An unmanned aerial vehicle perception and communication integrated imaging system and an energy consumption optimization method thereof

Abstract

The application relates to an unmanned aerial vehicle (UAV) integrated sensing and communication imaging system and an energy consumption optimization method thereof. The system comprises a detection UAV carrying a synthetic aperture radar (SAR), a relay UAV and a ground data processing center. In order to minimize the energy consumption of the system while ensuring the performance of the system, an energy consumption optimization method based on the system constructs a joint optimization problem, which considers the trajectory, flight speed, beamforming design of the detection UAV and the deployment of the relay UAV. In order to solve the non-convex optimization problem, a block coordinate descent algorithm based on a penalty coefficient is proposed, which solves a series of convex sub-problems in each iteration. Compared with the prior art, the application has the advantages of being capable of significantly reducing energy consumption and improving system performance.

Description

Technical Field

[0001] This invention relates to the field of integrated sensing technology based on unmanned aerial vehicles (UAVs), and in particular to an integrated sensing and communication imaging system for UAVs and its energy consumption optimization method. Background Technology

[0002] With the rapid development of wireless communication, sensor-communication integration (ISAC) has become a key technology for sixth-generation mobile communication (6G), enabling communication and sensing to share resources and operate simultaneously. Unmanned aerial vehicles (UAVs), due to their mobility and flexibility, are well-suited for ISAC applications (such as location, surveillance, and disaster response), and have therefore attracted widespread attention.

[0003] In recent years, research on UAV-driven IAC (Infrastructure Sensing and Communication) has covered various detection and communication scenarios. Existing work includes studies proposing a networked IAC framework for low-altitude economic applications, jointly optimizing the transmit beam and UAV trajectory through alternating optimization and successive convex approximation methods; studies have also proposed a rotorcraft UAV that simultaneously performs radar sensing and sensor data fusion communication, optimizing the average cooperative sensing area; and studies have proposed an adaptive IAC mechanism that enables rotorcraft UAVs to sense target position and velocity while communicating with multiple users, thereby improving system throughput. While these works explore trade-offs between sensing and communication, most neglect synthetic aperture radar (SAR) imaging, which is crucial for high-resolution scene reconstruction and target area detection.

[0004] To fill this gap, research has begun to study UAV SAR imaging within the ISAC framework. Existing research has proposed an energy-efficient UAV ISAC system for detection, jointly optimizing trajectory, velocity, beam, and power allocation to maximize data volume while reducing energy consumption. Other studies have examined the UAV trajectory optimization problem under joint sensing and communication requirements to minimize propulsion power, proposing algorithms based on successive convex approximation and block coordinate descent. Furthermore, research has proposed trajectory and resource optimization methods for lightweight detection UAVs, obtaining low-complexity algorithms through successive convex approximation. For example, Chinese patent CN118647045A proposes a SAR-based ISAC UAV joint trajectory, beam, and resource optimization method. It employs SAR-based integrated sensing and communication (ISAC) technology, constructing a UAV ground target detection and communication scenario, establishing UAV mobile energy consumption and sensing energy consumption models, and combining convex optimization algorithms to optimize UAV trajectory, beam, and resource configuration, achieving energy-optimal flight trajectory and beamforming. However, in scenarios where the target area is obscured or far away, the assumption of direct communication with the ground or data center may no longer hold.

[0005] Therefore, integrating relay mechanisms into the design of energy-constrained reconnaissance drones is crucial for ensuring reliable data transmission in actual missions. Summary of the Invention

[0006] The purpose of this invention is to provide an integrated imaging system for UAV perception and communication and its energy consumption optimization method, which enables target area imaging assisted by relay UAVs and SAR-equipped detection UAVs, and minimizes system energy consumption while ensuring system performance through an energy consumption optimization method based on block coordinate descent algorithm.

