Optimization method based on energy consumption minimization of fixed-wing unmanned aerial vehicle

Abstract

The invention relates to the technical field of communication perception integration, in particular to an optimization method based on energy consumption minimization of a fixed-wing unmanned aerial vehicle. In the invention, the fixed-wing unmanned aerial vehicle and the base station jointly form a bistatic synthetic aperture radar, and the unmanned aerial vehicle sequentially collects and senses base station downlink signals reflected by a target and returns the base station downlink signals to a base station end for target identification and imaging. A corresponding optimization problem is constructed by analyzing a system model. According to the method, the technical means of continuous convex approximation, auxiliary variable introduction, convex difference planning and the like are adopted, a non-convex problem which is originally difficult to solve is converted into a convex problem which is easy to solve, and joint design and optimization of the three-dimensional flight path and the return communication power of the unmanned aerial vehicle are achieved. Compared with the prior art, the method has the advantages that the energy consumption of the unmanned aerial vehicle for executing the communication perception integrated task is obviously reduced, and the system energy efficiency is improved. Meanwhile, good imaging quality can be obtained for targets at different distances, and universality and practicability of the method are fully embodied.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication and sensing integration. Specifically, it relates to an optimization method based on the minimum energy consumption of a fixed-wing unmanned aerial vehicle (UAV). Background Art

[0002] In recent years, in various fields of daily life and industrial production, higher requirements have been put forward for the reliability of environmental perception and the immediacy of wireless communication. To overcome the drawbacks caused by the separation of traditional communication and sensing systems, researchers have proposed the technology of communication and sensing integration. This technology effectively promotes the deep integration of communication functions and sensing functions in the system through means such as spectrum resource sharing and hardware function reuse. However, in practical applications, the line-of-sight link of the signal transmitted by the ground base station is often blocked by obstacles, which greatly affects the overall performance of the communication and sensing integration system.

[0003] Considering the unique advantage of UAVs in flexibly establishing line-of-sight air-to-ground links, combining UAV technology with communication and sensing integration technology is beneficial to overcome this limitation. Compared with other types of UAVs, fixed-wing UAVs fly faster and have stronger payload capabilities, making them more suitable for long-term execution of communication and sensing integration tasks. Since the battery capacity of UAVs is limited, in order to reduce energy consumption and ensure the smooth completion of tasks, it is necessary to design their flight trajectories and communication powers. However, existing research has insufficient consideration of the flight requirements of fixed-wing UAVs and the performance requirements of communication and sensing integration, lacks relevant joint design technologies, and does not give the imaging results of the sensed target area.

[0004] Constructing and solving an optimization problem based on the minimum energy consumption of a fixed-wing UAV is the key to obtaining the optimal flight trajectory and power allocation scheme of the UAV. However, due to the highly non-convex nature of the objective function and some constraints in the problem, this problem is a non-convex problem that is difficult to solve. Therefore, researching an optimization method based on the minimum energy consumption of a fixed-wing UAV has great practical value. Summary of the Invention

[0005] To solve the above technical problems, the present invention discloses an optimization method based on the minimum energy consumption of a fixed-wing UAV. For a communication and sensing integration system composed of a ground base station, a fixed-wing UAV, and multiple sensing targets, the total energy consumption of the UAV is taken as the optimization objective to achieve the joint optimization of the UAV trajectory and the backhaul communication power.

[0006] Specifically, the purpose of the present invention is to provide an optimization method based on the minimum energy consumption of a fixed-wing UAV, including the following steps:

[0007] Step 1: Establish a communication and sensing integration system model according to the three-dimensional motion requirements, target sensing requirements, and data backhaul communication requirements of the fixed-wing UAV.

[0008] Step 2: Take the total energy consumption of the UAV as the objective function and construct the corresponding optimization problem;

[0009] Step 3: By introducing methods such as auxiliary variables, convex difference programming, and successive convex approximation, transform the non-convex objective function and constraints in this problem into convex forms;

[0010] Step 4: Solve the transformed convex problem to obtain the optimal three-dimensional flight trajectory and backhaul communication power of the UAV;

[0011] Step 5: According to the data obtained from the solution, use synthetic aperture radar imaging technology based on OFDM signals to image the perceived target area.

