CN115801095A

CN115801095A - Air-ground relay communication control method for unmanned aerial vehicle cluster application

Info

Publication number: CN115801095A
Application number: CN202211296516.6A
Authority: CN
Inventors: 尹栋; 王祥科; 段碧琦; 杨璇
Original assignee: National University of Defense Technology
Current assignee: National University of Defense Technology
Priority date: 2022-10-21
Filing date: 2022-10-21
Publication date: 2023-03-14

Abstract

The invention discloses an air-ground relay communication control method for unmanned aerial vehicle cluster application, which comprises the following steps: s01, modeling the communication process of the unmanned aerial vehicle; s02, acquiring spatial distribution of task points, judging whether current air-ground communication conditions need relaying or not, and determining the number of unmanned aerial vehicles needing relaying and the spatial positions of the unmanned aerial vehicles needing relaying by taking the minimum relay nodes as targets if the current air-ground communication conditions need relaying; s03, acquiring a task point time sequence in the cluster, constructing an objective function by taking the relay communication income of the maximized unmanned aerial vehicle to the multi-task points as a target, constructing a dynamic relay unmanned aerial vehicle task decision model based on the objective function and required constraint conditions, and obtaining a planning result of a relay task by solving the dynamic relay unmanned aerial vehicle task decision model. The method has the advantages of simple implementation method, low complexity, strong safety and stability, capability of eliminating task point conflicts among the multi-relay unmanned aerial vehicles and minimum quantity of relay unmanned aerial vehicles required by the cluster tasks of air-ground communication.

Description

Air-ground relay communication control method for unmanned aerial vehicle cluster application

Technical Field

The invention relates to the technical field of unmanned aerial vehicle cluster communication control, in particular to an air-ground relay communication control method for unmanned aerial vehicle cluster application.

Background

The unmanned aerial vehicle cluster has the characteristics of distributed unmanned aerial vehicle nodes and flying across task areas in the application process, and the unmanned aerial vehicle establishes communication with a ground control station and other unmanned aerial vehicles by virtue of a data link in the task execution process, transmits service data and task information, and maintains cluster organization architecture and integral task execution. However, the distribution range of each task point is wide and distributed, and communication is not smooth due to environmental influences such as long distance (non-line-of-sight), terrain, buildings and the like, so that the air-ground communication between the cluster and the ground control station cannot be guaranteed. Therefore, the problem that air-ground direct information transfer is blocked can be well solved by introducing the relay unmanned aerial vehicle.

Compared with the communication based on a ground base station and a satellite system, as shown in fig. 1, the unmanned aerial vehicle relay communication has obvious advantages: firstly, rapid deployment can be realized, and by means of the rapid maneuvering characteristic of the medium and low altitude unmanned aerial vehicle, a short-range line-of-sight and medium and long-range non-line-of-sight communication link can be established under many conditions, so that the communication performance between communication nodes is improved; secondly, the pose is convenient to adjust, the motion and the pose of the aerial unmanned aerial vehicle are dynamically adjusted to adapt to the change of a communication environment (such as terrain shielding, attenuation in rainy days, communication noise or interference and the like), and the environmental adaptability of a communication link can be improved; finally, the maintenance cost is low, the operation and maintenance cost of the unmanned aerial vehicle system is lower, the taking-off and recovery are flexible, the deployment and the deployment operation can be carried out at any time, and the unmanned aerial vehicle system is suitable for tasks which occur unexpectedly or have short duration.

Unmanned aerial vehicle plays important effect in the aspect of supplementary wireless communication, has the typical application of three aspects: (1) area coverage and emergency communication: deploying drones to assist existing communication infrastructure to provide seamless wireless coverage in service areas, application scenarios of which include infrastructure damage due to natural disasters and base station overload in extremely congested areas; (2) remote communication: the UAV communication relay provides wireless connection between two remote users or user groups without reliable direct connection links; (3) Information collection and distribution, drone-assisted information distribution and data collection, dispatching drones to distribute to a large number of distributed wireless devices (e.g., wireless sensors in a farm field) or collecting delay tolerant messages from wireless devices. The unmanned aerial vehicle cluster is wide in task execution area range, communication between the ground control station and the task unmanned aerial vehicle is likely to be blocked due to too far distance or terrain, buildings and the like in the middle, even the air-ground control communication is interrupted, and at the moment, the command control message and the unmanned aerial vehicle state information are forwarded by means of the relay communication effect of the relay unmanned aerial vehicle.

However, due to characteristics of the unmanned aerial vehicle such as the operating environment and the motion mode, the unmanned aerial vehicle is affected by many factors such as terrain, weather, motion attitude and speed, signal interference and the like during the relay task of ground, sea and air communication, and therefore, a great challenge is faced to the relevant research of the relay communication of the unmanned aerial vehicle. In the prior art, when the mission planning and multi-machine online coordination of the unmanned aerial vehicle are performed, the unmanned aerial vehicle communication is usually restricted by only setting a distance and does not go deep into a channel model, and when the unmanned aerial vehicle cluster coordinates missions, the position change also causes the change of the communication topology of the inter-machine network, so that when the unmanned aerial vehicle cluster is controlled, the mission planning, coordination control, flight control and the like obtained in advance or online are not suitable for the changed inter-machine network communication topology, and further, the mission points between the unmanned aerial vehicles may conflict.

For relay communication planning in unmanned aerial vehicle cluster application, in the prior art, task point conflicts among multi-relay unmanned aerial vehicles are difficult to eliminate, and the number of relay unmanned aerial vehicles required by cluster tasks is large, so that an air-ground relay communication control method oriented to unmanned aerial vehicle cluster application is urgently needed, so that the task point conflicts among the multi-relay unmanned aerial vehicles can be eliminated in air-ground communication, and meanwhile, the relay unmanned aerial vehicles of cluster tasks can be reduced as much as possible.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the air-ground relay communication control method for the unmanned aerial vehicle cluster application, which is simple in implementation method, high in efficiency and strong in flexibility, and can eliminate task point conflicts among multiple relay unmanned aerial vehicles and realize air-ground communication in the process that the least relay unmanned aerial vehicles guarantee cluster tasks.

In order to solve the technical problems, the technical scheme provided by the invention is as follows:

an air-ground relay communication control method for unmanned aerial vehicle cluster application comprises the following steps:

s01, modeling unmanned aerial vehicle communication, and constructing a path loss calculation model between an unmanned aerial vehicle and a ground control station, a communication model between the unmanned aerial vehicle and the ground station, and a channel model between the unmanned aerial vehicle and a relay unmanned aerial vehicle;

s02, performing static analysis on relay demands based on space distribution of task points, namely acquiring the space distribution of the task points, judging whether the current air-ground communication condition needs relaying or not according to the acquired space distribution and the model constructed in the step S01, and determining the number of unmanned aerial vehicles needing relaying and the space position where the unmanned aerial vehicles need to be accessed by relaying by taking the minimum relay nodes as targets if the current air-ground communication condition needs relaying;

s03, relay task planning based on cluster unmanned aerial vehicle task time sequence: the method comprises the steps of obtaining a task point time sequence in a cluster, constructing an objective function by taking the relay communication income of a maximized unmanned aerial vehicle to a multi-task point as a target, constructing a dynamic relay unmanned aerial vehicle task decision model based on the objective function and a required constraint condition, and obtaining a planning result of a relay task by solving the dynamic relay unmanned aerial vehicle task decision model.

