A method for beamforming and resource scheduling of a drone communication and perception integrated system

Abstract

The present application relates to a kind of unmanned plane communication perception integrated system beam forming and resource scheduling method, belong to wireless communication technical field.The method includes: modeling unmanned plane communication perception integrated system model;Modeling unmanned plane communication channel model;Modeling unmanned plane communication signal;Modeling user communication rate and communication time;Modeling target perception resolution and perception correlation variable;Modeling unmanned plane signal power constraint and flight energy consumption;Modeling unmanned plane resource scheduling and flight trajectory constraint;Based on system performance optimization determines unmanned plane beam forming, correlation strategy and flight trajectory.The present application minimizes communication time, maximizes correlation coefficient and minimizes unmanned plane flight energy consumption as optimization goal, realizes the joint optimization of unmanned plane beam forming, correlation strategy and flight trajectory.

Description

Technical Field

[0001] This invention belongs to the field of UAV communication technology and relates to a beamforming and resource scheduling method for an integrated UAV communication and sensing system. Background Technology

[0002] Unmanned aerial vehicles (UAVs), with their high mobility, low cost, strong stealth, and ease of deployment, are widely used in both military and civilian fields, capable of performing various tasks such as reconnaissance, tracking, precision guidance, electromagnetic interference, and material delivery. By deploying communication and sensing modules, UAVs can serve as integrated communication and sensing platforms, enabling high-precision, flexible target perception and efficient information exchange through multi-UAV collaboration. However, UAV integrated communication and sensing systems face competition for multi-dimensional resources such as spectrum, power, and time slots between communication and sensing functions, as well as interference within and between communication systems. Designing beamforming and resource allocation strategies to optimize system performance is a pressing issue that needs to be addressed.

[0003] Current literature has investigated resource allocation in integrated communication and sensing systems for unmanned aerial vehicles (UAVs). Some studies have designed resource allocation schemes based on the signal-to-interference-plus-noise ratio (SIR) constraints at the sensing system receiver to optimize the transmission rate of the communication system. Other studies have designed the transmitting signal of the sensing system to optimize its SIR while ensuring the communication system's tolerance to interference. However, existing research has largely neglected the joint optimization of communication and sensing system performance, as well as the beamforming and bistatic SAR sensing trajectory design problems in UAV MIMO communication systems, leading to severely limited system performance. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a beamforming and resource scheduling method for an integrated communication and sensing system for unmanned aerial vehicles (UAVs). This method is applicable to systems comprising one main UAV, one secondary UAV, and... K individual users and G For a system scenario that senses a target, the optimization objectives are to minimize communication time, maximize correlation coefficient, and minimize UAV flight energy consumption, thereby achieving joint optimization of UAV beamforming, correlation strategy, and flight trajectory.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A beamforming and resource scheduling method for an integrated communication and sensing system for unmanned aerial vehicles (UAVs), the method comprising:

[0007] S1. Modeling an integrated communication and sensing system for unmanned aerial vehicles (UAVs); this system model specifically includes one main UAV and one secondary UAV. K individual users and G One target is detected, and the number of transmitting antennas of the main UAV is [number missing]. MThe number of user receiving antennas is N The main UAV is equipped with onboard communication equipment, which transmits data to communication users based on multiple-input multiple-output (MIMO) technology; the secondary UAV is equipped with an onboard radar receiver, forming a bistatic SAR system with the main UAV to receive target echo signals and perceive target information; the third k The coordinates of each user are represented as follows: , , No. g The coordinates of each perceived target are represented as follows: , The deployment location of the main UAV is represented as follows: Discretize the system time into T There are 1 time slot, and the length of each time slot is 1. τ The drone in t The location of a time slot is represented as follows: The main UAV and K The user communicates with the target. After receiving the communication signal from the main UAV, the target reflects the signal. The secondary UAV receives the reflected signal from the target and then perceives the target.

