Map-assisted drone communication and positioning integrated deployment method

Abstract

The present application relates to a kind of map-assisted unmanned plane communication positioning integrated deployment method, including the building position information given based on map the flight area of unmanned plane is divided into obstacle region and non-obstacle region based on the non-obstacle region deployment the unmanned plane.The present application is assisted by map, the flight area of unmanned plane is divided into non-obstacle region that can establish line-of-sight link and obstacle region that cannot establish line-of-sight link, then under the condition that positioning accuracy demand and communication rate demand constraint are satisfied, unmanned plane is deployed in non-obstacle region, to improve the total communication rate of system.The present application also proposes the algorithm based on Lagrange relaxation to study the influence of unmanned plane position deployment on communication and positioning performance, by using D optimal criterion as positioning accuracy performance index, and using the algorithm based on Lagrange relaxation and continuous convex approximation, solve the unmanned plane deployment optimization problem in the present application, significantly reduce the computational complexity.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication, and more specifically, to a map-aided integrated deployment method for UAV communication and positioning, and a UAV communication and positioning system constructed using the map-aided integrated deployment method for UAV communication and positioning. Background Technology

[0002] To meet the demands of intelligent mobile computing applications, future wireless networks need to provide not only ubiquitous, high-performance communication connectivity but also high-precision positioning services complementary to the Global Navigation Satellite System. In 4G / 5G standards, base stations (BS) can serve as both communication access points and positioning anchors to provide Integrated Localization and Communication (ILAC) services, enabling numerous mobile applications such as autonomous driving and social networking. In ultra-dense urban environments, link congestion between base stations and ground users can lead to a significant decrease in communication speed and positioning accuracy. To mitigate the performance degradation of terrestrial networks, unmanned aerial vehicles (UAVs) have been widely deployed at high altitudes to establish line-of-sight links with users due to their flexibility and low cost.

[0003] Numerous studies have explored using drones to enhance the communication performance of terrestrial networks. One of the most effective methods is optimizing drone positioning to improve network communication speeds and reduce device power consumption. Beyond serving as communication base stations, drones can also act as aerial positioning anchors, effectively increasing the spatial diversity of anchor points and thus improving the user's 3D positioning accuracy. Several technologies exist related to drone-assisted positioning, such as Received Signal Strength (RSS), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). TDOA technology requires four anchor points to obtain the 3D position of the mobile device, and optimizing the anchor point positions can improve relevant performance indicators related to mobile device positioning accuracy, such as the Camera-Rao Lower Bound (CRLB). The bandwidth and power allocation problem of CRLB for anchor points can be formulated as a convex positive semidefinite programming problem. However, CRLB is non-convex with respect to the location of anchor points, and its closed-form expression cannot be obtained. This requires the use of computationally complex algorithms to solve the network deployment problem, which leads to excessive latency and resource waste.

[0004] Another important consideration for drone deployment is hardware and management costs. Compared to using single-function drones to provide communication or location services, using a single drone for both purposes can effectively reduce hardware costs, collaboration complexity, and deployment delays. The biggest challenge with this approach is achieving a balance between communication and location performance.

[0005] Moreover, most current research uses pure line-of-sight link models and probabilistic link models for system modeling. However, these ideal, statistically based link models are only suitable for average system performance analysis. In real-world applications, they cannot classify links based on actual occlusion conditions to obtain more accurate link information, which weakens the system's practicality.

[0006] Therefore, current UAV communication and positioning deployments generally suffer from the following drawbacks: 1. Treating the UAV as a single-function platform to provide communication or positioning services without fully utilizing existing ground infrastructure results in higher network deployment costs and lower resource utilization efficiency. 2. Employing a relatively simple link model makes it difficult to overcome increasingly complex signal propagation environments, suitable only for average system performance analysis, and thus having low practicality. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a map-assisted UAV communication and positioning integrated deployment method and system, which can improve communication and positioning performance by dividing the UAV flight area and optimizing the UAV location deployment with the assistance of a physical environment map.