[0012] Furthermore, Step 1 specifically includes:

[0013] The three-dimensional motion requirements of the fixed-wing UAV include:

[0014] The flight altitude, speed, acceleration, pitch angle, starting position, and ending position of the UAV should meet the system limitations and the requirements of the integrated communication and sensing mission;

[0015] The target sensing requirements include:

[0016] The intensity of the echo signal reflected by the perceived target received by the UAV should not be lower than the minimum received power, so that it can be distinguished from the receiver noise; the UAV should maintain a constant flight speed and altitude during the sensing mission to obtain better sensing performance; at the central moment of each sensing mission, the flight speed of the UAV is perpendicular to the line connecting it to the center of the target area, so that the synthetic aperture radar is in the side-looking mode; the resolution of the synthetic aperture radar must be better than the required resolution of the system, that is, the minimum distance between two adjacent targets;

[0017] The backhaul communication requirements include:

[0018] After the UAV completes a sensing mission, the amount of data transmitted back to the base station is not less than the amount of data of the original echo signal received; the transmission power of the UAV backhaul signal cannot exceed the maximum power limit;

[0019] Establish an integrated communication and sensing system model, including:

[0020] The mission duration of the system is J seconds and is evenly divided into T time slots, which are respectively labeled The length of each time slot is Δt = J / T seconds; in order for the UAV to complete the sensing mission of K target areas and realize the transmission of the echo signal, the T time slots are further divided into K + 1 time periods of equal duration; in the initial period Among them, the UAV flies from the initial position to the first target area; subsequently, in each time period k = 1, …, K, the UAV will allocate T a time slots for the sensing task of the target area, and the remaining time slots are used for the backhaul communication task; define as the set of time slots used for the sensing task of the k-th target area, as the set of time slots used for the k-th backhaul communication task; is the set of all time slots used for the sensing task, is the set of all time slots used for the backhaul communication task; the coordinates of the center of the k-th sensed target area are The base station coordinates are p BS = [x BS , y BS , z BS T ; The UAV starts from the initial position at t = 0 and finally returns to this position. In the discretized time model, the speed, acceleration, and position coordinates of the UAV in each time slot are v[t] = [v x [t], v y [t], v z [t]] T , a[t] = [a x [t], a y [t], a z [t]] T , and p u [t] = [x u [t], y u [t], z u [t]] T .

[0021] Furthermore, step 2 specifically includes:

[0022] Taking the total energy consumption of the UAV as the objective function, its calculation expression is:

[0023]

[0024] Among them, P fly [t] is the flight power consumption generated by the UAV in time slot t, and P c [t] is the transmission power of the backhaul signal of the UAV in time slot t; the calculation expression of the flight power consumption is:

[0025]

[0026] Among them, f1 and f2 are constant parameters related to various factors such as the zero-lift drag coefficient, wing area, and air density of the UAV, and g is the acceleration due to gravity;​

[0027] Construct the corresponding optimization problem, including:

[0028]

[0029]

[0030] Among them, the optimization objective is the total energy consumption E of the UAV UAV to be minimized, and the optimization variables are the speed v[t], acceleration a[t], trajectory p u [t] and the backhaul signal transmission power P c [t], where the subsequent expressions represent the constraint conditions; p i and v i represent the initial position and speed of the UAV at the initial moment respectively; H min and H max represent the minimum and maximum heights of the UAV's three-dimensional flight respectively; a max represents the maximum acceleration that the UAV can withstand; ε represents the maximum pitch angle of the UAV; v min and v max represent the minimum and maximum flight speeds of the UAV; P r [t] and G r represent the signal power and antenna gain at the receiving end of the UAV within time slot t respectively; P t and G t represent the transmit signal power and antenna gain of the base station respectively; S RCS represents the radar cross-section coefficient; represent the distances from the base station or the UAV to the center of the k-th target area respectively; represents the minimum received power of the echo signal required by the system; for each sensing task, P t , G t , G r , S RCS and ρ k,1 are all constants; c represents the speed of light; B s is the system bandwidth; represents the azimuth angle of the base station relative to the center of the k-th target area within time slot t, represents the angle between the line connecting the UAV to the center of the k-th target area and the z-axis; d min represents the minimum resolution required by the system; R s is a constant representing the instantaneous rate of the original echo signal; represents the maximum transmission power of the UAV; R c [t] represents the data transmission rate from the UAV to the base station within time slot t, and the calculation expression is:

[0031]

[0032] Among them, B c represents the bandwidth of the UAV transmission signal; r a represents the channel power fading coefficient at a distance of 1 meter from the emission source; represents the power of additive white Gaussian noise at the base station; ρ3[t] represents the distance between the base station and the UAV within time slot t.