[0008] S2. Model the UAV communication channel;

[0009] S3, Modeling UAV communication signals;

[0010] S4. Model user communication rate and communication time;

[0011] S5. Modeling target perception resolution and perception-related variables;

[0012] S6. Modeling UAV signal power constraints and flight energy consumption;

[0013] S7, Modeling UAV resource scheduling and flight trajectory constraints;

[0014] S8. Determine the beamforming, correlation strategy, and flight trajectory of the UAV based on system performance optimization.

[0015] Furthermore, step S2 specifically involves: Let express t Time-slotted main unmanned aerial vehicle (UAV) m root antenna and the first k The user n The channel gain of the link between the root antennas, taking into account both channel transmission loss and random fading characteristics, is modeled as follows:

[0016]

[0017] In the formula, This represents the channel loss coefficient per unit distance. L Indicates the deployment altitude of the main drone. This indicates the performance gain of a small-scale MIMO antenna;

[0018] make express t Time-slot main drone and users k The communication channel matrix between them is modeled as follows:

[0019] .

[0020] Furthermore, step S3 specifically involves: Let express t Time-slot master drone sends to user k The signal is modeled as follows:

[0021]

[0022] In the formula, Represents user communication variables. Indicates the main drone is in t Time slots and users k Communication, or conversely, ; for t Time-slot main drone for users k Communication beamforming matrix, Indicates user k communication signals.

[0023] Furthermore, step S4 specifically involves: assuming t Time-slot main drone and users k To communicate, the user k The received signal power is modeled as follows:

[0024]

[0025] user k The received interference power from other antennas is modeled as follows:

[0026]

[0027] make Indicates user k exist t The signal-to-interference-plus-noise ratio (SIR) of a time slot is modeled as follows:

[0028]

[0029] In the formula, Indicates noise power;

[0030] make Indicates user k exist tThe communication rate of a time slot is modeled as follows:

[0031]

[0032] In the formula, B The communication bandwidth of the main drone;

[0033] make Indicates the main drone and the user k The start time slot of the communication is modeled as follows:

[0034]

[0035] make Indicates the main drone and the user k The end gap of communication is modeled as follows: ;

[0036] make Indicates the main drone and the user k Communication duration is modeled as follows: ;

[0037] make The total communication duration of the main UAV is represented by the model as follows: .

[0038] Furthermore, step S5 specifically involves: assuming t Time-slot sub-UAV targets g The sensing area can be approximated as a circular region with a radius of . R ;

[0039] make express t Time-slot sub-UAV perception center O The coordinates are modeled as follows:

[0040]

[0041]

[0042]

[0043] In the formula, H Indicates the flight altitude of the drone. This indicates the observation angle of the UAV's side-looking SAR receiver; Indicates time slot t The drone's heading and X The included angle of the axis;

[0044] make From main drone to perception center O The angle between the line connecting them and the vertical direction is modeled as follows:

[0045]

[0046] make and They represent time slots respectively. t The distance resolution and orientation resolution of the sensing area are modeled as follows:

[0047] ,

[0048] In the formula, c Represents the speed of light. λ Indicates the signal wavelength. Indicates the SAR coherence integration time;

[0049] make Indicates user k With perceived target g The correlation coefficient is modeled as follows:

[0050]

[0051] make The sum of correlation coefficients between users and perceived targets is represented by the model: ;

[0052] make This represents the matching variable between the user and the target. Indicates user k With perceived target g Match, or conversely, ;

[0053] make To represent the target perceived variable, This indicates that the drone was in t Time slot to target g To perceive, and vice versa. The model is as follows:

[0054]

[0055] In the formula, Indicates an indicator variable. This indicates that the drone was in t Time slots for sensing targets g satisfy ,on the contrary, .

[0056] Furthermore, step S6 specifically involves: assuming t Time-slot main drone and users k To communicate, expresst The transmission power corresponding to the communication signal transmitted by the time-slot master UAV is modeled as follows:

[0057]

[0058]

[0059] The main drone's communication power must be lower than the given maximum power. The constraint is modeled as follows: ;

[0060] make The energy consumed by this drone flight is modeled as follows:

[0061]

[0062] in, This indicates that the drone was in the time slot. t The propulsion power required for flight is expressed as:

[0063]

[0064] In the formula, This indicates the flight energy coefficient of the drone.