[0008] The technical solution adopted by this invention to solve its technical problem is: constructing a map-aided UAV communication and positioning integrated deployment method, comprising:

[0009] S1. Based on the building location information given in the map, the flight area of ​​the UAV is divided into obstacle area and non-obstacle area;

[0010] S2. Deploy the drone based on the non-obstacle area.

[0011] Preferably, step S1 further includes:

[0012] S11. Based on the building location information and user location information, obtain the expression for the i-th obstacle region (1).

[0013]

[0014] in This represents the i-th obstacle region. Let v = [x] represent the set of hyperplanes representing the i-th obstacle region.v ,y v ,h v The ] indicates the location of the drone. Let represent the normal vector of the j-th hyperplane in the i-th obstacle region. Represents a three-dimensional variable, with constant b. ij This represents the offset of hyperplane j relative to the origin, whose coordinates are [0,0,0].

[0015] S12. Based on the expression (1), obtain the expression (2) for the corresponding non-obstacle region.

[0016]

[0017] in Indicates the total flight area of ​​the unmanned aerial vehicle (UAV). Indicates a non-obstacle area.

[0018] Preferably, step S2 further includes:

[0019] S2a. Deploy the drone in the non-obstacle area; or

[0020] S2b, Deploy the UAV based on positioning accuracy constraints, communication rate constraints, and the non-obstacle area.

[0021] Preferably, step S2b specifically includes:

[0022] Based on the positioning accuracy constraint, the communication rate constraint, and the non-obstacle region, the position of the UAV in the non-obstacle region is transformed into solving the following problem P1:

[0023]

[0024]

[0025]

[0026]

[0027] Where R kv This represents the upload rate between user k and the UAV (User Aerial Vehicle). This represents the total communication rate between all users and the drone. u represents the given communication rate threshold for user k. k Let v represent the location coordinates of the k-th user, and v represent the location of the drone. kThe given positioning accuracy threshold for user k is represented; expressions (13b) and (13c) represent the positioning accuracy constraints and communication rate constraints for each user, respectively; and expression (13d) represents the non-obstacle area constraint for the UAV.

[0028] Preferably, step S2b further includes:

[0029] S2b1. The feasible positioning accuracy region of the UAV is represented by approximately restoring the positioning accuracy constraint as follows:

[0030]

[0031] in Indicates based on a given ∈ k The 3D region where user k satisfies the positioning accuracy constraint; ∈ k This represents the given positioning accuracy threshold for user k;

[0032] S2b2. Based on the communication rate constraint, the feasible communication accuracy region of the UAV is represented as follows:

[0033]

[0034] in Indicates based on a given The 3D region where user k satisfies the communication rate constraint; Represents the given communication rate threshold for user k;

[0035] S2b3, Introduce a binary variable l ij The non-obstacle region constraint is represented as:

[0036]

[0037]

[0038]

[0039]

[0040] in Let v = [x] represent the set of hyperplanes representing the i-th obstacle region. v ,y v ,h v The ] indicates the location of the drone. Let represent the normal vector of the j-th hyperplane in the i-th obstacle region. Represents a three-dimensional variable, with constant b. ij Let C represent the offset of hyperplane j relative to the origin, where the coordinates of the origin are [0,0,0]; C is a sufficiently large constant. It is the number of hyperplanes in the i-th obstacle region. Indicates the total flight area of ​​the unmanned aerial vehicle (UAV);

[0041] When position v is inside hyperplane j, the corresponding l is set ij =1, thus setting the binary variable l ij Equivalent replacement

[0042]

[0043]

[0044] S2b4. Simplify the problem P1 into the problem P2:

[0045]

[0046] st(27),(30),(31a)-(31c),(32),(33);

[0047] S2b5 uses Lagrange relaxation to solve problem P2 to obtain the drone deployment location and then deploys the drone at the drone deployment location.