Further, in step S1, the position of the ground control station is (x) _bs ,y _bs ,h _bs ) The coordinate of drone m is (x) _m ,y _m ,h _m ) Elevation angle of unmanned plane m to ground control station is

The expected path loss Lambda calculation model of the unmanned aerial vehicle and the ground control station is constructed as follows:

wherein, A = eta _LoS -η _NLoS ，

η _LoS 、η _NLoS Additional path loss, f, of line-of-sight communication link LoS and non-line-of-sight communication link NLoS, respectively _c Is the carrier frequency of the radio wave, d _mb Is the distance between the unmanned plane m and the ground control station,

c is the light wave speed, and alpha and beta are environmental parameters;

the total antenna gain calculation expression is:

the method comprises the following steps that k is total antenna gain, F (theta) is antenna power gain, theta is an angle caused by the height difference of a receiving and transmitting antenna, and eta is an included angle formed by the receiving and transmitting antenna due to the change of the attitude of the unmanned aerial vehicle;

according to the signal path Loss and the antenna gain, constructing and obtaining a total Loss calculation expression of the air-ground channel signal, wherein the total Loss calculation expression is as follows:

Loss＝Λ-log 10(F(θ)·cosη)。

further, in step S01, the communication model between the unmanned aerial vehicle and the ground station is constructed as follows:

wherein, A = eta _LoS -η _NLOS ，B＝20log f _c +20log(4π/c)+η _NLOS ，SNR _th Is a preset signal-to-noise ratio threshold;

the channel model between the unmanned aerial vehicle and the relay unmanned aerial vehicle is as follows:

further, in step S02, the optimization objective is that the relay unmanned aerial vehicles with the least deployment meet the communication requirements between all task points and the ground control station, the decision variables are the number of the relay unmanned aerial vehicles and the corresponding relay deployment positions, the position of the relay unmanned aerial vehicle and the association between the relay unmanned aerial vehicle and the corresponding task area are decided by taking the minimum number of the relay unmanned aerial vehicles as a static coverage optimization objective, and the formed optimization problem is specifically:

wherein, C ₁ Boolean decision variable, C, for the current problem ₂ The communication between the task point and the ground control station can be relayed only by one unmanned aerial vehicle, C ₃ Representing the upper bound of the number of task points relayed by the relay unmanned aerial vehicle at the same time, C ₄ Defining that a mission point cannot be associated with a relay drone not adopted, C ₅ And C ₆ The distance requirement between the relay unmanned aerial vehicle and the associated task point and between the ground control stations is represented, L is a preset numerical value and can enable alpha to be achieved _mk And u _m When the distance is equal to 0, no constraint is imposed on the m distance of the relay unmanned aerial vehicle, and x is _re,m ,y _re,m Abscissa and ordinate indicating relay position, u = [ u = [) _m ] _1×K Set of states, u, representing candidate relay drones _m =1 denotes that the mth candidate relay drone is adopted, u _m =0 then indicates that the alternative drone is deleted, R _bs Denotes a communication radius, α = [ α ] _mk ] _K×K Represents the correlation matrix, alpha, between the relay drone and the mission point _mk =1 indicates that the mth relay drone performs relay communication for the kth task point, that is, the task point k is within the communication range of the relay drone m, N _max The number of the task unmanned aerial vehicles which are connected at most at the same time and are one relay unmanned aerial vehicle is represented, x _ts,k ,y _ts,k The abscissa and ordinate of the k-th task point are shown.

Further, in step S03, the unmanned aerial vehicle relays are simultaneously arranged for all task points according to the deployment method of the static relays, so as to obtain unmanned aerial vehicle relay static deployment points; calculating a communication time window of the relay static deployment point, allocating the relay unmanned aerial vehicle to sequentially traverse the relay static deployment point according to the time window requirement, and integrating the communication income of the task point, the path of the relay unmanned aerial vehicle, the time for the relay unmanned aerial vehicle to reach the corresponding task point and the energy consumption factor of the relay unmanned aerial vehicle to construct an objective function by taking the relay communication income of the maximized unmanned aerial vehicle to the multi-task point as a target.

Further, an objective function constructed by maximizing the relay communication income of the unmanned aerial vehicle to the multi-task point is as follows:

wherein the function c _mn (β _m ,p _m ,τ _m ) Relaying unmanned aerial vehicle for mth frame according to path p _m Task time τ _m The total energy consumption of the flight path is subtracted from the relay communication income obtained by reaching the corresponding relay deployment point n,

for relaying the correspondence between the unmanned aerial vehicle m and the static coverage deployment point, beta _mn =1 denotes that the nth static coverage deployment point is allocated to the mth relay drone, L _m For the total number of static coverage deployment points allocated to relay drone m,

relaying the flight path of the drone for the mth frame, p _mk To relay the static coverage deployment point to which the drone belongs,

is corresponding to p _m Time of arrival, τ, at each static coverage deployment point _mk Indicating relay unmanned aerial vehicle m to reach task point p _mk The time of (d);

the constraint conditions include:

τ _mk ≤O _re,n1 ,τ _m(k+1) ≤O _re,n2 ,

(O _re,n2 -C _re,n1 )*v≥||(x _re,n1 -x _re,n2 ,y _re,n1 -y _re,n2 )|| ₂ ,

p _mk ＝n1,p _mk+1 ＝n2,

wherein the time window starts O _re,n For corresponding task point sets Ta _n The earliest of the start of all time windows, the end of the time window C _re,n Is Ta _n The latest time of the end points of all task time windows in the system represents the horizontal and vertical coordinates of the relay position.

Further, in the step S03, a consistency-based beam set algorithm CBBA is used to solve the dynamic relay drone task decision model, and two stages of task bundle construction and conflict elimination are continuously executed in an iterative manner until all tasks are distributed and completed in the solving process, where in the task bundle construction stage, each relay drone creates a task bundle y to store its own distributed relay task point, relay task point execution sequence and task execution time, and saves distributors corresponding to all tasks and gains brought by the tasks, and continuously updates in the iterative process; and in the conflict elimination stage, the adjacent relay unmanned aerial vehicles communicate with each other, the respective stored successful bidder list and the successful bid value list are compared, and the successful bid value list is updated according to the sequence of the timestamps.

Further, in the stage of task bundle construction, the relay unmanned aerial vehicle continuously adds the relay task points into the task bundle until the maximum relay task point number L of the relay unmanned aerial vehicle is reached _max Each relay unmanned aerial vehicle maintains a task bundle list, a path task point list and a path task point arrival time list; according to each listRespectively calculating the income increment which is caused by relaying the communication task at each insertion position, and taking the position with the maximum income increment as the path sequence corresponding to the new relay task point n, wherein the task bundle b of the relay task point n added into the relay unmanned aerial vehicle m is calculated according to the following formula _m The increment of the income brought in:

wherein, | p _m L is the number of assigned relay drone mission points,

indicating the insertion of a new relay task point n into the path p _m The total gain before the upper delta path point,

indicates following path p _m The total profit that the sequential execution of relay tasks will bring;

calculate relay drone m along p _m The expression for the total benefit of executing the task is:

wherein q is ₀ Coordinates of departure point, v, for relaying the drone ₀ For relaying cruising speed of unmanned aerial vehicle, s _mk (τ _mk ,b _mk ) For relaying drone m at τ _mk The time reaches relay task point b _mk The gains obtained in time; s _mk (τ _mk ,b _mk ) Is determined by three factors, the first being the time to reach the task point τ _mk The second factor is the relay communication value Val of the mission point itself _mk Said relay communication value Val _mk And relay task point b _mk Sum of communication time of all task points covered

In positive correlation, the third factor is a penalty term, so that the unmanned aerial vehicle m can relay the task point b from the last relay task point _m(k-1) Reach the current task point b _mk The fuel consumed is proportional to the distance between the two task points.