[0065] Furthermore, step S7 specifically states that the master UAV can communicate with at most one user in any given time slot, and this constraint is expressed as: ;

[0066] The communication time slots between the main UAV and a user are continuous, and this constraint is expressed as:

[0067]

[0068] make The main drone needs to transmit to the user k Given the amount of data, the data transmission constraint of the main UAV is expressed as:

[0069]

[0070] Will K individual users and G Each perceived target must be matched with one user. This constraint is expressed as follows: ;

[0071] make Given a minimum resolution requirement, the sub-UAV perception resolution constraint is expressed as:

[0072]

[0073]

[0074] The drone targeted the target. g The perception time must be greater than a given time. This constraint is expressed as:

[0075]

[0076] After completing the perception task, the drone needs to fly back to the starting point. This constraint is expressed as: ;

[0077] make Let the maximum flight speed of the secondary UAV be denoted as , then the speed constraint of the secondary UAV is expressed as: .

[0078] Furthermore, step S8 specifically involves: modeling the system performance metrics as follows: , , Determining the beamforming matrix based on system performance optimization Main drone deployment location The trajectory of the drone and related strategies , and ,get:

[0079]

[0080] In the formula, These are the optimal communication beam matrix, the deployment location of the primary UAV, the trajectory of the secondary UAV, communication-related variables, communication-sensing matching variables, and sensing-related variables, respectively.

[0081] The beneficial effects of the present invention are as follows: The present invention is designed for a system scenario that includes one multi-antenna main UAV, one sortie UAV, multiple users and multiple sensing targets. It models the optimization objectives of minimizing communication time, maximizing correlation coefficient and minimizing UAV flight energy consumption, and achieves joint optimization of UAV beamforming, correlation strategy and flight trajectory.

[0082] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0083] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0084] Figure 1 A schematic diagram of a UAV communication and perception fusion system.

[0085] Figure 2 This is a flowchart illustrating a beamforming and resource scheduling method for an integrated communication and sensing system for unmanned aerial vehicles (UAVs) proposed in this invention. Detailed Implementation

[0086] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0087] This invention relates to a system comprising one main UAV, one secondary UAV, and... K individual users and G For a system scenario that senses a target, the optimization objectives are to minimize communication time, maximize correlation coefficient, and minimize UAV flight energy consumption, thereby achieving joint optimization of UAV beamforming, correlation strategy, and flight trajectory.

[0088] Specifically, the system scenarios are as follows: Figure 1 As shown, the scene contains 1 main drone, 1 secondary drone, and K individual users and G One target is detected, and the number of transmitting antennas of the main UAV is [number missing]. M The number of user receiving antennas is N The main UAV is equipped with onboard communication equipment, enabling it to transmit data to communication users based on multiple-input multiple-output (MIMO) technology. The secondary UAV is equipped with an onboard radar receiver, forming a bistatic SAR system with the main UAV to receive target echo signals and perceive target information. The main UAV and... K The user communicates with the target. After receiving the communication signal from the main UAV, the target reflects the signal. The secondary UAV then receives the reflected signal from the target and uses it to perceive the target.

[0089] For the above system scenarios, the beamforming and resource scheduling method for the UAV communication and sensing integrated system proposed in this invention is as follows: Figure 2 As shown, it specifically includes:

[0090] 1) Modeling an integrated communication and sensing system for unmanned aerial vehicles (UAVs);

[0091] Modeling an integrated communication and sensing system for unmanned aerial vehicles (UAVs), the system includes one main UAV, one secondary UAV, and...K individual users and G One target is detected, and the number of transmitting antennas of the main UAV is [number missing]. M The number of user receiving antennas is N The main UAV is equipped with airborne communication equipment, which can send data to communication users based on multiple-input multiple-output (MIMO) technology; the secondary UAV is equipped with an airborne radar receiver, which, together with the main UAV, forms a bistatic SAR to receive target echo signals in order to sense target information.

