CN117767988A - Unmanned aerial vehicle communication perception fusion system beam forming and flight path design method - Google Patents
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Abstract
The invention relates to a beam forming and flight track design method of an unmanned aerial vehicle communication perception fusion system, and belongs to the technical field of wireless communication. The method comprises the following steps: s1: modeling an unmanned aerial vehicle communication perception fusion system model; s2: modeling the joint sending signal of the unmanned aerial vehicle communication perception fusion system; s3: modeling an unmanned aerial vehicle communication channel model; s4: modeling a perception channel model of the unmanned aerial vehicle; s5: modeling a user communication rate; s6: modeling a target discovery probability; s7: modeling unmanned aerial vehicle flight energy consumption and joint signal power constraint; s8: modeling unmanned aerial vehicle communication perception associated variable constraint; s9: and determining unmanned aerial vehicle flight trajectories, beam forming and association strategies based on system performance optimization. The invention maximizes the minimum user average communication rate as an optimization target, and realizes the joint optimization of communication perception beam forming, unmanned aerial vehicle flight track and unmanned aerial vehicle communication perception association strategy.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a beam forming and flight track design method of an unmanned aerial vehicle communication perception fusion system.
Background
Unmanned Aerial Vehicle (UAV) has the advantages of high maneuverability, low cost, strong concealment, easy deployment and the like, is widely applied to the military and civil fields, can bear various tasks such as reconnaissance detection, tracking and positioning, accurate guidance, electromagnetic interference, material throwing and the like, and the integration of unmanned aerial vehicle communication perception is an important technical basis for realizing multi-machine cooperation high-precision, flexible target perception and high-efficiency information interaction. However, the unmanned aerial vehicle communication perception integrated system faces competition of communication and perception functions on multidimensional resources such as frequency spectrum, power and time slot, and interference inside the communication and perception system and among systems, and how to design beam forming and resource allocation strategies to realize performance optimization of the communication perception system is a problem to be solved.
At present, the problem of integration of unmanned aerial vehicle communication perception is studied in literature, if literature is based on the limiting condition of signal-to-interference-and-noise ratio of a receiving end of a perception system, a resource allocation scheme is designed to realize optimization of the transmission rate of a communication system; under the condition that the tolerable interference constraint condition of the communication system is met, the transmitting signals of the sensing system are designed to realize the optimization of the signal-to-interference-and-noise ratio of the sensing system, however, the joint optimization of the performance of the communication sensing system, the joint beam forming and track design problems of the multi-unmanned-plane multi-antenna communication sensing system are less considered in the prior art, and the performance optimization of the communication sensing integrated system in a complex scene is difficult to realize.
The prior art with publication number of CN116149368A provides an unmanned aerial vehicle cluster behavior mapping method based on communication and perception information fusion, which is applied to the unmanned aerial vehicle cluster autonomous decision control field and comprises the following steps: establishing a communication and perception model of the unmanned aerial vehicle, fusing to obtain friend unmanned aerial vehicle information, obstacle information and ground target information, and designing basic behavior rules of the unmanned aerial vehicle based on the friend unmanned aerial vehicle information, the obstacle information and the ground target information; setting information weight and rule weight of each cluster behavior; carrying out characterization processing on friend unmanned aerial vehicle information, obstacle information and ground target information to obtain characterization information, combining information weights of each group of group behaviors to obtain optimal group behaviors, carrying out weighted summation on the output of basic behavior rules of the unmanned aerial vehicle based on rule weights of the optimal group behaviors to obtain decision output, and converting the decision output to obtain expected waypoints.