Further, in the stage of conflict elimination, the relay unmanned aerial vehicle adopts a consistency strategy to converge a bid-winning list of the relay task, and allocates a relay task point to be reached for the relay unmanned aerial vehicle according to the bid-winning list, so that communication relay service is performed on the task unmanned aerial vehicle of the task point corresponding to the relay task point; wherein when relay unmanned aerial vehicle m ₁ And relay m ₂ With direct links in between, i.e.

Relay m ₁ And relay m ₂ The last communication time is the message receiving time t _r (ii) a When no direct connection channel exists between the relay unmanned aerial vehicle m and the relay unmanned aerial vehicle m, the relay unmanned aerial vehicle m is searched ₁ All the rest of the directly connected relay unmanned aerial vehicles

Finding the nearest timestamp in the found relay unmanned aerial vehicle set C

When relay unmanned aerial vehicle m ₁ Slave relay m ₂ Update information, relay unmanned aerial vehicle m ₁ Stored winning bid winner vector

Bid and bid vector

And (5) fusion updating.

Further, in the collision elimination phase, if the unmanned aerial vehicle m is relayed ₂ In storage ofBid-winning ratio relay m of relay task point n ₁ Or one of the two relays has a stored winning bid for relay task point n is m, m ≠ m ₁ ∩m≠m ₂ And relay m ₂ The timestamp of the last received message about relay m is later than that of relay m ₁ Then relay unmanned aerial vehicle m ₁ Performing the update operation of the winning bid vector, and relaying m ₂ The bid price vector and the bid winner vector of the bid are correspondingly assigned to the relay m ₁ Bid-winning bid vector of

And winning bid vector

When relay unmanned aerial vehicle m ₁ Determine that the winner in task point n is self, and relay m ₂ If the winning bidder is relay i or null, no operation is executed, and relay m ₁ The winning bid vector quantity of (1) is kept unchanged, and when the winning bid persons stored by the two relay unmanned aerial vehicles conflict, the winning bid vector quantity of (m) is added to the relay m ₁ The two winning vectors are reset.

Compared with the prior art, the invention has the advantages that: according to the method, by considering the narrow-band real-time communication requirement between the unmanned aerial vehicle and the ground station and the broadband high-speed communication requirement during inter-station cooperative operation, firstly, the number of the unmanned aerial vehicles needing to be relayed and the space position where the unmanned aerial vehicles need to be relayed are determined based on the task point space distribution and with the minimum relay nodes as targets, then, an objective function is constructed by taking the relay communication benefit of the unmanned aerial vehicle to the multi-task point as the target according to the task timing sequence of the cluster unmanned aerial vehicle, and then, a dynamic relay unmanned aerial vehicle task decision model is constructed, relay planning is carried out by solving the dynamic relay unmanned aerial vehicle task decision model, the requirements of relay unmanned aerial vehicle path planning, online path adjustment and the like of cluster tasks can be met, continuous and stable transmission of the command information between the unmanned aerial vehicle and the ground and the inter-station cooperative information can be realized, task point conflict between the multi-relay unmanned aerial vehicles can be eliminated, and the number of the relay unmanned aerial vehicles needed by the cluster tasks of air-ground communication can be minimized.

Drawings

Fig. 1 is a schematic diagram of an unmanned aerial vehicle relay communication architecture.

Fig. 2 is a schematic key flow diagram of the air-ground relay communication control method for unmanned aerial vehicle cluster application according to the embodiment.

Fig. 3 is a schematic diagram of the principle of signal propagation of the drone-ground control station.

Fig. 4 is a pattern of the basic element.

Fig. 5 is a polar downward directional diagram of a monopole antenna.

Fig. 6 is a schematic diagram of the principle of the antenna factors affecting the received signal strength.

Fig. 7 is a schematic diagram of the principle of the task point full-time relay communication coverage.

Fig. 8 is a schematic diagram of the dynamic planning of the relay drone in the embodiment.

Fig. 9 is a schematic flowchart of relay deployment point allocation based on the CBBA algorithm in this embodiment.

Detailed Description

The invention is further described below with reference to the drawings and the specific preferred embodiments, without thereby limiting the scope of protection of the invention.

As shown in fig. 2, the steps of the air-ground relay communication control method for the unmanned aerial vehicle cluster application in this embodiment include:

s01, modeling a communication process of the unmanned aerial vehicle, and constructing a path loss calculation model between the unmanned aerial vehicle and a ground control station, a communication model between the unmanned aerial vehicle and the ground station, and a channel model between the unmanned aerial vehicle and a relay unmanned aerial vehicle;

s02, performing static analysis on relay demand based on spatial distribution of the task points, namely acquiring spatial distribution of the task points, judging whether the current air-ground communication condition needs relaying or not according to the acquired spatial distribution and the model constructed in the step S01, and determining the number of unmanned aerial vehicles needing relaying and the spatial position of the unmanned aerial vehicles needing relaying by targeting at least relay nodes if the current air-ground communication condition needs relaying;

s03, relay task planning based on cluster unmanned aerial vehicle task time sequence: the method comprises the steps of obtaining a task point time sequence in a cluster, constructing an objective function by taking the relay communication income of a maximized unmanned aerial vehicle to a multi-task point as a target, constructing a dynamic relay unmanned aerial vehicle task decision model based on the objective function and required constraint conditions, and obtaining a planning result of a relay task by solving the dynamic relay unmanned aerial vehicle task decision model.

Aiming at the relay communication requirement with a long time, under the conditions of multi-path effect of signal propagation, platform antenna gain, communication interference and the like, by considering the narrow-band real-time communication requirement between an unmanned aerial vehicle and a ground station and the broadband high-speed communication requirement during inter-vehicle cooperative operation, the number of the unmanned aerial vehicles needing to be relayed and the space positions where the unmanned aerial vehicles need to be accessed are determined on the basis of the spatial distribution of task points by taking the minimum relay nodes as targets, then an objective function is constructed by taking the relay communication benefits of the unmanned aerial vehicles to multi-task points as targets according to the task sequence of the cluster unmanned aerial vehicles, and then a dynamic relay unmanned aerial vehicle task decision model is constructed.

The unmanned aerial vehicle adopts the mode of wireless communication, and the propagation of radio wave is susceptible to various propagation media. When electromagnetic waves propagate in the air, energy is lost, power is reduced, and the multipath effect introduces intersymbol interference in transmission signals, so that the signal-to-noise ratio of a receiving party is reduced. In addition, when the transmitting and receiving antennas have a height difference or a certain angle, different gains are exerted on the received power of the signal. In the embodiment, the basic modeling of the air-ground communication of the unmanned aerial vehicle is firstly carried out, and the factors are considered during modeling so as to construct a channel model which is more consistent with the characteristics of the unmanned aerial vehicle.