[0007] The objective of this invention can be achieved through the following technical solutions: An integrated imaging system for sensing and communication of unmanned aerial vehicles (UAVs) includes a detection UAV equipped with SAR (Sensitive Radar), a relay UAV, and a ground data processing center. The detection UAV images a target area and transmits the sensing data to the relay UAV, which then forwards the sensing data to the ground data processing center. The detection drone is equipped with an antenna array for performing synthetic aperture radar imaging of the target area and transmitting the sensing data to the relay drone through beamforming. The relay UAV is equipped with an antenna array and forwards the sensing data to the ground data processing center through beamforming. Both the detection drone and the relay drone flew at the same preset altitude; The target area is a circular region with a radius of r.

[0008] An energy consumption optimization method for the aforementioned UAV sensing, communication, and integrated imaging system includes the following steps: The optimization objective and constraints for the energy consumption optimization of the UAV perception and communication integrated imaging system are determined, wherein the optimization objective is to minimize the total energy consumption of the detection UAV and the relay UAV; Determine the set of optimization variables, which includes the trajectory variables, flight speed sequence, synthetic aperture radar beamforming parameters, communication beamforming parameters, and the communication beamforming parameters and relay position of the relay UAV. The block coordinate descent algorithm is used to iteratively optimize each variable in the set of optimization variables. In each iteration, a penalty coefficient is introduced to transform the non-convex equality constraint into a penalty term. Other variables are fixed, and the trajectory variables, flight speed sequence, synthetic aperture radar beamforming parameters, communication beamforming parameters, communication beamforming parameters of relay UAV, and relay position of relay UAV are optimized in sequence. The penalty coefficient is iteratively updated to promote the satisfaction of the constraints. When the objective converges or the maximum number of iterations is reached, the optimal solution is output, and the energy consumption optimization result is obtained.

[0009] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention introduces a relay drone, which solves the problem that SAR drones cannot establish a reliable link with the data center in obstructed or long-distance scenarios; 2. This invention jointly optimizes the trajectory, flight speed, beamforming design, and deployment location of the relay drone, thereby reducing the total system energy consumption and improving energy efficiency. 3. In the joint optimization problem of this invention, a block coordinate descent (BCD) algorithm based on a penalty mechanism is adopted to ensure the stable convergence of the algorithm and obtain an optimized resource allocation scheme. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 This is an energy consumption diagram of the optimization algorithm under different conditions in an embodiment of the present invention; Figure 4 This is a diagram showing the trajectory of a third-region detection drone, the deployment of a relay drone, and the power allocation diagram in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0012] This embodiment first presents an integrated imaging system for sensing and communication of unmanned aerial vehicles (UAVs), such as... Figure 1 As shown, the system includes a detection drone equipped with synthetic aperture radar (SAR) (UAV-SAR), a relay drone (UAV-relay) that forwards collected data to a ground data processing center (DC) when the detection drone cannot communicate directly, and a ground data processing center. The detection drone is used to image the target area and send the sensing data to the relay drone, which in turn forwards the sensing data to the ground data processing center. The detection drone is equipped with A uniform antenna array is used to perform synthetic aperture radar imaging of the target area, and the sensing data is transmitted to the relay UAV through beamforming. The relay drone is equipped with A uniform antenna array forwards the sensing data to the ground data processing center through beamforming.

[0013] In this embodiment, both the detection drone and the relay drone are rotorcraft, and they operate at a fixed altitude. Flight; reconnaissance drones in spotlight mode Randomly distributed target areas Imaging is performed, and each target region is modeled as having a radius of... A circular area.

[0014] The reconnaissance drone takes off from its initial position and follows its trajectory. Flight, among which Indicates time The horizontal position. Upon reaching the target area, the detection drone performs SAR imaging and transmits the sensing data to the relay drone via beamforming. The relay drone is located at... It is responsible for forwarding the received data to the location located The remote data center. After the mission, both drones returned to their respective destinations.

