
3. The drone detects the location of the eavesdropper from a device (e.g., radar) carried by the drone.
4. Determining the number N of drones forming the transmission information of the virtual array antenna UAV . Determining the number of drones required by the following formula:
wherein, P T Representing the total power, P, required to transmit a certain piece of data UAV_max Representing the maximum power of a single drone node. According to the electromagnetic wave superposition principle, if N nodes transmit information with the same power P, the information received by a receiving base station is equivalent to N through information superposition 2 Data sent by each node.
Therefore, only the array needs to have
Each drone node has sufficient power to transmit information to a recipient.
5. The ground user selects an unmanned aerial vehicle from the unmanned aerial vehicle swarm and records the unmanned aerial vehicle as UAV selected Transmitting the communication data with the base station to the UAV selected . The specific selection process is as follows:
the ground user calculates the distance d from the ground user to each unmanned aerial vehicle node k in the unmanned aerial vehicle swarm k ：
In the formula (x) G ,y G ,z G ) And
and respectively representing the three-dimensional coordinates of the ground user needing to transmit information to the remote base station and each unmanned aerial vehicle k above the monitoring area.
The ground user selects the unmanned aerial vehicle closest to the ground user, and the unmanned aerial vehicle is recorded as UAV selected 。
Ground user-oriented UAV selected And sending control information, wherein the unmanned aerial vehicle needs to respond to the control information within the delay time. If the ground user successfully receives the response message within the delay time, the ground user transmits the data to be transmitted to the UAV, and if the ground user does not receive the response message from the selected UAV within the delay time, the ground user continuously transmits control information to the UAV selected Until it receives a message from the UAV selected Then transmits the communication data with the base station to the UAV selected 。
6. Unmanned Aerial Vehicle (UAV) selected Selecting N nearest to the unmanned plane in the swarm UAV -1 drone, constituting a virtual array antenna.
The energy consumption for mutually sending messages among the unmanned aerial vehicles is more than the energy consumed for the ground users to sequentially send own information to the unmanned aerial vehicles in cooperative beam forming. In the communication method provided by the invention, the ground user transmits the information to be transmitted to one unmanned aerial vehicle in the swarm, and then the unmanned aerial vehicle transmits the information to be transmitted to the unmanned aerial vehicle which cooperates with the unmanned aerial vehicle to form the beam together with the unmanned aerial vehicle, so that the energy consumption in the information transmission process can be reduced.
Selected Unmanned Aerial Vehicle (UAV) during communication selected After receiving the communication data transmitted by the ground user, the communication data transmitted by the ground user are sequentially transmitted to N in the selected virtual array antenna UAV -1 cooperative node. Because the communication between the unmanned aerial vehicles adopts the wireless network, the channel is unreliable, so the reliable transmission of communication data is realized by establishing connection between the unmanned aerial vehicles, and the specific process is as follows:
(1)UAV selected initiating a request for establishing connection to a cooperative node;
(2) After receiving the communication request, the cooperative node returns a message for confirming the establishment of the connection;
(3)UAV selected transmitting data to the cooperating node that requires communication to the remote base station.
7. Three optimization functions are designed according to the safety communication requirement, a multi-objective optimization problem is planned according to a multi-objective optimization theory, and then a communication sequence with a remote base station, the optimal position of each unmanned aerial vehicle when communicating with each base station and the optimal excitation current weight are designed by utilizing an NSGA-II algorithm.
(1) Determining the Total objective function (minimum solution)
min F＝{-f 1 ,f 2 ,f 3 }
f 1 Is the sum of the signal-to-noise ratios of the base stations for virtual array antenna communications, f 2 Is the signal-to-noise ratio, f, of an eavesdropper when the virtual array antenna communicates with the base station 3 Is the energy consumption that unmanned aerial vehicle removed in whole communication process. The total objective function is to simultaneously realize the maximum signal-to-noise ratio of the base station and the signal-to-noise ratio of the eavesdropperThe ratio and energy consumption are minimal.
First objective function f 1 Representing the sum of the signal-to-noise ratios of the base stations communicating with the virtual array antenna. Wherein the content of the first and second substances,
representing the signal-to-noise ratio of the jth base station in communication with the virtual array antenna. Wherein the content of the first and second substances,
indicating the position of the drone or drone,
represents the excitation current weight of the drone,
representing the order of the communication base stations, N BS Indicating the number of base stations.