As shown in fig. 3, the radio signal emitted by the drone is first propagated in free space to reach a low-altitude environment, and due to the influence of buildings, mountains, leaves, and the like, the signal is shaded and scattered, thereby causing extra signal loss in the air-ground communication link. That is, the path loss of the air-ground propagation signal is composed of two parts, the free space propagation loss and the additional loss caused by shadow scattering and other phenomena are gaussian distributed, and the mean value η of the additional loss is adopted in the modeling of the embodiment, rather than the random value of a certain experiment. The air-ground channel model average path loss (unit: dB) is, without considering the influence of small-scale fluctuations caused by rapid changes in the propagation environment:

L _ξ ＝FSPL+η _ξ (1)

where FSPL represents the free space propagation loss between the drone and the ground control station, ξ represents the propagation group and the additional path loss η depends to a large extent on the signal propagation group ξ to which it belongs.

To find the path loss spatial expectation of the drone with all users at elevation θ, the following rule applies:

wherein P (xi, theta) represents the probability of appearance of the xi signal propagation group when the elevation angle is theta, and L _ξ Is the signal path loss value of the ξ -th transmission group. The present embodiment follows the assumption of two propagation groups, corresponding strictly to line-of-sight LoS propagation conditions and non-line-of-sight NLoS propagation conditions, and therefore:

the NLoS link has a higher path loss than the LoS link due to shadowing effects and reflections of signals by obstacles. The probability of the occurrence of the line-of-sight communication link is related to the elevation angle and the environment, and the environment can be further divided into suburban areas, urban areas and high-density urban areas and is characterized by using the parameters alpha and beta. Thus, the line-of-sight link probability can be regarded as a continuous function of the elevation angle θ and the environmental parameters α and β, and can be approximated as a Sigmod function.

The probability of occurrence of the LoS link is:

the expected path loss Λ for the drone and the ground control station is expressed as:

Λ＝P(LoS,θ)×L _LoS +(1-P(LoS,θ))×L _NLoS (5)

wherein L is _LoS And L _NLoS The average path loss of the LoS link and the NLoS link, respectively.

Suppose the ground control station is located at (x) _bs ,y _bs ,h _bs ) Unmanned plane m coordinate is (x) _m ,y _m ,h _m ) Then the path loss (dB) can be expressed as:

wherein, f _c Is the carrier frequency of the radio wave, d _mb Is the distance between the unmanned plane m and the ground control station,

c is the speed of light wave, eta _LoS And η _NLoS The average additional path loss of the Los link and the NLoS link respectively.

Elevation angle of unmanned aerial vehicle m to ground control station is

Then:

wherein, A = eta _LoS -η _NLoS ,

Namely, in the embodiment, the expected path loss Λ calculation model of the unmanned aerial vehicle and the ground control station is constructed according to the above equation (7).

The present embodiment further constructs an antenna gain calculation model:

the radiation characteristic of the antenna can be reflected by the directional diagram of the antenna, and the directional diagram of the antenna generally represents the distribution of the power of electromagnetic waves radiated by the antenna in all directions. The spatial solid pattern of the basic array and the patterns of the two main surfaces (E surface and H surface) are shown in fig. 4, wherein (a) in fig. 4 is a solid pattern, (b) is an E surface pattern, and (c) is an H surface pattern. Unlike an ideal power antenna, the basic array has directivity; the vertical antenna directly above and below corresponds to θ =0 and θ = pi of the E-plane pattern, respectively, and the radio wave radiation intensity is 0. The radio wave radiation intensity is maximum when the horizontal side direction of the antenna corresponds to E-plane directional patterns theta = pi/2 and theta =3 pi/2; from the H-plane directional diagram in FIG. 4 (c), the intensity of the radio wave radiation and the intensity of the radio wave radiation are known

Independently, it is a circle in the plane perpendicular to the antenna.

In this embodiment, a monopole antenna is specifically adopted, and its basic directional diagram function is:

where F (θ) is the antenna power gain.

The resulting monopole antenna E-plane pattern in a specific application embodiment is shown in fig. 5, where the operating frequency is 900MHZ,

the monopole antenna E-plane pattern is completely symmetrical.

In the embodiment, two factors influencing the strength of the received signal are considered, and an included angle is formed between the receiving and transmitting antenna due to the theta angle caused by the height difference of the receiving and transmitting antenna and the change of the self attitude of the airplane. As shown in fig. 6, because the antenna length is small, θ is negligible compared with the distance between the drone, so this embodiment regards the antenna as a mass point, the θ angle is the angle between the central connecting line of the antenna and the vertical line, and the antenna gain formed by this angle can be obtained by the directional diagram function or the E-plane directional diagram of the antenna. When the receiving and transmitting antennas are not parallel and have a certain included angle eta, the electromagnetic wave is decomposed on the receiving antennas when propagating, and the power is changed into the original cos eta. Then the total antenna gain from both factors is:

combining the signal path Loss and the antenna gain, the total Loss (dB) of the air-ground channel signal is:

Loss＝Λ-log 10(F(θ)·cosη) (10)

the embodiment further constructs a communication model between the unmanned aerial vehicle and the ground station:

when the flying height of the unmanned aerial vehicle is H, the height of the ground control station is set to be 0, and all unmanned aerial vehicles with the horizontal distance from the ground control station have the same path loss L _th And the path loss of the unmanned plane with the horizontal distance less than r is less than L _th Therefore, the communication quality requirement (the signal-to-noise ratio is less than the SNR) is satisfied _th ) Within the corresponding communication radius of the ground control station. According to the air-ground channel model and the requirement of the task unmanned aerial vehicle on the lowest signal to noise ratio, the following results are obtained:

wherein, A = η _LoS -η _NLOS ，B＝20logf _c +20log(4π/c)+η _NLOS 。

A communication model between the unmanned aerial vehicle and the ground station is constructed according to the formula (11), and the coverage radius R of the ground control station to the plane with the height H can be easily obtained according to the formula (11) _bs 。

The embodiment further constructs a channel model between the unmanned aerial vehicle and the relay unmanned aerial vehicle:

the drone-drone channel is dominated primarily by line of sight (LoS). Despite the presence of multipath effects caused by ground reflections, these are negligible compared to the drone-ground channel or the ground-ground channel. Considering that the unmanned aerial vehicle flies on a plane with similar height or the same height, the height difference is almost negligible compared with the distance, the included angle theta formed by the height search of the antenna is approximately 0, and the received signal strength is almost not influenced, so that the following results are obtained:

L _uav-uav ＝20logd _uu +20logf _c +20log(4π/c)+η _LoS (12)

wherein d is _uu The distance between the unmanned aerial vehicle and the unmanned aerial vehicle.

In the large-scale fixed wing unmanned aerial vehicle cluster task execution, the communication link between the unmanned aerial vehicles does not consider the sheltering brought by the landform and the landform, and only the sight distance transmission is considered. The relay unmanned aerial vehicle and the task unmanned aerial vehicle are located on the same height plane, namely theta =90 degrees, the antenna gain is 1, and the signal-to-noise ratio of the unmanned aerial vehicle and the unmanned aerial vehicle is also required to be larger than a set threshold SNR (signal-to-noise ratio) when the unmanned aerial vehicle and the task unmanned aerial vehicle communicate with each other _th The maximum communication distance R between the unmanned planes on the same plane can be obtained _uav The channel model between the unmanned aerial vehicle and the unmanned aerial vehicle constructed by the embodiment is as follows:

in this embodiment, based on the model obtained by the above construction, relay demand analysis and relay planning are further performed.