[0015] A. ISAC Working Model Based on SAR Imaging UAV This embodiment will use each time slot It is divided into two sub-slots. The duration of the first sub-slot is... The detection drone performs focused SAR imaging; the duration of the second sub-slot is The detection drone transmits the sensing data to the relay drone. Here, This indicates the proportion of time allocated.

[0016] During the imaging process, the detection drone flies along a straight line across a radius of... The circular detection area. Imaging the first... The number of time slots required for each target area is denoted as . Its constraints are: ; in, Indicates the first Flight speed per time slot For the first The total number of time slots required to image a target region. This embodiment models sensing and communication separately under a unified architecture.

[0017] 1) SAR sensing model The reconnaissance drone operates in spotlight mode and employs beamforming to achieve high-resolution perception of the target area. Let the transmitted SAR signal be... In the first In each time slot, the elevation angle and azimuth angle between the UAV and the target center are respectively... and .

[0018] The launch steering vector is: ; in: ; Radar beamforming is determined by the transmitted beamforming vector. Implementation complete. The received echo signal is: ; in, The complex reflection coefficient of the target. The variance is Additive white Gaussian noise. Transmit power is: .

[0019] SAR images in time slots The signal-to-noise ratio (SNR) is: ; in: For wavelength, For transmit and receive antenna gain, The backscattering coefficient is... These are the speed of light, pulse duration, and pulse repetition frequency, respectively. The pitch angle, These represent Boltzmann constant, noise temperature, noise figure, bandwidth, total loss, and UAV speed, respectively.

[0020] SAR imaging data rate of detection drones Represented as: ; in, This represents the ratio of the near and far edges of the target area to the flight altitude of the reconnaissance drone: ; in: .

[0021] Therefore, in time slots In the process, the amount of SAR imaging data generated by the detection drone is: .

[0022] 2) UAV communication model Drone communication consists of two phases: Phase 1: The detection drone transmits the sensing data to the relay drone; Phase 2: The relay drone forwards the data to the data center (DC).

[0023] In phase 1, let... This indicates the transmission signal of the detected drone. (In the time slot) The pitch and azimuth angles between the detection drone and the relay drone are respectively and .

[0024] The transmit and receive steering vectors are as follows: ; The launch steering vector is defined as: ; in: ; .

[0025] The receiving guide vector is: .

[0026] communication channel Represented as: ; in To detect the channel fading coefficient between the UAV and the relay UAV.

[0027] Signal received by relay drone for: ; in These represent the transmit beamforming vector of the detection drone and the receive beamforming vector of the relay drone, respectively.

[0028] The communication transmission power of the detection drone is: .

[0029] Signal-to-noise ratio (SNR) of communication from reconnaissance drones to relay drones Represented as: ; The corresponding communication rate is: ; in This indicates the communication bandwidth of the probe drone.

[0030] Therefore, in time slots ISAC power consumption Represented as: .

[0031] Communication between the relay drone and the data center is modeled similarly. Let the transmit and receive steering vectors be: The line-of-sight (LOS) channel between the two is: ,in This represents the path loss coefficient of the communication link.

[0032] The signal received by the data center is: ; in Data transmitted by relay drones.

[0033] The corresponding communication SNR is: ; The communication rate is: ; in This indicates the communication bandwidth of the relay drone.

[0034] Its communication transmission power is: B. System Energy Consumption Model Rotary-wing drones at speed The propulsion power modeling is as follows: ; in: Propulsion power includes blade profile power. and induced power Both are mainly determined by hovering conditions. Indicates air density, Indicates the rotor disk area, Indicates the drag coefficient. Indicates rotor solidity.

[0035] Based on the above system, this embodiment also provides an energy consumption optimization method for the system, such as... Figure 2 As shown, the method includes the following steps: S1, determine the optimization objective and constraints for the energy consumption optimization of the UAV perception and communication integrated imaging system, wherein the optimization objective is to minimize the total energy consumption of the detection UAV and the relay UAV.

