CN111885504B - Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle - Google Patents

Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle Download PDF

Info

Publication number
CN111885504B
CN111885504B CN202010775851.9A CN202010775851A CN111885504B CN 111885504 B CN111885504 B CN 111885504B CN 202010775851 A CN202010775851 A CN 202010775851A CN 111885504 B CN111885504 B CN 111885504B
Authority
CN
China
Prior art keywords
vehicle
unmanned aerial
aerial vehicle
track
time
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
Application number
CN202010775851.9A
Other languages
Chinese (zh)
Other versions
CN111885504A (en
Inventor
黄高飞
刘兆年
郑晖
赵赛
唐冬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou University
Original Assignee
Guangzhou University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Guangzhou University filed Critical Guangzhou University
Priority to CN202010775851.9A priority Critical patent/CN111885504B/en
Publication of CN111885504A publication Critical patent/CN111885504A/en
Application granted granted Critical
Publication of CN111885504B publication Critical patent/CN111885504B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses an unmanned aerial vehicle track optimization method for assisting wireless communication of a mobile vehicle, which comprises the following steps: s1, establishing an unmanned aerial vehicle auxiliary traveling vehicle communication system model and a corresponding mathematical expression thereof by taking the maximum unmanned aerial vehicle flight energy efficiency as an optimization target, wherein the mathematical expression is an optimization problem P1; s2, obtaining the current state information of the vehicle in the system model, and obtaining the future movement track of the vehicle through the vehicle track prediction model according to the current state information of the vehicle; s3, according to the future moving track of the vehicle, carrying out approximate processing on the optimization problem P1 to be converted into a convex optimization problem, and obtaining an approximate convex optimization problem P2 of the optimization problem P1; s4, solving the approximate convex optimization problem P2 to obtain the flight path and power distribution of the unmanned aerial vehicle. The invention can improve the communication performance between the unmanned aerial vehicle base station and the ground mobile terminal and greatly reduce the energy loss caused by the flight of the unmanned aerial vehicle.

Description

Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle track optimization method for assisting wireless communication of a mobile vehicle, which is applied to an unmanned aerial vehicle assisted traveling vehicle communication system.
Background
In recent years, unmanned aerial vehicles have been a popular field, and the unmanned aerial vehicles are used as carriers for cargo distribution, real-time video transmission, agricultural plant protection and the like, and are rapidly developed in over ten years. Because it has characteristics such as mobility is strong, small and the cost is low will also see unmanned aerial vehicle's shadow in more fields in the future. The unmanned aerial vehicle auxiliary communication system is a wireless communication system which combines a traditional fixed base station with an unmanned aerial vehicle and further provides communication service. Depending on hot spots areas where communication services are dense (e.g., stadiums during a sporting event) or areas without wireless communication infrastructure (e.g., areas with communication interruptions due to natural disasters), drone flight base stations can be free of geographic conditions, provide instant messaging services, and can adjust hover position and altitude to maintain a line-of-sight (LoS) communication link with ground users at all times. Meanwhile, the unmanned aerial vehicle auxiliary communication system can also be applied to the field of the Internet of things, and has the characteristics of smaller transmitting power and incapability of carrying out remote communication on the nodes of the Internet of things generally, and the unmanned aerial vehicle can be used as a mobile base station or a mobile relay to assist the nodes of the Internet of things to complete data transmission service in a shorter time by planning the flight trajectory of the unmanned aerial vehicle.
With the gradual commercial start of the fifth generation mobile communication technology (5G), the technology has the characteristics of higher transmission rate, larger capacity, lower time delay and the like, the traditional communication mode in the past is greatly changed, and the development of the internet also enters the intelligent internet era from the mobile internet. In the future, 5G communication is not only human communication, but also businesses such as Internet of things, industrial automation, unmanned driving and the like are introduced, and communication starts to be switched to human-to-object communication until communication between machines. However, it is expected that with the increasing demand of high-speed wireless access in the 5G era, the existing wireless cellular network technology cannot meet the corresponding demand, wherein end-to-end (D2D) communication, Ultra Dense Networking (UDN) and millimeter wave (mmW) technology, etc. are key to solve the problem of high-speed wireless connection in the fifth generation mobile communication technology (5G). Although these technologies can meet the communication needs of the future 5G era, these solutions still have certain limitations. For example, end-to-end (D2D) communication requires better frequency planning and resource usage in cellular networks. At the same time, ultra-dense networking (UDN) faces many challenges in backhaul, interference and overall network modeling. Similarly, millimeter wave (mmW) communications are more susceptible to interference from obstacles, and require line-of-sight (LoS) communications to achieve high speed, low latency communications requirements. It is very helpful to solve these problems by introducing the unmanned aerial vehicle flight base station scheme, it can deploy fast in a short time, provides the growth demand of the high-speed wireless access of effective alleviating region of line of sight (LoS) communication link.
Currently, the high-traffic company and the AT & T company have plans to perform communication tests on the unmanned aerial vehicle so as to realize large-scale wireless communication application in the upcoming fifth generation (5G) wireless network. Meanwhile, a japanese operator KDDI proposes an assumption of a drone base station, the unmanned aerial vehicle realizes wireless backhaul through a nearby ground macro base station or an emergency communication vehicle, a power supply of the unmanned aerial vehicle is a battery carried by the unmanned aerial vehicle, the unmanned aerial vehicle is limited by battery endurance time, the flight time is only 30 minutes, and two groups of unmanned aerial vehicles need to alternately fly to prolong the network service time. The application of the domestic unmanned aerial vehicle aerial base station is mainly that emergency communication service is provided for natural disaster areas for many times by a tethered unmanned aerial vehicle scheme adopted by China mobile in mountainous areas where communication is interrupted after natural disasters occur and places where emergency communication vehicles cannot reach. But it is difficult to fly autonomously according to the communication needs of the ground user because of the need for a wired connection to the ground mobile station. At present, the application of vehicle-mounted unmanned aerial vehicle is that the unmanned aerial vehicle is matched with the speed of a faster vehicle and flies, and the road condition information in the front of the vehicle running process is transmitted to the ground running vehicle, so that the auxiliary vehicle can run safely. With the development of the internet of vehicles and the automatic driving technology, vehicles in the driving process need to continuously detect surrounding terrains and evaluate road conditions so as to ensure the safe driving of the vehicles. The unmanned aerial vehicle can move quickly and is not limited by geographical conditions, and front road condition information can be fed back to the mobile vehicle to assist the vehicle in judging road conditions and selecting paths. The above prior art has the following disadvantages:
1. at present, most of research on unmanned aerial vehicle flying base stations only considers the condition that a ground terminal is always static, but in fact, the communication demand of the ground terminal is always dynamically changed. Although some existing technologies utilize a user movement model to describe the movement condition of the ground terminal, the future movement position coordinates of each terminal cannot be accurately obtained. With the rapid development and large-scale application of vehicle networking in the 5G era, higher requirements are put forward on the delay and reliability of communication, and the requirement for high-quality communication in the vehicle driving process is far from being met only by considering the application scene that a ground terminal is always static or only knows the moving distribution condition of a user.
2. Because the energy that unmanned aerial vehicle self carried is limited, the energy consumption management to unmanned aerial vehicle has always been a key research problem, lacks the scheme that can not only guarantee communication quality but also can reduce the flight energy consumption as far as possible at unmanned aerial vehicle auxiliary communication in-process under the vehicle removes the condition at present.
3. Tethered unmanned aerial vehicle still need carry out wired connection with ground in providing communication service process, and this can restrict the mobility of unmanned aerial vehicle basic station greatly, can't realize independently flying according to ground communication terminal's demand simultaneously.
In summary, there is a need in the industry to design a method or system for greatly reducing the energy loss caused by the flight of an unmanned aerial vehicle while improving the communication performance between the unmanned aerial vehicle base station and a ground traveling vehicle in the moving process of a ground vehicle, so as to further prolong the operation time of the unmanned aerial vehicle flight base station.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an unmanned aerial vehicle track optimization method for assisting wireless communication of a mobile vehicle with high energy efficiency.
The purpose of the invention is realized by the following technical scheme:
an unmanned aerial vehicle trajectory optimization method for assisting wireless communication of a mobile vehicle comprises the following steps:
s1, establishing an unmanned aerial vehicle auxiliary traveling vehicle communication system model and a corresponding mathematical optimization problem P1 by taking the maximum unmanned aerial vehicle flight energy efficiency as an optimization target;
s2, obtaining the current state information of the vehicle in the system model, and obtaining the future movement track of the vehicle through the vehicle track prediction model according to the current state information of the vehicle;
s3, according to the future moving track of the vehicle, carrying out approximate processing on the optimization problem P1 to be converted into a convex optimization problem, and obtaining an approximate convex optimization problem P2 of the optimization problem P1;
s4, solving the approximately convex optimization problem P2, solving the optimization problem P1 of the unmanned aerial vehicle auxiliary advancing vehicle communication system model, and further obtaining the unmanned aerial vehicle flight track and power distribution when the unmanned aerial vehicle flight energy efficiency is maximized.
Preferably, the unmanned aerial vehicle assists vehicle communication system model to assist vehicle D of marcing to communicate for unmanned aerial vehicle S, and vehicle D of marcing carries positioner, and positioner is used for the current position information of real-time transmission vehicle when unmanned aerial vehicle S communicates with vehicle D of marcing.
Preferably, step S1 includes:
the unmanned aerial vehicle flies at a constant height H after taking off, and the time interval for predicting the vehicle track of each wheel is assumed to be T f The running vehicle will go every T f The time sends the future predicted track coordinates of the vehicle to the unmanned aerial vehicle flight base station, and the unmanned aerial vehicle flight base station can plan the self-flying track according to the future predicted track coordinates of the vehicle;
supposing that the unmanned aerial vehicle flight base station has enough computing power, the time delay of the process of optimizing the flight trajectory of the unmanned aerial vehicle can be ignored; wherein any time slot N [1, N ] of the flight base station of the unmanned aerial vehicle in the ith path planning]The position coordinates of the unmanned aerial vehicle S and the traveling vehicle D are respectively expressed as S [ n ]]、p[n]Wherein s [ n ]]＝(x s [n]，y s [n])、p[n]＝(x p [n]，y p [n]) (ii) a And the predicted position coordinates of the traveling vehicle within the time slot are represented as q n]＝(x q [n]，y q [n]) Then the predicted distance of the drone from the traveling vehicle at time slot n is denoted as
Figure GDA0003699803310000051
Wherein s is i [n]And q is i [n]Respectively representing the position coordinates of the unmanned aerial vehicle and the predicted position coordinates of a running vehicle when the ith round of unmanned aerial vehicle runs the track plan, i belongs to {1, 2, 3, · }, N belongs to [1, N · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · N · s · N, which is · · · · · · · · · · · N, and · · · · · · · · · · · · · · · · · · · · N, which is used for · · · · · · · · can be used for · · and the position coordinates, and the position coordinates of the unmanned aerial vehicle, and the coordinate system and the position coordinates of the unmanned aerial vehicle and the position coordinates of the driving trajectory planning and the position coordinates of the driving trajectory of the driving of the unmanned aerial vehicle and the driving trajectory of the driving of the unmanned aerial vehicle are respectively]；
In any time slot N belongs to [1, N ], using h [ N ] to represent the channel coefficient between the unmanned aerial vehicle and the ground terminal, then
Figure GDA0003699803310000052
Wherein beta [ n ]]＝β 0 d -2 [n]Expressed as a large scale fading factor affected by path loss,
Figure GDA0003699803310000058
expressed as small-scale fading factors affected by multipath; suppose that in an OFDM communication system, K e [1, K ] is in any subcarrier]The transmission power of the communication between the upper unmanned aerial vehicle and the running vehicle in the time slot n is P k [n]The communication transmission achievable rate between the drone and the traveling vehicle is then expressed as
Figure GDA0003699803310000053
Wherein the content of the first and second substances,
Figure GDA0003699803310000054
denotes the channel bandwidth of each subcarrier, W being the total bandwidth of the system, P k [n]For the transmission power on the k subcarrier of the nth slot, σ 2 Is the noise power at the receiver, Γ > 1 represents the gap from the channel capacity due to the actual modulation and coding employed, and defines the received signal-to-noise ratio at a reference distance of 1 meter as γ 0 ＝P k [n]H k [n](ii) a While
Figure GDA0003699803310000055
The moving tracks { x (t), y (t), H } of the unmanned aerial vehicle at constant flying height continuously change along with time are N long time sequences
Figure GDA0003699803310000056
To approximate; when the unmanned plane is in any time slot N ∈ [1, N ]]When the flying speed is V, the flying power loss of the unmanned aerial vehicle in the time slot is modeled as
Figure GDA0003699803310000057
Wherein P is p And P i Two defined constants related to hardware of the unmanned aerial vehicle respectively represent cascade contour power and inductive power in a hovering state, U tip Representing the tip speed, v, of the rotor blade 0 Referred to as mean induced speed of rotor at suspension, d 0 And s is fuselage drag ratio and rotor solidity, respectively, and ρ and A represent air density and rotor disk area, respectively; by substituting V in 0 for expression (3), power consumption P [ n ] in the hovering state is obtained]＝P p +P i ；
The energy loss of the unmanned aerial vehicle in any time slot N epsilon [1, N ] of the communication task is expressed as follows:
E[n]＝(P[n]+P c )δ t (4)
wherein, P c Circuit power consumption for a communication transmitter;
therefore, the optimization problem P1 of the unmanned aerial vehicle auxiliary traveling vehicle communication system model is as follows by taking the maximum unmanned aerial vehicle flight energy efficiency as an optimization target:
Figure GDA0003699803310000061
s.t||s i [n+1]-s i [n]||≤V max δ t ，n＝1，…，N-1 (5)
Figure GDA0003699803310000062
s 0 ＝s I ，i＝1 (7)
s i0 ＝s i-1 [N]，i＝2，3… (8)
wherein the content of the first and second substances,
Figure GDA0003699803310000063
for the transmit power on the kth subcarrier of the nth slot of the ith round,
Figure GDA0003699803310000064
for the track-optimized (i-1) set of sums of the communication rates of the unmanned aerial vehicle and the ground terminal within the prediction time range,
Figure GDA0003699803310000065
for the sum of the communication rates of the unmanned aerial vehicle and the ground terminal in the j-th prediction time range after the track optimization, i.e.
Figure GDA0003699803310000066
Wherein
Figure GDA0003699803310000067
Figure GDA0003699803310000068
Refers to the jth round time window T that has elapsed f The transmission rate on the kth subcarrier of the mth time slot in the range, M is the jth time window T f The number of time slots in the range is,
Figure GDA0003699803310000069
the average value of the transmission data quantity on the kth subcarrier of the nth time slot; also, in the same manner as above,
Figure GDA00036998033100000610
for the sum of the flight energy losses of the unmanned aerial vehicle in the (i-1) group prediction time range after the track optimization,
Figure GDA00036998033100000611
for predicting the sum of flight energy losses of the unmanned aerial vehicle in the time range of the j th time after the track optimization, i.e.
Figure GDA00036998033100000612
Wherein the content of the first and second substances,
Figure GDA00036998033100000613
for the jth elapsed time window T f Energy consumption of the m-th slot drone within the range, E i [n]Energy consumption of unmanned aerial vehicle for nth time slot of ith wheelCalculated by formula (4); equation (5) is used to limit the speed of the drone in each time slot, where V max Representing a maximum horizontal velocity of the drone; p in formula (6) max Represents a maximum transmit power of a transmitter mounted on the drone; s in the formula (7) I Representing an initial position of the projection of the unmanned aerial vehicle on a horizontal plane; equation (8) is to constrain the initial position of the unmanned aerial vehicle flight base station for trajectory optimization according to the predicted position information of the traveling vehicle to be the final position of the last trajectory optimization.
Preferably, step S2 includes:
acquiring current running state information of the vehicle, wherein the current running state information comprises the current position of the vehicle, the destination position, the running speed and the acceleration of the current vehicle and the yaw angle information of the vehicle;
judging whether the yaw angle changes in the current vehicle running process; if the vehicle speed changes, a constant rotation rate and acceleration model CTRA is adopted, and the constant rotation rate and acceleration model CTRA is combined with an unscented Kalman filtering algorithm UKF to calculate the movement track of the vehicle in the future short term; when the vehicle does not change, a constant speed model CV is adopted, and the constant speed model CV is combined with an unscented Kalman filtering algorithm UKF to calculate the movement track of the vehicle in the future short term;
dividing a road on which a vehicle is going into a plurality of road sections according to the current position information and the destination position information of the vehicle, and predicting the current vehicle passing time through the basic information of the road and the historical vehicle passing time of the road sections; according to the length of each road section and the corresponding predicted passing time, obtaining the position information of the vehicles at different moments;
splicing the short-term driving track prediction result of the vehicle and the vehicle passing time prediction result according to the time sequence, wherein the front part of the splicing is the short-term driving track prediction result of the vehicle, and the rear part of the splicing is the vehicle driving track prediction result obtained by predicting the passing time of different road sections;
and continuously acquiring the current running state information of the vehicle to circularly predict the moving track of the vehicle after the vehicle runs the travel corresponding to the short-term prediction time each time.
Preferably, step S3 includes:
since the optimization problem P1 is non-convex, the optimization problem P1 needs to be transformed by first introducing a slack variable y into the denominator of the objective function of the optimization problem P1 in ≧ 0}, wherein
Figure GDA0003699803310000081
Wherein, Delta in The flight distance of the ith round of unmanned aerial vehicle at the nth time slot is expressed;
converting equation (9):
Figure GDA0003699803310000082
the denominator form of the objective function of the following optimization problem P1 is obtained:
Figure GDA0003699803310000083
the global lower estimate is obtained by approximating the first order Taylor expansion to the right of equation (10):
Figure GDA0003699803310000084
wherein
Figure GDA0003699803310000085
And s i [n](l) Is the value of the corresponding variable for the first iteration;
for the numerator of the objective function of the optimization problem P1
Figure GDA0003699803310000086
Carrying out general division treatment to obtain:
Figure GDA0003699803310000087
wherein ε {. } refers to a mathematical expectation operation;
let theta [ n ]]＝H 2 +||s[n]-p[n]|| 2 (14)
Equation (13) then translates to:
Figure GDA0003699803310000088
the molecular transformation of the objective function of the optimization problem P1
Figure GDA0003699803310000091
Order to
Figure GDA0003699803310000092
The numerator of the objective function of the optimization problem P1 is
Figure GDA0003699803310000093
Let theta i [n]Is the ith wheel theta n]A value of (d); therefore, given any local point
Figure GDA0003699803310000094
The optimization problem P1 of the unmanned aerial vehicle auxiliary traveling vehicle communication system is changed into:
Figure GDA0003699803310000095
Figure GDA0003699803310000096
Figure GDA0003699803310000097
H 2 +||s[n]-p[n]|| 2 ≤θ i [n].n＝1，2，…，N
for the above formula (P1.1), which is also solved by solving the pseudo-convex optimization problem, an auxiliary variable μ is introduced, and the formula (P1.1) is written as:
Figure GDA0003699803310000098
taking the minimum value of mu as the upper bound of the objective function, namely the maximum value of the objective function, so as to find the flight path s of the unmanned aerial vehicle for maximizing the objective function i [n](ii) a Equation (19) can be further written as:
Figure GDA0003699803310000099
wherein
Figure GDA00036998033100000910
Is a positive value, with μ as the upper bound of the objective function; taking the formula (P1.1) to a maximum when the following near convex optimization problem P2 is non-negative;
Figure GDA00036998033100000911
Figure GDA0003699803310000101
Figure GDA0003699803310000102
H 2 +||s[n]-p[n]|| 2 ≤θ i [n].n＝1，2，…，N
at the moment, the optimal solution of the flight path and the power distribution of the unmanned aerial vehicle is solved through convex optimization software.
Preferably, step S4 includes:
s411: initialization s (0) 、y (0) 、θ (0) Setting the objective function as EE (0) The iteration number l is 0, and the error margin epsilon is set 1 (ii) a Wherein S is the flight trajectory of the unmanned aerial vehicle, P is the transmitting power, EE is the objective function, and l is the iteration number;
s412: calculating the future predicted driving tracks q 1, q 2, q 3, …, qn of the vehicle;
s413: will { s } (l) 、y (l) 、θ (l) And q [1]]，q[2]，q[3]，…，q[N]Substituting into formula (P2) to obtain optimal solution s (l +1) .P (l+1) Wherein the objective function is EE (l+1) ；
S414: judgment | EE (l+1) -EE (l) |≤∈ 1 Whether the result is true or not; if yes, go to step S415, otherwise go to step S416;
s415: obtaining optimal power distribution P of unmanned aerial vehicle (l+1) And a flight trajectory s (l+1) ；
S416: let l be l + 1; while returning to S413.
Preferably, step S4 further includes:
s421: initialization μ l is 0, μ u U, the objective function is W (0) The iteration number m is 0, and the error margin epsilon is set 2 ；
S422: calculating mu (m) ＝(μ l +μ u )/2；
S423: if W (m) > 0, then mu u ＝μ (m) Otherwise mu l ＝μ (m) ；
S424: mu.f u -μ l ＜∈ 2 Then the objective function W is obtained (m) Is best solution { P } (m) ，s (m) Otherwise, let m be m +1, and return to step S422.
Preferably, the drone is a rotary wing drone.
The invention introduces a vehicle movement track prediction model in the energy efficiency problem of the unmanned aerial vehicle flight base station, and compared with other models describing the movement of a ground terminal, the model can obtain the specific track coordinates of a traveling vehicle, so that the flight track of the unmanned aerial vehicle with maximized energy efficiency can be optimized. In order to realize the maximum flight of the energy efficiency of the auxiliary communication system of the unmanned aerial vehicle, an unmanned aerial vehicle communication system track optimization framework capable of combining a moving vehicle path prediction technology is designed according to the practical situation, a practical and feasible unmanned aerial vehicle communication system track online optimization algorithm is derived by pushing down on the framework, a non-convex problem appearing in the unmanned aerial vehicle communication system track online optimization algorithm is converted into a convex problem through the optimization algorithm, and then the optimization problem can be solved by using a convex optimization tool box.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: according to the invention, aiming at the characteristic of rapid change of the position of a user of a traveling vehicle, a vehicle movement track prediction model and an unmanned aerial vehicle energy loss model are introduced, so that the future movement track of the vehicle can be predicted in advance, and the flight track of the unmanned aerial vehicle is optimized according to the future movement information of the vehicle, so that the communication performance between an unmanned aerial vehicle base station and a ground mobile terminal is improved, the energy loss caused by the flight of the unmanned aerial vehicle is greatly reduced, the running time of the unmanned aerial vehicle flight base station can be further prolonged, and the application value of the unmanned aerial vehicle as the flight base station for auxiliary communication is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a model diagram of a communication system of an unmanned-vehicle-assisted traveling vehicle according to the present invention.
Fig. 2 is a schematic flow chart of the unmanned aerial vehicle trajectory optimization method for assisting mobile vehicle wireless communication of the present invention.
FIG. 3 is a detailed flowchart of solving the optimization problem P1 in the present invention.
FIG. 4 is a detailed flowchart of solving the approximate convex optimization problem P2 in the present invention.
Fig. 5 is a detailed flowchart of the vehicle trajectory prediction.
Detailed Description
The invention is further illustrated by the following figures and examples.
Referring to fig. 1-5, a method for unmanned aerial vehicle trajectory optimization to assist in wireless communication with a traveling vehicle, comprising the steps of:
1. the method comprises the steps of establishing a wireless communication system model of an auxiliary advancing vehicle of a rotary wing unmanned aerial vehicle (hereinafter referred to as unmanned aerial vehicle), wherein an unmanned aerial vehicle S is used for communicating the auxiliary advancing vehicle D, and the advancing vehicle D carries a positioning device which can transmit position information of the current vehicle in real time when the unmanned aerial vehicle S communicates with the unmanned aerial vehicle S, as shown in figure 1.
As shown in fig. 5, the method for predicting the trajectory of the vehicle includes the steps of:
construction of Current vehicle State information (vehicle Current position (x) t ，y t ) Destination location (x) F ，y F ) The current vehicle running speed v and acceleration a, and the yaw angle information omega) of the vehicle), obtaining the total distance between the current position and the destination position of the vehicle as L by an electronic map of a car navigation system, wherein the whole road can be divided into a plurality of road sections L according to curves, intersections and vehicle starting stages in the road 1 ，L 2 ，...，L N 。
Short-term (several seconds) prediction of the vehicle's travel path is made, assuming a time interval delta between adjacent positions of the predicted path of the vehicle t The predicted time length of the vehicle track is T I Selecting a constant rotation rate and acceleration model (CTRA) when the yaw angle | ω | is greater than zero, according to the yaw angle of the vehicle; when the yaw angle | ω | is equal to zero, a constant velocity model (CV) is selected. Wherein the constant velocity model (CV) and the constant rate and acceleration model (CTRA) are expressed as follows
The state space expression of the constant velocity model CV is:
Figure GDA0003699803310000121
wherein x t And y t Are respectively the abscissa and ordinate, and v x And v y The speed in the abscissa direction is the speed. It is a linear motionMoving model, linear state conversion of which
Figure GDA0003699803310000131
And delta t Representing the time it takes to move to the next location, the state transfer function vector may instead be:
Figure GDA0003699803310000132
for use within the unscented kalman filter framework.
The state space expression of a constant slew rate and acceleration model (CTRA) is
Figure GDA0003699803310000133
The state transition equation of the model is as follows:
Figure GDA0003699803310000134
Figure GDA0003699803310000135
Figure GDA0003699803310000136
where θ is the yaw angle of the vehicle, v is the speed, a is the acceleration, and ω is the vehicle yaw rate. Constant rate and acceleration models (CTRAs) describe the state of the vehicle more realistically because they take into account both the yaw rate and the acceleration values during the movement of the vehicle. Since the yaw rate and acceleration in the constant-rate and acceleration model (CTRA) are assumed to remain constant, and in order to facilitate the prediction of the future travel trajectory of the vehicle, the state space of the constant-rate and acceleration model (CTRA) may be rewritten as
Figure GDA0003699803310000137
At the elapsed time interval delta t Predicted trajectory information of the vehicle
x(t+δ t )＝x(t)+[v x (t)sin(ωδ t )-v y (t)(cos(ωδ t )-1)]/ω+[a(sin(θ(t)+ωδ t )-sin(θ(t)))-aωδ t cos(θ(t)+ωδ t )]/ω 2
y(t+δ t )＝y(t)+[v y (t)sin(ωδ t )-v x (t)(cos(ωδ t )-1)]/ω+[a(cos(θ(t)+ωδ t )-cos(θ(t)))+aωδ t sin(θ(t)+ωδ t )]/ω 2
v x (t+δ t )＝v x (t)cos(ωδ t )+v y (t)sin(ωt)+aδ t sin(θ(t)+ωδ t )
v y (t+δ t )＝v y (t)cos(ωδ t )-v x (t)sin(ωt)+aδ t cos(θ(t)+ωδ t )
θ(t+δ t )＝θ(δ t )+ωδ t
Combining the vehicle motion model (constant velocity model (CV), constant rotation rate and acceleration model (CTRA)) with the unscented Kalman filtering algorithm to obtain the vehicle future T I Predicted movement trajectory (x) of vehicle over time 1 ，y 1 )，(x 2 ，y 2 )，...，(x I ，y I ) Wherein
Figure GDA0003699803310000141
The above vehicle trajectory prediction method is to calculate the future moving position of the vehicle through a corresponding motion model according to the current motion information (including position coordinates, speed, acceleration, deviation angle, yaw rate, etc.) of the vehicle, and this usually can only predict the sustainable position information in a short period. As another possible embodiment, a vehicle long-term trajectory prediction may also be adopted, which is proposed for a vehicle motion model (e.g., constant angular velocity and acceleration CYRA) only suitable for vehicle trajectory prediction in a short period, and it is often to increase the judgment on the intention of the driver on the basis of the vehicle motion model to improve the accuracy of the vehicle trajectory prediction, especially in the case of a curve and a lane change. Compared with the vehicle short-term track prediction, the vehicle long-term track prediction further analyzes the driving intention of a driver, and improves the accuracy of track prediction under the conditions of curves and lane changes. The vehicle long-term trajectory prediction may be substituted for the vehicle short-term trajectory prediction.
Aiming at the defects of the vehicle short-term track prediction method, the invention provides the following solutions: according to the current vehicle state information, road attributes (road length, road width, lane number, lane width, maximum speed limit and the like) and historical passing time (batch passing time corresponding to the previous three days, batch passing time mean value corresponding to the previous seven days, two average passing times before and after the same year) of the road section L 1 ，L 2 ，...，L N So as to obtain T 1 ，T 2 ，...，T N . Assuming that the speed of the vehicle is constant during the running process, the moving track of the vehicle on the remaining distance can be approximately obtained
Figure GDA0003699803310000151
Wherein the time interval between each coordinate is δ t 。
The vehicle motion model can accurately predict the moving track of the vehicle in a short time, the whole-course moving track of the vehicle can be obtained by predicting the passing time, and the accurate whole-course vehicle moving track and the passing time T in a short time can be predicted by combining the two prediction results.
Assuming that the short-term predicted time of the vehicle is less than the predicted transit time for the first road segment, T I ＜T 1 So that the actual predicted trajectory of the vehicle at the current time t is
Figure GDA0003699803310000152
Meanwhile, the vehicle may have to be decelerated or emergently braked due to an emergency condition during the running process, and in order to avoid serious distortion of the predicted moving track of the vehicle caused by the emergency deceleration, the interval time length T is selected f And performing a round of vehicle movement track prediction again.
2. After the unmanned aerial vehicle takes off, the unmanned aerial vehicle flies at a constant height H, the track of the ground vehicle is predicted to obtain the vehicle passing time T of the whole travel, and the vehicle passing time T is used as the time for the unmanned aerial vehicle to carry out the auxiliary communication task in the flying base station. The time T for the unmanned aerial vehicle flight base station to perform the auxiliary communication task can be divided into N time slot lengths, that is, T is N δ t ，δ t Indicating the basic slot length. Suppose that the time interval for vehicle trajectory prediction per round is T f The running vehicle will go every T f And the time sends the future predicted track coordinates of the vehicle to the unmanned aerial vehicle flight base station, and the unmanned aerial vehicle flight base station plans the self-flying track according to the future predicted track coordinates of the vehicle. Assuming that the unmanned aerial vehicle flight base station has enough computing power, the time delay of the process of optimizing the flight trajectory of the unmanned aerial vehicle can be ignored. Wherein any time slot N [ e [1, N ] of the ith unmanned aerial vehicle flight base station path planning]The position coordinates of the unmanned aerial vehicle S and the traveling vehicle D may be respectively expressed as S [ n ]]、p[n]Wherein s [ n ]]＝(x s [n]，y s [n])、p[n]＝(x p [n]，y p [n]). And the predicted position coordinates of the traveling vehicle within the time slot may be represented as q n]＝(x q [n]，y q [n]) Then the predicted distance of the drone from the traveling vehicle at time slot n may be expressed as
Figure GDA0003699803310000161
Wherein s is i [n]And q is i [n]Respectively representing the position coordinates of the unmanned aerial vehicle and the predicted position coordinates of a running vehicle when the ith round of unmanned aerial vehicle runs the track plan, i belongs to [1, M ∈ [ ]]，n∈[1，N]。
3. At any time slot N ∈ [1, N ], the channel coefficient between the drone and the ground terminal can be represented by h [ N ]:
Figure GDA0003699803310000162
wherein beta [ n ]]＝β 0 d -2 [n]Expressed as a large-scale fading factor, h n, affected by path loss]Expressed as small-scale fading factors affected by multipath. Suppose that in an OFDM communication system, K e [1, K ] is in any subcarrier]The transmission power of the communication between the unmanned aerial vehicle and the mobile terminal in the time slot n is P k [n]The communication transmission reachable rate between the drone and the mobile terminal may be expressed as
Figure GDA0003699803310000163
Wherein the content of the first and second substances,
Figure GDA0003699803310000164
denotes the channel bandwidth of each subcarrier, W being the total bandwidth of the system, P k [n]For the transmission power on the k subcarrier of the nth slot, σ 2 Is the noise power at the receiver, Γ > 1 represents the gap from the channel capacity due to the actual modulation and coding employed, and defines the received signal-to-noise ratio γ at a reference distance of 1 meter 0 ＝P k [n]H k [n]To do so
Figure GDA0003699803310000165
When one of the transmitter and the receiver is moving, the received signal will have doppler shift, but for a typical vehicle speed (75km/h) and frequency (about 1GHz), the doppler shift is only about 100Hz, so it will be assumed herein that the doppler shift is well compensated.
4. The moving tracks { x (t), y (t), H } of the unmanned aerial vehicle at constant flying height continuously change along with time can be N long time sequence
Figure GDA0003699803310000166
To approximate. When the unmanned plane is in any time slot N ∈ [1, N ]]When the flying speed is V, the flying power loss of the unmanned aerial vehicle in the time slotThe consumption can be modeled as
Figure GDA0003699803310000171
Wherein P is p And P i Two defined constants related to hardware of the unmanned aerial vehicle respectively represent cascade contour power and inductive power in a hovering state, U tip Representing the tip speed, v, of the rotor blade 0 Referred to as mean induced speed of rotor at suspension, d 0 And s is the fuselage drag ratio and rotor solidity, respectively, and ρ and a represent the air density and rotor disk area, respectively. By substituting V-0 into (15), we get the power consumption of the hover state to be P [ n [ ]]＝P p +P i This is a finite value that depends on aircraft weight, air density and rotor disc area. As V increased, P [ n ] in (15) was confirmed]First decreasing and then increasing, one conclusion may be drawn that the power consumption of the drone in the hovering state is not minimal.
The energy loss of the unmanned aerial vehicle in any time slot N epsilon [1, N ] of the communication task can be expressed by the method
E[n]＝(P[n]+P c )δ t (4)
Wherein, P c Is the circuit power consumption of the communication transmitter.
5. The optimization problem of the unmanned aerial vehicle auxiliary traveling vehicle communication system can be established by taking the maximum unmanned aerial vehicle flight energy efficiency as an optimization target.
Figure GDA0003699803310000172
s.t||s i [n+1]-s i [n]||≤V max δ t ，n＝1，…，N-1 (5)
Figure GDA0003699803310000173
s 0 ＝s I ，i＝1 (7)
s i0 ＝s i-1 [N]，i＝2，3… (8)
Wherein the content of the first and second substances,
Figure GDA0003699803310000174
for the transmit power on the kth subcarrier of the nth slot of the ith round,
Figure GDA0003699803310000175
for the track-optimized (i-1) set of sums of the communication rates of the unmanned aerial vehicle and the ground terminal within the prediction time range,
Figure GDA0003699803310000176
for the sum of the communication rates of the unmanned aerial vehicle and the ground terminal in the j-th prediction time range after the track optimization, i.e.
Figure GDA0003699803310000177
Wherein
Figure GDA0003699803310000178
Figure GDA0003699803310000179
Refers to the jth round time window T that has elapsed f The transmission rate on the kth subcarrier of the mth time slot in the range, M is the jth time window T f The number of time slots in the range is,
Figure GDA0003699803310000181
is the average value of the transmission data quantity on the k subcarrier of the nth time slot. Also, in the same manner as above,
Figure GDA0003699803310000182
for the sum of the flight energy losses of the unmanned aerial vehicle in the (i-1) group prediction time range after the track optimization,
Figure GDA0003699803310000183
for predicting the sum of flight energy losses of the unmanned aerial vehicle in the time range of the j th time after the track optimization, i.e.
Figure GDA0003699803310000184
Wherein the content of the first and second substances,
Figure GDA0003699803310000185
for the jth elapsed time window T f Energy consumption of the m-th slot drone within the range, E i [n]And (4) calculating the energy consumption of the unmanned aerial vehicle for the nth time slot of the ith round by the formula (4).
The constraint (17) limits the speed of the drone in each time slot, where V max Representing the maximum horizontal velocity of the drone.
Constraint (18) of P max Representing the maximum transmit power of the transmitter mounted on the drone.
S in constraint (19) I Representing the initial position of the drone projection on the horizontal plane.
(20) The initial position of the unmanned aerial vehicle flight base station for performing track optimization according to the predicted position information of the traveling vehicle is constrained to be the final position of the last track optimization.
6. Since the optimization problem is non-convex, it needs to be transformed. Firstly, a relaxation variable { y ] is introduced into the denominator of the objective function of the optimization problem in ≧ 0}, wherein
Figure GDA0003699803310000186
Wherein, Delta in Indicated as the flight distance of the ith round of drones in the nth time slot.
Convert the above equation
Figure GDA0003699803310000187
Therefore, the following denominator form of the objective function is obtained
Figure GDA0003699803310000188
Approximating the first order Taylor expansion to the right of equation (22) may obtain a global lower estimate:
Figure GDA0003699803310000191
wherein
Figure GDA0003699803310000192
And s i [n] (l) Is the value of the corresponding variable for the ith iteration.
7. For in the target function molecule
Figure GDA0003699803310000193
Can be obtained by carrying out general separation treatment
Figure GDA0003699803310000194
Wherein ε {. } refers to a mathematical expectation operation;
can make theta n]＝H 2 +||s[n]-p[n]|| 2 (14)
Then (13) can be converted into
Figure GDA0003699803310000195
Therefore, the objective function of the optimization problem P1 is converted into
Figure GDA0003699803310000196
Order to
Figure GDA0003699803310000197
The objective function numerator of the optimization problem P1 is
Figure GDA0003699803310000198
8. Let theta i [n]Is the ith wheel theta n]Is given at random, so that any given local point
Figure GDA0003699803310000199
The problem (P1) can be re-described as
Figure GDA00036998033100001910
Figure GDA0003699803310000201
Figure GDA0003699803310000202
H 2 +||s[n]-p[n]|| 2 ≤θ i [n].n＝1.2，…，N-1
9. For the above optimization problem (P1.1), which can also be solved by solving the pseudo-convex optimization problem, an auxiliary variable μ is introduced, and the problem (P1.1) can be written as
Figure GDA0003699803310000203
Wherein the minimum value of μ is taken as the upper bound of the objective function, i.e. the maximum value of the objective function, to find the flight trajectory s of the drone that maximizes the objective function i [n]. Then (31) can be further written as
Figure GDA0003699803310000204
Wherein
Figure GDA0003699803310000205
Is a positive value, and μ is the upper bound of the objective function, which causes the optimization problem (P1.1) to be achieved when the problem (P2) is non-negativeA maximum value.
10. The optimization problem (P2) is
Figure GDA0003699803310000206
Figure GDA0003699803310000207
Figure GDA0003699803310000208
H 2 +||s[n]-p[n]|| 2 ≤θ i [n].n＝1，2，…，N
At the moment, the optimal solution of the flight path of the unmanned aerial vehicle can be solved through the convex optimization toolbox.
As shown in fig. 3, the solution method for the optimization problem (P1) includes the following steps:
s411: and setting the flight track of the unmanned aerial vehicle as S, the transmitting power as P, the objective function as EE and l as the iteration number.
Initialization s (0) 、y (0) 、θ (0) The objective function is EE (0) The iteration number l is 0, and the error margin epsilon is set 1
S412: obtaining the future predicted driving tracks q 1, q 2, q 3, …, q N of the vehicle
S413: will { s } (l) 、y (l) 、θ (l) And q [1]]，q[2]，q[3]，…，q[N]Solving the optimization problem (P2) to obtain the optimal solution s (l+1) ，P (l+1) }, the objective function is EE (l+1)
S414: judgment | EE (l+1) -E[ (l) |≤∈ 1 Whether the result is true or not; if yes, go to step S415, otherwise go to step S416;
s415: obtaining optimal power distribution P of unmanned aerial vehicle (l+1) And a flight trajectory s (l+1) ；
S416: let l be l + 1;
the solution process for the problem (P2) is shown in fig. 4, the method comprising the steps of:
s421: initializing mu l ＝0，μ u Set the objective function W as u (0) The iteration number m is 0, and the error margin e is set 2
S422: calculating mu (m) ＝(μ l +μ u )/2
S423: if W (m) > 0, then mu u ＝μ (m) Else mu l ＝μ (m)
S424: mu.s of u -μ l ＜∈ 2 Then the objective function W is obtained (m) Optimal solution { P (m) ，s (m) Otherwise, let m be m +1, and return to S22.
In summary, the invention designs an unmanned aerial vehicle track optimization method for assisting a traveling vehicle in wireless communication, and in an urban road environment, an unmanned aerial vehicle can optimize the flight track of the unmanned aerial vehicle according to the movement condition of the vehicle, so that the communication performance between the unmanned aerial vehicle and a ground traveling vehicle is improved, and the energy loss caused by the flight of the unmanned aerial vehicle is greatly reduced. Meanwhile, a vehicle movement track prediction model is introduced into the energy efficiency problem of the unmanned aerial vehicle, and compared with other models for describing the movement of the ground terminal, the model can obtain the specific track coordinates of the traveling vehicle, so that the flight track of the unmanned aerial vehicle with maximized energy efficiency can be optimized. Compared with the prior art, the invention mainly has the following advantages:
1. the future whole-course moving track of the vehicle can be predicted. The method comprises the steps of constructing vehicle state information by utilizing vehicle position information, vehicle speed and acceleration and vehicle course angle acquired by GPS equipment of a vehicle, and predicting the movement track of the vehicle in a short term in the future through a vehicle motion model and an unscented Kalman filtering algorithm; meanwhile, the automobile navigation system can predict the running time of the vehicle passing through the road section according to the road attribute information and the historical passing time of the road, and further can approximately obtain the moving track of the vehicle in the whole travel.
2. The flight energy loss of the unmanned aerial vehicle is reduced. Aiming at the characteristic that the self-carried energy of the unmanned aerial vehicle is limited, the flight trajectory with the maximum flight energy efficiency of the unmanned aerial vehicle is found by analyzing the flight energy loss model of the unmanned aerial vehicle, so that the communication performance between the unmanned aerial vehicle and a ground travelling vehicle is improved, the energy loss generated by the flight of the unmanned aerial vehicle is reduced as much as possible, the running time of the unmanned aerial vehicle can be prolonged, and the application value of the unmanned aerial vehicle as a flight base station for auxiliary communication is improved.
3. And the scene of ground user movement is met. At present, most of research on unmanned aerial vehicle flying base stations only considers the condition that a ground terminal is always static, but in fact, the communication demand of a ground user is always dynamically changed. The invention is suitable for scenes that ground users move quickly in urban roads, and particularly provides an unmanned aerial vehicle track optimization method for assisting a traveling vehicle in wireless communication aiming at the vehicle in the traveling process.
The above-mentioned embodiments are preferred embodiments of the present invention, and the present invention is not limited thereto, and any other modifications or equivalent substitutions that do not depart from the technical spirit of the present invention are included in the scope of the present invention.

Claims (4)

1. An unmanned aerial vehicle track optimization method for assisting wireless communication of a mobile vehicle is characterized by comprising the following steps:
s1, establishing an unmanned aerial vehicle auxiliary traveling vehicle communication system model and a corresponding mathematical optimization problem P1 by taking the maximum unmanned aerial vehicle flight energy efficiency as an optimization target;
s2, obtaining the current state information of the vehicle in the system model, and obtaining the future movement track of the vehicle through the vehicle track prediction model according to the current state information of the vehicle;
s3, according to the future moving track of the vehicle, carrying out approximate processing on the optimization problem P1 to be converted into a convex optimization problem, and obtaining an approximate convex optimization problem P2 of the optimization problem P1;
s4, solving the approximately convex optimization problem P2, solving an optimization problem P1 of an unmanned aerial vehicle auxiliary advancing vehicle communication system model, and further obtaining the flight trajectory and power distribution of the unmanned aerial vehicle when the flight energy efficiency of the unmanned aerial vehicle is maximized;
step S1 includes:
the unmanned aerial vehicle flies at a constant height H after taking off, and the time interval for predicting the vehicle track of each wheel is assumed to be T f The running vehicle will go every T f The time sends the future predicted track coordinates of the vehicle to the unmanned aerial vehicle flight base station, and the unmanned aerial vehicle flight base station can plan the self-flying track according to the future predicted track coordinates of the vehicle;
supposing that the unmanned aerial vehicle flight base station has enough computing power, the time delay of the process of optimizing the flight trajectory of the unmanned aerial vehicle can be ignored; wherein any time slot N [1, N ] of the flight base station of the unmanned aerial vehicle in the ith path planning]The position coordinates of the unmanned aerial vehicle S and the traveling vehicle D are respectively expressed as S [ n ]]、p[n]Wherein s [ n ]]＝(x s [n]，y s [n])、p[n]＝(x p [n]，y p [n]) (ii) a And the predicted position coordinates of the traveling vehicle within the time slot are represented as q n]＝(x q [n]，y q [n]) Then the predicted distance of the drone from the traveling vehicle at time slot n is denoted as
Figure FDA0003699803300000011
Wherein s is i [n]And q is i [n]Respectively representing the position coordinates of the unmanned aerial vehicle and the predicted position coordinates of the traveling vehicle when the ith round of unmanned aerial vehicle traveling track is planned, wherein i belongs to {1, 2, 3, … }, N belongs to [1, N ]]；
In any time slot N belongs to [1, N ], using h [ N ] to represent the channel coefficient between the unmanned aerial vehicle and the ground terminal, then
Figure FDA0003699803300000021
Wherein beta [ n ]]＝β 0 d -2 [n]Expressed as a large scale fading factor affected by path loss,
Figure FDA0003699803300000022
expressed as small-scale fading factors affected by multipath; suppose in OFDM communicationIn a communication system, in any subcarrier K ∈ [1, K ]]The transmission power of the communication between the upper unmanned aerial vehicle and the running vehicle in the time slot n is P k [n]The communication transmission achievable rate between the drone and the traveling vehicle is then expressed as
Figure FDA0003699803300000023
Wherein the content of the first and second substances,
Figure FDA0003699803300000024
denotes the channel bandwidth of each subcarrier, W being the total bandwidth of the system, P k [n]For the transmission power on the k subcarrier of the nth slot, σ 2 Is the noise power at the receiver, Γ > 1 represents the gap from the channel capacity due to the actual modulation and coding employed, and defines the received signal-to-noise ratio at a reference distance of 1 meter as γ 0 ＝P k [n]H k [n](ii) a While
Figure FDA0003699803300000025
The moving tracks { x (t), y (t), H } of the unmanned aerial vehicle at constant flying height continuously change along with time are N long time sequences
Figure FDA0003699803300000026
To approximate; when the unmanned plane is in any time slot N ∈ [1, N ]]When the flying speed is V, the flying power loss of the unmanned aerial vehicle in the time slot is modeled as
Figure FDA0003699803300000027
Wherein P is p And P i Two defined constants related to hardware of the unmanned aerial vehicle respectively represent cascade contour power and inductive power in a hovering state, U tip Representing the tip speed, v, of the rotor blade 0 Referred to as mean induced speed of rotor at suspension, d 0 And s is respectively the fuselage resistanceForce ratio and rotor solidity, ρ and a representing air density and rotor disk area, respectively; by substituting V in 0 for expression (3), power consumption P [ n ] in the hovering state is obtained]＝P p +P i ；
The energy loss of the unmanned aerial vehicle in any time slot N epsilon [1, N ] of the communication task is expressed as follows:
E[n]＝(P[n]+P c )δ t (4)
wherein, P c Circuit power consumption for a communication transmitter;
therefore, the optimization problem P1 of the unmanned aerial vehicle auxiliary traveling vehicle communication system model is as follows by taking the maximum unmanned aerial vehicle flight energy efficiency as an optimization target:
Figure FDA0003699803300000031
s.t||s i [n+1]-s i [n]||≤V max δ t ，n＝1，…，N-1 (5)
Figure FDA0003699803300000032
s 0 ＝s I ，i＝1 (7)
s i0 ＝s i-1 [N]，i＝2，3… (8)
wherein the content of the first and second substances,
Figure FDA0003699803300000033
for the transmit power on the kth subcarrier of the nth slot of the ith round,
Figure FDA0003699803300000034
for the track-optimized (i-1) set of sums of the communication rates of the unmanned aerial vehicle and the ground terminal within the prediction time range,
Figure FDA0003699803300000035
for the j prediction after the optimization of the trackThe sum of the communication rates of the drone and the ground terminal in the time frame, i.e.
Figure FDA0003699803300000036
Wherein the content of the first and second substances,
Figure FDA0003699803300000037
Figure FDA0003699803300000038
refers to the jth round time window T that has elapsed f The transmission rate on the kth subcarrier of the mth time slot in the range, M is the jth time window T f The number of time slots in the range is,
Figure FDA0003699803300000039
the average value of the transmission data quantity on the kth subcarrier of the nth time slot; also, in the same manner as above,
Figure FDA00036998033000000310
for the sum of the flight energy losses of the unmanned aerial vehicle in the (i-1) group prediction time range after the track optimization,
Figure FDA00036998033000000311
for predicting the sum of flight energy losses of the unmanned aerial vehicle in the time range of the j th time after the track optimization, i.e.
Figure FDA00036998033000000312
Wherein the content of the first and second substances,
Figure FDA00036998033000000313
for the jth elapsed time window T f Energy consumption of the m-th slot drone within the range, E i [n]Calculating the energy consumption of the unmanned aerial vehicle for the nth time slot of the ith wheel by using a formula (4); equation (5) is used to limit the speed of the drone in each time slot, where V max Representing a maximum horizontal velocity of the drone; p in formula (6) max Indicating maximum transmission of transmitter mounted on dronePower; s in the formula (7) I Representing an initial position of the projection of the unmanned aerial vehicle on a horizontal plane; formula (8) is that the initial position of the unmanned aerial vehicle flight base station for performing track optimization according to the predicted position information of the traveling vehicle is constrained to be the final position of the last track optimization;
step S3 includes:
since the optimization problem P1 is non-convex, the optimization problem P1 needs to be transformed by first introducing a slack variable y into the denominator of the objective function of the optimization problem P1 in ≧ 0}, wherein
Figure FDA0003699803300000041
Wherein, Delta in The flight distance of the ith round of unmanned aerial vehicle in the nth time slot is represented;
converting equation (9):
Figure FDA0003699803300000042
the denominator form of the objective function of the following optimization problem P1 is obtained:
Figure FDA0003699803300000043
the global lower estimate is obtained by approximating the first order Taylor expansion to the right of equation (10):
Figure FDA0003699803300000044
wherein
Figure FDA0003699803300000045
And s i [n] (l) Is the value of the corresponding variable for the first iteration;
for the numerator of the objective function of the optimization problem P1
Figure FDA0003699803300000046
Carrying out general division treatment to obtain:
Figure FDA0003699803300000047
wherein ε {. } refers to a mathematical expectation operation;
let theta [ n ]]＝H 2 +||s[n]-p[n]|| 2 (14)
Equation (13) then translates to:
Figure FDA0003699803300000048
the molecular transformation of the objective function of the optimization problem P1
Figure FDA0003699803300000049
Order to
Figure FDA0003699803300000051
The numerator of the objective function of the optimization problem P1 is
Figure FDA0003699803300000052
Let theta i [n]Is the ith wheel theta n]A value of (d); therefore, given any local point
Figure FDA0003699803300000053
The optimization problem P1 of the unmanned aerial vehicle auxiliary traveling vehicle communication system is changed into:
Figure FDA0003699803300000054
Figure FDA0003699803300000055
H 2 +||s[n]-p[n]|| 2 ≤θ i [n].n＝1，2，…，N
for the above formula (P1.1), which is also solved by solving the pseudo-convex optimization problem, an auxiliary variable μ is introduced, and the formula (P1.1) is written as:
Figure FDA0003699803300000056
taking the minimum value of mu as the upper bound of the objective function, namely the maximum value of the objective function, so as to find the flight path s of the unmanned aerial vehicle for maximizing the objective function i [n](ii) a Equation (19) can be further written as:
Figure FDA0003699803300000057
wherein
Figure FDA0003699803300000058
Is a positive value, with μ as the upper bound of the objective function; taking the formula (P1.1) to a maximum when the following near convex optimization problem P2 is non-negative;
Figure FDA0003699803300000059
Figure FDA00036998033000000510
Figure FDA0003699803300000061
H 2 +||s[n]-p[n]|| 2 ≤θ i [n].n＝1，2，…，N
at the moment, solving the optimal solution of the flight path and the power distribution of the unmanned aerial vehicle through convex optimization software;
step S4 includes:
s411: initialization s (0) 、y (0) 、θ (0) Setting the objective function as EE (0) The iteration number l is 0, and the error margin epsilon is set 1 (ii) a Wherein S is the flight trajectory of the unmanned aerial vehicle, P is the transmitting power, EE is the objective function, and l is the iteration number;
s412: calculating the future predicted driving tracks q 1, q 2, q 3, …, qn of the vehicle;
s413: will { s } (l) 、y (l) 、θ (l) And q [1]]，q[2]，q[3]，…，q[N]Substituting into formula (P2) to obtain optimal solution s (l+1) ，P (l+1) Wherein the objective function is EE (l+1) ；
S414: judgment | EE (l+1) -EE (l) |≤∈ 1 Whether the result is true or not; if yes, go to step S415, otherwise go to step S416;
s415: obtaining optimal power distribution P of unmanned aerial vehicle (l+1) And a flight trajectory s (l+1) ；
S416: let l be l + 1; returning to S413;
step S4 further includes:
s421: initializing mu l ＝0，μ u U, the objective function is W (0) The iteration number m is 0, and the error margin epsilon is set 2 ；
S422: calculating mu (m) ＝(μ l +μ u )/2；
S423: if W (m) > 0, then mu u ＝μ (m) Else mu l ＝μ (m) ；
S424: mu.f u -μ l ＜∈ 2 Then the objective function W is obtained (m) Is best solution { P } (m) ，s (m) Otherwise, let m be m +1, and return to step S422.
2. The unmanned aerial vehicle trajectory optimization method for assisting mobile vehicle wireless communication according to claim 1, wherein the unmanned aerial vehicle-assisted traveling vehicle communication system model communicates with an unmanned aerial vehicle S-assisted traveling vehicle D, the traveling vehicle D carrying a positioning device for transmitting current position information of the vehicle in real time when the unmanned aerial vehicle S communicates with the traveling vehicle D.
3. The unmanned aerial vehicle trajectory optimization method for assisting mobile vehicle wireless communication according to claim 1, wherein step S2 comprises:
acquiring current running state information of the vehicle, wherein the current running state information comprises the current position of the vehicle, the destination position, the running speed and the acceleration of the current vehicle and the yaw angle information of the vehicle;
judging whether the yaw angle changes in the current vehicle running process; if the vehicle speed changes, a constant rotation rate and acceleration model CTRA is adopted, and the constant rotation rate and acceleration model CTRA is combined with an unscented Kalman filtering algorithm UKF to calculate the movement track of the vehicle in the future short term; when the vehicle does not change, a constant speed model CV is adopted, and the constant speed model CV is combined with an unscented Kalman filtering algorithm UKF to calculate the movement track of the vehicle in the future short term;
dividing a road on which a vehicle is going into a plurality of road sections according to the current position information and the destination position information of the vehicle, and predicting the current vehicle passing time through the basic information of the road and the historical vehicle passing time of the road sections; according to the length of each road section and the corresponding predicted passing time, obtaining the position information of the vehicles at different moments;
splicing the short-term driving track prediction result of the vehicle and the vehicle passing time prediction result according to the time sequence, wherein the front part of the splicing is the short-term driving track prediction result of the vehicle, and the rear part of the splicing is the vehicle driving track prediction result obtained by predicting the passing time of different road sections;
and continuously acquiring the current running state information of the vehicle to circularly predict the moving track of the vehicle after the vehicle runs the travel corresponding to the short-term prediction time each time.
4. The method of claim 1, wherein the drone is a rotary wing drone.
CN202010775851.9A 2020-08-05 2020-08-05 Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle Active CN111885504B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010775851.9A CN111885504B (en) 2020-08-05 2020-08-05 Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010775851.9A CN111885504B (en) 2020-08-05 2020-08-05 Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle

Publications (2)

Publication Number Publication Date
CN111885504A CN111885504A (en) 2020-11-03
CN111885504B true CN111885504B (en) 2022-08-02

Family

ID=73210588

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010775851.9A Active CN111885504B (en) 2020-08-05 2020-08-05 Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle

Country Status (1)

Country Link
CN (1) CN111885504B (en)

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112435504B (en) * 2020-11-11 2022-07-08 清华大学 Centralized collaborative track planning method and device under vehicle-road collaborative environment
CN112243239B (en) * 2020-12-21 2021-03-12 长沙理工大学 Unmanned aerial vehicle deployment method based on overpass and related device
CN113115217B (en) * 2021-03-01 2022-06-21 山东师范大学 Post-disaster UAV auxiliary communication area division method and system based on optimal transmission theory
CN113055898B (en) * 2021-03-11 2022-10-04 西北工业大学 Air-ground cooperative Internet of vehicles unmanned aerial vehicle position deployment and data chain anti-interference method
CN113259835B (en) * 2021-03-24 2022-01-28 北京交通大学 Unmanned aerial vehicle cluster deployment method based on ground cellular system
CN113034981B (en) * 2021-04-14 2022-07-08 北京航空航天大学 Multi-relay unmanned aerial vehicle flight path planning method and system in uncertain channel environment and storage medium
CN113507717A (en) * 2021-06-08 2021-10-15 山东师范大学 Unmanned aerial vehicle track optimization method and system based on vehicle track prediction
CN113518361B (en) * 2021-06-18 2022-06-17 中南林业科技大学 Trajectory optimization method, device and medium for unmanned aerial vehicle-assisted wireless communication
CN113596854B (en) * 2021-07-21 2022-08-09 东南大学 Wide area coverage high-speed transmission method assisted by autonomous mobile relay
CN113747397B (en) * 2021-09-08 2024-03-29 广州大学 Unmanned aerial vehicle assisted traveling vehicle communication system optimization method
CN116367087A (en) * 2021-12-27 2023-06-30 大唐移动通信设备有限公司 Communication method, communication device, base station, removable relay and storage medium
CN114339667B (en) * 2022-01-06 2022-12-20 北京邮电大学 Relay method and device based on hybrid unmanned aerial vehicle aerial mobile base station
CN115134370B (en) * 2022-06-23 2023-06-02 广东工业大学 Multi-unmanned aerial vehicle assisted mobile edge computing and unloading method
CN115278849B (en) * 2022-09-29 2022-12-20 香港中文大学（深圳） Transmission opportunity and power control method for dynamic topology of unmanned aerial vehicle

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110380773A (en) * 2019-06-13 2019-10-25 广东工业大学 A kind of track optimizing and resource allocation methods of unmanned plane multi-hop relay communication system
CN110730031A (en) * 2019-10-22 2020-01-24 大连海事大学 Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107238388B (en) * 2017-05-27 2018-02-23 合肥工业大学 Multiple no-manned plane task is distributed and trajectory planning combined optimization method and device
CN110381444B (en) * 2019-06-24 2020-10-09 广东工业大学 Unmanned aerial vehicle track optimization and resource allocation method
CN111031513B (en) * 2019-12-02 2020-12-15 北京邮电大学 Multi-unmanned-aerial-vehicle-assisted Internet-of-things communication method and system
CN111050286B (en) * 2019-12-18 2020-11-24 北京邮电大学 Trajectory and resource optimization method in unmanned aerial vehicle auxiliary sensor network
CN111479226B (en) * 2020-05-22 2022-03-11 南京邮电大学 Unmanned aerial vehicle communication system and unmanned aerial vehicle communication optimization method based on user track

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110380773A (en) * 2019-06-13 2019-10-25 广东工业大学 A kind of track optimizing and resource allocation methods of unmanned plane multi-hop relay communication system
CN110730031A (en) * 2019-10-22 2020-01-24 大连海事大学 Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication

Also Published As

Publication number Publication date
CN111885504A (en) 2020-11-03

Similar Documents

Publication Publication Date Title
CN111885504B (en) Unmanned aerial vehicle track optimization method for assisting wireless communication of mobile vehicle
CN110381444B (en) Unmanned aerial vehicle track optimization and resource allocation method
Zhang et al. Energy-efficient trajectory optimization for UAV-assisted IoT networks
Mei et al. 3D-trajectory and phase-shift design for RIS-assisted UAV systems using deep reinforcement learning
CN107017940B (en) Unmanned plane repeat broadcast communication system route optimization method
CN108337034A (en) Full duplex unmanned plane mobile-relay system and its method for optimizing route
CN109885088A (en) Unmanned plane during flying track optimizing method in edge calculations network based on machine learning
Chen et al. Edge computing assisted autonomous flight for UAV: Synergies between vision and communications
CN112902969A (en) Path planning method for unmanned aerial vehicle in data collection process
CN112327907A (en) Unmanned aerial vehicle path planning method
CN114466309A (en) Efficient communication wireless federal learning architecture construction method based on unmanned aerial vehicle
Liu et al. UAV-aided vehicular communication design with vehicle trajectory’s prediction
Wu et al. On the interplay between sensing and communications for UAV trajectory design
Lu et al. Relay in the sky: A UAV-aided cooperative data dissemination scheduling strategy in VANETs
Gesbert et al. UAV-aided RF mapping for sensing and connectivity in wireless networks
Wang et al. Density‐aware deployment with multi‐layer UAV‐V2X communication networks
CN113552898B (en) Unmanned aerial vehicle robust trajectory planning method under uncertain interference environment
CN115390584B (en) Multi-machine collaborative searching method
CN116321237A (en) Unmanned aerial vehicle auxiliary internet of vehicles data collection method based on deep reinforcement learning
CN112243239B (en) Unmanned aerial vehicle deployment method based on overpass and related device
CN114785408A (en) Unmanned aerial vehicle auxiliary communication method based on resource allocation and track optimization
Eskandari et al. AI-based navigation and communication control for a team of UAVs with reconfigurable intelligent surfaces supporting mobile internet of vehicles
Huang et al. Boosting research for carbon neutral on edge UWB nodes integration communication localization technology of IoV
CN113747397B (en) Unmanned aerial vehicle assisted traveling vehicle communication system optimization method
Linpei et al. Energy-efficient computation offloading assisted by RIS-based UAV

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