Multi-UAV integrated communication and sensing system, UAV deployment and power control methods

Abstract

This invention discloses a multi-UAV communication and sensing integrated system, and a UAV deployment and power control method. In the integrated system, multiple single-antenna UAVs transmit individual information to their respective single-antenna ground users, simultaneously and collaboratively detecting ground targets to generate echo signals. A central processing unit aggregates the filtered echo signals from all UAVs and performs joint target detection. Each UAV flies to an optimized position and transmits individual information with optimal power according to an optimal scheme. The optimal scheme is determined by jointly optimizing the deployment position and transmission power of each UAV to maximize the minimum detection probability within the target area, under constraints of a single minimum signal-to-interference-plus-noise ratio at each ground user, a maximum transmit power constraint for each UAV, and a minimum inter-UAV distance constraint to avoid collisions. This is achieved using alternating optimization and continuous convex approximation algorithms. This invention improves coverage while enhancing communication and sensing performance.

Description

Technical Field

[0001] This invention relates to a multi-UAV communication system and its control method in the field of wireless communication technology, and particularly to a multi-UAV integrated communication and sensing system, UAV deployment and power control method. Background Technology

[0002] Integrated sensing and communications (ISAC) systems, by utilizing existing wireless infrastructure and spectrum / power resources to simultaneously perform sensing and communication tasks, significantly improve spectrum, energy, and hardware utilization efficiency, making them an important application scenario for sixth-generation (6G) wireless networks. The sensing performance of traditional terrestrial ISAC systems is highly dependent on the line-of-sight link between the base station and the sensing area due to obstructions such as buildings and trees. Providing a line-of-sight link between the ISAC base station and the sensing area, enabling the base station to achieve better sensing effects while transmitting communication, is a key challenge for enhancing the performance of integrated sensing and communications systems.

[0003] Unmanned aerial vehicles (UAVs) have been widely used in ISAC wireless networks due to their advantages such as high altitude capability, high maneuverability, low cost, and ease of deployment. In ISAC systems, UAVs can act as aerial access points, aerial base stations, and relays to establish a three-dimensional wireless communication network while simultaneously performing sensing tasks. Currently, existing technologies address this by combining a single UAV with a ground base station to achieve coverage of the sensing area. However, existing technologies still need improvement in sensing performance. Single-UAV integrated communication and sensing systems suffer from limited coverage and performance degradation due to their size, weight, and power limitations. Therefore, there is an urgent need in this field for a technical solution that improves coverage while enhancing communication and sensing performance. Summary of the Invention

[0004] To address the technical problems of insufficient coverage, weak communication, and unsatisfactory sensing performance in existing technologies, this invention provides an integrated multi-UAV communication and sensing system, as well as a UAV deployment and power control method.

[0005] This invention is achieved using the following technical solution: a multi-UAV communication and sensing integrated system, comprising:

[0006] Multiple single-antenna UAVs are used to send individual information to their respective single-antenna ground users and simultaneously use the echo signals formed by the individual information to collaboratively detect ground targets. Each UAV performs matched filtering to remove cross-link echoes generated by other UAVs after receiving the echo signal from the sensing area, and transmits the filtered echo signal to the central processing unit.

[0007] The central processing unit aggregates the filtered echo signals from all UAVs and performs joint target detection.

[0008] Each UAV flies to an optimized position according to the optimal scheme and transmits the individual information with the optimal power. The optimal scheme is obtained by jointly optimizing the deployment position and transmission power of each UAV to maximize the minimum detection probability in the target area under the constraints of the single minimum signal-to-interference-plus-noise ratio at the ground user, the maximum transmission power of each UAV, and the minimum inter-UAV distance for avoiding collisions. This is achieved by using alternating optimization and continuous convex approximation algorithms.

[0009] As a further improvement to the above scheme, the design method of the optimal scheme includes the following steps:

[0010] The first step is to design problem P1 as follows:

[0011]

[0012]

[0013]

[0014]

[0015] In the formula, u l u i u k These are the position coordinates of the l-th, i-th, and k-th UAVs, respectively. A collection of drones; p l p k These are the transmit powers of the l-th and k-th UAVs, respectively. Let t represent the coordinates of the nth sampling point, where t indicates that these coordinates are the coordinates of the detected target. The set of sampling points at the target location; Let d be the distance between the l-th drone and the n-th sampling point of the target. min P represents the minimum drone spacing required to avoid collisions. max This is the maximum transmission power of the drone. Let ρ0 be the reference distance, and ρ0 be the distance at the reference distance. Channel power at that location, Let l be the distance between the l-th drone and the k-th ground user. Let be the location coordinates of the k-th ground user, where c indicates that these coordinates are the location coordinates of a ground user, and Γ is the minimum signal-to-interference-plus-noise ratio at a single-antenna ground user location. The noise power in the received signal;

[0016] The second step is to initialize d. minP max Г,

[0017] The third step is to introduce an auxiliary variable ζ to transform problem P1 into problem P1.1:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] Fourth step: Under the constraint of a single minimum signal-to-interference-plus-noise ratio Γ, transform problem P1.1 into problem P2:

[0024]

[0025]

[0026]

[0027]

[0028] Fifth, consider an alternating optimization iteration with the number of iterations r≥1. For u l The local point in the r-th iteration is then used to perform a first-order Taylor expansion, thereby transforming problem P2 into problem P2.1:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, For u i Local points in the r-th iteration for At point The lower bound function obtained by performing a first-order Taylor expansion, where H is the flight altitude of the UAV, and δ l,k Auxiliary variables introduced;

[0035] Step 6: For the convex problem P2.1, use the standard solver CVX for optimized solution;

[0036] Step 7: Optimize the drone's transmit power p at the given drone deployment location. l Transform problem P1.1 into problem P3:

[0037]

[0038]

[0039]

[0040]

[0041] Step 8: For the linear programming problem P3, use the standard solver CVX to perform optimization.

[0042] In the ninth step, by merging the optimization solutions of problem P2.1 and problem P3, the objective value of problem P1 is improved alternately in each external iteration to ensure that the objective value is monotonically non-decreasing. At this point, the optimization solutions of problem P2.1 and problem P3 are the optimal position and optimal transmission power of the UAV, respectively.

[0043] Furthermore, in problem P2.1, the left-hand side expression of constraint C4 is approximated by its lower bound as:

[0044]

[0045] Furthermore, in problem P2.1, constraint C1 is approximated by its lower bound as:

[0046]

[0047] Furthermore, in problem P2.1, constraint C3 is first transformed into:

[0048]

[0049] The first term of the expression on the left is at the point. The lower bound obtained by the Taylor expansion is:

[0050]

[0051] When processing the second term of this expression, an auxiliary variable δ is introduced. l,k The C3 constraint is rewritten as:

[0052]

[0053] Furthermore, in problem P2.1, the left-hand side expression of constraint C5 is approximated by its lower bound as:

[0054]

[0055] Furthermore, problem P2.1 is solved using an optimized solver;

[0056] And / or, problem P3 is solved using an optimized solver.

[0057] The present invention also provides a method for deploying and controlling the power of a drone, which includes the following steps:

[0058] The first step is to design problem P1 as follows:

[0059]

[0060]

[0061]

[0062]

[0063] In the formula, u l u i u k These are the position coordinates of the l-th, i-th, and k-th UAVs, respectively. A collection of drones; p l p k These are the transmit powers of the l-th and k-th UAVs, respectively. Let n be the coordinates of the nth sampling point. The set of sampling points at the target location; Let d be the distance between the l-th drone and the n-th sampling point of the target. min P is the minimum inter-drone distance used to avoid collisions. max This is the maximum transmission power of the drone. Let ρ0 be the reference distance, and ρ0 be the distance at the reference distance. Channel power at that location, Let be the location coordinates of the k-th ground user, and Γ be the minimum signal-to-interference-plus-noise ratio at a single-antenna ground user. The noise power in the received signal;

[0064] The second step is to initialize d. min P max Г,

[0065] The third step is to introduce an auxiliary variable ζ to transform problem P1 into problem P1.1:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] Fourth step: Under the single minimum signal-to-interference-plus-noise ratio (SINR) constraint Γ, transform problem P1.1 into problem P2:

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[0073]

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[0075]

[0076] Fifth, consider an alternating optimization iteration with the number of iterations r≥1. For u l At a local point in the r-th iteration, and then performing a first-order Taylor expansion at that point, problem P2 is transformed into problem P2.1:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, For u i Local points in the r-th iteration for At point The lower bound function obtained by performing a first-order Taylor expansion, where H is the flight altitude of the UAV, and δ l,k Auxiliary variables introduced;

[0083] Step 6: For the convex problem P2.1, use the standard solver CVX for optimized solution;

[0084] Step 7: To optimize the drone's transmit power p at a given drone deployment location. l Transform problem P1.1 into problem P3:

[0085]

[0086]

[0087]

[0088]

[0089] Step 8: For the linear programming problem P3, use the standard solver CVX to perform optimization.

[0090] In the ninth step, by merging the optimization solutions of problem P2.1 and problem P3, the objective value of problem P1 is improved alternately in each external iteration to ensure that the objective value is monotonically non-decreasing. At this point, the optimization solutions of problem P2.1 and problem P3 are respectively the optimal location and optimal power of the UAV, and the optimal location of the UAV is the optimal deployment of the UAV.

[0091] As a further improvement to the above solution, the UAV deployment and power control method is applied to the above-mentioned multi-UAV communication and sensing integrated system.

[0092] As a further improvement to the above scheme, the UAV deployment and power control method adopts the design method of the optimal scheme in the above multi-UAV communication and sensing integrated system.

[0093] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0094] 1. The technical solution of this invention can maximize the minimum detection probability of the multi-UAV communication and sensing integrated system in the target area while ensuring user communication, thereby improving the overall performance of the system. Compared with existing technical solutions, this solution makes full use of the high mobility characteristics of UAVs that allow for flexible deployment and combines it with transmission power control for joint optimization.

[0095] 2. The method described in this invention uses information signals to achieve target detection, which is compatible with existing UAV communication systems and can solve the target detection requirement while supporting high-speed communication, thereby realizing the dual function of integrated communication and perception.

[0096] 3. The method described in this invention uses UAVs to achieve integrated communication and sensing functions, which can provide line-of-sight links for communication and sensing. Compared with ground-based integrated communication and sensing systems, it has a larger coverage range and better communication and sensing performance.

[0097] 4. The method described in this invention uses multiple UAVs for communication and sensing, which can not only improve the coverage of the UAV communication and sensing area, but also increase the detection probability within the sensing area.

[0098] 5. The method described in this invention uses a joint optimization scheme for UAV deployment and transmission power, which can better balance communication and perception compared to optimizing a single variable, thus greatly improving the overall performance of the system.

[0099] 6. The method described in this invention decouples highly coupled non-convex problems and transforms them into convex problems, which greatly simplifies the amount of computation for optimization, effectively reduces computational complexity, and lowers the computational power requirements of the central processing unit. Attached Figure Description

[0100] Figure 1 This is a schematic diagram of the structure of a multi-UAV communication and sensing integrated system provided in a preferred embodiment of the present invention.

[0101] Figure 2 For based on Figure 1 A flowchart of the deployment and power control method for a multi-UAV communication and sensing integrated system.

[0102] Figure 3 for Figure 1 A flowchart illustrating the design method of the optimal solution adopted in the multi-UAV communication and sensing integrated system. Detailed Implementation

[0103] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0104] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0105] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0106] The multi-UAV communication and sensing integrated system of the present invention is as follows: Figure 1The multi-UAV ISAC system shown in the figure has L single-antenna UAVs communicating with a single-antenna ground user, and their information signals are reused for target detection in the region of interest. This represents the set of drone / ground users. T represents the duration of the transport block of interest or the radar dwell time. and Let H and Y represent the position coordinates of UAV l and ground user k, respectively. All UAVs fly at a fixed altitude H above the ground. The UAVs know the location of the ground user in advance. Consider the communication channel from the UAV to the ground user as a multi-user interference line-of-sight (LOS) channel. Therefore, the channel power gain between UAV l and ground user k is: Where ρ0 is the reference distance Channel power at that location, Let s be the distance between the drone l and the ground user k. l (t) represents the information signal sent by UAV l at time t, s l (t) is an independent and identically distributed random variable with zero mean and unit variance. The received signal at ground user k is... p l The transmit power at point l of the UAV. Let K be the noise at the receiver of ground user k, with expectation of 0 and variance of .

[0107] The transmission power of each drone is no greater than the maximum transmission power P. max The signal-to-interference-plus-noise ratio at ground user k is... Consider using unmanned aerial vehicles (UAVs) to perform joint target detection by reusing information signals. The detected target is located at u. t =[x t ,y t ] T The distance between the drone and the target is... The echo signal received by the UAV at point l is in For unit antenna gain, σ r The radar cross section of the target. Let be the transmission delay from drone i to the target drone l, where c is the propagation speed of electromagnetic waves. This represents the Gaussian noise at the drone's receiver.

[0108] Based on the echo signals received at each drone, matched filtering is used to remove cross-link echo signals from other drones. The filtered signals are then sent to the central processing unit via a wireless backhaul link for joint target detection. Therefore, the detection probability is... Q is the complementary cumulative distribution function, and δ′ is the detector threshold. Since the detection probability monotonically increases with the sum of the reflected signal powers, maximizing the joint detection probability is equivalent to maximizing the sum of the reflected signal powers, i.e. Where α depends on the radar cross section of the target in reality.

[0109] This invention maximizes the minimum detection probability of the target area while ensuring the signal-to-interference-plus-noise ratio (SIR) constraint, maximum transmit power constraint, and minimum inter-UAV distance constraint to avoid collisions for each ground user. It considers taking N sampling points in the target area. These are the coordinates of the sampling point. This is the set of sampling points for the target location. Because maximizing the position... The detection probability is equivalent to maximizing Given a fixed α for different target locations, the optimization problem of maximizing the minimum detection probability in a given target area by optimizing the UAV position and transmission power is as follows:

[0110]

[0111]

[0112]

[0113]

[0114] Where, d min Let Γ be the minimum inter-UAV distance for collision avoidance, and let C1 be the minimum signal-to-interference-plus-noise ratio (SNR) at the ground user. Constraint C1 states that the distance between any two UAVs is not less than the minimum safe distance for collision avoidance. Constraint C2 states that the transmit power of any UAV is not greater than its maximum transmit power and is non-negative. Constraint C3 states that the SNR at any ground user is not less than the minimum SNR.

[0115] The multi-UAV communication and sensing integrated system of the present invention employs the following UAV deployment and power control method: Figure 2 The method for UAV position optimization and power control based on alternating optimization is shown below. The steps of this method are as follows:

[0116] (1) Initialization of the multi-UAV communication and sensing integrated system: Set the minimum inter-UAV distance d for each UAV. min Maximum transmit power P max Minimum signal-to-interference-plus-noise ratio (SIN / N) at the ground user location (GIN / N) Detection of target sampling point location wait.

[0117] (2) Ground base station issues optimization scheme: The ground base station optimizes the deployment and transmission power of UAVs based on the initialization information.

[0118] a) Introducing the auxiliary variable ζ transforms problem (P1) into the following problem:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] By alternately optimizing the drone's position u l and UAV transmission power p l It is possible to find suboptimal but high-quality solutions to problems (P1) or (P1.1).

[0125] First, optimize at a given transmit power p l The layout of the drone under u l Based on problem (P1.1), the optimization problem under the signal-to-interference-plus-noise ratio constraint is transformed into the following problem:

[0126]

[0127]

[0128]

[0129]

[0130] Consider a specific alternating optimization iteration with the number of iterations r≥1. Let be a local point in the r-th iteration, and then perform a first-order Taylor expansion at that point. Therefore, the left-hand side expression of constraint C4 is approximated by its lower bound as... Constraint C1 is approximated by its lower bound as Constraint C3 can first be converted to The first term of the expression on the left is at the point. The lower bound obtained by the Taylor expansion is When processing the second term of this expression, an auxiliary variable δ is introduced. l,k The C3 constraint is rewritten as The left-hand side expression of constraint C5 is approximated by its lower bound as follows: Based on the above transformation, problem (P2) is transformed into the following problem:

[0131]

[0132]

[0133]

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[0136] Problem (P2.1) is a convex problem, and therefore can be solved by optimization using CVX. Furthermore, the objective value of (P2.1) is monotonically non-decreasing throughout the optimization process. Therefore, the convergence of the SCA-based problem-solving algorithm (P2) is guaranteed.

[0137] b) Then, based on problem (P1.1), optimize the UAV transmit power p at the given UAV deployment location. l In this case, the optimization problem is expressed as:

[0138]

[0139]

[0140]

[0141]

[0142] Problem (P3) is a linear programming problem that can be solved using a standard solver (such as CVX) for optimization.

[0143] d) By combining the UAV position optimization in (P2.1) and the power control optimization in (P3), a global optimization algorithm based on alternating optimization is implemented to solve problem (P1). By merging these two components, the alternating optimization method increases the objective value of (P1) in each external iteration, ensuring that the objective value is monotonically non-decreasing. Since the objective value of problem (P1) is an upper bound of a finite value, the convergence of this algorithm is guaranteed.

[0144] (3) UAV position optimization and power control: Each UAV flies to the optimized position according to the optimization scheme given by the ground base station and transmits information signals with the optimal power. After the information signal reaches the target location, it forms an echo and returns to the UAV. Each UAV eliminates the cross-link echo signal formed by the signals transmitted by other UAVs through matched filtering, and uniformly transmits the filtered signal to the central processing unit located on the ground through the wireless backhaul link to achieve joint target detection.

[0145] By employing a joint optimization approach for target detection, the detection probability of the target area can be improved while ensuring the communication needs of ground users. As described above, in a multi-UAV communication and sensing integrated system, each UAV flies to an optimized position according to an optimal scheme and transmits its individual information with optimal power. This optimal scheme is achieved by jointly optimizing the deployment position and transmission power of each UAV to maximize the minimum detection probability within the target area, under constraints of a single minimum signal-to-interference-plus-noise ratio at the ground user, the maximum transmit power constraint of each UAV, and the minimum inter-UAV distance constraint to avoid collisions. This is obtained using alternating optimization and continuous convex approximation algorithms.

[0146] Based on the overall understanding of the above description, it can be seen that the design method of the optimal solution includes the following steps:

[0147] Step 1: Design problem P1;

[0148] The second step is to initialize d. min P max Г,

[0149] The third step is to introduce an auxiliary variable ζ to transform problem P1 into problem P1.1;

[0150] The fourth step is to transform problem P1.1 into problem P2 under the single minimum signal-to-interference-plus-noise ratio (SINR) constraint Γ.

[0151] Fifth step, transform problem P2 into problem P2.1;

[0152] Step 6: For the convex problem P2.1, use the standard solver CVX for optimized solution;

[0153] Step 7: Optimize the drone's transmit power p at the given drone deployment location. l Transform problem P1.1 into problem P3:

[0154] Step 8: For the linear programming problem P3, use the standard solver CVX to perform optimization.

[0155] In the ninth step, by merging the optimization solutions of problem P2.1 and problem P3, the objective value of problem P1 is improved alternately in each external iteration to ensure that the objective value is monotonically non-decreasing. At this point, the optimization solutions of problem P2.1 and problem P3 are the optimal position and optimal power of the UAV, respectively.

[0156] Compared with existing technologies, the target detection performance enhancement method in the multi-UAV communication and sensing integrated system designed in this invention involves multiple single-antenna UAVs transmitting individual information to their respective single-antenna ground users and simultaneously using the information signals to collaboratively detect ground targets. After receiving the echo signal from the sensing area, the UAVs perform matched filtering to remove cross-link echoes generated by other UAVs and transmit the filtered signal to the central processing unit. The central processing unit then aggregates all UAV information and performs joint target detection.

[0157] The problem this invention aims to solve is to maximize the minimum detection probability within a target area by jointly optimizing the deployment location and transmission power of UAVs. This problem is constrained by a single minimum signal-to-interference-plus-noise ratio at the ground user, the maximum transmission power of each UAV, and the minimum inter-UAV distance constraint to avoid collisions. The ground base station uses alternating optimization and continuous convex approximation algorithms to obtain the optimal solution and distributes it to each UAV for implementation. This method not only protects the safe flight of UAVs and ensures user communication, but also improves the target detection probability within the sensing area.

[0158] This invention's technical solution can maximize the minimum detection probability of a multi-UAV integrated communication and sensing system in the target area while ensuring user communication, thereby improving the overall system performance. Compared with existing technical solutions, this solution fully utilizes the high-altitude characteristics of UAVs, which can provide line-of-sight communication and sensing links, and their high mobility and flexible deployment capabilities. It also uses a multi-UAV system to overcome the limitations of single UAVs in terms of size and energy, ensuring the system's ability to perform complex tasks and improving its robustness, coverage, and communication and detection performance.

[0159] The technical solution of this invention can rapidly construct an integrated UAV communication and sensing network in post-disaster reconstruction scenarios, relying on a small number of base stations. This network ensures communication while simultaneously detecting targets in specific areas, assisting in reconnaissance and rescue operations. Furthermore, the technical solution of this invention can also achieve large-scale communication and sensing coverage in urban areas and other regions with relatively complete ground base station coverage, relying on a small number of UAVs supported by ground base stations.

[0160] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of this invention should be determined by the appended claims.

Claims

1. A multi-UAV communication and sensing integrated system, comprising: Multiple single-antenna UAVs are used to send individual information to their respective single-antenna ground users and simultaneously use the echo signals formed by the individual information to collaboratively detect ground targets. Each UAV performs matched filtering to remove cross-link echoes generated by other UAVs after receiving the echo signal from the sensing area, and transmits the filtered echo signal to the central processing unit. The central processing unit aggregates the filtered echo signals from all UAVs and performs joint target detection. The feature is that each UAV flies to an optimized position according to the optimal scheme and transmits the individual information with the optimal power; the optimal scheme is obtained by jointly optimizing the deployment position and transmission power of each UAV to maximize the minimum detection probability in the target area under the constraints of a single minimum signal-to-interference-plus-noise ratio at the ground user, the maximum transmission power of each UAV, and the minimum inter-UAV distance constraint to avoid collisions, and thereby using an alternating optimization and continuous convex approximation algorithm. The optimal solution design method includes the following steps: The first step is to design problem P1 as follows: In the formula, , , The first , No. , No. The location coordinates of the drone. A collection of drones; , The first , No. The transmit power of each drone, For the first The location coordinates of each sampling point This indicates that the coordinates are the location coordinates of the detected target. The set of sampling points at the target location; For the first The first drone and target The distance between each sampling point The minimum drone spacing distance used to avoid collisions; This is the maximum transmission power of the drone. For reference distance, To be at the reference distance Channel power at that location, For the first The drone and the first The distance between ground users For the first Location coordinates of a ground user This indicates that the coordinates are the location coordinates of the ground user. This represents the minimum signal-to-interference-plus-noise ratio (SIR) at a single-antenna ground user location. This represents the noise power in the received signal.

2. The multi-UAV communication and sensing integrated system according to claim 1, characterized in that, The design methods for optimal solutions also include: The second step is initialization. , , , , ; The third step is to introduce auxiliary variables. Transform problem P1 into problem P1.1: Fourth step: Under the constraint of a single minimum signal-to-interference-plus-noise ratio, transform problem P1.1 into problem P2: Fifth step, consider an alternating optimization iteration, with the number of iterations being... , for No. The local point in the next iteration is then used to perform a first-order Taylor expansion, thereby transforming problem P2 into problem P2.1: In the formula, for In the Local points in the next iteration for At point The lower bound function obtained by performing a first-order Taylor expansion, The flight altitude of the drone. Auxiliary variables introduced; Step 6: For the convex problem P2.1, use the standard solver CVX for optimized solution; Step 7: Optimize the drone's transmission power at the given drone deployment location. Transform problem P1.1 into problem P3: Step 8: For the linear programming problem P3, use the standard solver CVX to perform optimization. In the ninth step, by merging the optimization solutions of problem P2.1 and problem P3, the objective value of problem P1 is improved alternately in each external iteration to ensure that the objective value is monotonically non-decreasing. At this point, the optimization solutions of problem P2.1 and problem P3 are the optimal position and optimal transmission power of the UAV, respectively.

