Multi-uav deployment method for integrated communication and positioning emergency service coverage

Abstract

The present application relates to a multi-unmanned aerial vehicle deployment method and a multi-unmanned aerial vehicle system for communication and positioning integrated emergency service coverage, which solves a simplified 2D projection deployment problem by converting the problem into a minimum hitting set problem and providing a low complexity algorithm to solve the problem by adopting a D optimality criterion as a positioning performance metric, so as to realize an optimization problem of deploying the least number of unmanned aerial vehicles to meet the communication and positioning requirements of ground users by using a low complexity algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle deployment, and more particularly, to a multi-unmanned aerial vehicle deployment method and system for communication positioning integrated emergency service coverage. BACKGROUND

[0002] In emergency scenarios, such as earthquakes, floods and other scenarios requiring emergency rescue in cities, due to hardware facility damage, ground obstacles and scattering, the positioning performance of cellular communication and global satellite navigation system is sharply reduced. In recent years, unmanned aerial vehicle technology has developed rapidly. Due to the advantages of reliable line-of-sight link and flexible deployment, unmanned aerial vehicles can provide high-speed and reliable communication services for ground users and can also serve as positioning anchors to improve the positioning performance of the network, so unmanned aerial vehicle technology has become an important solution in emergency scenarios.

[0003] By installing base stations on unmanned aerial vehicles, unmanned aerial vehicle-based aerial base stations can help ground networks improve communication and positioning services. There has been a lot of research on deploying unmanned aerial vehicles to improve communication coverage, data rate, communication delay and energy consumption performance, etc. A common design method is to jointly optimize the flight trajectory (or deployment position) of the unmanned aerial vehicle and the resource allocation (such as power, bandwidth and transmission time) to increase the capacity of the communication link under the limited on-board battery. At present, 1D, 2D and 3D deployment of unmanned aerial vehicles have been fully researched. In 4 / 5G networks, time difference of arrival (TDoA) positioning technology is commonly used for high-precision positioning. Unlike providing communication services, positioning a 3D position by TDoA requires at least four positioning anchors, and the positioning accuracy depends on the deployment and resource allocation of all participating nodes. Improving the positioning performance of the network by deploying unmanned aerial vehicles is an important topic. The existing research commonly uses the Camer-Rao Lower Bound (CRLB) to analyze the positioning accuracy of unmanned aerial vehicle-assisted positioning networks. However, due to the non-convexity of CRLB for unmanned aerial vehicle positions and the inability to obtain a closed form of the CRLB expression, existing research usually uses heuristic algorithms to search for the optimal unmanned aerial vehicle deployment. However, the existing heuristic methods for optimizing the position of unmanned aerial vehicles have high computational complexity and are too time-consuming, which is not suitable for use in emergency situations. SUMMARY

[0004] The technical problem solved by the present application is to provide a multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage, which adopts D-optimality criterion as a positioning performance metric, converts the problem into a minimum hitting set problem to solve the simplified 2D projection deployment problem and provides a low-complexity algorithm to solve the problem, so as to realize the optimization problem of deploying the minimum number of unmanned aerial vehicles to meet the communication and positioning requirements of ground users with a low-complexity algorithm.

[0005] According to a first aspect of the present application, a multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage is provided, which is applied to a multi-unmanned aerial vehicle communication and positioning system comprising three ground base stations, a plurality of unmanned aerial vehicles and a plurality of users; characterized in that the method comprises the following steps:

[0006] S1, measuring the positioning accuracy requirement of each user based on the approximate D-optimality criterion and solving the first deployment area of the unmanned aerial vehicle based on the positioning accuracy requirement;

[0007] S2, solving the second deployment area of the unmanned aerial vehicle based on the communication demand of each user;

[0008] S3, obtaining the deployment area based on the first deployment area and the second deployment area;

[0009] S4, obtaining the number and position of the unmanned aerial vehicle deployment based on the deployment area by using the minimum hitting set.

[0010] In the multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage described in the present application, the step S1 further comprises:

[0011] S11, representing the positioning accuracy of user k as k , and measuring the positioning accuracy by using the D-optimality criterion, so that the positioning accuracy k satisfies:

[0012]

[0013] wherein, k m u represents the position coordinates of user k, b k represents the position coordinates of unmanned aerial vehicle u, m k = [x k , y k , h n ], H represents the Jacobian matrix of the time difference of arrival TDoA of the base station and the unmanned aerial vehicle u to user k; respectively represent the ToA measurement variance of the positioning signal sent by the three ground base stations to user k, b ndenotes the position coordinates of the ground base station n,

[0014] S12, based on det(H) > 0 or det(H) < 0, rewriting the constraint equation (17) to derive the first UAV flight ellipse and the second UAV flight ellipse at a given flight height, and solving the first deployment area of the UAV based on the first UAV flight ellipse and the second UAV flight ellipse.