[0036] This embodiment models the energy minimization problem of the system. The trajectory of the reconnaissance UAV is modeled as a sequential target entry point selection problem, while the deployment of the relay UAV is optimized to ensure the reliability of data forwarding.

[0037] set up These represent the initial and final positions of the SAR imaging UAV, respectively. The auxiliary variables are defined as follows: ; .

[0038] Let the set of optimization variables be: ; in , Indicates the first The second visit One target area; Indicates the first The first in the region One time slot; Represents the assignment variable, indicating the value at the first position. Does the first round visit the... Each region.

[0039] Constraints: Ensure that each region is visited only once, and only one region is visited per round.

[0040] The system power consumption can be written as: ; Overall energy minimization problem Represented as: ; The following constraints must be met: Task allocation constraints: ; Communication rate constraints: ; ; Power constraints: ; ; ; ; ; ; Imaging SNR Constraints: ; .

[0041] in, , These refer to the communication rates from the detection drone and the relay drone to the data center, respectively. To detect the SAR imaging data rate of the UAV, To indicate the proportion of time allocation, To detect the drone's transmission power, The propulsion power of the drone (the propulsion power of the reconnaissance drone and the relay drone is the same). For the maximum power limit of the drone, To detect the transmitted beamforming vector of the UAV, This is the transmit beamforming vector for the relay UAV. For time slots, The signal-to-noise ratio of the SAR image. This represents the minimum signal-to-noise ratio for SAR imaging. This is the maximum speed limit for drones.

[0042] To address the challenges of high coupling and non-convexity in the problem, this embodiment employs a penalized block coordinate descent (BCD) framework. The original problem is decomposed into a series of subproblems, each of which optimizes only one variable block while keeping other variables fixed. Non-convex equality constraints are introduced through penalized terms, with the penalty coefficient gradually increasing to promote constraint satisfaction.

[0043] The energy minimization problem (P0) jointly optimizes the trajectory, velocity, beamforming, and deployment and beamforming of the UAV relay. Since this is a mixed integer, non-differentiable problem involving fuzzy spatial traveling salesman problem (TSP) and continuous variables, we employ a penalized BCD method to obtain a continuously differentiable approximate solution, denoted as (P1): ; in, Represents the set of optimization variables. This represents the total energy consumption of the system. , It is a positive penalty coefficient. Indicates the number of target areas. As an assignment variable, it represents the first time... Does the first round visit the... One target area, Indicates that the detection drone is in the The second visit Entry point location when targeting a specific area. Indicates the first The central location of the target area Indicates the radius of the target area. Indicates the first The total number of time slots required to image a target region Indicates that the detection drone is in the The second visit The target area Flight speed per time slot This indicates the duration of each time slot. This indicates the system's power consumption.

[0044] The constraints are the same as those for problem (P0).

[0045] S2, determine the set of optimization variables, which includes the trajectory variables, flight speed sequence, synthetic aperture radar beamforming parameters, communication beamforming parameters, and the communication beamforming parameters and relay position of the relay UAV.

[0046] S3, the block coordinate descent algorithm is used to iteratively optimize each variable in the set of optimization variables. In each iteration, a penalty coefficient is introduced to transform the non-convex equality constraint into a penalty term. Other variables are fixed, and the trajectory variables (i), flight speed sequence (ii), synthetic aperture radar beamforming parameters (iii), communication beamforming parameters (iv), communication beamforming parameters (v), and relay position (vi) of the detection UAV are optimized in sequence: (i) Steps to update trajectory variables: The trajectory of the detected UAV is determined by... and The characterization is performed using the BCD method with alternating optimization and constraint elimination. Since the problem is convex, the global optimal solution can be quickly obtained using Newton's method. .