[0048] Preferably, step S2b5 further includes:

[0049] S2b51. Using the Lagrange multiplier λ constraint equation (33), a partial Lagrange dual problem (35) is obtained:

[0050]

[0051] Among them, R kv This represents the upload rate between user k and the UAV (User Aerial Vehicle), where l represents a binary variable.

[0052] S2b52, A continuous convex approximation is used to solve a partial Lagrange problem (35) to transform it into a convex problem to solve for the deployment location of the UAV.

[0053] Preferably, when the UAV deployment location obtained in step S2b52 is not located in the non-obstacle area, a UAV location that satisfies all positioning accuracy constraints and has a minimum height limit is searched. If the UAV location satisfies the non-obstacle area constraint formula (13d), the UAV location is taken as the preset deployment location; otherwise, its horizontal coordinate is fixed and its height coordinate is increased until its height coordinate satisfies the non-obstacle area constraint formula (13d), and then the UAV location obtained at this time is taken as the preset deployment location.

[0054] Calculate the corresponding binary variable l based on the preset deployment location. ij The value is used to obtain a closed, non-obstructive area including the preset deployment location, and the closed, non-obstructive area is used in problem P2 to solve for the deployment location of the UAV.

[0055] Preferably, step S2 further includes: S2c, when the deployment location of the UAV cannot be determined, reducing the given positioning accuracy threshold ∈ k ;

[0056] Step S2b5 further includes: after obtaining the deployment location of the UAV, updating the value of the Lagrange multiplier λ to reduce the duality gap.

[0057] Preferably, the map includes an urban environment map or a radio map.

[0058] Another technical solution adopted by the present invention to solve its technical problem is to construct a UAV communication and positioning system, including multiple ground base stations, multiple users and at least one UAV, characterized in that the UAV is deployed based on the map-assisted UAV communication and positioning integrated deployment method.

[0059] This invention, with map assistance, divides the UAV's flight area into non-obstacle areas where line-of-sight (LAS) links can be established and obstacle areas where LLS links cannot be established. The UAV is then deployed in the non-obstacle areas, thus improving the communication and positioning performance of the UAV communication and positioning system. Furthermore, by defining feasible communication, positioning, and non-obstacle areas, this invention proposes an algorithm based on Lagrange relaxation to study the impact of UAV deployment on communication and positioning performance. By employing the D-optimal criterion as a positioning accuracy performance indicator and using an algorithm based on Lagrange relaxation and continuous convex approximation, the UAV deployment optimization problem in this invention is solved, significantly reducing computational complexity. Attached Figure Description

[0060] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0061] Figure 1 This is a flowchart of a preferred embodiment of the map-assisted UAV communication and positioning integrated deployment method of the present invention;

[0062] Figure 2 This is a schematic diagram of a drone communication and positioning system constructed using the map-assisted drone communication and positioning integrated deployment method described above;

[0063] Figures 3A-3B Different configurations of the obstacle region are shown;

[0064] Figure 4 A schematic diagram of an algorithm according to a preferred embodiment of the present invention is shown. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0066] This invention relates to a map-assisted integrated deployment method for UAV communication and positioning, and a UAV communication and positioning system constructed using the aforementioned map-assisted integrated deployment method. One inventive concept is that, with map assistance, the UAV's flight area is divided into non-obstacle areas where line-of-sight (LAS) links can be established and obstacle areas where LAS cannot be established. The UAV is then deployed in the non-obstacle areas, thus improving the communication and positioning performance of the UAV communication and positioning system. Another inventive concept is that, with map assistance, the UAV's flight area is divided into non-obstacle areas where LAS can be established and obstacle areas where LAS cannot be established. Then, under the constraints of positioning accuracy and communication rate requirements, the UAV is deployed in the non-obstacle areas to improve the overall communication rate of the system. A further inventive concept is that, based on characterizing feasible communication areas, positioning areas, and non-obstacle areas, an algorithm based on Lagrange relaxation is proposed to study the impact of UAV location deployment on communication and positioning performance. By using the D-optimal criterion as the positioning accuracy performance index, and employing an algorithm based on Lagrange relaxation and continuous convex approximation, the UAV deployment optimization problem in this invention is solved, significantly reducing computational complexity.