[0015] In the multi-UAV deployment method for communication and positioning integrated emergency service coverage described in the present application, in the step S12, the first UAV flight ellipse ε When c2> 0, the first UAV flight ellipse ε 1 is defined for the first deployment area, otherwise the second UAV flight ellipse ε 2 is defined for the first deployment area; wherein the coefficient a 1-3 satisfies:

[0016] a1= (q 22 -q 12 )(q 33 -q 13 )-(q 23 -q 13 )(q 32 -q 12 ),#(19)

[0017] a2= (q 23 -q 13 )(q 31 -q 11 )-(q 21 -q 11 )(q 33 -q 13 ),#(20)

[0018] a3= (q 21 -q 11 )(q 32 -q 12 )-(q 22 -q 12 )(q 31 -q 11 ),#(21)

[0019] wherein the unit vector from the nth base station to the user k is defined as q n ,

[0020]

[0021] In the multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage, the first unmanned aerial vehicle flight ellipse ε 1 satisfies:

[0022]

[0023] the second unmanned aerial vehicle flight ellipse ε 2 satisfies:

[0024]

[0025] wherein,

[0026] In the multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage, the step S2 further comprises:

[0027] S21, for the ground-to-air link, the second deployment area of the unmanned aerial vehicle satisfying the communication request of the user k is calculated based on the following inequality

[0028]

[0029] wherein W ku is the communication bandwidth between the unmanned aerial vehicle and the user, P k is the transmission power of the user k, N0 is the noise power spectral density, α is the path loss index of the ground-to-air link, γ0 is the reference free space path loss at a distance of 1m, P(LoS, θ ku ) represents the LoS probability between the unmanned aerial vehicle u located at position b u and the user k, θ ku is the elevation angle between the unmanned aerial vehicle u and the user k, represents the communication rate requirement of the user k.

[0030] In the multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage, the step S3 further comprises:

[0031] S31, for the ground-to-ground link, the base station position satisfying the communication request of the user k is calculated based on the following inequality:

[0032]

[0033] wherein W kn is the communication bandwidth between the base station and the user, P k is the transmission power of the user k, N0 is the noise power spectral density, ε is the communication interruption probability, F -1(ε) is the inverse function of the cumulative distribution function of the Rayleigh fading link coefficient, β represents the path loss exponent for the ground-to-ground path, γ0 is the reference free space path loss at a distance of 1 m, denotes the communication rate requirement of user k;

[0034] S32, determining the user set that can be served by all base stations according to inequality (27) and calculating the base station feasible region corresponding to the user

[0035] S33, based on the second deployment area the first deployment area ε k , the base station feasible region obtaining the deployment area wherein

[0036] In the multi-unmanned aerial vehicle deployment method for communication positioning integrated emergency service coverage provided by the application, the step S4 further comprises:

[0037] S41, discretizing the area capable of deploying unmanned aerial vehicles into a set of unmanned aerial vehicle candidate deployment points

[0038] A binary variable v ij is used to represent whether the grid point g i is in the area

[0039] S42, converting the unmanned aerial vehicle candidate deployment problem into solving the deployment number and position of the unmanned aerial vehicle satisfying the following constraints and using a depth-first algorithm:

[0040]

[0041]

[0042] wherein L is a positive integer, v i = 1 when the grid point g ij is in the area , otherwise v ij = 0, wherein denotes the number of areas containing g l .

[0043] In the multi-unmanned aerial vehicle deployment method for communication positioning integrated emergency service coverage provided by the application, in step S42, the depth of the grid point g l is defined as Δ l , wherein the value of Δ l is equal to the number of areas containing g l ​the number of regions, then select the point g with the largest depth k as the first position of the drone, then from the deployment region remove all regions containing point g k and update the depth of each point, repeat the previous steps until all regions are covered by at least one drone.