[0033] Furthermore, step 3 specifically includes:

[0034] Converting the non-convex objective function in the problem into a convex form, including:

[0035] By introducing an auxiliary variable converting the non-convex term P fly [t] in the objective function into the following form:

[0036]

[0037] When introducing the auxiliary variable, additional constraints will also be introduced, including:

[0038]

[0039]

[0040] At the optimal solution of the problem, except for v min ≤γ[t] and otherwise, the newly introduced constraints take the equal sign. Otherwise, changing the value of the auxiliary variable will further reduce the value of the objective function. Therefore and are equivalent;

[0041] In , is a convex difference function, showing non-convex properties. Using the convex difference programming method, performing a first-order Taylor expansion on the second term of this function can complete the convexification of the objective function;

[0042] Converting the non-convex constraints in the problem into a convex form, including:

[0043] By reconstructing the UAV received power constraint, converting it into an equivalent convex constraint, the expression is:

[0044] ρ k,2 [t]≤ρ max ;

[0045] Among them, represents the maximum effective sensing distance of the UAV. For each sensing task, ρ max is a constant;

[0046] By introducing slack variables the backhaul communication constraint of the UAV is replaced with the following form:

[0047]

[0048] Through the sequential convex approximation method, the non-convex terms in the non-convex constraints in the original problem and the newly introduced non-convex constraints are expanded by the first-order Taylor expansion, and they are transformed into convex forms.

[0049] After the convexification of the non-convex objective function and constraints is completed, the solution of the optimization problem in step 2 is transformed into solving a series of standard convex optimization problems.

[0050] Furthermore, in step 4, the transformed convex problem is solved to obtain the optimal three-dimensional flight trajectory and backhaul communication power of the UAV, specifically including:

[0051] Set the initial values of the variables v[t], p u [t], n[t], ξ[t], ζ[t], and iteratively solve the transformed convex problem until the difference between the optimization objectives in two consecutive iterations is less than the set threshold, that is, the algorithm converges, so as to obtain the minimum energy consumption of the fixed-wing UAV.

[0052] Furthermore, in step 5, according to the data obtained from the solution, the synthetic aperture radar imaging technology based on OFDM signals is used to image the sensed target area, specifically including:

[0053] According to the UAV motion parameters in the case of the minimum energy consumption obtained in step 4, the echo signals of each sensed target area collected by the UAV are processed by using the OFDM synthetic aperture radar imaging algorithm based on the cyclic prefix to obtain the imaging results of the sensed targets.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] The present invention comprehensively considers the flight requirements and system performance of the fixed-wing UAV in the communication-sensing integrated system. Through optimization methods such as variable substitution, convex difference programming, and sequential convex approximation, the problem of minimizing the energy consumption of the fixed-wing UAV, which was originally difficult to solve, is transformed into a convex problem that is easy to solve; the present invention proposes an optimization method based on minimizing the energy consumption of the fixed-wing UAV, which can ensure the convergence of the objective function when solving the convex problem and solve the optimal flight trajectory and power allocation scheme of the UAV; by introducing the three-dimensional propulsion energy consumption model of the UAV, the optimal flight trajectory of the UAV is made more accurate and in line with the actual situation, and at the same time, the imaging results of target sensing are shown.

[0056] The experimental results prove that, compared with the existing solutions, the optimization method based on minimizing the energy consumption of fixed-wing UAVs proposed in the present invention can significantly reduce the energy consumption of UAVs and obtain good imaging quality for targets at different distances. Description of the Drawings

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only a part of the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0058] Figure 1 It is a schematic diagram of the communication and sensing integrated system of the fixed-wing UAV considered in the present invention.

[0059] Figure 2 It is a flowchart of an optimization method based on minimizing the energy consumption of fixed-wing UAVs in the present invention.

[0060] Figure 3 It is an imaging result diagram of the sensing target area in the embodiment of the present invention. Detailed Embodiments

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe the embodiments of the present invention in detail in conjunction with the drawings.

[0062] Embodiment 1

[0063] This embodiment considers a communication and sensing integrated system of a fixed-wing UAV as shown in Figure 1 . The UAV and the base station together form a bistatic synthetic aperture radar. By collecting the echo signals reflected by the target and transmitting them back to the base station for processing, target sensing and imaging can be achieved. Since the battery capacity of the UAV is limited, in order to ensure the smooth completion of the task, it is necessary to reasonably design its flight path and transmission power. Based on this, the present invention provides an optimization method based on minimizing the energy consumption of fixed-wing UAVs. As shown in Figure 2 , this method includes the following steps:

[0064] Step 1: Establish a communication and sensing integrated system model according to the three-dimensional motion requirements, target sensing requirements, and data transmission and communication requirements of the fixed-wing UAV.

[0065] The three-dimensional motion requirements of the fixed-wing UAV include:

[0066] The flight altitude, speed, acceleration, pitch angle, starting position, and ending position of the UAV should meet the system limitations and the requirements of the communication and sensing integrated task;

[0067] The target sensing requirements include:

[0068] The echo signal intensity received by the UAV from the sensed target should not be lower than the minimum received power so that it can be distinguished from the receiver noise; the UAV should maintain a constant flight speed and altitude during the sensing mission to obtain better sensing performance; at the central moment of each sensing mission, the flight speed of the UAV should be perpendicular to the line connecting it to the center of the target area, so that the synthetic aperture radar is in the side-looking mode; the resolution of the synthetic aperture radar must be better than the required resolution of the system, that is, the minimum distance between two adjacent targets.

[0069] Requirements for backhaul communication, including:

[0070] After the UAV completes a sensing mission, the amount of data transmitted back to the base station should not be less than the amount of data of the original echo signal received; the transmission power of the UAV's backhaul signal cannot exceed the maximum power limit.

[0071] Establish a communication-sensing integrated system model, including:

[0072] The mission duration of the system is J seconds and is evenly divided into T time slots, respectively labeled The length of each time slot is Δt = J / T seconds; in order for the UAV to complete the sensing mission of K target areas and achieve the transmission of the echo signal, the T time slots are further divided into K + 1 time periods of equal duration; in the initial time period the UAV flies from the initial position to the first target area; subsequently, in each time period the UAV will allocate T a time slots for the sensing mission of the target area, and the remaining time slots are used for the backhaul communication mission; define as the set of time slots used for the sensing mission of the k-th target area, as the set of time slots used for the k-th backhaul communication mission; is the set of all time slots used for the sensing mission, is the set of all time slots used for the backhaul communication mission; the coordinates of the center of the k-th sensing target area are The base station coordinates are p BS =[x BS ,y BS ,z BS T ; The UAV starts from the initial position at t = 0 and finally returns to this position. In the discretized time model, the speed, acceleration, and position coordinates of the UAV in each time slot are v[t]=[v x [t],v y [t],v z [t]] T ​, a[t] = [a x [t], a y [t], a z [t]] T , and p u [t] = [x u [t], y u [t], z u [t]] T . Table 1 shows the parameter settings in the above system

[0073] Table 1 System Parameter Settings

[0074]

[0075]

[0076] Step 2: Take the total energy consumption of the UAV as the objective function and construct the corresponding optimization problem

[0077] Take the total energy consumption of the UAV as the objective function, and its calculation expression is

[0078]

[0079] where P fly [t] is the flight power consumption generated by the UAV in time slot t, and P c [t] is the transmit power of the backhaul signal of the UAV in time slot t; the calculation expression of the flight power consumption is

[0080]

[0081] where f1 and f2 are constant parameters related to various factors such as the zero-lift drag coefficient, wing area, and air density of the UAV, and g is the acceleration due to gravity

[0082] Construct the corresponding optimization problem, including

[0083]

[0084]

[0085] where the optimization objective is to minimize the total energy consumption E of the UAV UAV , the optimization variables are the speed v[t], acceleration a[t], trajectory p u [t] and the transmit power P of the backhaul signal c [t], and the expressions after s.t. represent the constraint conditions; p i and v i represent the initial position and speed of the UAV at the initial moment respectively; H min and H maxrespectively represent the minimum and maximum heights of the UAV's three-dimensional flight; a max represents the maximum acceleration that the UAV can withstand; ε represents the maximum pitch angle of the UAV; v min and v max respectively represent the minimum and maximum flight speeds of the UAV; P r [t] and G r respectively represent the signal power and antenna gain of the UAV receiver within time slot t; P t and G t represent the transmit signal power and antenna gain of the base station; S RCS represents the radar cross-section coefficient; respectively represent the distances from the base station or the UAV to the center of the k-th target area; represents the minimum received power of the echo signal required by the system; for each sensing task, P t 、G t 、G r 、S RCS and ρ k,1 are all constants; c represents the speed of light; B s is the system bandwidth; represents the azimuth angle of the base station relative to the center of the k-th target area within time slot t, represents the angle between the line connecting the UAV to the center of the k-th target area and the z-axis; d min represents the minimum resolution required by the system; R s is a constant representing the instantaneous rate of the original echo signal; represents the maximum transmission power of the UAV; R c [t] represents the data transmission rate of the UAV to the base station within time slot t, and the calculation expression is:

[0086]

[0087] where, B c represents the bandwidth of the UAV's transmitted signal; r a represents the channel power fading coefficient at a distance of 1 meter from the transmission source; represents the additive Gaussian white noise power at the base station; ρ3[t] represents the distance between the base station and the UAV within time slot t.

[0088] Step 3: By introducing methods such as auxiliary variables, convex difference programming, and successive convex approximation, transform the non-convex objective function and constraints in this problem into convex forms.

[0089] Transform the non-convex objective function in the problem into a convex form, including:

[0090] By introducing auxiliary variables Convert the non-convex term P in the objective function fly [t] into the following form:

[0091]

[0092] When introducing auxiliary variables, additional constraints will also be introduced, including:

[0093]

[0094] At the optimal solution of the problem, except for v min ≤γ[t] and otherwise, the newly introduced constraints take the equal sign. Otherwise, changing the value of the auxiliary variable will further decrease the value of the objective function. Therefore is equivalent to;

[0095] In where is a convex difference function, showing non-convex properties. Using the convex difference programming method, perform a first-order Taylor expansion on the second term of this function to complete the convexification of the objective function;

[0096] Convert the non-convex constraints in the problem into convex forms, including:

[0097] By reconstructing the UAV received power constraint, convert it into an equivalent convex constraint, and the expression is:

[0098] ρ k,2 [t]≤ρ max ;

[0099] where represents the maximum effective sensing distance of the UAV. For each sensing task, ρ max is a constant;

[0100] By introducing a slack variable replace the UAV backhaul communication constraint with the following form:

[0101]

[0102] Through the continuous convex approximation method, perform a first-order Taylor expansion on the non-convex terms in the non-convex constraints in the original problem and the newly introduced non-convex constraints to convert them into convex forms; after completing the convexification of the non-convex objective function and constraints, the solution step of the optimization problem in step 2 is transformed into solving a series of standard convex optimization problems.

[0103] Step 4: Solve the transformed convex problem to obtain the optimal 3D flight trajectory and backhaul communication power of the UAV.

[0104] Set the variable v[t], p uThe initial values of [t], n[t], ξ[t], and ζ[t] are used to iteratively solve the transformed convex problem until the difference in the optimization objective between two consecutive iterations is less than the set threshold, that is, until the algorithm converges, thereby obtaining the minimum energy consumption of the fixed-wing UAV.

[0105] Step 5: According to the data obtained from the solution, use synthetic aperture radar imaging technology based on OFDM signals to image the sensed target area.

[0106] According to the UAV motion parameters in the case of minimum energy consumption obtained in Step 4, use the OFDM synthetic aperture radar imaging algorithm based on cyclic prefix to process the echo signals of each sensed target area collected by the UAV, and obtain the imaging results of the sensed targets.

[0107] To measure the performance of the method, the optimized UAV energy consumption and motion parameters obtained through Steps 1 to 5 in the present invention (UAV three-dimensional trajectory design) are compared with the results obtained under the two-dimensional trajectory design scheme commonly used in existing research, as shown in Table 2. The simulation results show that after being optimized by the method of the present invention, the average acceleration and flight altitude of the UAV have both increased. In addition, its average speed remains at a relatively low level, which is conducive to reducing the total energy consumption during the execution of the communication and sensing integration task.

[0108] Table 2 Comparison of simulation results

[0109] Method Average speed Average acceleration Average flight altitude Total energy consumption of UAV Three-dimensional trajectory design of UAV 28.2 m / s <![CDATA[4.8m / s 2 > 127.7m 28028J Two-dimensional trajectory design of UAV 28.6 m / s <![CDATA[3.9m / s 2 > 120m 29666J

[0110] The imaging results of each target area obtained in the above Steps 1 to 5 are as Figure 3 shown. For target areas with different distances, the synthetic aperture radar can always present a clear imaging effect, fully demonstrating the universality of this method in maintaining the sensing performance of the system.

Claims

1. An optimization method based on minimizing the energy consumption of a fixed-wing unmanned aerial vehicle, characterized in that, It includes the following steps: Step 1: Establish a communication and sensing integrated system model according to the three-dimensional motion requirements, target sensing requirements, and data transmission communication requirements of a fixed-wing unmanned aerial vehicle (UAV). Step 2: Take the total energy consumption of the UAV as the objective function and construct the corresponding optimization problem. Step 3: By introducing auxiliary variables, convex difference programming, and successive convex approximation methods, transform the non-convex objective function and constraints in the optimization problem into convex forms. Step 4: Solve the transformed convex problem to obtain the optimal three-dimensional flight trajectory and data transmission communication power of the UAV. Step 5: According to the data obtained from the solution, use the synthetic aperture radar imaging technology based on OFDM signals to image the sensed target area.

2. The optimization method based on minimizing the energy consumption of a fixed-wing unmanned aerial vehicle according to claim 1, wherein In Step 1, the three-dimensional motion requirements of the fixed-wing UAV include: the flight altitude, speed, acceleration, pitch angle, starting position, and ending position of the UAV need to meet the system limitations and the requirements of the communication and sensing integrated task; the target sensing requirements include: the intensity of the echo signal reflected by the sensed target received by the UAV is not lower than the minimum received power, so that it can be distinguished from the receiver noise; the UAV maintains a constant flight speed and altitude during the sensing task to obtain better sensing performance; at the central moment of each sensing task, the flight speed of the UAV is perpendicular to the line connecting it to the center of the target area, so that the synthetic aperture radar is in the side-looking mode; the resolution of the synthetic aperture radar is better than the required resolution of the system, that is, the minimum distance between two adjacent targets; the data transmission communication requirements include: after the UAV completes a sensing task, the amount of data transmitted back to the base station is not less than the amount of data of the original echo signal received; the transmission power of the UAV transmission signal cannot exceed the maximum power limit.

3. The optimization method based on minimizing the energy consumption of a fixed-wing UAV according to claim 1, wherein, In Step 1, establishing the communication and sensing integrated system model specifically includes: The mission duration of the system is J seconds and is evenly divided into T time slots, labeled respectively as The length of each time slot is Δt = J / T seconds; in order for the UAV to complete the sensing mission of K target areas and achieve the transmission of echo signals, the T time slots are further divided into K + 1 time periods of equal duration; in the initial time period the UAV flies from the initial position to the first target area; subsequently, in each time period the UAV will allocate T a time slots for the sensing mission of the target area, and the remaining time slots are used for the backhaul communication mission; define as the set of time slots used for the sensing mission of the k-th target area, as the set of time slots used for the k-th backhaul communication mission; is the set of all time slots used for the sensing mission, is the set of all time slots used for the backhaul communication mission; the coordinates of the center of the k-th sensing target area are The base station coordinates are p BS = [x BS , y BS , z BS T ; The UAV starts from the initial position at t = 0 and finally returns to this position. In the discrete-time model, the speed, acceleration, and position coordinates of the UAV in each time slot are v[t] = [v x [t], v y [t], v z [t]] T , a[t] = [a x [t], a y [t], a z [t]] T , and p u [t] = [x u [t], y u [t], z u [t]] T .​ 4. The optimization method based on minimizing the energy consumption of a fixed-wing UAV according to claim 3, characterized in that In Step 2, taking the total energy consumption of the UAV as the objective function, its calculation expression is: Among them, P fly [t] is the flight power consumption generated by the UAV in time slot t, and P c [t] is the transmission power of the backhaul signal of the UAV in time slot t; The calculation expression of the flight power consumption is: Among them, f1 and f2 are constant parameters related to the zero-lift drag coefficient, wing area, and air density factors of the UAV, and g is the acceleration due to gravity.