The calculation is performed by the following formula:
f 2 And a second objective function representing the sum of the signal-to-noise ratios of the eavesdropper when the virtual array antenna communicates with each base station. Wherein the SNR KE The calculation is made by the following formula:
d KE representing the distance, K, between the virtual array antenna center and a known eavesdropper KE Constant path loss coefficient representing the direction of an eavesdropper; g KE Antenna gain (θ) representing eavesdropper direction KE ,φ KE ) Representing the direction of an eavesdropper, theta KE Indicates the elevation angle phi KE Indicating the azimuth angle.
f 3 And the third objective function represents the energy consumption of the unmanned aerial vehicle during the flight process. Wherein, t i,j Indicating the time required for the ith drone to prepare for communication with the jth base station, E i,j And the energy consumption of the ith unmanned aerial vehicle in communication with the jth base station is shown.
T represents the end time of flight, v (T) represents the speed of the drone at time T, m D Representing the weight of the drone, v (T) representing the end time T, the speed of the drone; v (0) represents the velocity of the unmanned aerial vehicle at the initial time, g represents the gravitational acceleration, h (T) represents the height of the unmanned aerial vehicle at the end of the flight, and h (0) represents the height of the unmanned aerial vehicle at the initial time; p (v) represents the energy consumption of the unmanned plane in two-dimensional horizontal space flight, P B And P I Respectively representing the profile and the induced power of the blade in the hovering state, v representing the speed of the unmanned aerial vehicle, v tip Representing the tip speed, v, of the rotor blade 0 Indicating average rotor blade induced velocity in hover, d 0 Denotes fuselage drag ratio, ρ denotes air density, s denotes rotor blade stiffness, and a denotes rotor disk area.
(2) Based on the objective function, the optimum position of the unmanned aerial vehicle, the optimum excitation current weight of the unmanned aerial vehicle and the optimum base station communication sequence are designed by utilizing an NSGA-II algorithm.
The specific calculation process is as follows:
the input parameters are: population size (number of solutions) N pop Maximum number of iterations t max The number of crossing elements is N crossover The number of the variant elements is N mutate The rate of variation mu, the objective function.
The output parameters are: the pareto optimal solution corresponds to the optimal communication sequence of the base stations, and the optimal position and the optimal excitation current weight of the unmanned aerial vehicle when each base station communicates.
The basic principle of the algorithm is as follows: and on the basis of the intersection and variation of the genetic algorithm, selecting a pareto optimal solution by using non-dominated sorting and crowding distance.
The specific steps of the algorithm are as follows:
(1) Initialize solutions in the population, each solution in the population containing 3 optimal design variables (communication order of remote base stations, position of each drone in the virtual array antenna and excitation current weight)
Each solution in the population comprises a continuous part and a discrete part, and the continuous solutions in the population are initialized by a random generation method, namely the position and excitation current weight of each unmanned aerial vehicle in the virtual array antenna:
X p,q ＝lb q +(ub q -lb q )*rand
in the formula, X p,q Represents the qth solution, lb, of the p-th particle in the population q Lower bound, ub, representing the solution of dimension q q Representing the upper bound of the solution of dimension q, rand is a random number generated by the system, and the value is between 0 and 1.
Initializing discrete solutions in the population by using a random generation method, namely a communication sequence of the base stations:
S＝randperm(a,b)
in the formula, S represents the communication sequence of the base stations, randderm is a function which randomly generates b unequal values from 1-a, wherein the values of a and b are the number N of the base stations BS 。
(2) Calculating an objective function value from the current solution.
(3) For each iteration:
a. generating cross population, wherein the number of cross elements generated by each iteration is N crossover Selecting two elements in the population to cross each time, wherein the total cross N is required crossover 2 times of treatment.
The intersection of successive solutions is achieved using the following pair of equations:
wherein, X 1 And X 2 Is a continuous solution part of two solutions in the population, one row has m dimensions, m represents the dimension of the continuous solution, alpha is a random number array with 1 row and m dimensions, Y 1 And Y 2 Representing two successive solutions after the crossover. The multiplication of two arrays in the formula is the multiplication of elements at corresponding positions in the arrays.
The interleaving of discrete solutions is achieved by partial match interleaving (PMX). The principle of PMX is to take the same-positioned segments of the two solutions to interchange.
b. Generating variant populations.
The variation method of the continuous solution comprises the following steps: first, m × mu elements to be mutated are randomly selected from an m-dimensional continuous solution. Then, the variation of the continuous solution in the element is realized by using the following formula:
Y(i)＝X(i)+σ*randn
where σ represents the variation step size of the ith dimension element in the continuous solution, and randn is a random number generated by the system and obeying normal distribution.
The variation method of the discrete solution comprises the following steps: two different base stations are randomly selected from the base station communication order and the two values are exchanged for position.
c. And combining the original population, the cross population and the variant population to form a mixed population.
d. And (4) performing non-dominated sorting on the mixed population.
e. And calculating the crowding distance of each particle in the mixed population.
f. Sorting the mixed population according to the non-dominant sorting result and the crowding distance of the particles, and reserving the top N POP And (5) taking the solution as a solution basis for the next iteration. The ordering rule is as follows: the elements belonging to each same level are sorted in descending order according to the congestion distance after sorting in ascending order according to the level obtained by non-dominated sorting.