The above prior art fuses the communication and sensing systems to calculate the expected flight trajectory, but the prior art does not consider that certain interference exists between the communication system and the sensing system of the unmanned aerial vehicle and between the systems, and the interference affects the joint optimization of the performance of the communication sensing system of the unmanned aerial vehicle and the joint beam forming and trajectory design problem of the multi-unmanned aerial vehicle multi-antenna communication sensing system to a certain extent.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for designing a beam forming and a flight path of an unmanned aerial vehicle communication perception fusion system. Aiming at a system scene comprising N multi-antenna unmanned aerial vehicles, K users and J perception targets, modeling communication minimum user rate as an optimization target, and realizing joint optimization of joint beam forming, track design and multi-unmanned aerial vehicle communication perception association.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a beam forming and flight path design method of an unmanned aerial vehicle communication perception fusion system comprises the following steps:
s1: modeling an unmanned aerial vehicle communication perception fusion system model;
s2: modeling the joint sending signal of the unmanned aerial vehicle communication perception fusion system;
s3: modeling an unmanned aerial vehicle communication channel model;
s4: modeling a perception channel model of the unmanned aerial vehicle;
s5: modeling a user communication rate;
s6: modeling a target discovery probability;
s7: modeling unmanned aerial vehicle flight energy consumption and joint signal power constraint;
s8: modeling unmanned aerial vehicle communication perception associated variable constraint;
s9: and determining unmanned aerial vehicle flight trajectories, beam forming and association strategies based on system performance optimization.
Further, in step S1, the communication perception fusion system model includes N unmanned aerial vehicles, K users, and J perception targets, where the number of transmitting antennas of the unmanned aerial vehicles is M; the coordinates of the kth user are expressed as:k is more than or equal to 1 and less than or equal to K; the j-th perception target coordinates are: />J is more than or equal to 1 and less than or equal to J; discretizing the system time into T time slots, and the length of each time slot is tau, let q n (t) represents the position of the nth unmanned aerial vehicle in the t time slot; each unmanned aerial vehicle in each time slot is in a communication state or a communication perception state, the unmanned aerial vehicle in the communication state can communicate with a plurality of users at the same time, and the unmanned aerial vehicle in the communication perception state also perceives a target when communicating with the users.
Further, in step S2, the modeling unmanned aerial vehicle communication perception fusion system jointly transmits signals, which specifically includes: the transmitting signal of the unmanned aerial vehicle consists of communication and sensing signals, and x is the same as that of the unmanned aerial vehicle n (t) represents the sum of communication and perception signals transmitted by the nth unmanned aerial vehicle in t time slots, namelyWherein alpha is n,k (t) ∈ {0,1} is the user communication variable, α n,k (t) =1 means that drone n communicates with user k in time slot t, +.>Forming vectors for communication beams c k Communication signal for kth user, beta n,j (t) ∈ {0,1} as the target perceptual variable, β n,j (t) =1 represents that the drone n perceives the target j in t time slots, and +.>To perceive a beamforming vector.
Further, in step S3, modeling the unmanned aerial vehicle communication channel characteristic model specifically includes: order theChannel gain of a link between an mth antenna of the t-slot unmanned plane n and a kth user is represented, channel transmission loss and random fading are comprehensively considered, and modeling is performed +.>The method comprises the following steps:
wherein ρ is 0 The channel loss coefficient representing unit distance, L is the flying height of the unmanned aerial vehicle, and ψ is the channel loss coefficient n,k,m Representing the performance gain of the small-scale MIMO antenna, modeling as a complex Gaussian distribution random variable with the mean value of 0 and the variance of 1; order theRepresenting a communication channel vector between t time slot drone n and user k, then +.>
Further, in step S4, modeling the unmanned aerial vehicle perception channel model specifically includes: order theRepresenting an actual channel matrix between the t time slot unmanned plane n and the jth perception target, and modeling under the assumption of imperfect channel state estimationThe method comprises the following steps:
wherein,for a perceived channel matrix estimate between t-slot drone n and the jth target,for an n-slot perceived channel error matrix, assume +.>The values of (2) are distributed in the ellipsoidal region, namely:wherein C is W ∈C M×M Is a symmetrical semi-positive definite matrix, V W Representing the volume of an ellipsoid.