In step S02 of this embodiment, the detailed steps of the relay demand analysis based on the spatial distribution of task points include:

the multiple drone clusters execute tasks at different task points sequentially according to time sequence, for example, as shown in fig. 7, a relay drone static coverage manner is adopted, and communication relay service is provided for all task points in a full time period. The task unmanned aerial vehicle needs to be within the communication coverage radius of the relay unmanned aerial vehicle corresponding to the task unmanned aerial vehicle, and the relay unmanned aerial vehicle also needs to be within the communication range of the ground control station. In this embodiment, for realizing relay demand analysis, the optimization target is that the relay unmanned aerial vehicles with the least deployment meet the communication requirements between all task points and the ground control station, and the decision variables are the number of the relay unmanned aerial vehicles and the corresponding relay deployment positions.

Assuming that K task points are on a plane with height H, the plane coordinate is x _ts ＝[x _ts,k ,y _ts,k ] ^T K =1, \\ 8230;. K indicates that the relay unmanned aerial vehicle is out of the communication range of the ground control station, the relay unmanned aerial vehicle is introduced to assist the communication between the mission unmanned aerial vehicle and the ground control station, enough K relay unmanned aerial vehicles are initially set, and the relay position is indicated as x _re ＝[x _re,m ,y _re,m ] ^T M =1, \8230;, K. Introducing vector u = [ u ] _m ] _1×K Represents the status of candidate relay drones, u _m =1 denotes that the mth candidate relay drone is adopted, u _m =0 then represents the deletion of this candidate drone. The adopted alternative relay unmanned aerial vehicle has to be in the communication radius R of the ground control station _bs The condition can be written as:

wherein (x) _bs ,y _bs ) And the coordinates are the plane coordinates of the ground control station. To make the above formula at u _m Not restrict relay unmanned aerial vehicle's position when =0, further convertible to:

where L is a constant sufficiently large.

It should be noted that, the communication device of the unmanned aerial vehicle is simple and limited, the communication radius between the unmanned aerial vehicle is generally smaller than the communication radius of the ground control station, however, when the unmanned aerial vehicle transmits a message to the ground control station, the communication distance from the unmanned aerial vehicle to the ground station can be greatly increased by virtue of the larger receiving antenna gain of the ground control station, and is larger than the communication distance between the unmanned aerial vehicle and the ground station, so that when the relay unmanned aerial vehicle is within the communication radius of the ground control station, the air-ground two-way communication can be realized.

Matrix α = [ α ] _mk ] _K×K Representing the association between the relay drone and the mission point, α _mk And =1 indicates that the mth relay drone performs relay communication for the kth task point, that is, the task point k is within the communication range of the relay drone m. The condition can be written as:

2.

to make the above formula at alpha _mk If =0, further written as:

3.

limited by the capacity of the communication channel of the unmanned aerial vehicle, one relay unmanned aerial vehicle is connected with N at most at the same time _max Individual task unmanned aerial vehicle.

In this embodiment, the static coverage optimization target is to minimize the number of relay drones, and to decide the positions of the relay drones and the association between the relay drones and the corresponding task areas, so that the following optimization problem may be formed:

wherein, C ₁ Is the boolean decision variable for that problem. C ₂ The communication between the task point and the ground control station can be relayed by one unmanned aerial vehicle only, C ₃ Representing the upper bound of the number of task points relayed by the relay unmanned aerial vehicle at the same time, C ₄ Defining that a mission point cannot be associated with a relay drone not adopted, C ₅ And C ₆ The distance requirement between the relay unmanned aerial vehicle and the associated task point and the ground control station is represented, L is a preset constant which is large enough, and alpha can be ensured _mk And u _m When the distance is equal to 0, no constraint is made on the m distance of the relay unmanned aerial vehicle.

The equation (18) includes quadratic terms and binary terms, which can form an MINLP mixed integer nonlinear programming problem, and in a specific application embodiment, the solution can be performed by using an interior point optimizer in a MOSEK optimization software package.

In step S03 of this embodiment, the detailed steps of performing relay planning based on task point timing are as follows:

s301, modeling of relay task time sequence problem

And each unmanned aerial vehicle in the cluster flies to each region in sequence according to the distributed subtask sequence to execute the task, the detection information is supposed to be returned when the unmanned aerial vehicle flies in the task region, and the detection information is not required to be returned when the unmanned aerial vehicle flies between the subtask regions in a cross-region manner. When the task unmanned aerial vehicle reaches the task point, the position of the task point is required to be within the range capable of communicating. Therefore, the communication time required by the mission point and the ground control station is different, and respective communication time windows are formed. Only in the task point time window, the corresponding relay unmanned aerial vehicle relays communication to the task unmanned aerial vehicle at the point. On the basis of the relay static coverage deployment point obtained in the step S02, by means of the fast mobility of the unmanned aerial vehicle, the unmanned aerial vehicle flies to the deployment point in the corresponding time window of the static coverage deployment point as required, so that the unmanned aerial vehicle can fly to a plurality of static coverage deployment points, relay communication is performed on a plurality of task points according to time sequence, and the number of relays is further reduced. As shown in fig. 8, there are three static deployment points, so that full-time relay communication coverage for 8 task points is achieved, and a relay dynamic coverage manner is adopted, so that the relay communication coverage can be reduced to two relay unmanned aerial vehicles, where the relay unmanned aerial vehicle 1 reaches the deployment point 1 in the time window of the static deployment point 1, and then reaches the deployment point 2 before the start of the time window of the static deployment point 2.

According to the deployment method of the static relays, the relays of the unmanned aerial vehicles are simultaneously deployed to all the task points, the static deployment points of the relays of the unmanned aerial vehicles are obtained, and the communication time windows of the static deployment points of the relays are calculated. Task point

Corresponding to the communication time window required to be communicated with the ground control station as O _ts,k ,C _ts,k ]Suppose the relay static deployment point n is V _n Each task point provides relay communication, and the corresponding task point set is recorded as Ta _n ＝{ta _n,v |v＝1,…,V _n ,1≤ta _n,v Less than or equal to K }. The time window of the deployment point n must include the time windows of all corresponding task points, and the time window is marked as [ O ] _re,n ,C _re,n ]Time window starting point O _re,n For corresponding task point sets Ta _n Starting point of all time windowsAt the earliest time, end of time window C _re,n Is Ta _n Recording the latest time of the end points of all task time windows as:

after the position and the time window of each relay static coverage deployment point are obtained, the relay unmanned aerial vehicle is distributed to sequentially traverse the relay deployment points according to the requirement of the time window, and the problem is immediately converted into a relay deployment point distribution problem with the time window.

When the objective function is constructed, the relay communication income of the unmanned aerial vehicle to the multi-task point is maximized, and factors such as the communication income of the task point, the path of the relay aircraft, the time for the relay to reach the corresponding task point, the energy consumption of the relay aircraft and the like are comprehensively considered. The global objective function is assumed to be the sum of local reward values of all relay unmanned aerial vehicles, and it is assumed that M relay unmanned aerial vehicles need to be dispatched to N static coverage deployment points at least, the set of relay unmanned aerial vehicles is denoted as V = {1,2, \8230;, M }, and the set of static coverage deployment points is denoted as Re = {1,2, \8230;, N }.

For relaying the correspondence between the unmanned aerial vehicle m and the static coverage deployment point, beta _mn =1 indicates that the nth static coverage deployment point is allocated to the mth relay drone. L is a radical of an alcohol _m For the total number of static coverage deployment points allocated to relay drone m,

relaying the flight path of the drone for the mth frame, p _mk For relaying the static coverage deployment point, p, to which the drone belongs _mk ∈Re。

Is corresponding to p _m Time of arrival, τ, at each static coverage deployment point _mk Indicating relay unmanned aerial vehicle m to reach task point p _mk The objective function is specifically defined as follows:

wherein the function c _mn (β _m ,p _m ,τ _m ) Relaying the unmanned aerial vehicle for the mth frame according to the path p _m Task time τ _m And subtracting the total energy consumption of the flight path from the relay communication income obtained by reaching the corresponding relay deployment point n.