(4) And returning to the finally obtained population (optimal solution set) after the set iteration number is reached. (in this case, the preferred solution for the communication order of the remote base stations, the positions of the drones in the virtual array antenna, and the excitation current weights are placed in the population.)
And then, selecting a solution which can realize the maximum total signal-to-noise ratio in the direction of the communication base station in the optimal solution set as a final solution, wherein the solution corresponding to the final solution is the optimal communication sequence of the remote base station, the optimal position of each unmanned aerial vehicle in the virtual array antenna and the optimal excitation current weight.
8. After the unmanned aerial vehicle in the virtual array antenna moves to the optimal position, the ground user transmits the communication data to the unmanned aerial vehicle UAV selected Unmanned aerial vehicle UAV selected And respectively transmitting the communication data to other unmanned aerial vehicles in the virtual array antenna, and transmitting the communication data to the remote base station in a virtual array antenna mode according to the optimal communication sequence and the optimal excitation current weight.
In this embodiment, an optimum position of the drone is designed by using NSGA-ii, and the specific implementation processes of the optimum excitation current weight of the drone and the optimum base station communication sequence are as follows:
(one) experimental basic configuration:
1.Matlab version number: matlabR2020a
2. A device processor: 11thGenIntel (R) Core (TM) i7-11700@2.50GHz
3. Memory: 8G
(II) experimental parameters:
population size N pop Maximum number of iterations t max The number of crossing elements is N crossover The number of the variant elements is N mutate Rate of variation mu, objective function
1. Maximum number of iterations t max ：500
2. Population size N pop ：30
3. Number of crossing elements N crossover ：24
4. Number of mutation elements N mutate ：12
5. The rate of variation mu:0.02
6. Number of unmanned aerial vehicles N UAV :16
7. Of the desired communicationNumber of base stations N BS ：8
Initial information of 8.16 drones:
uav_origin＝[0.427062347759695,0.955372102971021,0.724247033520084,0.580891712363774,0.540257907420780,0.705441191564081,0.00502888330112106,0.782515778836242,0.926859573385509,0.00829565739263971,0.824628342666394,0.767335868027880,0.997136895348686,0.227653072058219,0.919542206194465,0.641999305521780,7.38418767360661,12.0508166401978,98.1596208916383,49.6799421779149,2.24136544401531,5.38315489695107,14.0873797601841,89.3474311740186,46.5820073116048,56.0856738133542,49.4456347383399,6.77854884858151,89.7646542373691,28.8565308881261,26.9046814753517,59.4194175430363,47.5879024056308,36.8311038073403,65.5611090496500,93.8200405804263,62.0425185783994,28.2840091614325,20.5181251932987,43.9134081593786,2.72502196608166,87.6184335960346,61.0092246274455,20.3592380287419,51.9916824790401,5.38243021501204,86.2187431116878,44.2934666053869,89.9231313097571,83.3401165511889,76.3135368534335,60.5978347118252,84.2573618067275,73.3270757440223,77.5940116085254,67.1302176736209,80.2231478168383,78.9735710082504,69.7910978255359,74.9390363957855,81.0447845777941,64.4490864632728,89.9044221513849,79.3794655977227]；
wherein the first 16 terms represent excitation current weights of the drone; items 17-32 represent the x-axis coordinates of the drone; items 33-48 represent the y-axis coordinates of the drone; items 49-64 represent the z-axis coordinates of the drone;
9. coordinate information of the base station:
BS＝[580.2231478168383,613.9735710082504,0；900.2231478168383,513.9735710082504,0；402.5790742078009,313.9735710082504,0；754.4119156408130,419.9930552178037,0；808.9171236377485,270.6234775969576,0；513.6544401531348,646.5423736919012,0；242.4703352008421,553.7210297102178,0；419.9930552178024,241.8767360661453,0]；
wherein items 1-3 represent the three-dimensional coordinates of the first base station, items 4-6 represent the three-dimensional coordinates of the second base station, and so on.