Further, in step S5, modeling the user communication rate specifically includes: assuming that the t-slot unmanned plane n communicates with the user k, the received signal power of the user k is:
the interference power received by user k from other users under the same unmanned plane can be expressed as:
interference power from other drones may be expressed as:
the interference received by user k from the perceived signal can be expressed as:
let sigma 2 For noise power, the signal-to-interference-and-noise ratio of user k can be expressed as:
the average communication rate for user k can be expressed as:
further, in step S6, modeling the target discovery probability specifically includes: assuming that the t-slot unmanned plane n perceives the target j, the target discovery probability is modeled as:
wherein I is 0 (. Cndot.) represents the zero-order Bessel function of the first class, V T Judging a threshold for the radar receiver; false alarm probability P given radar detection fa Decision threshold V T The method meets the following conditions:
modeling for power at the radar receiving antenna is:
further, in step S7, modeling the unmanned aerial vehicle flight energy consumption and the joint signal power constraint specifically includes: order theRepresenting the energy consumed by the flight of the nth unmanned aerial vehicle, and modeling as follows: />Wherein (1)>The propulsion power required by the unmanned aerial vehicle n to fly in the time slot t is represented, and the modeling is as follows: />The unmanned aerial vehicle flight energy consumption constraint is expressed as: />The combined signal power of each time slot needs to be lower than a given maximum power p max Unmanned aerial vehicle flight energy consumption constraint modeling is: />
Further, in step S8, modeling the unmanned aerial vehicle communication perception associated variable constraint specifically includes: each user communicates with at most one drone at the same time in any slot, the constraint can be expressed as:each target is perceived at most by one unmanned aerial vehicle at the same time and each unmanned aerial vehicle perceives at most one target at the same time, which can be expressed as:and->
Further, in step S9, the flight trajectory, beam forming and unmanned aerial vehicle association policy are determined based on the system performance optimization, which specifically includes: the modeling system performance metrics were:determining beamforming vectors based on system performance optimizationUnmanned aerial vehicle track q n (t) and association policy α n,k (t) and beta n,j (t), namely:wherein->The method comprises the steps of optimal communication beamforming, perception beamforming, unmanned aerial vehicle track, unmanned aerial vehicle communication related variable and perception related variable.
The invention has the beneficial effects that:
aiming at a system scene comprising a plurality of unmanned aerial vehicles with multiple antennas, a plurality of users and a plurality of perception targets, the invention realizes joint optimization of joint beam forming, track design and communication perception association of the unmanned aerial vehicles by modeling the communication rate of the users and taking the minimum communication rate of the users as an optimization target according to a modeling correlation model.
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.
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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 diagram of a multi-unmanned aerial vehicle sensing communication system scene according to the present invention;
fig. 2 is a flow chart of the method for supporting communication perception integration of multiple unmanned aerial vehicles in the invention.
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.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
Referring to fig. 1-2, the method for supporting multi-unmanned aerial vehicle communication perception integrated beam forming and flight path design is provided. Aiming at a system scene comprising N multi-antenna unmanned aerial vehicles, K users and J perception targets, modeling communication minimum user rate as an optimization target, and realizing joint optimization of joint beam forming, track design and multi-unmanned aerial vehicle communication perception association.
Fig. 1 is a schematic diagram of a scene of a multi-unmanned aerial vehicle sensing communication system, as shown in fig. 1, in which a multi-antenna unmanned aerial vehicle of a frame, K users and J sensing targets exist, and the number of transmitting antennas of the unmanned aerial vehicle is M (M > K). The unmanned aerial vehicle is configured with airborne communication and radar equipment, can send data to communication users based on a downlink communication link, and meanwhile, the unmanned aerial vehicle can also send radar signals through an airborne radar and receive target echo signals so as to sense target information.