[0044] According to a first aspect of the present application, a multi-drone system for communication and positioning integrated emergency service coverage is provided, comprising three ground base stations, a plurality of drones and a plurality of users, the plurality of drones being deployed based on the multi-drone deployment method for communication and positioning integrated emergency service coverage.

[0045] The multi-drone method and system for communication and positioning integrated emergency service coverage of the present application can measure the positioning accuracy of users by using the approximate D-optimal criterion, thereby being able to depict the feasible positions of drone positioning from a geometric perspective, and converting the NP-hard problem of the minimum deployment problem into the minimum hitting set problem, and providing a low-complexity algorithm using the minimum collision set. BRIEF DESCRIPTION OF DRAWINGS

[0046] The present application will be further described below with reference to the accompanying drawings and examples, in which:

[0047] Figure 1 is a schematic diagram of the multi-drone system for communication and positioning integrated emergency service coverage of the present application;

[0048] Figure 2 is a flowchart of the multi-drone method for communication and positioning integrated emergency service coverage of the present application;

[0049] Figures 3A-3E shows the number of drones required and the layout under different methods. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0051] The present application uses dual-function unmanned aerial vehicle assisted ground network to improve communication and positioning performance. The present application studies the optimization problem of deploying the minimum number of unmanned aerial vehicles to meet the communication and positioning needs of ground users. There are several technical difficulties in this problem, including cardinality minimization, non-convexity of unmanned aerial vehicle positioning performance metrics, and association between users and communication terminals. In order to solve this problem, we use the D-optimality criterion as the positioning performance metric, solve the simplified 2D projection deployment problem by converting the problem into a minimum hitting set problem, and propose a low complexity algorithm to solve the problem.

[0052] The present application relates to a multi-unmanned aerial vehicle deployment method for communication and positioning integrated emergency service coverage, which is used to deploy Figure 1 each unmanned aerial vehicle in the multi-unmanned aerial vehicle system for communication and positioning integrated emergency service coverage as shown. As Figure 1 shown, the multi-unmanned aerial vehicle system for communication and positioning integrated emergency service coverage includes an emergency network composed of 3 ground base stations, N u rotor unmanned aerial vehicles and K users. These unmanned aerial vehicles work with ground base stations to provide communication and positioning services for ground users. The positions of the nth base station, the u unmanned aerial vehicle and the k user are represented as m k = [x k ,y k ,h k ], wherein, as Figure 1 shown, x and y represent the horizontal and vertical coordinates, respectively, and h represents the height coordinate, i.e. the coordinate on the Z axis in Figure 1 . N u and K are positive integers.

[0053] Our goal is to find the minimum number of unmanned aerial vehicle deployment and its position. Therefore, we propose a multi-unmanned aerial vehicle method for communication and positioning integrated emergency service coverage. Figure 2 is the flowchart of the multi-unmanned aerial vehicle method for communication and positioning integrated emergency service coverage of the present application. It should be understood that the sequence of steps in the embodiment does not mean the order of execution, the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0054] As Figure 2 shown, in step S1, the positioning accuracy requirement of each user is measured based on the approximate D-optimal criterion, and the first deployment area of the unmanned aerial vehicle is solved based on the positioning accuracy requirement.

[0055] Specifically, we represent the positioning accuracy of user k as ∈k The positioning accuracy is measured by using the D-optimal criterion, so that the positioning accuracy ∈ k satisfies:

[0056]

[0057] Then, constraint (17) is rewritten based on det(H) > 0 or det(H) < 0 to derive the first and second UAV flight ellipses at a given flight height, and a first deployment area of the UAV is solved based on the first and second UAV flight ellipses.

[0058] The specific derivation process is described in detail as follows.

[0059] Considering occasional blockage between the UAV and the ground user, we use a commonly used probability Line of Sight (LoS) link model to determine the large-scale attenuation of the ground-to-air (G2A) link. The probability of the geometric LoS link between the UAV and the user depends on the statistical parameters related to the environment and the elevation angle. Specifically, we represent the LoS probability between the UAV u located at position b u and the user k as

[0060]

[0061] where e1 and e2 are the environment-related parameters, θ ku is the elevation angle between the user and the UAV:

[0062]

[0063] θ ku ranges from [0°, 90°]. Therefore, the expected power gain is:

[0064]

[0065] where κ < 1 is the attenuation effect of the non-LoS link, α ≥ 2 is the path loss coefficient, and γ0 is the reference free-space path loss at a distance of 1 m. Thus, the upload rate between the UAV and the user k is

[0066]

[0067] where W ku is the communication bandwidth between the UAV and the user, P k is the transmission power of the user k, N0 is the noise power spectral density, and α is the path loss exponent of the G2A link.