10. Coordinate information of the eavesdropper:
eavesdropper＝[2534.247033520084,2554.0257907420780,1670.5441191564081]；
(III) simulation results
An example was randomly selected in the population to demonstrate the effectiveness of this scheme:
1.0.647907379438624 0.553201387166822 0.339921967618492 0.702167577858676 0.461521904146291 0.656735947729896 0.624894529194936 0.751874482967324 0.686840619684287 0.743387088058726 0.442132876034677 0.418750781838277 0.393205528306055 0.198685254132320 0.527439862945699 0.379322226185264 52.3118243808143 56.5569395814034 69.9392116188394 41.0041810121479 44.5546263528944 67.8988620688247 38.0338991128211 42.2761249893986 59.8019193515844 56.1008836668486 19.1998409999629 62.3904973165376 37.7528788165014 67.2517554738795 46.7460328651915 70.9442205586905 34.2764105549966 46.4859062483575 68.4749077650813 33.5477255684990 33.2180596150036 49.5887079164846 26.3267865130270 62.2081180314605 40.1380918273869 34.2356200920226 55.1199778978000 29.5982932993651 40.5126589222279 30.9075954369163 42.7238062671031 67.2204302035956 108.056758004521 108.061376373895 113.169461913981 112.918867118003 109.338217067741 109.489361792886 112.032778372626 109.065773950177 107.286306827652 110.313798328468 108.586560920282 108.494134816068 108.531168341016 110.779156193040 107.711443325424 105.867806725418 0.358034427533296 0.809078970672187 0.437631616666453 0.501902961608387 0.611948809342742 0.332695688895867 0.596120887225676 0.275943213808113 0.573882081928291 0.488940180261821 0.359077978757829 0.243729744451544 0.499696336518574 0.581814782127400 0.564851989000931 0.613618602321553 44.3549658047651 70.6743676109999 59.3658681107324 35.2758282788648 25.5356249686287 40.0218845578982 38.9975653357515 32.9852722569013 44.7426058655432 60.5807914165607 43.8648784592767 47.6746378552470 46.7135669754730 50.3334315502928 20.4346676439785 64.0323990680284 43.4646600071516 51.8243991650609 39.1135253680481 60.3396647853951 44.3977351265203 62.8060361889309 48.2842687825558 52.3789272120774 34.7034983009694 59.8976226540394 51.3672796520750 50.8491229664927 65.1440762860292 40.6333627786589 38.7844188072182 52.2016686329932 110.647133377094 109.329038576403 112.417048274969 109.712021583637 109.813407593617 106.943876346325 111.683679011006 111.265240451940 110.769566587524 111.737262188811 111.532686737128 112.666284333957 109.166100307889 108.820630881895 113.040153815868 110.764224262805 0.591389349014854 0.748701147923573 0.547256860944394 0.663125772547253 0.676284988044552 0.343081004459606 0.199688986975344 0.502690497906972 0.675703248790619 0.526384637964811 0.682434526975063 0.428295080568912 0.798008266300060 0.576852356057562 0.256783001915367 0.327437827833588 64.3294305250359 48.8132982099453 41.4672121935279 44.5895582749437 46.0692822077518 80.6744111170567 71.3878683601376 58.7068812613637 24.6793847544436 56.5360710935253 57.9096926862701 60.2445384078069 58.6668712288725 51.2674867849158 37.3223201000171 43.2345043756686 33.7540142676805 44.0999361644380 49.7068878811866 37.1545146309101 46.8818308834317 50.1602340167542 40.8075665213904 38.4957192605938 62.6469652838378 66.2700167082548 75.9058884766066 45.4789203457255 39.8313590781577 53.3407161413684 56.5096924412346 61.4567279693238 107.557909285033 107.335799665465 109.808610303404 112.221724190096 110.342526796396 112.423188109679 110.886068927826 109.215762802991 112.156052003750 108.496365680560 109.299672524832 110.632879784003 106.114738023276 113.293134860273 113.271456581404 109.353548784450 0.268833425137430 0.601182704647581 0.490276471648505 0.633104649459664 0.621650773734265 0.428494331445088 0.642284729974386 0.698507260196174 0.349211328072138 0.501485680614896 0.413876480945375 0.668081720476163 0.501089220753572 0.369966728093721 0.432404631619782 0.357013296700476 46.9782590616945 51.2671163557057 64.5050827215833 46.4994981039495 51.9653684445103 65.0256167224912 57.2611654521448 46.9695941138212 63.8015569661668 64.8304851636497 44.2473516370166 15.0909890124030 58.1465292117197 51.0249525015257 48.3680668311670 59.5954774567522 50.1796354385866 50.1961661608363 49.5179659516954 41.4027867113685 39.1874190051675 61.1744829602255 63.4167032471030 43.4899054418353 34.9725782002533 36.5787299184147 47.8219719872983 19.9946493481426 49.9996135769927 64.0574434240509 43.3222739989734 44.2789995760974 105.165615026341 109.066414513874 108.723952398347 107.833181258337 111.317622469870 110.783415417646 107.447542562873 110.956383042492 112.601685955979 106.520727538066 113.530399222382 108.731843286138 111.527202943854 109.601449186722 113.621398060746 107.697147612621 0.347031976331836 0.543572303748268 0.764646194796906 0.308653740455136 0.447425544203309 0.537274186776363 0.629819551373774 0.265223381484702 0.531925387371921 