[0047] The trajectory of the detection drone is determined by and Characterization was performed by alternating optimization using the BCD method; With other variables fixed and constant terms ignored, update The subproblems are: ; in: ; Define function and for: ; ; in, To detect drones in the The second visit The departure point location when targeting a specific area. To detect the flight speed of drones between different target areas, The propulsion power of the drone during flight. Power of the drone while hovering. To detect drones in the The second visit The first target area Flight speed per time slot To detect drones in the The second visit The first target area The transmit beamforming vector for each time slot, where the superscript "-" indicates a quantity known in the previous iteration or fixed in the current subproblem. Due to constraints It is linear, updating The subproblem is convex, and the optimal solution is: ; In fixed Then, update the allocation variable. : After ignoring the constant term, we obtain the updated allocation variable. The subproblems are: ; The objective function is: ; For the deployment location of relay drones, Using constraints eliminate The gradient expression is obtained. This problem is a convex problem, and the global optimal solution can be quickly obtained using Newton's method. .

[0048] (ii) Velocity sequence optimization steps: The successive convex approximation (SCA) method is used to solve the problem: at each step of the iteration, a convex upper bound is constructed to approximate the non-convex term.

[0049] In fixed In the case of ignoring constant terms, the optimization subproblem of the flight speed sequence of the detection drone can be expressed as: ; in, , st ; ; That is, it satisfies the SAR imaging SNR constraint and velocity limit constraint.

[0050] in, To detect drones in the The second visit The first target area Transmit beamforming vector for each time slot, For the deployment location of relay drones, For the first The total number of time slots required to image a target region question The problem can be solved using the Continuous Convex Approximation (SCA) method, and its approximation problem is constructed as follows: ; st ; in, For gradient operators, The propulsion power of a drone when it flies at a specific speed. This refers to the propulsion power of the drone while it is hovering. This represents the velocity value from the previous iteration. For penalty parameters, To detect drones in the The second visit The first target area Signal-to-noise ratio of SAR images in each time slot; This approximate problem is a convex optimization problem, which can be solved efficiently using convex optimization tools, and the optimal solution can be obtained through updating. .

[0051] (iii) SAR beam optimization steps: Optimize SAR beam .

[0052] (iv) Steps for optimizing the communication beam of the detection drone: Optimize the communication beam of the detection drone. From Shannon's capacity formula, the rate constraint can be transformed into a lower power bound, resulting in the optimal unbounded form.

[0053] With other variables fixed, ignoring the constant term, the first... The second visit The first target area The sub-problem of optimizing the synthetic aperture radar beamforming parameters for each time slot is: ; st ; in, To detect drones in the The second visit The first target area Transmit beamforming vector for each time slot, These are the combined parameters of the radar equations. The synthetic aperture radar transmit power is: ; Therefore, the optimal solution for synthetic aperture radar beamforming is: ; in, , For wavelength, For the transmit antenna gain, For receiving antenna gain, The backscattering coefficient of the target region. At the speed of light, For radar pulse duration, The pulse repetition frequency, Boltzmann's constant, For noise temperature, Noise figure For radar bandwidth, The total loss of the radar system, To detect the flight speed of the drone while performing the ISAC mission, This refers to the flight altitude of the drone.

[0054] Ignoring constant terms, the subproblem of optimizing the communication beamforming parameters for the detection UAV is: ; st ; in, To detect drones in the The second visit The first target area Each time slot transmits communication signals. The standard deviation of additive white Gaussian noise. This is the minimum communication rate requirement.

[0055] Based on the monotonicity of the objective function and constraints, the optimal communication beamforming vector is: .

[0056] (v) Relay UAV communication beam optimization steps: Optimize the beam of the relay UAV. Similarly, using Shannon's formula, we can obtain the optimal solution.

[0057] The specific optimization of the communication beamforming parameters for relay UAVs is as follows: Constraints Substitution ,get: ; set up: Given a fixed location and parameters, the communication beamforming optimization problem for a relay UAV is as follows: ; st The optimal communication beamforming for the relay UAV is then: ; in, To meet the minimum communication rate requirement, The standard deviation of additive white Gaussian noise. For relay drones in the The second visit The first target area Each time slot forwards the signal. For relay drones in the The second visit The first target area Each time slot communication transmission power, This is the normalized direction vector for beamforming of relay UAVs.