[0092] No. k The coordinates of each user are represented as follows: , , No. g The coordinates of the sensing target are: , The deployment location of the main UAV is represented as follows: Discretize the system time into T There are 1 time slot, and the length of each time slot is 1. τ The drone in t The location of a time slot is represented as follows: The main UAV and K The user communicates with the target. After receiving the communication signal from the main UAV, the target reflects the signal. The secondary UAV then receives the reflected signal from the target and uses it to perceive the target.

[0093] 2) Model the UAV communication channel;

[0094] Modeling the UAV communication channel model, specifically including:

[0095] make express t Time-slotted main unmanned aerial vehicle (UAV) m root antenna and the first k The user n The channel gain of the link between the root antennas, taking into account both channel transmission loss and random fading characteristics, can be modeled as follows:

[0096]

[0097] in, This represents the channel loss coefficient per unit distance. L Indicates the deployment altitude of the main drone. The performance gain of a small-scale MIMO antenna is represented by a complex Gaussian random variable with a mean of 0 and a variance of 1.

[0098] make express t Time-slot main drone and users k The communication channel matrix between them can be modeled as follows:

[0099]

[0100] 3) Modeling UAV communication signals;

[0101] Modeling drone communication signals, specifically including:

[0102] make express t Time-slot master drone sends to user k The signal can be modeled as:

[0103]

[0104] in, Represents user communication variables. Indicates the main drone is in t Time slots and users k Communication, or conversely, ; for t Time-slot main drone for users k Communication beamforming matrix, For users k The communication signal can be modeled as: , .

[0105] 4) Model user communication rates and communication times;

[0106] Modeling user communication rates and communication times, specifically including:

[0107] assumed t Time-slot main drone and users k To communicate, the user k The received signal power can be modeled as:

[0108]

[0109] user k The received interference power from other antennas can be modeled as follows:

[0110]

[0111] make Indicates user k exist t The signal-to-interference-plus-noise ratio (SIR) of a time slot can be modeled as follows:

[0112]

[0113] in, Indicates noise power;

[0114] make Indicates user k exist t The communication rate of a time slot can be modeled as follows:

[0115]

[0116] in, B The communication bandwidth of the main drone;

[0117] make Indicates the main drone and the user k The start time slot of communication can be modeled as follows:

[0118]

[0119] make Indicates the main drone and the user k The end gap of communication can be modeled as: ;

[0120] make Indicates the main drone and the user k Communication duration can be modeled as follows: ;

[0121] make The total communication duration of the main UAV can be modeled as follows: .

[0122] 5) Model the target's perceptual resolution and perceptual correlation variables;

[0123] Modeling the target perception resolution and perception-related variables, specifically including:

[0124] assumed t Time-slot sub-UAV targets g The sensing area can be approximated as a circular region with a radius of . R ;

[0125] make express t Time-slot sub-UAV perception center O The coordinates can be modeled as follows:

[0126]

[0127] in, , , H This is the flight altitude of the drone. The observation angle of the UAV side-looking SAR receiver; Indicates time slot t The drone's heading and X The included angle of the axes can be modeled as:

[0128] ,

[0129] in, This indicates that the drone was in the time slot. t The speed can be modeled as ;

[0130] make From main drone to perception center O The angle between the line connecting them and the perpendicular direction can be modeled as:

[0131]

[0132] make and They represent time slots respectively. t The distance resolution and orientation resolution of the sensing area can be modeled as follows:

[0133] ,

[0134] in, c At the speed of light, λ For the signal wavelength, The SAR coherence integration time;

[0135] make Indicates user k With perceived target g The correlation coefficient can be modeled as follows:

[0136]

[0137] make The sum of correlation coefficients between users and perceived targets can be modeled as follows: ;

[0138] make This represents the matching variable between the user and the target. Indicates user k With perceived target g Match, or conversely, ;

[0139] make To represent the target perceived variable, This indicates that the drone was in t Time slot to target g To perceive, and vice versa. It can be modeled as:

[0140]

[0141] in, As an indicator variable, This indicates that the drone was in t Time slots for sensing targets g satisfy ,on the contrary, .