0.648416042187015 0.538964161648714 0.477086914211216 0.598969221677388 0.535927772956954 0.539531422965487 0.584491352357998 45.7327830027798 56.3489658552642 56.0885908110727 62.9122346308487 61.8084701151307 60.7792781288780 61.1769166673367 56.3766349423776 61.2352125183968 44.7570092629724 39.3910726699367 73.7210674812364 55.3632050147819 57.0543107372590 36.9319699666312 28.5313378715471 61.5546909488093 33.5031430398140 35.4497230510516 49.6844700573252 45.0136887503243 20.2116149674749 44.3187204886523 56.3144887273539 48.2047075524814 38.5702214889377 45.3459628006447 36.6033141384686 52.9459000907060 53.9458271490870 49.1470667627733 30.1463907147469 112.322771132449 114.534054085259 108.399612226624 113.812832228146 109.471762643592 113.018057797538 109.715512189993 111.904069657847 112.944874453176 110.551024354817 114.006421371094 108.893216536330 109.035983949846 110.993155265382 116.139102968412 108.242998469325 0.446926304958227 0.422420103048309 0.526857765515390 0.549822097089250 0.591264884174443 0.275849159015416 0.313496114419833 0.344018779180497 0.238270927630402 0.391955764195281 0.387212934026438 0.556552397660957 0.363185292299233 0.740899246135922 0.386551628262251 0.395547746318850 72.9756467995432 39.4717426560303 25.1616894599137 55.2100313002535 48.7986034483299 46.9721857273502 57.6065861068375 53.0092495733979 43.0671923515808 67.5755879740426 42.7326867310065 33.7351093065569 38.5411849613164 60.0324727176442 52.2626476971511 43.2577333755380 42.0826048723248 69.3323763241208 65.0020381176623 48.3995908467006 57.7377864847807 39.9008037845262 50.6217860393919 47.6670451328144 63.7709151986140 62.4399616587474 64.9525299085237 46.5670178838331 53.0187990153329 52.2063859307167 35.4494609700146 44.9373746062818 114.624532262547 106.360827530321 107.063521561093 109.552641828593 113.332754504831 112.496709895094 107.008986027428 111.132404349987 107.397970651933 112.002639400353 114.846865783479 112.027761851077 107.711879944691 114.574392204314 110.323538541937 108.593566881471 0.443190519962141 0.264253468453701 0.803721623664142 0.320092130501574 0.545486572427172 0.761358613965589 0.632494974690846 0.559766529011758 0.439525845061868 0.436651147701905 0.718774661241170 0.521916129673007 0.418681969655850 0.583627949847253 0.730270079989681 0.451824321692174 59.2089424274206 45.7146455613179 50.1309839644559 55.4467882455196 42.0624075484376 47.9805384410750 46.8456429644092 25.4823058524988 58.0328535785372 32.4720497636497 66.8636164323121 53.2243429498649 66.2690308791849 51.5001859410395 34.8903173063010 59.5275582813650 39.4088318843761 52.6771059324874 60.6166159213270 48.3979056595211 79.5729688194494 49.5402997232260 49.4658688592913 34.7274676982077 53.9952859500897 72.3016726135494 44.2773853806641 44.7558629348753 58.4420326444033 36.9791587313576 51.8535667309859 67.7391453542550 112.101373799953 105.619726633609 108.614070481476 109.928526601934 110.037506013389 107.335574001449 110.617198715507 108.850311514773 107.757323761858 109.716157537028 107.092475551104 107.939379395739 110.369606620028 109.843717018942 110.239485800184 109.414693280933 0.732108291763698 0.257922453804334 0.520986213516605 0.296819965551859 0.531990470901699 0.667900447175836 0.598643651983319 0.314718216573038 0.432544547086741 0.414692686269118 0.663135944287953 0.537585227266209 0.607203941656811 0.515294747944977 0.378813757317247 0.576741176000765 30.4620799109775 58.1143103656068 54.0901844058039 55.0260343399368 40.4570607931632 54.9440566165778 54.6938838332920 51.4420659997745 51.4225130534006 39.8104228664582 26.3946676448318 60.5616989397671 68.5499661015430 45.7179136203255 49.0984362468729 46.7977621874968 38.5676851275393 68.8617372731852 41.7576159254384 57.3209914549790 50.1930821062706 43.0198278764074 45.1766219721173 52.1818487245213 51.4368742794672 63.0258291667641 48.3301497925310 45.1108432911503 60.4047655616662 64.3849466392211 66.5982115139460 36.5134865728088 109.156650847486 106.968120363005 108.931719125807 106.239922420757 108.455171923353 116.366366628231 111.944109688050 110.376281594405 112.124677676475 107.103356205683 110.465468350588 109.426295526306 109.932436051514 109.915781191725 107.396150027283 110.356757648670 7 6 4 2 5 38 1
wherein items 1-64 represent excitation currents when the drone swarm communicates with the first base station, and three-dimensional coordinates of 16 drones, items 65-128 represent excitation currents when the drone swarm communicates with the second base station, and three-dimensional coordinates of 16 drones, and so on, items 449-512 represent excitation currents when the drone swarm communicates with the eighth base station, and three-dimensional coordinates of 16 drones, and items 513-520 represent communication orders of the base stations.