The constraint conditions constructed in this embodiment are specifically:

(1) Each relay task point has and can only be allocated to one relay unmanned aerial vehicle, namely:

(2) Each relay drone is allocated L at most _max Each relay task point is:

(3) The relay unmanned aerial vehicle has to be at one static deployment point p on the path _mk End of time window O _re,n1 Back-away, at the next static deployment point p _mk+1 The unmanned aerial vehicle can fly in a flight path within two time differences, wherein the flight path reaches from the starting point of the time window, and the distance between the front relay deployment point and the rear relay deployment point is necessarily greater than that between the front relay deployment point and the rear relay deployment point:

wherein v is the airspeed of relaying unmanned aerial vehicle.

And (3) integrating the objective function and the constraint condition to construct a dynamic relay unmanned aerial vehicle task decision model, namely:

wherein x is _re,n1 、y _re,n1 Respectively represents the abscissa and ordinate, x, of the relay static deployment point n1 _re,n2 、y _re,n2 Respectively represents the abscissa and ordinate of the relay static deployment point n2, O _re,n1 、O _re,n2 Respectively representing the clock window end points of the relay static deployment points n1 and n2, wherein the correspondence between n1 and n2 is p _mk 、p _mk+1 。

And S302, solving the dynamic relay planning model.

In this embodiment, a consistency-based Bundle set Algorithm (CBBA) is specifically adopted to solve the dynamic relay unmanned aerial vehicle task decision model, and two stages of task Bundle construction and conflict elimination are continuously and iteratively executed in a solving process until all tasks are completely distributed. Aiming at the problem of multi-task allocation, the CBBA algorithm reduces the complexity of the problem of multi-agent cooperative task allocation, not only can avoid conflict, but also has the advantage of rapid robustness. In the embodiment, the distribution problem of the relay deployment points is solved by adopting the CBBA algorithm, and the relay static coverage deployment points calculated in the step S02 are distributed to each dynamic relay unmanned aerial vehicle as the relay task points to be distributed in the CBBA algorithm, so that the distribution complexity can be greatly reduced, the distribution efficiency can be improved, and meanwhile, conflicts can be avoided.

As shown in fig. 9, in the CBBA algorithm of this embodiment, two stages, namely task bundle construction and conflict elimination, are iterated continuously, the first stage is the construction of a task bundle, each relay drone creates a task bundle y to store relay task points, relay task point execution sequences and task execution times allocated to itself, and also stores five variables including an allocator (i.e., a winner of the task) corresponding to all tasks and revenue (a bid-winning value of the task) brought by the task, and the five variables are updated continuously along with the iteration of the algorithm; and in the second stage, conflict elimination is carried out, communication is carried out between adjacent relay unmanned aerial vehicles, the respective stored successful bidder list and the successful bid value list are compared, and the lists are updated according to the sequence of the timestamps, so that conflict is solved. The two processes are iterated until all task assignments are completed.

The detailed steps of the task bundle construction phase in this embodiment include:

the relay unmanned aerial vehicle continuously adds the relay task points into the task bundle until the maximum relay task point number L of the relay unmanned aerial vehicle is reached _max Or no free relay points, no other tasks. Each relay drone needs to maintain three lists, namely a task bundle list b _m ＝{b _mk |b _mk ∈{1,…,N},k≤L _max List p of path task points _m ＝{p _mk |p _mk ∈{1,…,N},k≤L _max And path task point arrival time list τ _m ＝{τ _mk |k≤L _max },b _m And the relay task point numbers which represent the m-th relay distribution are arranged according to the time sequence of the relay task points added into the task bundle. p is a radical of formula _m And storing the path points of the relay m, namely the serial numbers of the relay task points, and arranging according to the sequence of passing by the flight.

New joining task bundle b _m Should be inserted into the corresponding p _m And calculating the profit increment which is caused by the relay communication task at each insertion position at which position of the path needs to be calculated, wherein the position with the maximum profit increment is the path sequence corresponding to the new relay task point n. In this embodiment, the profit increase amount that would be caused by relaying the communication task at each insertion position is calculated according to each list, and the position with the largest profit increase amount is used as the path sequence corresponding to the new relay task point n.

Use of

Indicates following path p _m The total income brought by sequentially executing the relay tasks, and the relay task point n is added into the task bundle b of the relay unmanned aerial vehicle m _m The gain increase amount brought by (1) can be expressed as:

wherein, | p _m L is the number of assigned relay drone mission points,

indicating the insertion of a new relay task point n into the path p _m The total profit before the δ -th waypoint.

Relay drone m along p _m The total benefit of performing the task is expressed as:

wherein q is ₀ Coordinates of departure point, v, for relaying the drone ₀ For relaying the cruising speed of the drone. s _mk (τ _mk ,b _mk ) For relaying unmanned aerial vehicle m at tau _mk Time to relay task point b _mk The gain obtained is determined by three factors: the first factor is the time τ to reach the task point _mk Corresponding to relay task point b _mk The time window is

The relay unmanned aerial vehicle needs to arrive at a task point in a time window, and the time tau of the arrival time window _mk From the start of the time window

The more recent, the greater the profit; the second factor is the relay communication value Val of the mission point itself _mk It is related to the amount of relay communication task and is set as the relay task point b _mk Sum of communication time of all task points covered

Forming positive correlation; the third factor is a penalty item, and the unmanned aerial vehicle m relays from the last relay task point b _m(k-1) Reach the current task point b _mk Fuel consumed and aligned with the distance between two task pointsAnd (4) the ratio.

Wherein λ is ₁ Is the coefficient of return related to the time of arrival of the relay drone at the relay mission point, is positive, gamma is the energy consumed per unit distance,

and

are respectively relay task points b _mk And b _m(k-1) The position coordinates of (a) are determined,

for relaying task point b _mk The sum of all the task point communication times of the relay service,

for relaying task point b _mk Relay communication

The value of time, i.e.

In the task bundle updating process, each unmanned aerial vehicle not only stores a task bundle, a path task point list and a path task point time list, but also comprises two vectors, namely a winner vector in a relay task point

And bid amount vector of winning bidder

In the specific application embodiment, a specific algorithm for task bundle construction is shown in the following table.

The above algorithm describes an iterative process of the mth relay unmanned aerial vehicle in the task bundle construction phase, how the relay unmanned aerial vehicle adds the task into the respective task bundle to update the five maintained vectors, wherein at the initial moment, the five vectors b maintained by each relay unmanned aerial vehicle _m (0),p _m (0),τ _m (0),z _m (0),y _m (0) The set is set to be empty,

the algorithm inputs the vector value of t-1 times of iteration and outputs the updated vector of the t-th iteration after updating, h _m The relay task points marked in the last iteration relay unmanned aerial vehicle are stored, and when the length of the task bundle is less than the maximum task point number L which can be distributed by the relay m of the unmanned aerial vehicle _max And there is a task of winning a bid on the last iteration

The algorithm is iterated continuously (lines 6-14).