[0068] Considering the scattering and reflection of the ground, we model the ground-to-ground (G2G) link between user equipment and ground base stations as a Rayleigh fading link. Thus, the achievable rate between base station n and user k is

[0069]

[0070] where ε is the outage probability, F -1 (ε) is the inverse function of the cumulative distribution function of the Rayleigh fading link coefficient. And the power gain of the G2G link is:

[0071]

[0072] where β represents the ground-to-ground path loss exponent.

[0073] This method considers using the observed time difference of arrival (OTDoA) to locate the user's position. To reduce the computational complexity of the user, each user estimates its 3D position according to the reference positioning signals sent by four anchors (including three ground base stations and one unmanned aerial vehicle). We assume that the accurate positions of the ground base stations and the unmanned aerial vehicle are known, and all anchors are time-accurately synchronized. Each user measures the time of arrival (ToA) of the positioning signals from the aerial and ground anchors. We assume that base station 1 is the reference anchor, and the time difference of arrival (TDoA) is the time difference of ToA of the signal from the reference anchor and the ToA of the remaining anchors. We take base station 1 as the reference anchor, then the time difference of arrival TDoA of the positioning signal received by user k is:

[0074]

[0075] where τ n1 n represents the time difference of arrival TDoA of the nth anchor and the reference anchor (base station 1). τ n =||b n -m k || / c represents the time of arrival of the positioning signal from anchor n. c represents the signal speed. σ n is the measurement error of the positioning signal from anchor n that satisfies , the measurement variance of the ToA of the positioning signal from user k to unmanned aerial vehicle u is:

[0076] In the G2A link, the measurement variance of the ToA of the positioning signal from unmanned aerial vehicle u to user k is:

[0077]

[0078] ​where ψ is a constant related to the characteristics of the positioning signal, SNR is the signal-to-noise ratio of the received signal. Where P u is the transmission power of the UAV u, W ku is the communication bandwidth from the UAV u to the user k, g uk is the expected power gain. And in the G2G link, advanced signal processing algorithms are needed to eliminate the effects of multipath effects and non-line-of-sight propagation on ToA measurement, whose random estimation error is usually modeled as an additional Gaussian noise term with variance Therefore, in the G2G link, the ToA measurement variance of the positioning signal sent from the base station n to the user k is:

[0079]

[0080] where P n is the transmission power of the base station n. W nk is the communication bandwidth from the base station n to the user k, g nk is the expected power gain, N0 is the noise power spectral density, then we can get the TDoA covariance matrix of the user k, that is, the TDoA covariance matrix of the base stations 1-3 and the UAV u to the user k:

[0081]

[0082] where, respectively represent the ToA measurement variance of the positioning signal sent by the three ground base stations to the user k, representing the ToA measurement variance of the positioning signal from the UAV u to the user k.

[0083] We define the unit vector of the nth anchor point to the user k as q n , where

[0084]

[0085] The TDoA Jacobian matrix of the base stations 1-3 and the UAV u to the user k is:

[0086]

[0087] Since the Fisher Information Matrix (FIM) quantifies the amount of information carried by the measurement vector about the unknown parameters. Therefore, the TDoA Fisher Information Matrix FIM of the base stations 1-3 and the UAV u to the user k is:

[0088] F = H T R -1 H#(13)

[0089] We use D-optimal criterion to measure the positioning accuracy, that is:

[0090]

[0091] Due to the blocking and scattering effects in the actual emergency scene, the measurement variance of the positioning signal received by the user from the base station can be much larger than the measurement variance of the positioning signal received from the UAV, so in this application we finally use the approximate D-optimal criterion to measure the positioning accuracy, defined as:

[0092]

[0093] Of course, in other preferred embodiments of the application, the covariance matrix calculated based on the Hessian matrix method, Jacobian matrix, sandwich matrix, etc. can also be used to measure the positioning accuracy.

[0094] Based on the above analysis, our goal is to deploy the minimum number of UAVs to meet the positioning accuracy and communication needs of all users. Therefore, the problem can be expressed as follows:

[0095]

[0096]

[0097]

[0098] where is the number of UAVs, is the area where the UAV can be deployed.(16a) is the positioning accuracy requirement constraint of each user, and (16b) is the communication requirement constraint of each user. The max term in (16a) means that each user uses all three ground base stations and a UAV to estimate its position. At the same time, the max term in (16b) means that each user only sends information to one of the base stations (UAV or ground base station).

[0099] Problem (P1) is an NP-hard problem about cardinality minimization, and (16a) is a non-convex constraint about the location of the UAV, and the problem also contains implicit association between users and base stations or UAVs, making the problem more difficult to solve. We will analyze the feasible region of the UAV from a geometric point of view and then solve the problem.

[0100] We first study the location of the UAV that meets the positioning requirement. For user k, there is the following positioning accuracy requirement, where ∈ k represents the positioning accuracy of user k:

[0101]

[0102] We can substitute equation (7), equation (12) into equation (17), and derive the following implicit geometric expression of (17) according to the value of det(H).

[0103] Case 1: det(H) > 0, constraint (17) can be written as:

[0104]

[0105] where coefficient a 1-3 is expressed as:

[0106] a1= (q 22 -q 12 )(q 33 -q 13 )-(q 23 -q 13 )(q 32 -q 12 ),#(19)

[0107] a2= (q 23 -q 13 )(q 31 -q 11 )-(q 21 -q 11 )(q 33 -q 13 ),#(20)

[0108] a3= (q 21 -q 11 )(q 32 -q 12 )-(q 22 -q 12 )(q 31 -q 11 ),#(21)

[0109] To satisfy constraint (17), when the given flight height h k , the UAV position should be located in the following area ε 1 :

[0110] Case 2: det(H) < 0, constraint (17) can be written as:

[0111]

[0112] where At this time, when the given flight height h k , the UAV position should be located in the following area ε 2 :

[0113]

[0114] We define Then, when the positioning accuracy ∈ k satisfies:

[0115]

[0116] At this time, the constraint (17) is satisfied, and the 2D feasible region of the UAV is the ellipse ε 1 defined by (22) and (24). 2 Specifically, when c2> 0, the feasible region of the UAV is the ellipse ε 1 , and vice versa, which is located in the ellipse ε 2 .

[0117] Next, we analyze the communication requirements. Referring back to Figure 2 , in step S2, the second deployment region of the UAV is solved based on the communication requirements of each user.

[0118] For the G2A link, satisfying the communication requirements of the user is equivalent to the 2D position of the UAV satisfying the following inequality:

[0119]

[0120] where W ku is the communication bandwidth between the UAV and the user, P k is the transmission power of the user k, N0is the noise power spectral density, a is the path loss exponent of the ground-to-air link, γ0is the reference free space path loss at a distance of 1 m, P(LoS, θ ku ) represents the LoS probability between the UAV u located at position b u and the user k, θ ku is the elevation angle between the UAV u and the user k, denotes the communication rate requirement of the user k.

[0121] For the G2G link, when the base station position satisfies the following inequality, it can satisfy the communication requirements of the user:

[0122]

[0123] where W kn is the communication bandwidth between the base station and the user, P k is the transmission power of the user k, N0is the noise power spectral density, ε is the communication interruption probability, F -1 (ε) is the inverse function of the cumulative distribution function of the Rayleigh fading link coefficient, β represents the ground-to-ground path loss exponent, γ0is the reference free space path loss at a distance of 1 m, denotes the communication rate requirement of the user k.

[0124] In summary, when given a fixed height, optD(m k ,b u )>ε k The feasible region of the communication demand is a circle, which we define as k ; The feasible region of the communication demand is a circle, which we define as We define the set of all feasible regions as

[0125] We first consider the communication demand of each user. If the location of user k satisfies (27), then the base station can already satisfy the communication demand of user k. Therefore, in step S3, we obtain the deployment region based on the first deployment region and the second deployment region. Specifically, we denote the set of users that can be served by all base stations as The corresponding circular feasible region is denoted as Then, when considering the deployment of the UAV, we can exclude from Therefore, we obtain a new set of regions

[0126] In step S4, we obtain the number and location of the UAV deployments based on the deployment region using the minimum hitting set. Our goal is to find the minimum number of UAV deployments and their locations, and therefore, the problem is finally simplified to finding a minimum point set such that each region in has a non-empty intersection with This is a classic minimum hitting set (MHS) problem, and the specific algorithm 1 is shown below.

[0127]

[0128] The MHS problem can be converted into an integer linear programming (ILP) problem. We discretize the deployable region of the UAV into a set of candidate deployment points of the UAV We use a binary variable v ij = 1 to indicate that the grid point g i is in the region , where S j can be an ellipse in or a circle. Conversely, v ij = 0.

[0129] The ILP problem is as follows:

[0130]

[0131]

[0132] This problem is NP-hard, and obtaining the optimal solution requires very high computational complexity. In a preferred embodiment of the present invention, an improved Boolean algebra method can be used to solve it.

[0133] In this preferred embodiment, we propose a depth-first algorithm to solve the minimum hit set problem. We define grid point g l The depth is Δ l , where Δ l The value is equal to containing g l The number of regions, that is We first select point g with the maximum depth. k As the first location for the drone. Then from Remove point g k We then process all regions and update the depth of each point. We repeat this process until all regions are covered by at least one drone. The specific algorithm steps are shown in Algorithm 2.

[0134]

[0135] This invention proposes a low-cost network using dual-function drones, which assist terrestrial networks in improving communication and location services. We consider using the OTDoA method for location, where users estimate their own location from location signals transmitted by the drone and three ground base stations. Furthermore, each user communicates with the drone or base station that generates the highest communication rate. This dual-function drone scheme significantly reduces the cost and latency of network deployment in emergency situations.

[0136] Furthermore, we first analyze the feasible deployment locations of UAVs that meet user positioning accuracy requirements. Instead of the traditional CRLB performance metric, we propose using the D-optimal criterion as the key positioning accuracy metric for analyzing tractability. We show that under the D-optimal criterion, the feasible location of the UAV can be characterized as a second-order cone in three-dimensional space. Simultaneously, given a fixed altitude, it shrinks to an ellipse on the 2D projection plane. We derive closed-form expressions for the 3D and 2D feasible regions of the UAV, and then, through the geometric characteristics of the feasible region, we prove that the minimum deployment problem can be equivalently transformed into the minimum hit set problem, a classic NP-complete problem lacking efficient solutions. Based on the equivalent graphical formula, we propose a low-complexity approximation algorithm to solve the NP-hard problem in large bursty networks.

[0137] To verify the beneficial effects of the multi-UAV deployment method for communication and positioning integrated emergency service coverage of the present application, we deploy the UAVs in the area According to the equal interval Δ = 20m discretization, we obtain L = 26x26 = 676 grid points for deploying the UAVs. Figures 3A-3E The number of UAVs required and the layout under different methods are shown. The communication requirement is set to Figures 3A-3E In the middle, the large black dot represents the base station, the triangle represents the UAV, the pentagram represents the user, and the dense dashed area around the base station represents the coverage of the base station. The positioning accuracy requirement is randomly selected from the specified range to ensure feasibility. Each UAV is associated with a circular area that meets the communication performance and an elliptical area that meets the positioning performance. As long as there is a UAV hovering in its corresponding sparse dashed area, its positioning accuracy requirement can be met. Similarly, as long as there is a UAV or a ground base station in the circular solid line area, the communication requirement can be met. As shown in Figure 3A and 3B As shown in Figures 3C-3E , the integer linear programming (ILP) method and the deep-first algorithm of the present application achieve the minimum number of UAVs, i.e. 8 UAVs. In the same case, as shown in , the spiral search method and the strip search method require 13 UAVs and 10 UAVs respectively, and the communication priority method requires 14 UAVs. And although we use the approximate opt-D1 value to determine the feasible positioning area, the obtained UAV position always meets the original positioning requirement in the opt-D value (16b).

[0138] In a further preferred embodiment of the present application, a multi-UAV system for communication and positioning integrated emergency service coverage is also involved, comprising three ground base stations, a plurality of UAVs and a plurality of users, the plurality of UAVs being deployed based on the aforementioned multi-UAV deployment method for communication and positioning integrated emergency service coverage.

[0139] In the embodiments described, each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of the present application.

[0140] While the application has been described by way of example, it should be appreciated that modifications and additions can be made without departing from the scope of the application. Accordingly, the application is not to be limited by what has been particularly shown and described.

[0141] The above description is merely illustrative of the application, and not restrictive. Since the application can be modified in arrangement and details by those with skill in the art, it is intended that all such modifications come within the scope of the application as defined by the claims.

Claims

1. A method for deploying multiple unmanned aerial vehicles (UAVs) for integrated communication and positioning emergency service coverage, the method being applied to a multi-UAV communication and positioning system comprising three ground base stations, multiple UAVs, and multiple users; characterized in that, The method includes the following steps: S1. The positioning accuracy requirement of each user is measured based on the approximate D-optimal criterion, and the first deployment area of ​​the UAV is solved based on the positioning accuracy requirement. S2. Solve for the second deployment area of ​​the drone based on the communication needs of each user; S3. Determine the deployment area based on the first deployment area and the second deployment area; S4. Use the minimum collision set to determine the number and location of the deployed drones based on the deployment area; Step S1 further includes: S11, will the user The positioning accuracy is expressed as The positioning accuracy is measured using the D-optimal criterion, resulting in the positioning accuracy... satisfy: in, Indicates user Location coordinates, The coordinates of the position of the drone u are shown. ; H Indicating base stations and drones To users arrival time difference The Jacobian matrix; These represent the data sent to the user by the three ground base stations. Positioning signal Measure variance This represents the location coordinates of ground base station n. ; S12, based on or Rewrite constraint (17) to derive the first UAV flight ellipse and the second UAV flight ellipse at a given flight altitude, and solve the first deployment area of ​​the UAV based on the first UAV flight ellipse and the second UAV flight ellipse.

2. The multi-UAV deployment method for integrated communication and positioning emergency service coverage according to claim 1, characterized in that, In step S12, define when At that time, define the flight ellipse of the first UAV. Define the first deployment area; otherwise, define the second UAV flight ellipse. The first deployment area; where the coefficient satisfy: Among them, the From base station to user The unit vector is defined as , 。 3. The multi-UAV deployment method for integrated communication and positioning emergency service coverage according to claim 2, characterized in that, The first UAV flies in an elliptical path satisfy: The second UAV flies in an elliptical path. satisfy: in, , .

4. The multi-UAV deployment method for integrated communication and positioning emergency service coverage according to any one of claims 2-3, characterized in that, Step S2 further includes: S21. For ground-to-air links, determine the conditions that satisfy the user's requirements based on the following inequality. The second deployment area of ​​the drone when making a communication request : in It refers to the communication bandwidth between the drone and the user. User Transmission power, α is the noise power spectral density, and α is the path loss exponent of the ground-to-air link. It is the reference free space path loss at a distance of 1m. Indicates the location drones and users The probability of Loss between them It is a drone and users The angle of elevation between them Indicates user The communication rate requirements.

5. The multi-UAV deployment method for integrated communication and positioning emergency service coverage according to claim 4, characterized in that, Step S3 further includes: S31. For ground-to-ground links, determine the inequality that satisfies the user's requirements based on the following inequality. The location of the base station when making a communication request: in It is the communication bandwidth between the base station and the user. User Transmission power, It is the noise power spectral density. It is the probability of communication interruption. It is the inverse function of the cumulative distribution function of the Rayleigh fading link coefficient, where β represents the ground-to-ground path loss exponent. It is the reference free space path loss at a distance of 1m. Indicates user Communication rate requirements; S32. Determine the set of users that all base stations can serve based on inequality (27). And calculate the feasible area of ​​the base station corresponding to the user. ; S33, Based on the second deployment area The first deployment area The feasible area of ​​the base station Determine the deployment area ,in .

6. The multi-UAV deployment method for integrated communication and positioning emergency service coverage according to claim 5, characterized in that, Step S4 further includes: S41, Areas where drones can be deployed Discretized into a set of candidate deployment points for unmanned aerial vehicles Using binary variables To represent grid points Is it in the region? ; S42. The problem of candidate drone deployment is transformed into finding drones that satisfy the following constraints, and the depth-first search algorithm is used to solve for the number and location of drones to be deployed: in, L The value is a positive integer, when the grid point In the region China Times, Conversely ,in Indicates inclusion The number of regions.

7. The multi-UAV deployment method for integrated communication and positioning emergency service coverage according to claim 6, characterized in that, In step S42, grid points are defined. The depth is ,in The value is equal to containing The number of regions is determined, and then the point with the maximum depth is selected. As the first location of the drone, then from the deployment area Remove containment points Update the depth of each point in all regions, and repeat the above steps until all regions are covered by at least one drone.

8. A multi-UAV system for integrated communication and positioning emergency service coverage, comprising three ground base stations, multiple UAVs and multiple users, wherein the multiple UAVs are deployed based on the multi-UAV deployment method for integrated communication and positioning emergency service coverage as described in any one of claims 1-7.

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