Based on the above data, three objective function values are achieved:
eavesdropper direction signal-to-noise ratio: 0.158201693200681 (-8.0078 dB);
base station directional signal-to-noise ratio: 12.110650889256902 (10.8317 dB);
unmanned aerial vehicle total energy consumption: 1.759851083342175 × 10 5 ；
The information of the excitation current weight and the coordinate information of the unmanned aerial vehicle is obtained after optimization and when the unmanned aerial vehicle is communicated with the base station.
The three objective function values achieved without optimization are:
eavesdropper direction signal-to-noise ratio: 0.179506435928853; (-7.4591 dB);
base station directional signal-to-noise ratio: 3.459241040827076 (5.3898 dB);
unmanned aerial vehicle total energy consumption: 2.786511460251500 × 10 5 ；
By comparing the objective function value of the optimization result with the objective function value obtained without optimization, it can be seen that: the NSGA-II algorithm can solve the planned multi-objective optimization problem, and through iteration layer by layer, the optimal communication sequence of the same base station and the optimal position and the optimal excitation current weight of the unmanned aerial vehicle when communicating with different base stations can be finally selected, so that the aims of reducing the signal-to-noise ratio of the eavesdropper direction, improving the signal-to-noise ratio of the base station direction and reducing the energy consumption of the unmanned aerial vehicle are fulfilled.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. An unmanned aerial vehicle swarm security communication method is characterized by comprising the following steps:
step 1, acquiring the number and the position of unmanned aerial vehicles standing by above a monitoring area, the number and the position of remote base stations needing communication and the position of an eavesdropper; determining the number of unmanned aerial vehicles forming the virtual array antenna for transmitting information;
step 2, the ground user selects an unmanned aerial vehicle from the unmanned aerial vehicle swarm as a first communication unmanned aerial vehicle;
step 3, selecting a plurality of second communication unmanned aerial vehicles by the first communication unmanned aerial vehicle;
the first communication unmanned aerial vehicle and the plurality of second communication unmanned aerial vehicles jointly form a virtual array antenna;
step 4, determining the optimal communication sequence of the remote base station, the optimal position of each unmanned aerial vehicle in the virtual array antenna and the optimal excitation current weight by taking the maximum sum of the signal-to-noise ratios of the base stations communicated by the virtual array antenna, the minimum sum of the signal-to-noise ratios of the eavesdropper when the virtual array antenna is communicated with each base station and the minimum energy consumption of the unmanned aerial vehicle in the flight process as optimization targets;
step 5, after the unmanned aerial vehicle in the virtual array antenna moves to the optimal position, the ground user transmits communication data to the first communication unmanned aerial vehicle, the first communication unmanned aerial vehicle transmits the communication data to the plurality of second communication unmanned aerial vehicles respectively, and the communication data are transmitted to the remote base station in a virtual array antenna mode according to the optimal communication sequence and the optimal excitation current weight;
in step 4, the optimum communication order of the remote base station, the optimum position of each drone in the virtual array antenna, and the optimum excitation current weight are determined based on the NSGA-ii algorithm, which includes the following steps:
step a, randomly generating a first generation population, wherein each element in the first generation population comprises a continuous solution and a discrete solution:
the continuous solution corresponds to the position and the excitation current weight of each unmanned aerial vehicle in the virtual array antenna, and the discrete solution corresponds to the communication sequence of the remote base station;
b, calculating an objective function value corresponding to each element in the first generation population;
c, selecting a plurality of elements from the previous generation population to cross each other by adopting a random method to generate a cross population, and selecting a plurality of elements to perform variation to generate a variation population;
d, combining the generated cross and variation populations with the previous generation population, carrying out non-dominated sorting on the combined population to obtain the level of a solution, calculating the congestion distance of each element, sorting the populations according to the sorting rule of ascending order of the level of the solution and descending order of the congestion distance, and reserving the top N POP An element; circularly executing the steps c-d until reaching the specified circulation times to obtain an optimal solution set;
and e, selecting the element which can realize the maximum total signal-to-noise ratio of the direction of the communication base station from the optimal solution set as a final solution.