[0142] 6) Model the UAV signal power constraints and flight energy consumption;

[0143] Modeling UAV signal power constraints and flight energy consumption, specifically including:

[0144] assumed t Time-slot main drone and users k To communicate, express t The transmission power corresponding to the communication signal transmitted by the time-slot master UAV can be modeled as follows:

[0145]

[0146] in, ;

[0147] The main drone's communication power must be lower than the given maximum power. This constraint can be modeled as: ;

[0148] make The energy consumed by this drone flight can be modeled as follows:

[0149]

[0150] in, This indicates that the drone was in the time slot. t The propulsion power required for flight can be expressed as:

[0151]

[0152] in, This represents the flight energy coefficient of the drone.

[0153] 7) Model UAV resource scheduling and flight trajectory constraints;

[0154] Modeling UAV resource scheduling and flight trajectory constraints, specifically including:

[0155] The master UAV can communicate with at most one user in any time slot. This constraint can be expressed as: ;

[0156] The communication time slots between the main UAV and a user are continuous, and this constraint can be expressed as:

[0157]

[0158] make The main drone needs to transmit to the user k Given the amount of data, the data transmission constraint of the main UAV can be expressed as:

[0159]

[0160] Will K individual users and G Each perceived target must be matched with a user. This constraint can be expressed as: ;

[0161] make Given a minimum resolution requirement, the sub-UAV perception resolution constraint can be expressed as:

[0162]

[0163]

[0164] The drone targeted the target. g The perception time must be greater than a given time. This constraint can be expressed as:

[0165]

[0166] After completing the perception task, the drone needs to fly back to the starting point. This constraint can be expressed as: ;

[0167] make Let $\frac{ ...

[0168] .

[0169] 8) Determine the beamforming, correlation strategy, and flight trajectory of the UAV based on system performance optimization;

[0170] Based on system performance optimization, the beamforming, correlation strategy, and flight trajectory of the UAV are determined, specifically including:

[0171] The performance metric for the modeling system is: , , Determining the beamforming matrix based on system performance optimization Main drone deployment location The trajectory of the drone and related strategies , and ,get:

[0172]