[0058] (vi) Relay location optimization steps: A first-order Taylor expansion is used to make a linear approximation at the current location, resulting in a convex optimization problem. This convex problem can be solved efficiently using standard methods to obtain updated... .

[0059] The specific optimization of relay location for relay drones is as follows: Under other parameters, the sub-problem of optimizing relay drone deployment is: ; in, For the deployment location of relay drones, For drones at speed Power during flight To detect the initial position of the drone, To detect the final location of the drone; The standard convex optimization method is used to solve the subproblem of relay UAV deployment optimization to obtain the optimal relay location. .

[0060] S4 iteratively updates the penalty coefficient to promote the satisfaction of the constraints. When the objective converges or the maximum number of iterations is reached, the optimal solution is output, and the energy consumption optimization result is obtained.

[0061] The specific algorithms include: 1. Input: Initialize variables Punishment factor Threshold Iterative index 2. Solving the problem :renew ; 3. Solving the problem :renew ; 4. Solving the problem Update SAR imaging drone speed (through solution) ); 5. Solving the problem Update SAR beamforming ; 6. Solving the problem Updated beamforming for SAR imaging UAVs ; 7. Solving the problem Update the beamforming of relay drones. ; 8. Solving the problem Update relay drone deployment location ; 9. Update penalty status: ; 10. Until the goal is improved ; 11. Update penalty coefficient: , To update the step size, , , , This is a penalty item; 12. Until ; 13. Output: Optimal SAR imaging UAV trajectory Beamforming vector ,speed Relay drone deployment location and total energy consumption .

[0062] In this embodiment, the performance of the proposed algorithm is verified through simulation. Five target regions are randomly distributed in... Within a square area. Both drones are equipped with A uniform planar antenna array. The data center (DC) is located at coordinates. SAR imaging UAV from Departure, SAR imaging is performed sequentially on all target areas, and then the system returns to its final location. The flight altitude is fixed at 200 meters. Detailed simulation parameters are shown in Table 1 below.

[0063] Table 1 Figure 3 and Figure 4 The simulation results are compared. Figure 3 As shown in (a), the total energy consumption of all schemes increases almost linearly with the increase in the number of target areas, due to longer flight paths and more sensing operations. The proposed beamforming-assisted scheme consistently outperforms the non-beamforming baseline and the stochastic scheme. Joint optimization can effectively reduce unnecessary detours and energy consumption, while stochastic scheduling leads to overflight and inefficiency.

[0064] Figure 3 (b) shows that the stricter the SAR SNR threshold, the higher the total energy consumption, because the UAV needs to increase its transmit power, reduce its speed, and extend its hovering time. The proposed solution still achieves significant energy savings under high SNR requirements.

[0065] Figure 3 (c) shows that energy consumption increases sharply as the detection radius increases, because a larger area means a longer path and a greater distance between the entry / exit point and the target center, resulting in increased energy consumption during flight and hovering.

[0066] Figure 3 Figure (d) shows the trend of energy consumption as a function of UAV speed: it first decreases and then increases, reaching a minimum near the optimal cruise speed. Higher speeds lead to a non-linear increase in propulsion power, thus increasing total energy consumption. Regardless of speed, the energy consumption of the proposed beamforming scheme is consistently lower than the benchmark.

[0067] Figure 4 (a) shows the flight trajectory of the SAR imaging UAV and the deployment location of the relay UAV. The green and blue dots represent the entry and exit points of each target area, respectively.

[0068] Figure 4 (b) illustrates the power distribution of the SAR imaging UAV in the third detection area. SAR power exhibits a convex trend due to the requirements of angle imaging, while flight power remains almost constant. Notably, communication power is significantly reduced with the help of beamforming, demonstrating its energy-saving effect in real-time data transmission.