2. The drone swarm security communication method of claim 1, wherein in step 2, the ground user calculates k distances d between the ground user and each drone above the monitoring area k And selecting the minimum d i The unmanned aerial vehicle corresponding to the value is used as the first communication unmanned aerial vehicle; wherein:
in the formula (x) G ,y G ,z G ) And
and respectively representing the three-dimensional coordinates of the ground user needing to transmit information to the remote base station and each unmanned aerial vehicle k above the monitoring area.
3. The drone swarm security communication method of claim 2, wherein before the first communicating drone transmits the communication data to the plurality of second communicating drones, respectively, in step 5, further comprising:
the first communication unmanned aerial vehicle initiates a communication request to the second communication unmanned aerial vehicle;
and the second communication unmanned aerial vehicle returns a message for confirming the establishment of the connection after receiving the communication request.
4. The drone swarm security communication method according to claim 1 or 3, further comprising, at the step 4:
the optimization objective function is established as follows: minF = { -f 1 ,f 2 ,f 3 }；
Wherein:
in the formula (I), the compound is shown in the specification,
indicating the position of the drone or drone,
represents the excitation current weight of the drone,
representing the order of the communication base stations, N BS Representing the number of base stations, f 1 Representing the sum of the signal-to-noise ratios of the base stations, f 2 Sum of signal-to-noise ratios, f, representing an eavesdropper 3 The energy consumption of the unmanned aerial vehicle is shown,
representing the signal-to-noise ratio, SNR, of the jth base station in communication with the virtual array antenna KE-j Representing the signal-to-noise ratio of an eavesdropper while communicating at the jth base station, E i,j And the energy consumption of the ith unmanned aerial vehicle in communication with the jth base station is shown.
5. The unmanned aerial vehicle swarm security communication method of claim 4, wherein a model for calculating the signal-to-noise ratio of the base station is as follows:
wherein the content of the first and second substances,
denotes a distance between the virtual array antenna and the jth base station, alpha denotes a path loss exponent, P CB Represents the total transmission power of the virtual array antenna,
representing the constant path loss coefficient, σ, of the virtual array antenna and the jth base station 2 Representing the noise power;
represents the antenna gain of the jth base station; (x) U ,y U ,z U ) A set of three-dimensional position coordinates representing a drone in a virtual array antenna,
indicating the direction of the jth base station,
which represents the elevation angle of the vehicle,
the azimuth angle is represented, and omega (theta, phi) represents the size of a unit far-field beam pattern of the unmanned aerial vehicle; eta ∈ [0,1]Representing virtual array antenna efficiency;
as a function of array factors for the base station direction of the jth station, I i Represents the excitation current weight of the ith drone in the virtual array antenna, λ represents the wavelength, k =2 π/λ represents the phase constant,
and representing the three-dimensional coordinates of the ith unmanned aerial vehicle in the virtual array antenna.
6. The unmanned aerial vehicle swarm security communication method of claim 5, wherein when the virtual array antenna communication is communicated with a base station, a computational model of signal-to-noise ratio of an eavesdropper is as follows:
wherein d is KE Representing the distance, P, between the virtual array antenna center and a known eavesdropper CB Representing the total transmission power of the virtual array antenna; k KE Constant path loss coefficient representing the direction of an eavesdropper; g KE Antenna gain (θ) representing eavesdropper direction KE ,φ KE ) Representing the direction of an eavesdropper, theta KE Indicates the elevation angle phi KE Denotes the azimuth angle, AF (θ) KE ,φ KE ) An array factor function representing the direction of a known eavesdropper.