3. The multi-UAV communication and sensing integrated system according to claim 2, characterized in that, In problem P2.1, constraints The expression on the left-hand side is approximated by its lower bound as: 。 4. The multi-UAV communication and sensing integrated system according to claim 2, characterized in that, In problem P2.1, constraints Its lower bound approximates as: 。 5. The multi-UAV communication and sensing integrated system according to claim 2, characterized in that, In problem P2.1, constraints First, convert it to: ， The first term of the expression on the left is at the point. The lower bound obtained by the Taylor expansion is: ， When processing the second term of the expression, an auxiliary variable is introduced. of The constraints are rewritten as: 。 6. The multi-UAV communication and sensing integrated system according to claim 2, characterized in that, In problem P2.1, constraints The expression on the left-hand side is approximated by its lower bound as: 。 7. The multi-UAV communication and sensing integrated system according to claim 2, characterized in that, Problem P2.1 is solved using an optimized solver. And / or, problem P3 is solved using an optimized solver.

8. A method for deploying and controlling the power of an unmanned aerial vehicle (UAV), characterized in that, It includes the following steps: The first step is to design problem P1 as follows: In the formula, , , The first , No. , No. The location coordinates of the drone. A collection of drones; , The first , No. The transmit power of each drone, For the first The location coordinates of each sampling point This indicates that the coordinates are the location coordinates of the detected target. The set of sampling points at the target location; For the first The first drone and target The distance between each sampling point The minimum drone spacing distance used to avoid collisions; This is the maximum transmission power of the drone. For reference distance, To be at the reference distance Channel power at that location, For the first The drone and the first The distance between ground users For the first Location coordinates of a ground user This indicates that the coordinates are the location coordinates of the ground user. This represents the minimum signal-to-interference-plus-noise ratio (SIR) at a single-antenna ground user location. The noise power in the received signal; The second step is initialization. , ... , ; The third step is to introduce auxiliary variables. Transform problem P1 into problem P1.1: The fourth step is to achieve a single minimum signal-to-interference-plus-noise ratio. Under constraints, problem P1.1 is transformed into problem P2: Fifth step, consider an alternating optimization iteration, with the number of iterations being... , for In the The local point in the next iteration is then used for a first-order Taylor expansion, thus transforming problem P2 into problem P2.1: In the formula, for In the Local points in the next iteration for At point The lower bound function obtained by performing a first-order Taylor expansion, The flight altitude of the drone. Auxiliary variables introduced; Step 6: For the convex problem P2.1, use the standard solver CVX for optimized solution; Step 7: Optimize the drone's transmit power at a given drone deployment location. Transform problem P1.1 into problem P3: Step 8: For the linear programming problem P3, use the standard solver CVX to perform optimization. In the ninth step, by merging the optimization solutions of problem P2.1 and problem P3, the objective value of problem P1 is improved alternately in each external iteration to ensure that the objective value is monotonically non-decreasing. At this point, the optimization solutions of problem P2.1 and problem P3 are respectively the optimal location and optimal power of the UAV, and the optimal location of the UAV is the optimal deployment of the UAV.

9. The UAV deployment and power control method according to claim 8, characterized in that, The UAV deployment and power control method is applied to the multi-UAV communication and sensing integrated system as described in claim 1.

10. The UAV deployment and power control method according to claim 8, characterized in that, The UAV deployment and power control method adopts the design method of the optimal solution in the multi-UAV communication and sensing integrated system as described in any one of claims 3 to 7.

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