[0173] in, These are the optimal communication beam matrix, the deployment location of the primary UAV, the trajectory of the secondary UAV, communication-related variables, communication-sensing matching variables, and sensing-related variables, respectively.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A beamforming and resource scheduling method for an integrated communication and sensing system for unmanned aerial vehicles (UAVs), characterized in that: The method includes the following steps: S1. Modeling an integrated communication and sensing system for unmanned aerial vehicles (UAVs); this system model specifically includes one main UAV and one secondary UAV. K individual users and G One target is detected, and the number of transmitting antennas of the main UAV is [number missing]. M The number of user receiving antennas is N The main UAV is equipped with onboard communication equipment, which transmits data to communication users based on multiple-input multiple-output (MIMO) technology; the secondary UAV is equipped with an onboard radar receiver, forming a bistatic SAR system with the main UAV to receive target echo signals and perceive target information; the third k The coordinates of each user are represented as follows: , , No. g The coordinates of each perceived target are represented as follows: , The deployment location of the main UAV is represented as follows: Discretize the system time into T There are 1 time slot, and the length of each time slot is 1. τ The drone in t The location of a time slot is represented as follows: The main UAV and K The user communicates with the target. After receiving the communication signal from the main UAV, the target reflects the signal. The secondary UAV receives the reflected signal from the target and then perceives the target. S2. Model the UAV communication channel; S3, Modeling UAV Communication Signals; S4, modeling user communication rates and communication times; in t Time-slot main drone and users k To communicate, the user k The received signal power is modeled as follows: user k The received interference power from other antennas is modeled as follows: make Indicates user k exist t The signal-to-interference-plus-noise ratio (SIR) of a time slot is modeled as follows: In the formula, Indicates noise power; make Indicates user k exist t The communication rate of a time slot is modeled as follows: In the formula, B The communication bandwidth of the main drone; Represents user communication variables. Indicates the main drone is in t Time slots and users k Communication, or conversely, ; make Indicates the main drone and the user k The start time slot of the communication is modeled as follows: make Indicates the main drone and the user k The end gap of communication is modeled as follows: ; make Indicates the main drone and the user k Communication duration is modeled as follows: ; make The total communication duration of the main UAV is represented by the model as follows: ; S5, Modeling target perception resolution and perception-related variables; in t Time-slot sub-UAV targets g The sensing area is approximately a circular region with a radius of [missing information]. R ; make express t Time-slot sub-UAV perception center O The coordinates are modeled as follows: In the formula, H Indicates the flight altitude of the drone. This indicates the observation angle of the UAV's side-looking SAR receiver; Indicates time slot t The drone's heading and X The included angle of the axis; make From main drone to perception center O The angle between the line connecting them and the vertical direction is modeled as follows: make and They represent time slots respectively. t The distance resolution and orientation resolution of the sensing area are modeled as follows: ， In the formula, c Represents the speed of light. λ Indicates the signal wavelength. Indicates the SAR coherence integration time; make Indicates user k With perceived target g The correlation coefficient is modeled as follows: make The sum of correlation coefficients between users and perceived targets is represented by the model: ; make This represents the matching variable between the user and the target. Indicates user k With perceived target g Match, or conversely, ; make To represent the target perceived variable, This indicates that the drone was in t Time slot to target g To perceive, and vice versa. The model is as follows: In the formula, Indicates an indicator variable. This indicates that the drone was in t Time slots for sensing targets g satisfy ,on the contrary, ; S6. Modeling UAV signal power constraints and flight energy consumption; assuming... t Time-slot main drone and users k To communicate, express t The transmission power corresponding to the communication signal transmitted by the time-slot master UAV is modeled as follows: The main drone's communication power must be lower than the given maximum power. The constraint is modeled as follows: ; make The energy consumed by this drone flight is modeled as follows: in, This indicates that the drone was in the time slot. t The propulsion power required for flight is expressed as: In the formula, The flight energy coefficient of the drone; S7. Modeling UAV resource scheduling and flight trajectory constraints; the master UAV can communicate with at most one user in any time slot, represented as: ; The communication time slots between the main drone and a user are continuous, as shown below: make The main drone needs to transmit to the user k Given the amount of data, the data transmission constraint of the main UAV is expressed as: Will K individual users and G Each perceived target is matched with a user, as follows: ; make Given a minimum resolution requirement, the sub-UAV perception resolution constraint is expressed as: The drone targeted the target. g The perception time must be greater than a given time. , represented as: ; After completing the perception task, the drone needs to fly back to the starting point, which is represented as: ; make Let the maximum flight speed of the secondary UAV be denoted as , then the speed constraint of the secondary UAV is expressed as: ; S8. Based on user communication duration and UAV flight energy optimization, determine the UAV beamforming, association strategy, and flight trajectory; model the system performance metrics as follows: , , Beamforming matrix determined based on user communication duration and UAV flight energy optimization. Main drone deployment location The trajectory of the drone and related strategies , and ,get: In the formula, These are the optimal communication beam matrix, the deployment location of the primary UAV, the trajectory of the secondary UAV, communication-related variables, communication-sensing matching variables, and sensing-related variables, respectively.

2. The method according to claim 1, characterized in that: Step S2 specifically involves: Let express t Time-slotted main UAV m root antenna and the first k The user n The channel gain of the link between the root antennas, taking into account both channel transmission loss and random fading characteristics, is modeled as follows: In the formula, This represents the channel loss coefficient per unit distance. L Indicates the deployment altitude of the main drone. This indicates the performance gain of a small-scale MIMO antenna; make express t Time-slot main drone and users k The communication channel matrix between them is modeled as follows: 。 3. The method according to claim 1, characterized in that: Step S3 specifically involves: Let express t Time-slot master drone sends to user k The signal is modeled as follows: In the formula, Represents user communication variables. Indicates the main drone is in t Time slots and users k Communication, or conversely, ; for t Time-slot main drone for users k Communication beamforming matrix, Indicates user k communication signal.

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