5. The optimization method based on minimizing the energy consumption of a fixed-wing UAV according to claim 4, characterized in that, In Step 2, constructing the corresponding optimization problem specifically is: s.t.p u [0] = p u [T] = p i , v[0] = v i , Among them, the optimization objective is the total energy consumption \(E\) of the UAV UAV to be minimized, and the optimization variables are the speed \(v[t]\), acceleration \(a[t]\), trajectory \(p\) u [t] of the UAV and the transmit power \(P\) c [t] of the backhaul signal, where the subsequent expressions represent the constraint conditions; \(p\) i and \(v\) i represent the initial position and speed of the UAV at the initial moment, respectively; \(H\) min and \(H\) max represent the minimum and maximum heights of the UAV's three-dimensional flight, respectively; \(a\) max represents the maximum acceleration that the UAV can withstand; \(\varepsilon\) represents the maximum pitch angle of the UAV; \(v\) min and \(v\) max represent the minimum and maximum flight speeds of the UAV, respectively; \(P\) r [t] and \(G\) r represent the signal power and antenna gain of the UAV's receiving end within time slot \(t\), respectively; \(P\) t and \(G\) t represent the transmit signal power and antenna gain of the base station, respectively; \(S\) RCS represents the radar cross-section coefficient; and represent the distances from the base station or the UAV to the center of the \(k\)-th target area, respectively; represents the minimum received power of the echo signal required by the system; for each sensing task, \(P\) t , \(G\) t , \(G\) r , \(S\) RCS and \(\rho\) k,1 are all constants; \(c\) represents the speed of light; \(B\) s is the system bandwidth; represents the azimuth angle of the base station relative to the center of the \(k\)-th target area within time slot \(t\), represents the angle between the line connecting the UAV to the center of the \(k\)-th target area and the \(z\)-axis; \(d\) min represents the minimum resolution required by the system; \(R\) s is a constant representing the instantaneous rate of the original echo signal; represents the maximum transmission power of the UAV; \(R\) c [t] represents the data transmission rate of the UAV to the base station within time slot \(t\), and the calculation expression is: Among them, B c represents the bandwidth of the UAV transmission signal; r a represents the channel power fading coefficient at a distance of 1 meter from the transmitter; represents the power of additive white Gaussian noise at the base station; ρ3[t] represents the distance between the base station and the UAV within time slot t.

6. The optimization method based on minimizing the energy consumption of a fixed-wing UAV according to claim 5, characterized in that In the said step 3, the non-convex objective function in the optimization problem is transformed into a convex form, and the method includes: by introducing auxiliary variables and the non-convex term P fly [t] in the objective function is transformed into the following form: When introducing auxiliary variables, additional constraints will also be introduced, specifically including: At the optimal solution of the problem, except for v min ≤γ[t] and otherwise, the newly introduced constraints hold with equality. Otherwise, changing the value of the auxiliary variable would further decrease the value of the objective function. Therefore is equivalent to P fly [t]; In , is a convex difference function that exhibits non-convex properties. Using the convex difference programming method, a first-order Taylor expansion is performed on the second term of this function, thereby completing the convexification of the objective function.

7. The optimization method based on minimizing the energy consumption of a fixed-wing unmanned aerial vehicle according to claim 5, characterized in that, In Step 3, transforming the non-convex constraints in the problem into convex forms, the methods include: by reconstructing the UAV received power constraint, transforming it into an equivalent convex constraint, and the specific expression is: ρ k,2 [t] ≤ ρ max ; Among them, represents the maximum effective sensing distance of the UAV. For each sensing task, ρ max is a constant. By introducing slack variables Replace the backhaul communication constraint of the UAV with the following form: Through the successive convex approximation method, perform a first-order Taylor expansion on the non-convex terms in the non-convex constraints in the original problem and the newly introduced non-convex constraints, and transform them into convex forms.

8. The optimization method based on minimizing the energy consumption of a fixed-wing UAV according to claim 6 or 7, characterized in that, In step 4, solve the transformed convex problem. The methods include: setting the initial values of variables v[t], p u [t], n[t], ξ[t], ζ[t], and iteratively solving the transformed convex problem until the difference in the optimization objective between two consecutive iterations is less than the set threshold, that is, until the algorithm converges, so as to obtain the minimum energy consumption of the fixed-wing UAV.

9. The optimization method based on minimizing the energy consumption of a fixed-wing unmanned aerial vehicle according to claim 1, wherein, In Step 5, according to the UAV motion parameters in the case of minimum energy consumption obtained in Step 4, use the OFDM synthetic aperture radar imaging algorithm based on cyclic prefix to process the echo signals of each sensed target area collected by the UAV, and obtain the imaging results of the sensed targets.

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