Fig. 2 is a flow chart of a joint optimization method of joint beamforming, trajectory design and communication perception association of multiple unmanned aerial vehicles according to the present invention, as shown in fig. 2, the method specifically includes the following steps:
1) Modeling an unmanned aerial vehicle communication perception fusion system model;
modeling an unmanned aerial vehicle communication perception fusion system model comprises: the system comprises N unmanned aerial vehicles, K users and J perception targets, wherein the number of the transmitting antennas of the unmanned aerial vehicles is M; the unmanned aerial vehicle is configured with airborne communication and radar equipment, can send data to a communication user based on a downlink communication link, can also send radar signals through an airborne radar, and can receive target echo signals so as to sense target information; the coordinates of the kth user are expressed as:k is more than or equal to 1 and less than or equal to K, and the j-th perception target coordinates are as follows: />J is more than or equal to 1 and less than or equal to J; discretizing the system time into T time slots, and the length of each time slot is tau, let q n (t) represents the position of the nth unmanned aerial vehicle in the t time slot; each unmanned aerial vehicle in each time slot is in a communication state or a communication perception state, the unmanned aerial vehicle in the communication state can communicate with a plurality of users at the same time, and the unmanned aerial vehicle in the communication perception state also perceives a target when communicating with the users.
2) Modeling the joint sending signal of the unmanned aerial vehicle communication perception fusion system;
modeling unmanned aerial vehicle communication perception fusion system joint transmission signal includes: the transmitting signal of the unmanned aerial vehicle consists of communication and sensing signals, and x is the same as that of the unmanned aerial vehicle n (t) represents the sum of communication and perception signals sent by the nth unmanned aerial vehicle in t time slots, and can be modeled as follows:wherein alpha is n,k (t) ∈ {0,1} is the user communication variable, α n,k (t) =1 means that drone n communicates with user k in time slot t, +.>Forming vectors for communication beams c k Communication signal for kth user, beta n,j (t) ∈ {0,1} as the target perceptual variable, β n,j (t) =1 represents that the drone n perceives the target j in t time slots, and +.>To perceive a beamforming vector.
3) Modeling a communication channel characteristic model of the unmanned aerial vehicle;
modeling a drone communication channel characteristics model includes: order theChannel gain of a link between an mth antenna of the t-slot unmanned plane n and a kth user is represented, channel transmission loss and random fading are comprehensively considered, and modeling is performed +.>The method comprises the following steps:
wherein ρ is 0 The channel loss coefficient representing unit distance, L is the flying height of the unmanned aerial vehicle, and ψ is the channel loss coefficient n,k,m Representing the performance gain of the small-scale MIMO antenna, modeling as a complex Gaussian distribution random variable with the mean value of 0 and the variance of 1; order theRepresenting a communication channel vector between t time slot drone n and user k, then +.>
4) Modeling a perception channel model of the unmanned aerial vehicle;
modeling the unmanned aerial vehicle perception channel model includes: order theRepresenting the actual channel matrix between the t time slot unmanned plane n and the jth perceived target, modeling the imperfect channel state estimation is assumed to exist>The method comprises the following steps:
wherein,for a perceived channel matrix estimate between t-slot drone n and the jth target,for an n-slot perceived channel error matrix, assume +.>The values of (2) are distributed in the ellipsoidal region, namely:wherein C is W ∈C M×M Is a symmetrical semi-positive definite matrix, V W Representing the volume of an ellipsoid.
5) Modeling a user communication rate;
modeling user communication rates includes: assuming that the t-slot unmanned plane n communicates with the user k, the received signal power of the user k is:the interference power received by user k from other users under the same unmanned plane can be expressed as: />Interference power from other drones may be expressed as: />The interference received by user k from the perceived signal can be expressed as: />Let sigma 2 For noise power, the signal-to-interference-and-noise ratio of user k can be expressed as:
the average communication rate for user k can be expressed as:
6) Modeling a target discovery probability;
modeling the target discovery probability specifically includes: assuming that the t-slot drone n perceives the target j, the target discovery probability may be modeled as:
wherein I is 0 (. Cndot.) represents the zero-order Bessel function of the first class, V T Judging a threshold for the radar receiver; false alarm probability P given radar detection fa Decision threshold V T The method meets the following conditions: for power at the radar receiving antenna, it can be modeled as:
7) Modeling flight energy consumption and joint signal power constraint of multiple unmanned aerial vehicles;
modeling unmanned aerial vehicle flight energy consumption and joint signal power constraint specifically includes: order theRepresenting the energy consumed by the flight of the nth unmanned aerial vehicle, the method can be modeled as follows: />Wherein (1)>Representing the propulsion power required by unmanned plane n to fly in time slot t, it can be modeled as: />Unmanned aerial vehicle fliesThe row energy consumption constraint can be expressed as: />Assuming that the joint signal power requirement per slot is below a given maximum power p max The constraint may be modeled as:
8) Modeling unmanned aerial vehicle communication perception associated variable constraint;
modeling unmanned aerial vehicle communication perception associated variable constraint specifically comprises: each user communicates with at most one drone at the same time in any slot, the constraint can be expressed as:each target is perceived at most by one unmanned aerial vehicle at the same time and each unmanned aerial vehicle perceives at most one target at the same time, which can be expressed as: />And
9) Determining unmanned aerial vehicle flight trajectories, beam forming and association strategies based on system performance optimization;
the method for determining the flight trajectory, the beam forming and the unmanned aerial vehicle association strategy based on the system performance optimization specifically comprises the following steps: the modeling system performance metrics were:determining a beamforming vector based on system performance optimization>Unmanned aerial vehicle track q n (t) and association policy α n,k (t) and beta n,j (t), namely: />Wherein the method comprises the steps ofThe method comprises the steps of optimal communication beamforming, perception beamforming, unmanned aerial vehicle track, unmanned aerial vehicle communication related variable and perception related variable.
The invention maximizes the minimum user average communication rate as an optimization target, and realizes the joint optimization of communication perception beam forming, unmanned aerial vehicle flight track and unmanned aerial vehicle communication perception association strategy.
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 (10)
1. The unmanned aerial vehicle communication perception fusion system beam forming and flight trajectory design method is characterized by comprising the following steps of:
s1: modeling an unmanned aerial vehicle communication perception fusion system model;
s2: modeling the joint sending signal of the unmanned aerial vehicle communication perception fusion system;
s3: modeling an unmanned aerial vehicle communication channel model;
s4: modeling a perception channel model of the unmanned aerial vehicle;
s5: modeling a user communication rate;
s6: modeling a target discovery probability;
s7: modeling unmanned aerial vehicle flight energy consumption and joint signal power constraint;
s8: modeling unmanned aerial vehicle communication perception associated variable constraint;
s9: and determining unmanned aerial vehicle flight trajectories, beam forming and association strategies based on system performance optimization.
2. The unmanned aerial vehicle communication perception fusion system beam forming and flight path designing method according to claim 1, wherein in step S1, the communication perception fusion systemThe system model comprises N unmanned aerial vehicles, K users and J perception targets, wherein the number of the transmitting antennas of the unmanned aerial vehicles is M; the coordinates of the kth user are expressed as:the j-th perception target coordinates are: />Discretizing the system time into T time slots, and the length of each time slot is tau, let q n (t) represents the position of the nth unmanned aerial vehicle in the t time slot; each unmanned aerial vehicle in each time slot is in a communication state or a communication perception state, the unmanned aerial vehicle in the communication state is simultaneously communicated with a plurality of users, and the unmanned aerial vehicle in the communication perception state is also used for perceiving a target when being communicated with the users.
3. The method for designing the beam forming and the flight path of the unmanned aerial vehicle communication perception fusion system according to claim 2, wherein in step S2, the modeling unmanned aerial vehicle communication perception fusion system jointly transmits signals, specifically comprising: the transmitting signal of the unmanned aerial vehicle consists of communication and sensing signals, and x is the same as that of the unmanned aerial vehicle n (t) represents the sum of communication and perception signals transmitted by the nth unmanned aerial vehicle in t time slots, namelyWherein alpha is n,k (t) ∈ {0,1} is the user communication variable, α n,k (t) =1 means that drone n communicates with user k in time slot t, +.>Forming vectors for communication beams c k Communication signal for kth user, beta n,j (t) ∈ {0,1} as the target perceptual variable, β n,j (t) =1 represents that the drone n perceives the target j in t time slots, and +.>To perceive a beamforming vector.