The detailed steps of the collision elimination phase in this embodiment are:

the relay unmanned aerial vehicle adopts a consistency strategy to converge a relay task bid-winning list, and a relay task point required to be reached is distributed for the relay unmanned aerial vehicle according to the bid-winning list, so that communication relay service is carried out on the task unmanned aerial vehicle of the task point corresponding to the relay task point. The consistency strategy can dissolve the contradiction of all tasks without limiting the network to a specific structure, thereby achieving the consistency aim.

In the embodiment, an undirected graph for relaying unmanned aerial vehicle communication is represented by a symmetric adjacency matrix G (tau), and if the relaying unmanned aerial vehicle m relays at the time t ₁ And relay unmanned aerial vehicle m ₂ A communication link is available between them, then

Since the communication link is undirected, it corresponds

In a conflict elimination stage, three vectors need to be exchanged between relays of the unmanned aerial vehicle in real time, two vectors are used in a relay task bundle creation stage, and a winner vector z _m And bid vector y of winning bidder _m Another vector e _m ∈R ^M And recording a timestamp of the latest information exchange among the relay drones, wherein the timestamp represents the time when each relay drone updates information from other relay drones. When relay unmanned aerial vehicle m ₁ And relay m ₂ With direct link therebetween, i.e.

Time, relay m ₁ And relay m ₂ The last communication time is the message receiving time t _r . When no direct connection channel exists between the unmanned aerial vehicle m and the relay unmanned aerial vehicle m, the relay unmanned aerial vehicle m is found ₁ All the rest of the directly connected relay unmanned aerial vehicles

Then find the latest timestamp in these relay drone sets C, that is:

when relay unmanned aerial vehicle m ₁ Slave relay m ₂ Update information, need to relay unmanned aerial vehicle m ₁ Stored winning bid winner vector

Bid and bid vector

And (5) fusion updating. Take relay task point n as an example, relay m ₁ Three actions may be performed:

1) The winning bid vector is updated according to the standard bid quantity,

2) The winning bid vector is reset and the winning bid vector is reset,

3) The amount of the winning bid is not changed,

if relay unmanned aerial vehicle m ₂ Bid-winning ratio relay m of stored relay task point n ₁ Or one of the two relays has a stored winning bid for relay task point n of m, m ≠ m ₁ ∩m≠m ₂ And relay m ₂ The timestamp of the last received message about relay m is later than that of relay m ₁ Then relay unmanned aerial vehicle m ₁ Performing the update operation of the winning bid vector, and relaying m ₁ Is/are as follows

And

grant and relay m ₂ The same value. When relay unmanned aerial vehicle m ₁ The winner at task point n is regarded as self, relay m ₂ If the winning bid is relay i or null, no operation is performed, and relay m is performed ₁ The winning bid vector of (1) remains unchanged. Otherwise, if the winning bid of the two relay unmanned aerial vehicles conflict, the winning bid is transmitted to the relay m ₁ The two winning vectors are reset.

Assuming that there is a relay task point n in the task bundle of the relay drone m, and there are other relays with bids higher than m, m yields the task. Or in the consistency conflict resolution stage, the two relay unmanned aerial vehicles perform communication negotiation, the task point n in the task bundle is updated or reset, and all the added tasks after the task need to be released. The benefit due to the task is increased by its insertion in the task path p _m So that the tasks added into the m-task bundle after this task will all become invalid and the corresponding winning bidder vector elements and winning bidder bid vector elements will both be reset. Firstly, a task point n needs to be found out and a task bundle b is formed in a relay unmanned aerial vehicle m _m Position delta in _mn ：

Then to task bundle b _m Middle delta _mn Task reset after project:

wherein, b _in The nth item of the task bundle for relay drone i.

According to the data transmission requirements between the unmanned aerial vehicle cluster task point and the ground control station, the communication environment of the unmanned aerial vehicle is firstly modeled, and a channel model suitable for the platform characteristics of the unmanned aerial vehicle is established; then, fixed area communication coverage is carried out on all task points by adopting the relay unmanned aerial vehicles, and the number of the relay unmanned aerial vehicles and the corresponding deployment positions of static coverage are optimized; by means of the time sequence of the task points, namely different task points have different execution times and are different from communication time required by a ground control station, a relay dynamic coverage mode is adopted to successively reach a plurality of relay static coverage points, the distribution of the relay task points and the paths of the relay unmanned aerial vehicles are planned, the deployment and planning of the air-ground relay unmanned aerial vehicles can be quickly and efficiently realized, the task point conflict among the multi-relay unmanned aerial vehicles can be effectively eliminated, the number of the relay unmanned aerial vehicles required by the cluster tasks of the air-ground communication can be minimized, and the continuous and stable transmission of the command control information and the inter-aircraft cooperation information between the unmanned aerial vehicles and the ground can be ensured.

The foregoing is illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention shall fall within the protection scope of the technical solution of the present invention, unless the technical essence of the present invention departs from the content of the technical solution of the present invention.

Claims

1. An air-ground relay communication control method for unmanned aerial vehicle cluster application is characterized by comprising the following steps:

s01, modeling is carried out on the communication process of the unmanned aerial vehicle, and a path loss calculation model between the unmanned aerial vehicle and a ground control station, a communication model between the unmanned aerial vehicle and the ground station and a channel model between the unmanned aerial vehicle and a relay unmanned aerial vehicle are constructed;

s02, performing static analysis on relay demand based on spatial distribution of task points, namely acquiring spatial distribution of the task points, judging whether the current air-ground communication condition needs relaying or not according to the acquired spatial distribution and the model constructed in the step S01, and determining the number of unmanned aerial vehicles needing relaying and the spatial position of unmanned aerial vehicle access relaying by taking the minimum relay nodes as targets if the current air-ground communication condition needs relaying;

s03, relay task planning based on cluster unmanned aerial vehicle task time sequence: acquiring a task point time sequence in a cluster, constructing an objective function by taking the relay communication income of the maximized unmanned aerial vehicle to the multi-task point as a target, constructing a dynamic relay unmanned aerial vehicle task decision model based on the objective function and a required constraint condition, and obtaining a planning result of a relay task by solving the dynamic relay unmanned aerial vehicle task decision model.

2. The air-ground relay communication control method for unmanned aerial vehicle cluster application according to claim 1, wherein in step S1, the ground control station is located at (x) position _bs ,y _bs ,h _bs ) Coordinate of drone m is (x) _m ,y _m ,h _m ) Elevation angle of unmanned plane m to ground control station is

wherein, A = eta _LoS -η _NLoS ,

c is the speed of light wave, alpha and beta are environmental parameters;

the total antenna gain calculation expression is:

wherein κ is total antenna gain, F (θ) is antenna power gain, θ is an angle caused by a height difference of the receiving and transmitting antennas, and η is an included angle formed by the receiving and transmitting antennas due to a change in the attitude of the unmanned aerial vehicle;

Loss＝Λ-log 10(F(θ)·cosη)。

3. the air-ground relay communication control method for the unmanned aerial vehicle cluster application according to claim 2, wherein in the step S01, the communication model between the unmanned aerial vehicle and the ground station is constructed as follows:

4. the air-ground relay communication control method oriented to unmanned aerial vehicle cluster application of claim 1, wherein in step S02, the optimization objective is that the relay unmanned aerial vehicles with the least deployment meet the communication requirements between all mission points and the ground control station, the decision variables are the number of the relay unmanned aerial vehicles and the corresponding relay deployment positions, the positions of the relay unmanned aerial vehicles and the associations between the relay unmanned aerial vehicles and the corresponding mission areas are decided by taking the minimum number of the relay unmanned aerial vehicles as the static coverage optimization objective, and the formed optimization problem is specifically:

wherein, C ₁ Boolean decision variable, C, for the current problem ₂ The communication between the task point and the ground control station can be relayed by one unmanned aerial vehicle only, C ₃ Representing the upper bound of the number of task points relayed by the relay unmanned aerial vehicle at the same time, C ₄ Defining that a mission point cannot be associated with a relay drone not adopted, C ₅ And C ₆ The distance requirement between the relay unmanned aerial vehicle and the associated task point and the ground control station is represented, L is a preset constant and can enable alpha to be _mk And u _m When the distance is equal to 0, no constraint is made on the m distance of the relay unmanned aerial vehicle, and x is _re,m ,y _re,m In the representationHorizontal and vertical coordinates of relay position, u = [ u = [) _m ] _1×K Set of states, u, representing candidate relay drones _m =1 denotes that mth candidate relay drone is adopted, u _m =0 then indicates that the alternative drone is deleted, R _bs Denotes a communication radius, α = [ α = _mk ] _K×K Representing the correlation matrix, alpha, between the relay drone and the mission point _mk =1 indicates that the mth relay drone performs relay communication for the kth task point, that is, the task point k is within the communication range of the relay drone m, N _max The number of the task unmanned aerial vehicles which are connected at most at the same time and are one relay unmanned aerial vehicle is represented, x _ts,k ,y _ts,k The abscissa and ordinate of the kth task point are indicated.

5. The air-ground relay communication control method for unmanned aerial vehicle cluster application according to claim 1, wherein in step S03, unmanned aerial vehicle relays are arranged simultaneously for all task points according to a deployment method of static relays to obtain unmanned aerial vehicle relay static deployment points; calculating a communication time window of the relay static deployment point, allocating the relay static deployment point to be traversed by the relay unmanned aerial vehicle in sequence according to the time window requirement, and integrating the communication income of the task point, the path of the relay unmanned aerial vehicle, the time for the relay unmanned aerial vehicle to reach the corresponding task point and the energy consumption factor of the relay unmanned aerial vehicle to construct an objective function by taking the relay communication income of the maximized unmanned aerial vehicle to the multi-task point as a target.

6. The air-ground relay communication control method for the unmanned aerial vehicle cluster application, according to claim 5, wherein the objective function constructed with the objective of maximizing the relay communication profit of the unmanned aerial vehicle to the multitask point is:

wherein the function c _mn (β _m ,p _m ,τ _m ) Relaying unmanned aerial vehicle for mth frame according to path p _m Ren and renTime of service tau _m The total energy consumption of the flight path is subtracted from the relay communication income obtained by reaching the corresponding relay deployment point n,

for relaying the correspondence between the unmanned aerial vehicle m and the static coverage deployment point, beta _mn =1 denotes that the nth static coverage deployment point is allocated to the mth relay drone, L _m For the total number of static coverage deployment points allocated to relay drones m,

the constraint conditions include:

τ _mk ≤O _re,n1 ,τ _m(k+1) ≤O _re,n2 ,

(O _re,n2 -C _re,n1 )*v≥||(x _re,n1 -x _re,n2 ,y _re,n1 -y _re,n2 )|| ₂ ,

p _mk ＝n1,p _mk+1 ＝n2,

7. The air-ground relay communication control method for unmanned aerial vehicle cluster application according to any one of claims 1 to 6, wherein in step S03, a consistency-based beam set algorithm CBBA is used to solve the dynamic relay unmanned aerial vehicle task decision model, and in the solving process, two stages of task beam construction and conflict elimination are continuously and iteratively performed until all tasks are completely distributed, wherein in the stage of task beam construction, each relay unmanned aerial vehicle creates a task beam y to store its own distributed relay task points, relay task point execution sequence and task execution time, and saves distributors corresponding to all tasks and gains brought by the tasks, and continuously updates in the iteration process; and in the conflict elimination stage, communication is carried out between adjacent relay unmanned aerial vehicles, the respectively stored successful bid winner list and successful bid value list are compared, and the successful bid value list is updated according to the sequence of the time stamps.

8. The method of claim 7, wherein the relay drone continues to add relay mission points to the mission beam until a maximum number L of relay mission points for the relay drone is reached during the mission beam construction phase _max Each relay unmanned aerial vehicle maintains a task bundle list, a path task point list and a path task point arrival time list; respectively calculating the profit increment which is brought by the relay communication task at each insertion position according to each list, and taking the position with the maximum profit increment as the path sequence corresponding to the new relay task point n, wherein the task bundle b of the relay task point n added into the relay unmanned aerial vehicle m is calculated according to the following formula _m The increment of the income brought in:

wherein, | p _m L is the number of assigned relay drone mission points,

indicates following path p _m The total revenue that would be brought by sequentially executing relay tasks;

calculate relay drone m along p _m The expression for the total benefit of performing the task is:

wherein q is ₀ For relaying unmanned aerial vehicle departure coordinates, v ₀ For relaying cruising speed, s, of unmanned aerial vehicle _mk (τ _mk ,b _mk ) For relaying unmanned aerial vehicle m at tau _mk The time reaches relay task point b _mk The gains gained in time; s _mk (τ _mk ,b _mk ) Is determined by three factors, the first being the time to reach the task point τ _mk The second factor is the relay communication value Val of the mission Point itself _mk Said value of relay communication Val _mk And relay task point b _mk Sum of communication time of all task points covered

The third factor is a penalty item so that the unmanned aerial vehicle m can relay the task point b from the last relay task point _m(k-1) Reach the current task point b _mk Fuel consumption and two task pointsIs proportional to the distance between them.

9. The air-ground relay communication control method for unmanned aerial vehicle cluster application of claim 7, wherein in the conflict elimination phase, the relay unmanned aerial vehicle adopts a consistency strategy to converge a bid-winning list of the relay task, and allocates a relay task point to be reached for the relay unmanned aerial vehicle according to the bid-winning list, so as to perform communication relay service for the task unmanned aerial vehicle of the task point corresponding to the relay task point; wherein when relay unmanned aerial vehicle m ₁ And relay m ₂ With direct link therebetween, i.e.

Finding the nearest timestamp in the found relay unmanned aerial vehicle set C

When relay unmanned aerial vehicle m ₁ Slave relay m ₂ Update information, relay unmanned aerial vehicle m ₁ Stored winner vector

Bid and bid vector

And (5) fusion updating.

10. The method as claimed in claim 7, wherein in the collision elimination phase, if the drone m is relayed, the drone m is in relay communication control mode ₂ Bid-winning ratio relay m of stored relay task point n ₁ Or one of the two relays has a stored winning bid for relay task point n of m, m ≠ m ₁ ∩m≠m ₂ And relay m ₂ The timestamp of the last received message about Relay m is later than Relay m ₁ Then relay unmanned aerial vehicle m ₁ Executing bid-winning vector updating operation to relay m ₂ The bid-winning vector and the bid-winning person vector are correspondingly assigned to the relay m ₁ Bid-winning bid vector of

And winning bid vector

When relay unmanned aerial vehicle m ₁ Determine that the winner in task point n is self, and relay m ₂ If the winning bidder is relay i or null, no operation is executed, and relay m ₁ The winning bid vector quantity of (1) is kept unchanged, and when the winning bidders stored by the two relay unmanned planes conflict, the winning bidders to the relay m ₁ The two winning vectors are reset.