[0069] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. An integrated imaging system for UAV perception and communication, comprising a detection UAV equipped with SAR, a relay UAV, and a ground data processing center, wherein the detection UAV is used to image a target area and transmit the perception data to the relay UAV, and the relay UAV is used to forward the perception data to the ground data processing center, characterized in that, The detection drone is equipped with an antenna array for performing synthetic aperture radar imaging of the target area and transmitting the sensing data to the relay drone through beamforming. The relay UAV is equipped with an antenna array and forwards the sensing data to the ground data processing center through beamforming. Both the detection drone and the relay drone flew at the same preset altitude; The target area is a circular region with a radius of r.

2. A method for optimizing energy consumption in an integrated sensing, communication, and imaging system for unmanned aerial vehicles as described in claim 1, characterized in that, The method includes the following steps: The optimization objective and constraints for the energy consumption optimization of the UAV perception and communication integrated imaging system are determined, wherein the optimization objective is to minimize the total energy consumption of the detection UAV and the relay UAV; Determine the set of optimization variables, which includes the trajectory variables, flight speed sequence, synthetic aperture radar beamforming parameters, communication beamforming parameters, and the communication beamforming parameters and relay position of the relay UAV. The block coordinate descent algorithm is used to iteratively optimize each variable in the set of optimization variables. In each iteration, a penalty coefficient is introduced to transform the non-convex equality constraint into a penalty term. Other variables are fixed, and the trajectory variables, flight speed sequence, synthetic aperture radar beamforming parameters, communication beamforming parameters, communication beamforming parameters of relay UAV, and relay position of relay UAV are optimized in sequence. The penalty coefficient is iteratively updated to promote the satisfaction of the constraints. When the objective converges or the maximum number of iterations is reached, the optimal solution is output, and the energy consumption optimization result is obtained.

3. The energy consumption optimization method according to claim 2, characterized in that, The optimization objective is expressed as: ； in, Represents the set of optimization variables. This represents the total energy consumption of the system. , It is a positive penalty coefficient. Indicates the number of target areas. As an assignment variable, it represents the first time... Does the first round visit the... One target area, Indicates that the detection drone is in the The second visit Entry point location when targeting a specific area. Indicates the first The central location of the target area Indicates the radius of the target area. Indicates the first The total number of time slots required to image a target region Indicates that the detection drone is in the The second visit The target area Flight speed per time slot This indicates the duration of each time slot. This indicates the system's power consumption.

4. The energy consumption optimization method according to claim 3, characterized in that, The constraints include: ； ； ； ； ； ； ； ； ； in, , These refer to the communication rates from the detection drone and the relay drone to the data center, respectively. To detect the SAR imaging data rate of the UAV, To indicate the proportion of time allocation, To detect the drone's transmission power, For the propulsion power of the drone, For the maximum power limit of the drone, To detect the transmitted beamforming vector of the UAV, This is the transmit beamforming vector for the relay UAV. For time slots, The signal-to-noise ratio of the SAR image. This represents the minimum signal-to-noise ratio for SAR imaging. This is the maximum speed limit for drones.

5. The energy consumption optimization method according to claim 4, characterized in that, The optimization of the trajectory variables for the detection drone is specifically as follows: The trajectory of the detection drone is determined by and Characterization was performed by alternating optimization using the BCD method; With other variables fixed and constant terms ignored, update The subproblems are: ； in: ； Define function and for: ； ； in, To detect drones in the The second visit The departure point location when targeting a specific area. To detect the flight speed of drones between different target areas, The propulsion power of the drone during flight. Power of the drone while hovering. To detect drones in the The second visit The first target area Flight speed per time slot To detect drones in the The second visit The first target area Transmit beamforming vector for each time slot, To detect drones in the The second visit The first target area The transmit beamforming vector for each time slot, where the superscript "-" indicates a quantity known in the previous iteration or fixed in the current subproblem. Due to constraints It is linear, updating The subproblem is convex, and the optimal solution is: ； In fixed Then, update the allocation variable. : After ignoring the constant term, we obtain the updated allocation variable. The subproblems are: ； The objective function is: ; For the deployment location of relay drones, Using constraints eliminate The gradient expression is obtained. This problem is a convex problem, and the global optimal solution can be quickly obtained using Newton's method. .