7. The unmanned aerial vehicle swarm security communication method of claim 6, wherein the computational model of unmanned aerial vehicle energy consumption is:
where T represents the total flight time of the drone, v (T) represents the velocity of the drone at time T, m D Representing the weight of the unmanned aerial vehicle, g representing the acceleration of gravity, h (T) representing the height of the unmanned aerial vehicle at the end of flight, and h (0) representing the height of the unmanned aerial vehicle at the beginning; p (v) represents the energy consumption of the unmanned plane in two-dimensional horizontal space flight, P B And P I Is two constants respectively representing the profile of the blade and the induced power in the hovering state, v represents the speed of the unmanned aerial vehicle tip Representing the tip speed, v, of the rotor blade 0 Indicating average rotor blade induced velocity in hover, d 0 Denotes fuselage drag ratio, ρ denotes air density, s denotes rotor blade stiffness, and a denotes rotor disk area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210003107.6A CN114221694B (en) | 2022-01-04 | 2022-01-04 | Unmanned aerial vehicle swarm security communication method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210003107.6A CN114221694B (en) | 2022-01-04 | 2022-01-04 | Unmanned aerial vehicle swarm security communication method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114221694A CN114221694A (en) | 2022-03-22 |
| CN114221694B true CN114221694B (en) | 2023-01-03 |
Family
ID=80707790
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210003107.6A Active CN114221694B (en) | 2022-01-04 | 2022-01-04 | Unmanned aerial vehicle swarm security communication method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114221694B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115133976A (en) * | 2022-06-17 | 2022-09-30 | 深圳市道通智能航空技术股份有限公司 | Unmanned aerial vehicle networking communication system and method |
| CN117376937B (en) * | 2023-12-05 | 2024-03-19 | 吉林大学 | Unmanned aerial vehicle array thinning method for guaranteeing safety communication |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104035816A (en) * | 2014-05-22 | 2014-09-10 | 南京信息工程大学 | Cloud computing task scheduling method based on improved NSGA-II |
| CN112996117A (en) * | 2021-02-02 | 2021-06-18 | 清华大学 | Safe communication method and device in satellite unmanned aerial vehicle cooperative coverage network |
-
2022
- 2022-01-04 CN CN202210003107.6A patent/CN114221694B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104035816A (en) * | 2014-05-22 | 2014-09-10 | 南京信息工程大学 | Cloud computing task scheduling method based on improved NSGA-II |
| CN112996117A (en) * | 2021-02-02 | 2021-06-18 | 清华大学 | Safe communication method and device in satellite unmanned aerial vehicle cooperative coverage network |
Non-Patent Citations (2)
| Title |
|---|
| "A Joint Optimization Approach for UAV-enabled Collaborative Beamforming";Yanheng Liu等;《 2021 IEEE Symposium on Computers and Communications (ISCC)》;20211215;全文 * |
| "Physical Layer Secure Communications Based on Collaborative Beamforming for UAV Networks: A Multi-objective Optimization Approach";Jiahui Li等;《IEEE INFOCOM 2021 - IEEE Conference on Computer Communications》;20210726;第三部分至第5部分 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114221694A (en) | 2022-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mkiramweni et al. | A survey of game theory in unmanned aerial vehicles communications | |
| Cao et al. | Edge–cloud resource scheduling in space–air–ground-integrated networks for internet of vehicles | |
| Wang et al. | Task offloading for post-disaster rescue in unmanned aerial vehicles networks | |
| CN110673635B (en) | Unmanned aerial vehicle three-dimensional trajectory design method based on wireless energy transmission network | |
| Wang et al. | Task offloading and trajectory scheduling for UAV-enabled MEC networks: An optimal transport theory perspective | |
| CN114221694B (en) | Unmanned aerial vehicle swarm security communication method | |
| Luo et al. | Stability of cloud-based UAV systems supporting big data acquisition and processing | |
| Sun et al. | Energy efficient collaborative beamforming for reducing sidelobe in wireless sensor networks | |
| Shamsoshoara et al. | An autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations | |
| Alam et al. | Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey | |
| Sun et al. | Secure and energy-efficient UAV relay communications exploiting collaborative beamforming | |
| Hou et al. | Fog based computation offloading for swarm of drones | |
| CN113890588B (en) | Unmanned aerial vehicle relay communication method based on virtual array antenna cooperative beam forming | |
| He et al. | Towards 3D deployment of UAV base stations in uneven terrain | |
| Wang et al. | Joint power and QoE optimization scheme for multi-UAV assisted offloading in mobile computing | |
| CN113485409B (en) | Geographic fairness-oriented unmanned aerial vehicle path planning and distribution method and system | |
| Ghazzai et al. | Mobility and energy aware data routing for UAV-assisted VANETs | |
| Zhang et al. | Aerial edge computing: A survey | |
| Zhang et al. | Deep reinforcement learning for aerial data collection in hybrid-powered NOMA-IoT networks | |
| Vashisht et al. | Software‐defined network‐enabled opportunistic offloading and charging scheme in multi‐unmanned aerial vehicle ecosystem | |
| Wang et al. | Trajectory optimization and power allocation scheme based on DRL in energy efficient UAV‐aided communication networks | |
| Gupta et al. | Multi-UAV deployment for NOMA-enabled wireless networks based on IMOGWO algorithm | |
| Zhuo et al. | [Retracted] UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm | |
| Dang et al. | Low-latency mobile virtual reality content delivery for unmanned aerial vehicle-enabled wireless networks with energy constraints | |
| Xia et al. | Multiagent collaborative learning for uav enabled wireless networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |