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CN118301645A - Unmanned aerial vehicle communication perception integrated system beam forming and resource scheduling method - Google Patents

Unmanned aerial vehicle communication perception integrated system beam forming and resource scheduling method Download PDF

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Publication number
CN118301645A
CN118301645A CN202410548159.0A CN202410548159A CN118301645A CN 118301645 A CN118301645 A CN 118301645A CN 202410548159 A CN202410548159 A CN 202410548159A CN 118301645 A CN118301645 A CN 118301645A
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uav
communication
user
aerial vehicle
unmanned aerial
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柴蓉
郭子达
李立凡
陈前斌
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Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3911—Fading models or fading generators
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • 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]
    • H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radio Relay Systems (AREA)

Abstract

本发明涉及一种无人机通信感知一体化系统波束成形及资源调度方法，属于无线通信技术领域。该方法包括：建模无人机通信感知一体化系统模型；建模无人机通信信道模型；建模无人机通信信号；建模用户通信速率及通信时间；建模目标感知分辨率及感知关联变量；建模无人机信号功率约束及飞行能耗；建模无人机资源调度及飞行轨迹约束；基于系统性能优化确定无人机波束成形、关联策略及飞行轨迹。本发明以最小化通信时间、最大化相关系数及最小化无人机飞行能耗为优化目标，实现无人机波束成形、关联策略及飞行轨迹的联合优化。

The present invention relates to a method for beamforming and resource scheduling of an unmanned aerial vehicle communication perception integrated system, and belongs to the field of wireless communication technology. The method comprises: modeling an unmanned aerial vehicle communication perception integrated system model; modeling an unmanned aerial vehicle communication channel model; modeling an unmanned aerial vehicle communication signal; modeling a user communication rate and communication time; modeling a target perception resolution and perception-related variables; modeling an unmanned aerial vehicle signal power constraint and flight energy consumption; modeling an unmanned aerial vehicle resource scheduling and flight trajectory constraint; and determining the unmanned aerial vehicle beamforming, association strategy and flight trajectory based on system performance optimization. The present invention takes minimizing communication time, maximizing correlation coefficient and minimizing unmanned aerial vehicle flight energy consumption as optimization goals, and realizes the joint optimization of unmanned aerial vehicle beamforming, association strategy and flight trajectory.

Description

Unmanned aerial vehicle communication perception integrated system beam forming and resource scheduling method
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle communication, and relates to a beam forming and resource scheduling method of an unmanned aerial vehicle communication perception integrated system.
Background
The unmanned aerial vehicle is widely applied to the military and civil fields due to the characteristics of high maneuverability, low cost, strong concealment, easy deployment and the like, and can perform various tasks such as reconnaissance, tracking, accurate guidance, electromagnetic interference, material throwing and the like. Through deploying the communication and perception modules, the unmanned aerial vehicle can be used as a communication and perception integrated platform, and high-precision, flexible target perception and high-efficiency information interaction of multi-machine cooperation are realized. However, the unmanned aerial vehicle communication perception integrated system faces competition of multidimensional resources such as frequency spectrum, power and time slot between communication and perception functions, and interference inside the communication system and among systems, and how to design beam forming and resource allocation strategies to realize system performance optimization is a problem to be solved currently.
At present, the problem of resource allocation of an unmanned aerial vehicle communication perception integrated system is studied in literature, and if literature is based on the signal-to-interference-and-noise ratio limiting condition of a receiving end of a perception system, a resource allocation scheme is designed to optimize the transmission rate of a communication system. It is also studied to design the transmission signal of the sensing system to optimize the signal-to-interference-and-noise ratio of the sensing system under the condition that the communication system can tolerate interference. However, the existing research rarely considers the joint optimization of communication and perception system performance, and the problems of beam forming and bistatic SAR perception trajectory design of the unmanned aerial vehicle multi-input multi-output MIMO communication system, which results in serious limitation of system performance.
Disclosure of Invention
In view of the above, the present invention aims to provide a beam forming and resource scheduling method for an unmanned aerial vehicle communication perception integrated system. Aiming at a system scene comprising 1 main unmanned aerial vehicle, 1 secondary unmanned aerial vehicle, K users and G perception targets, modeling is carried out to minimize communication time, maximize correlation coefficient and minimize unmanned aerial vehicle flight energy consumption as optimization targets, and the combined optimization of unmanned aerial vehicle beam forming, association strategy and flight track is realized.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A beam forming and resource scheduling method of an unmanned aerial vehicle communication perception integrated system comprises the following steps:
S1, modeling an unmanned aerial vehicle communication perception integrated system model; the system model specifically comprises the following steps: the system comprises 1 main unmanned aerial vehicle, 1 secondary unmanned aerial vehicle, K users and G perception targets, wherein the number of transmitting antennas of the main unmanned aerial vehicle is M, and the number of receiving antennas of the users is N; the main unmanned aerial vehicle configures airborne communication equipment, and sends data to communication users based on a multiple-input multiple-output (MIMO) technology; the secondary unmanned aerial vehicle is configured with an airborne radar receiver, and forms a bistatic SAR with the main unmanned aerial vehicle, and receives a target echo signal so as to sense target information; the coordinates of the kth user are expressed as: q k＝[xk,yk, where K is equal to or greater than 1 and equal to or less than K, and the g-th perception target coordinates are expressed as: The deployment location of the master drone is expressed as: q c＝[xc,yc ]; discretizing the system time into T time slots, wherein the length of each time slot is tau; the position of the secondary drone at the t slot is expressed as: q s(t)＝[xs(t),ys (t) ]; the main unmanned aerial vehicle is respectively communicated with K users, the target receives the communication signals of the main unmanned aerial vehicle and then reflects signals, and the secondary unmanned aerial vehicle receives the reflected signals of the target and then perceives the target;
S2, modeling an unmanned aerial vehicle communication channel model;
s3, modeling a communication signal of the unmanned aerial vehicle;
s4, modeling the communication rate and the communication time of the user;
s5, modeling target perception resolution and perception associated variables;
S6, modeling unmanned aerial vehicle signal power constraint and flight energy consumption;
s7, modeling unmanned aerial vehicle resource scheduling and flight trajectory constraint;
S8, determining unmanned aerial vehicle beam forming, association strategy and flight track based on system performance optimization.
Further, the step S2 specifically includes: let h k,n,m (t) represent the channel gain of the link between the mth antenna of the main unmanned aerial vehicle with t time slots and the nth antenna of the kth user, comprehensively consider the channel transmission loss and the random fading characteristic, and model as follows:
Wherein ρ 0 represents a channel loss coefficient per unit distance, L represents a deployment height of the main unmanned aerial vehicle, χ k,n,m (t) represents a small-scale MIMO antenna performance gain;
Let H k(t)∈CN×M represent the communication channel matrix between the t-slot master drone and user k, modeled as:
[Hk(t)]n,m＝hk,n,m(t)。
Further, the step S3 specifically includes: let x k (t) represent the signal sent by the t-slot master drone to user k, modeled as:
xk(t)＝αk(t)Wk(t)ck
where α k (t) ∈ {0,1} represents a user communication variable, α k (t) =1 represents that the master drone communicates with user k in t slots, whereas α k(t)＝0;Wk(t)∈CM×N is a communication beamforming matrix of the master drone to user k in t slots, and c k∈CN×1 represents a communication signal of user k.
Further, the step S4 specifically includes: assuming that the t-slot master drone communicates with user k, the received signal power for user k is modeled as:
The interference power received by user k from other antennas is modeled as:
let γ k (t) denote the signal-to-interference-and-noise ratio of user k at time slot t, modeled as:
Wherein σ 2 represents the noise power;
Let R k (t) denote the communication rate of user k at time t slot, modeled as:
Rk(t)＝αk(t)B log2(1+γk(t))
Wherein B is the communication bandwidth of the main unmanned aerial vehicle;
Order the The starting time slot representing the communication between the master drone and user k is modeled as:
Order the The end time slot representing the communication between the master drone and user k is modeled as:
let T k denote the duration of communication between the master drone and user k, modeled as:
Let T 0 represent the total duration of the main unmanned aerial vehicle communication, modeled as:
further, the step S5 specifically includes: assuming that the target g is perceived by the t-time-slot secondary unmanned aerial vehicle, the perceived area can be approximately a circular area, and the radius of the perceived area is R;
Let q o (t) represent the coordinates of the t-slot secondary unmanned perception center O, modeled as:
qo(t)＝[xo(t),yo(t)]
xo(t)＝xs(t)+H tanηsin(θs(t))
yo(t)＝ys(t)-H tanηcos(θs(t))
Wherein H represents the flight altitude of the secondary unmanned aerial vehicle, Representing the observation angle of the side-looking SAR receiver of the secondary unmanned aerial vehicle; θ s (t) epsilon (0, 2 pi) represents an included angle between the course of the unmanned aerial vehicle and the X axis for t times in a time slot;
Order the The included angle between the connecting line and the vertical direction between the main unmanned aerial vehicle and the perception center O is modeled as follows:
Let δ r (t) and δ a (t) represent the distance resolution and azimuth resolution of the slot t-aware region, respectively, modeled as:
Wherein c represents the speed of light, λ represents the signal wavelength, and T d represents the SAR coherent integration time;
let ζ k,g represent the correlation coefficient of user k with perceived target g, modeled as:
let ζ represent the sum of correlation coefficients between the user and the perceived target, modeled as:
Order the Representing the matching variable between the user and the target,Indicating that user k matches perception target g, and vice versa,
Let β g (t) ∈ {0,1} be the target perception variable, β g (t) =1 represent that the secondary drone perceives the target g in t slots, whereas β g (t) =0, modeled as:
Wherein ω g (t) ∈ {0,1} represents an indicator variable, and ω g (t) =1 represents that the secondary unmanned plane satisfies the perception target g in t time slots Conversely, ω g (t) =0.
Further, the step S6 specifically includes: assuming that the t-slot main unmanned aerial vehicle communicates with the user k, let P c,k (t) represent the transmission power corresponding to the transmission communication signal of the t-slot main unmanned aerial vehicle, and modeling is as follows:
Pc,k(t)＝Tr[Rk(t)]
The communication power of the main unmanned aerial vehicle needs to be lower than the given maximum power The constraint modeling is:
Let E s represent the energy consumed by the secondary unmanned aerial vehicle flight, modeled as:
Wherein, P f (t) represents the propulsion power required by the secondary unmanned aerial vehicle to fly in the time slot t, and is expressed as:
Where k 1,k2 represents the unmanned aerial vehicle flight energy coefficient.
Further, the step S7 specifically includes: any slot master drone communicates with at most one user, the constraint being expressed as:
The communication time slots of the master drone with one user are continuous, and the constraint is expressed as:
Let D k be the data volume that the primary unmanned aerial vehicle needs to transmit to user k, then the primary unmanned aerial vehicle data transmission constraint is expressed as:
Matching K users with G perception targets, wherein each perception target is required to be matched with one user, and the constraint is expressed as:
let d min be the given minimum resolution requirement, then the secondary drone perceived resolution constraint is expressed as:
The time required for the secondary drone's perception of the target g is greater than a given time T g, the constraint being expressed as:
After the secondary unmanned aerial vehicle finishes the perception task, the secondary unmanned aerial vehicle needs to fly back to the starting point, and the constraint is expressed as: q s(0)＝qs (T);
Let V max be the maximum flight speed of the secondary unmanned aerial vehicle, then the secondary unmanned aerial vehicle speed constraint is expressed as:
Further, the step S8 specifically includes: the modeling system performance metrics were: t 0,ξ,Es; determining a beam forming matrix W k (t) based on system performance optimization, a primary unmanned aerial vehicle deployment position q c, a secondary unmanned aerial vehicle track q s (t) and an association strategy alpha k (t), And β g (t), yielding:
In the method, in the process of the invention, The method comprises the steps of respectively optimizing a communication beam matrix, a primary unmanned aerial vehicle deployment position, a secondary unmanned aerial vehicle track, a communication association variable, a communication perception matching variable and a perception association variable.
The invention has the beneficial effects that: aiming at a system scene comprising 1 multi-antenna main unmanned aerial vehicle, 1 sub-unmanned aerial vehicle, a plurality of users and a plurality of perception targets, the method models to minimize communication time, maximize correlation coefficient and minimize unmanned aerial vehicle flight energy consumption as optimization targets, and realizes the combined optimization of unmanned aerial vehicle beam forming, correlation strategy and flight track.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
fig. 1 is a schematic view of a scene of an unmanned aerial vehicle communication perception fusion system;
Fig. 2 is a schematic flow chart of a beam forming and resource scheduling method of an unmanned aerial vehicle communication perception integrated system.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Aiming at a system scene comprising 1 main unmanned aerial vehicle, 1 secondary unmanned aerial vehicle, K users and G perception targets, the invention models the minimum communication time, the maximum correlation coefficient and the minimum unmanned aerial vehicle flight energy consumption as optimization targets, and realizes the combined optimization of unmanned aerial vehicle beam forming, correlation strategy and flight track.
Specifically, the system scene is shown in fig. 1, 1 main unmanned aerial vehicle, 1 secondary unmanned aerial vehicle, K users and G perception targets exist in the scene, the number of transmitting antennas of the main unmanned aerial vehicle is M, the number of receiving antennas of the users is N, and the main unmanned aerial vehicle is configured with airborne communication equipment and can send data to communication users based on a multiple-input multiple-output (MIMO) technology. The secondary unmanned aerial vehicle is configured with an airborne radar receiver, and forms a bistatic SAR with the main unmanned aerial vehicle, and receives a target echo signal so as to sense target information. The main unmanned aerial vehicle is respectively communicated with K users, the target receives the communication signals of the main unmanned aerial vehicle and then reflects signals, and the secondary unmanned aerial vehicle receives the reflected signals of the target and then perceives the target.
For the above system scenario, the method for beam forming and resource scheduling of the unmanned aerial vehicle communication perception integrated system provided by the invention is shown in fig. 2, and specifically includes:
1) Modeling an unmanned aerial vehicle communication perception integrated system model;
modeling an unmanned aerial vehicle communication perception integrated system model, wherein the system comprises 1 main unmanned aerial vehicle, 1 secondary unmanned aerial vehicle, K users and G perception targets, the number of transmitting antennas of the main unmanned aerial vehicle is M, and the number of receiving antennas of the users is N; the main unmanned plane is configured with an onboard communication device, and can send data to a communication user based on a multiple-input multiple-output (MIMO) technology; the secondary unmanned aerial vehicle is configured with an airborne radar receiver, and forms a bistatic SAR with the main unmanned aerial vehicle, and receives a target echo signal so as to sense target information;
The coordinates of the kth user are expressed as: q k＝[xk,yk, wherein K is more than or equal to 1 and less than or equal to K, and the g-th perception target coordinates are as follows: G is more than or equal to 1 and less than or equal to G; the deployment location of the master drone is expressed as: q c＝[xc,yc ]; discretizing the system time into T time slots, wherein the length of each time slot is tau; the position of the secondary drone at the t slot is expressed as: q s(t)＝[xs(t),ys (t) ]; the main unmanned aerial vehicle is respectively communicated with K users, the target receives the communication signals of the main unmanned aerial vehicle and then reflects signals, and the secondary unmanned aerial vehicle receives the reflected signals of the target and then perceives the target.
2) Modeling an unmanned aerial vehicle communication channel model;
Modeling an unmanned aerial vehicle communication channel model, specifically comprising:
let h k,n,m (t) represent the channel gain of the link between the mth antenna of the main unmanned aerial vehicle with t time slots and the nth antenna of the kth user, comprehensively consider the channel transmission loss and the random fading characteristics, and can be modeled as follows:
Wherein ρ 0 represents the channel loss coefficient of unit distance, L represents the deployment height of the main unmanned aerial vehicle, χ k,n,m (t) represents the performance gain of the small-scale MIMO antenna, and the complex Gaussian distribution random variable with the mean value of 0 and the variance of 1 is modeled;
Let H k(t)∈CN×M denote the communication channel matrix between the t-slot master drone and user k, which can be modeled as:
[Hk(t)]n,m＝hk,n,m(t)
3) Modeling the unmanned aerial vehicle communication signal;
Modeling unmanned aerial vehicle communication signals, specifically includes:
Let x k (t) represent the signal sent by the t-slot master drone to user k, which can be modeled as:
xk(t)＝αk(t)Wk(t)ck
Wherein α k (t) ∈ {0,1} represents a user communication variable, α k (t) =1 represents that the master unmanned aerial vehicle communicates with user k in t time slots, whereas α k(t)＝0;Wk(t)∈CM×N is a communication beamforming matrix of the master unmanned aerial vehicle to user k in t time slots, c k∈CN×1 is a communication signal of user k, and can be modeled as:
4) Modeling user communication rate and communication time;
modeling the communication rate and communication time of a user specifically includes:
assuming that the t-slot master drone communicates with user k, the received signal power for user k can be modeled as:
The interference power received by user k from other antennas can be modeled as:
let γ k (t) denote the signal-to-interference-and-noise ratio of user k at time t, which can be modeled as:
Wherein σ 2 represents the noise power;
Let R k (t) denote the communication rate of user k at time t, which can be modeled as:
Rk(t)＝αk(t)B log2(1+γk(t))
B is the communication bandwidth of the main unmanned aerial vehicle;
Order the The starting time slot representing the communication of the master drone with user k can be modeled as:
Order the The end time slot representing the communication of the master drone with user k can be modeled as:
Let T k denote the duration of the communication between the master drone and user k, which can be modeled as:
Let T 0 denote the total duration of the primary drone communication, which can be modeled as:
5) Modeling target perception resolution and perception associated variables;
Modeling target perception resolution and perception associated variables specifically comprises:
Assuming that the target g is perceived by the t-time-slot secondary unmanned aerial vehicle, the perceived area can be approximately a circular area, and the radius of the perceived area is R;
Let q o (t) represent the coordinates of the t-slot secondary unmanned perception center O, which can be modeled as:
qo(t)＝[xo(t),yo(t)]
Wherein ,xo(t)＝xs(t)+H tanηsin(θs(t)),yo(t)＝ys(t)-H tanηcos(θs(t)),H is the secondary unmanned aerial vehicle flight altitude, Viewing an observation angle of the SAR receiver for the secondary unmanned aerial vehicle side; θ s (t) ∈ (0, 2 pi) represents the angle between the heading of the unmanned aerial vehicle and the X axis for t times in a time slot, and can be modeled as follows:
where v (t) represents the speed of the secondary unmanned aerial vehicle at time slot t, and can be modeled as
Order theThe included angle between the connecting line and the vertical direction between the main unmanned plane and the perception center O can be modeled as follows:
Let δ r (t) and δ a (t) represent the distance resolution and azimuth resolution, respectively, of the perceived region of the slot t, which can be modeled as:
Wherein c is the speed of light, lambda is the signal wavelength, and T d is SAR coherent integration time;
Let ζ k,g represent the correlation coefficient of user k with perceived target g, which can be modeled as:
Let ζ represent the sum of the correlation coefficients between the user and the perceived target, which can be modeled as:
Order the Representing the matching variable between the user and the target,Indicating that user k matches perception target g, and vice versa,
Let β g (t) ∈ {0,1} be the target perception variable, β g (t) =1 represent that the secondary drone perceives the target g in t slots, whereas β g (t) =0 can be modeled as:
wherein ω g (t) ∈ {0,1} is an indicator variable, and ω g (t) =1 indicates that the secondary unmanned plane satisfies the perception target g in t time slots Conversely, ω g (t) =0.
6) Modeling unmanned aerial vehicle signal power constraint and flight energy consumption;
Modeling unmanned aerial vehicle signal power constraint and flight energy consumption specifically includes:
assuming that the t-slot master drone communicates with the user k, let P c,k (t) represent the transmission power corresponding to the transmission communication signal of the t-slot master drone, which can be modeled as:
Pc,k(t)＝Tr[Rk(t)]
Wherein,
The communication power of the main unmanned aerial vehicle needs to be lower than the given maximum powerThe constraint can be modeled as:
Let E s represent the energy consumed by the secondary unmanned aerial vehicle flight, which can be modeled as:
Wherein, P f (t) represents the propulsion power required by the secondary unmanned aerial vehicle to fly in the time slot t, which can be expressed as:
Wherein k 1,k2 is the unmanned aerial vehicle flight energy coefficient.
7) Modeling unmanned aerial vehicle resource scheduling and flight trajectory constraint;
Modeling unmanned aerial vehicle resource scheduling and flight trajectory constraint specifically includes:
any slot master drone communicates with at most one user, the constraint can be expressed as:
the communication time slots of the master drone with one user are continuous, and the constraint can be expressed as:
Let D k be the data volume that the primary drone needs to transmit to user k, then the primary drone data transmission constraint may be expressed as:
matching K users with G perception targets, wherein each perception target needs to be matched with one user, and the constraint can be expressed as follows:
Let d min be the given minimum resolution requirement, then the secondary drone perceived resolution constraint can be expressed as:
The time required for the secondary drone's perception of target g is greater than a given time T g, and the constraint can be expressed as:
After the secondary unmanned aerial vehicle finishes the perception task, the secondary unmanned aerial vehicle needs to fly back to the starting point, and the constraint can be expressed as: q s(0)＝qs (T);
Let V max be the maximum flight speed of the secondary unmanned aerial vehicle, the secondary unmanned aerial vehicle speed constraint may be expressed as:
8) Determining unmanned aerial vehicle beam forming, association strategy and flight track based on system performance optimization;
Determining unmanned aerial vehicle beam forming, association strategy and flight trajectory based on system performance optimization, specifically comprising:
The modeling system performance metrics were: t 0,ξ,Es; determining a beam forming matrix W k (t) based on system performance optimization, a primary unmanned aerial vehicle deployment position q c, a secondary unmanned aerial vehicle track q s (t) and an association strategy alpha k (t), And β g (t), yielding:
Wherein, The method comprises the steps of respectively optimizing a communication beam matrix, a primary unmanned aerial vehicle deployment position, a secondary unmanned aerial vehicle track, a communication association variable, a communication perception matching variable and a perception association variable.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.

Claims (8)

1.一种无人机通信感知一体化系统波束成形及资源调度方法，其特征在于：该方法包括以下步骤：1. A beamforming and resource scheduling method for an integrated UAV communication and perception system, characterized in that the method comprises the following steps: S1、建模无人机通信感知一体化系统模型；该系统模型具体为：包含1架主无人机、1架次无人机、K个用户和G个感知目标，主无人机发射天线个数为M，用户接收天线个数为N；主无人机配置机载通信设备，基于多输入多输出MIMO技术向通信用户发送数据；次无人机配置机载雷达接收机，与主无人机组成双基地SAR，接收目标回波信号，以感知目标信息；第k个用户的坐标表示为：qk＝[xk,yk]，1≤k≤K，第g个感知目标坐标表示为：主无人机的部署位置表示为：qc＝[xc,yc]；将系统时间离散化为T个时隙，每个时隙的长度为τ；次无人机在t时隙的位置表示为：qs(t)＝[xs(t),ys(t)]；主无人机分别与K个用户进行通信，目标接收到主无人机的通信信号后反射信号，次无人机接收目标的反射信号后对目标进行感知；S1. Modeling the integrated system model of UAV communication and perception; the system model specifically includes 1 main UAV, 1 secondary UAV, K users and G perception targets. The number of transmitting antennas of the main UAV is M, and the number of receiving antennas of the user is N. The main UAV is equipped with airborne communication equipment and sends data to communication users based on multiple-input multiple-output MIMO technology. The secondary UAV is equipped with an airborne radar receiver and forms a dual-base SAR with the main UAV to receive target echo signals to perceive target information. The coordinates of the kth user are expressed as: q k = [x k , y k ], 1≤k≤K, and the coordinates of the gth perception target are expressed as: The deployment position of the main UAV is expressed as: q c = [x c , y c ]; the system time is discretized into T time slots, and the length of each time slot is τ; the position of the secondary UAV in time slot t is expressed as: q s (t) = [x s (t), y s (t)]; the main UAV communicates with K users respectively, and the target receives the communication signal of the main UAV and then reflects the signal. The secondary UAV receives the reflected signal of the target and then perceives the target; S2、建模无人机通信信道模型；S2, modeling UAV communication channel model; S3、建模无人机通信信号；S3, modeling UAV communication signals; S4、建模用户通信速率及通信时间；S4, modeling user communication rate and communication time; S5、建模目标感知分辨率及感知关联变量；S5, modeling target perception resolution and perception-related variables; S6、建模无人机信号功率约束及飞行能耗；S6, modeling UAV signal power constraints and flight energy consumption; S7、建模无人机资源调度及飞行轨迹约束；S7,modeling UAV resource scheduling and flight trajectory constraints; S8、基于系统性能优化确定无人机波束成形、关联策略及飞行轨迹。S8. Determine the drone beamforming, association strategy and flight trajectory based on system performance optimization. 2.根据权利要求1所述的方法，其特征在于：步骤S2具体为：令hk,n,m(t)表示t时隙主无人机第m根天线与第k个用户第n根天线之间链路的信道增益，综合考虑信道传输损耗及随机衰落特性，建模为：2. The method according to claim 1 is characterized in that: step S2 is specifically: let hk,n,m (t) represent the channel gain of the link between the mth antenna of the master drone and the nth antenna of the kth user in time slot t, comprehensively consider the channel transmission loss and random fading characteristics, and model it as follows: 式中，ρ0表示单位距离的信道损耗系数，L表示主无人机部署高度，χk,n,m(t)表示小尺度MIMO天线性能增益；Where ρ 0 represents the channel loss coefficient per unit distance, L represents the deployment height of the main UAV, and χ k,n,m (t) represents the performance gain of the small-scale MIMO antenna; 令Hk(t)∈CN×M表示t时隙主无人机与用户k之间的通信信道矩阵，建模为：Let Hk (t)∈CN ×M represent the communication channel matrix between the master UAV and user k in time slot t, which can be modeled as: [Hk(t)]n,m＝hk,n,m(t)。[H k (t)] n,m = h k,n,m (t). 3.根据权利要求1所述的方法，其特征在于：步骤S3具体为：令xk(t)表示t时隙主无人机发送至用户k的信号，建模为：3. The method according to claim 1 is characterized in that: Step S3 specifically comprises: Let x k (t) represent the signal sent by the master UAV to user k in time slot t, and model it as: xk(t)＝αk(t)Wk(t)ck x k (t) = α k (t) W k (t) c k 式中，αk(t)∈{0,1}表示用户通信变量，αk(t)＝1表示主无人机在t时隙与用户k通信，反之，αk(t)＝0；Wk(t)∈CM×N为t时隙主无人机对用户k的通信波束成形矩阵，ck∈CN×1表示用户k的通信信号。Where α k (t) ∈ {0, 1} represents the user communication variable, α k (t) = 1 means that the master UAV communicates with user k in time slot t, otherwise, α k (t) = 0; W k (t) ∈ CM×N is the communication beamforming matrix of the master UAV to user k in time slot t, and c k ∈ CN×1 represents the communication signal of user k. 4.根据权利要求1所述的方法，其特征在于：步骤S4具体为：假定t时隙主无人机与用户k进行通信，则用户k的接收信号功率建模为：4. The method according to claim 1 is characterized in that: step S4 specifically comprises: assuming that the master drone communicates with user k in time slot t, the received signal power of user k is modeled as: 用户k收到的来自其他天线的干扰功率建模为：The interference power received by user k from other antennas is modeled as: 令γk(t)表示用户k在t时隙的信干噪比，建模为：Let γ k (t) represent the signal to interference noise ratio of user k in time slot t, which can be modeled as: 式中，σ2表示噪声功率；Where σ 2 represents the noise power; 令Rk(t)表示用户k在t时隙的通信速率，建模为：Let R k (t) represent the communication rate of user k in time slot t, which can be modeled as: Rk(t)＝αk(t)Blog2(1+γk(t))R k (t) = α k (t) B log 2 (1 + γ k (t)) 式中，B为主无人机的通信带宽；Where B is the communication bandwidth of the main UAV; 令表示主无人机与用户k通信的开始时隙，建模为：make represents the starting time slot of the communication between the master drone and user k, which is modeled as: 令表示主无人机与用户k通信的结束时隙，建模为： make represents the end time slot of the communication between the master drone and user k, which is modeled as: 令Tk表示主无人机与用户k通信时长，建模为： Let Tk represent the communication time between the master UAV and user k, which can be modeled as: 令T0表示主无人机通信总时长，建模为： Let T 0 represent the total communication duration of the master UAV, which can be modeled as: 5.根据权利要求1所述的方法，其特征在于：步骤S5具体为：假定t时隙次无人机对目标g进行感知，感知区域可以近似为一个圆形区域，其半径为R；5. The method according to claim 1 is characterized in that: step S5 specifically comprises: assuming that the drone senses the target g at time slot t, the sensing area can be approximated as a circular area with a radius of R; 令qo(t)表示t时隙次无人机感知中心O的坐标，建模为：Let q o (t) represent the coordinates of the UAV sensing center O at time slot t, and model it as: qo(t)＝[xo(t),yo(t)] qo (t)＝[ xo (t), yo (t)] xo(t)＝xs(t)+Htanηsin(θs(t)) xo (t)＝ xs (t)+Htanηsin( θs (t)) yo(t)＝ys(t)-Htanηcos(θs(t)) yo (t) = ys (t) - Htanηcos( θs (t)) 式中，H表示次无人机飞行高度，表示次无人机侧视SAR接收机的观测角度；θs(t)∈(0,2π)表示时隙t次无人机航向与X轴的夹角；In the formula, H represents the flight altitude of the secondary UAV, represents the observation angle of the side-looking SAR receiver of the sub-UAV; θ s (t)∈(0,2π) represents the angle between the heading of the sub-UAV and the X-axis at time slot t; 令为主无人机到感知中心O之间连线与垂直方向的夹角，建模为：make The angle between the line from the main UAV to the sensing center O and the vertical direction is modeled as: 令δr(t)和δa(t)分别表示时隙t感知区域的距离分辨率和方位分辨率，建模为：Let δ r (t) and δ a (t) represent the range resolution and azimuth resolution of the sensing area at time slot t, respectively, and model it as: 式中，c表示光速，λ表示信号波长，Td表示SAR相干积分时间；In the formula, c represents the speed of light, λ represents the signal wavelength, and Td represents the SAR coherent integration time; 令ξk,g表示用户k与感知目标g的相关系数，建模为：Let ξ k,g represent the correlation coefficient between user k and perceived target g, which can be modeled as: 令ξ表示用户与感知目标之间相关系数之和，建模为： Let ξ represent the sum of the correlation coefficients between the user and the perceived target, and model it as: 令表示用户与目标之间的匹配变量，表示用户k与感知目标g匹配，反之， make A variable representing the match between a user and a target, indicates that user k matches the perceived target g, otherwise, 令βg(t)∈{0,1}为表示目标感知变量，βg(t)＝1表示次无人机在t时隙对目标g进行感知，反之，βg(t)＝0，建模为：Let β g (t)∈{0,1} be the target perception variable, β g (t)=1 means that the secondary UAV perceives the target g at time slot t, otherwise, β g (t)=0, and the model is: 式中，ωg(t)∈{0,1}表示指示变量，ωg(t)＝1表示次无人机在t时隙对感知目标g满足反之，ωg(t)＝0。In the formula, ω g (t)∈{0,1} represents the indicator variable, and ω g (t)＝1 means that the secondary UAV satisfies the sensed target g at time slot t. Otherwise, ω g (t) = 0. 6.根据权利要求1所述的方法，其特征在于：步骤S6具体为：假定t时隙主无人机与用户k进行通信，令Pc,k(t)表示t时隙主无人机发送通信信号对应的发送功率，建模为：6. The method according to claim 1 is characterized in that: step S6 specifically comprises: assuming that the master UAV communicates with user k in time slot t, let P c,k (t) represent the transmission power corresponding to the communication signal sent by the master UAV in time slot t, and model it as: Pc,k(t)＝Tr[Rk(t)]P c,k (t) = Tr [ R k (t)] 主无人机通信功率需低于给定最大功率该约束建模为： The communication power of the main drone must be lower than the given maximum power This constraint is modeled as: 令Es表示次无人机飞行所消耗能量，建模为：Let Es represent the energy consumed by the drone flight, and model it as: 其中，Pf(t)表示次无人机在时隙t飞行所需推进功率，表示为：Where P f (t) represents the propulsion power required for the secondary UAV to fly in time slot t, which is expressed as: 式中，k1,k2为无人机飞行能量系数。In the formula, k 1 and k 2 are the UAV flight energy coefficients. 7.根据权利要求1所述的方法，其特征在于：步骤S7具体为：任意时隙主无人机最多与一个用户进行通信，该约束表示为： 7. The method according to claim 1 is characterized in that: step S7 specifically comprises: the master drone communicates with at most one user in any time slot, and the constraint is expressed as: 主无人机与一个用户的通信时隙是连续的，该约束表示为：The communication time slots between the master drone and a user are continuous, and this constraint is expressed as: 令Dk为主无人机需传输至用户k的数据量，则主无人机数据传输约束表示为：Let Dk be the amount of data that the master UAV needs to transmit to user k, then the master UAV data transmission constraint is expressed as: 将K个用户与G个感知目标进行匹配，每个感知目标均需与一个用户匹配，该约束表示为： K users are matched with G sensing targets. Each sensing target must be matched with one user. The constraint is expressed as: 令dmin为给定的最低分辨率要求，则次无人机感知分辨率约束表示为：Let dmin be the given minimum resolution requirement, then the sub-UAV perception resolution constraint is expressed as: 次无人机对目标g的感知的时间需大于给定时间Tg，该约束表示为：The time that the secondary UAV perceives the target g must be greater than the given time T g . This constraint is expressed as: 次无人机完成感知任务后需飞回起始点，该约束表示为：qs(0)＝qs(T)；After completing the sensing task, the UAV needs to fly back to the starting point. This constraint is expressed as: q s (0) = q s (T); 令Vmax为次无人机最大飞行速度，则次无人机速度约束表示为： Let V max be the maximum flight speed of the secondary UAV, then the secondary UAV speed constraint is expressed as: 8.根据权利要求1所述的方法，其特征在于：步骤S8具体为：建模系统性能度量为：T0，ξ，Es；基于系统性能优化确定波束成形矩阵Wk(t)，主无人机部署位置qc，次无人机轨迹qs(t)及关联策略αk(t)、和βg(t)，得到：8. The method according to claim 1, characterized in that: step S8 specifically comprises: modeling system performance metrics: T 0 , ξ, Es ; determining the beamforming matrix W k (t), the primary UAV deployment position q c , the secondary UAV trajectory q s (t) and the associated strategy α k (t) based on system performance optimization; and β g (t), we get: 式中，分别为最优通信波束矩阵、主无人机部署位置、次无人机轨迹、通信关联变量、通信感知匹配变量及感知关联变量。In the formula, They are the optimal communication beam matrix, the primary UAV deployment position, the secondary UAV trajectory, the communication association variables, the communication perception matching variables and the perception association variables.
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