6. The energy consumption optimization method according to claim 4, characterized in that, The optimization of the flight speed sequence of the detection drone is specifically as follows: In fixed In the case of ignoring constant terms, the optimization subproblem of the flight speed sequence of the detection drone can be expressed as: ； in, , To detect drones in the The second visit The first target area Transmit beamforming vector for each time slot, For the deployment location of relay drones, For the first The total number of time slots required to image a target region The optimization subproblem of detecting the flight velocity sequence of the UAV is solved using the continuous convex approximation method, and its approximation problem is constructed as follows: ； s.t. ; in, For gradient operators, The propulsion power of a drone when it flies at a specific speed. This refers to the propulsion power of the drone while it is hovering. This represents the velocity value from the previous iteration. For penalty parameters, To detect drones in the The second visit The first target area Signal-to-noise ratio of SAR images in each time slot; This approximate problem is a convex optimization problem, which can be solved efficiently using convex optimization tools, and the optimal solution can be obtained through updating. .

7. The energy consumption optimization method according to claim 4, characterized in that, The optimization of the synthetic aperture radar beamforming parameters and communication beamforming parameters for the detection UAV is specifically as follows: With other variables fixed, ignoring the constant term, the first... The second visit The first target area The sub-problem of optimizing the synthetic aperture radar beamforming parameters for each time slot is: ； s.t. ; in, To detect drones in the The second visit The first target area Transmit beamforming vector for each time slot, These are the combined parameters of the radar equations. The synthetic aperture radar transmit power is: ; Therefore, the optimal solution for synthetic aperture radar beamforming is: ； in, , For wavelength, For the transmit antenna gain, For receiving antenna gain, The backscattering coefficient of the target region. At the speed of light, For radar pulse duration, The pulse repetition frequency, Boltzmann's constant, For noise temperature, Noise figure For radar bandwidth, The total loss of the radar system, To detect the flight speed of the drone while performing the ISAC mission, This refers to the flight altitude of the drone; Ignoring constant terms, the subproblem of optimizing the communication beamforming parameters for the detection UAV is: ； s.t. ; in, To detect drones in the The second visit The first target area Each time slot transmits communication signals. The standard deviation of additive white Gaussian noise. Minimum communication rate requirement; Based on the monotonicity of the objective function and constraints, the optimal communication beamforming vector is: 。 8. The energy consumption optimization method according to claim 4, characterized in that, The specific optimization of the communication beamforming parameters for relay UAVs is as follows: Constraints Substitution ,get: ； set up: Given a fixed location and parameters, the communication beamforming optimization problem for a relay UAV is as follows: ； s.t. The optimal communication beamforming for the relay UAV is then: ； in, To meet the minimum communication rate requirement, The standard deviation of additive white Gaussian noise. For relay drones in the The second visit The first target area Each time slot forwards the signal. For relay drones in the The second visit The first target area Each time slot communication transmission power, This is the normalized direction vector for beamforming of relay UAVs.

9. The energy consumption optimization method according to claim 4, characterized in that, The specific optimization of relay location for relay drones is as follows: Under other parameters, the sub-problem of optimizing relay drone deployment is: ； in, For the deployment location of relay drones, For drones at speed Power during flight To detect the initial position of the drone, To detect the final location of the drone; The standard convex optimization method is used to solve the subproblem of relay UAV deployment optimization to obtain the optimal relay location. .

10. The energy consumption optimization method according to claim 2, characterized in that, The update rule for the penalty coefficient is as follows: ； in, For the first The penalty coefficient for the next iteration To update the step size, This is a penalty item.

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