4. The unmanned aerial vehicle communication perception fusion system beam forming and flight path designing method according to claim 3, wherein in step S3, modeling the unmanned aerial vehicle communication channel characteristic model specifically includes: order theChannel gain of link between mth antenna and kth user of t time slot unmanned plane n is represented, channel transmission loss and random fading are comprehensively considered, and modeling is carried outThe method comprises the following steps:
wherein ρ is 0 The channel loss coefficient representing unit distance, L is the flying height of the unmanned aerial vehicle, and ψ is the channel loss coefficient n,k,m Representing the performance gain of the small-scale MIMO antenna, modeling as a complex Gaussian distribution random variable with the mean value of 0 and the variance of 1; order theRepresenting a communication channel vector between t time slot drone n and user k, then +.>
5. The method for designing a beam forming and a flight path of an unmanned aerial vehicle communication perception fusion system according to claim 4, wherein in step S4, modeling an unmanned aerial vehicle perception channel model specifically comprises: order theRepresenting the actual channel matrix between the t time slot unmanned plane n and the jth perceived target, modeling the imperfect channel state estimation is assumed to exist>The method comprises the following steps:
wherein,for a perceived channel matrix estimate between t-slot drone n and the jth target,for an n-slot perceived channel error matrix, assume +.>The values of (2) are distributed in the ellipsoidal region, namely:wherein C is W ∈C M×M Is a symmetrical semi-positive definite matrix, V W Representing the volume of an ellipsoid.
6. The unmanned aerial vehicle communication perception fusion system beam forming and flight path design method of claim 5, wherein in step S5, modeling the user communication rate specifically comprises: assuming that the t-slot unmanned plane n communicates with the user k, the received signal power of the user k is:
the interference power received by the user k from other users under the same unmanned plane is expressed as:
interference power from other drones is expressed as:
the interference from the perceived signal received by user k is expressed as:
let sigma 2 For noise power, the signal-to-interference-and-noise ratio of user k is expressed as:
the average communication rate for user k is expressed as:
7. the unmanned aerial vehicle communication perception fusion system beam forming and flight path designing method according to claim 6, wherein in step S6, modeling the target discovery probability specifically includes: assuming that the t-slot unmanned plane n perceives the target j, the target discovery probability is modeled as:
wherein I is 0 (. Cndot.) represents the zero-order Bessel function of the first class, V T Judging a threshold for the radar receiver; false alarm probability P given radar detection fa Decision threshold V T The method meets the following conditions:
modeling for power at the radar receiving antenna is:
8. the method for designing the beam forming and the flight path of the unmanned aerial vehicle communication perception fusion system according to claim 7, wherein in step S7, modeling the unmanned aerial vehicle flight energy consumption and the joint signal power constraint specifically comprises: order theRepresenting the energy consumed by the flight of the nth unmanned aerial vehicle, and modeling as follows: />Wherein (1)>The propulsion power required by the unmanned aerial vehicle n to fly in the time slot t is represented, and the modeling is as follows: />The unmanned aerial vehicle flight energy consumption constraint is expressed as: />The combined signal power of each time slot needs to be lower than a given maximum power p max Unmanned aerial vehicle flight energy consumption constraint modeling is: />
9. The unmanned aerial vehicle communication perception fusion system beam forming and flight path design method according to claim 8, wherein in step S8, modeling the unmanned aerial vehicle communication perception associated variable constraint specifically comprises: each user at most communicates with one unmanned aerial vehicle at the same time in any time slot, and the constraint is expressed as:each target is perceived at most simultaneously by one unmanned aerial vehicle and each unmanned aerial vehicle perceives at most one target simultaneously, expressed as: />And
10. the unmanned aerial vehicle communication perception fusion system beam forming and flight path design method according to claim 9, wherein in step S9, the flight path, beam forming and unmanned aerial vehicle association strategy is determined based on system performance optimization, specifically comprising: the modeling system performance metrics were:determining a beamforming vector based on system performance optimization>Unmanned aerial vehicle track q n (t) and association policy α n,k (t) and beta n,j (t), namely:wherein->The method comprises the steps of optimal communication beamforming, perception beamforming, unmanned aerial vehicle track, unmanned aerial vehicle communication related variable and perception related variable.
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