[0067] Figure 1 This is a flowchart of a preferred embodiment of the map-aided UAV communication and positioning integrated deployment method of the present invention. Figure 1 As shown, in step S1, the drone's flight area is divided into obstacle areas and non-obstacle areas based on the building location information provided by the map. In step S2, the drone is deployed based on the non-obstacle areas.

[0068] In a preferred embodiment of the present invention, the map includes an urban environment map or a radio map. An urban environment map will be used as an example below. However, those skilled in the art will recognize that radio maps or other types of maps are equally feasible, and based on the teachings of this invention, those skilled in the art can implement the present invention using other types of maps.

[0069] In a preferred embodiment of the present invention, an expression for each obstacle region can be obtained based on the building location information and the user location information, and then an expression for the corresponding non-obstacle region can be obtained based on the expressions for all obstacle regions. The drone is then directly deployed in the non-obstacle region. With map assistance, the drone's flight area is divided into non-obstacle regions where a line-of-sight link can be established and obstacle regions where a line-of-sight link cannot be established. The drone is then deployed in the non-obstacle region, thus improving the communication and positioning performance of the drone communication and positioning system.

[0070] In another preferred embodiment of the invention, after obtaining the non-obstacle area, positioning accuracy constraints and communication rate constraints are considered simultaneously, and the UAV is deployed based on these constraints and the non-obstacle area. This approach not only improves the communication and positioning performance of the UAV communication and positioning system but also further enhances the overall communication rate of the system.

[0071] To better illustrate the invention, we combine Figure 2 The UAV communication and positioning system constructed using the map-assisted integrated deployment method is described below. Figure 2 This paper illustrates a drone communication and positioning system consisting of three ground base stations BS1-BS3, K user units (UEs), and one unmanned aerial vehicle (UAV). Because buildings or other obstacles on the ground can obstruct ground-to-ground communication links, leading to a decrease in communication and positioning performance, a dual-function UAV is employed as an aerial platform to assist the ground base stations in providing communication and positioning services. Furthermore, by classifying the UAV's flight area using a physical environment map, the UAV is deployed in unobstructed areas where line-of-sight links can be established with ground users, further improving the network's communication and positioning performance and practicality. The position coordinates of the nth base station, the kth user, and the UAV represent b... n =[x n ,y n ,h n ], u k =[x k ,y k ,h k ], v = [x v ,y v ,h v Unless otherwise defined, the symbols used to express quantities, such as K and M, are all positive integers, and x, y, and h represent three-dimensional coordinates.

[0072] Assume there is The buildings are randomly distributed across a square area with sides of 500m. The total flight area of ​​the UAV is a three-dimensional region ranging from 100m to 600m above this square area. Here, M is also a positive integer. Based on whether the communication link between the UAV and the user is obstructed, we can divide the total flight area of ​​the UAV into obstructed and unobstructed areas. M buildings and K users correspond to MK obstructed areas, which can be represented as... The parameter i = (k-1)M+m represents the number of the obstacle zone between the k-th user and the m-th building.

[0073] The obstruction region corresponding to the user-building pair of the k-th user and the m-th building is a polyhedron. For example... Figure 3A and 3B As shown, the boundary of each obstacle region can be formed by three or four hyperplanes. Each hyperplane is formed by two adjacent vertices of the user's position and the building's surface. Therefore, depending on the positions of the user and the building, each obstacle region is a polyhedron enclosed by three or four hyperplanes. From analytic geometry, the i-th obstacle region of the UAV can be expressed as:

[0074]

[0075] in Let v = [x] represent the set of hyperplanes representing the i-th obstacle region. v ,y v ,h v The ] indicates the location of the drone. It is the normal vector of the j-th hyperplane in the i-th obstacle region, and the constant is b. ij Let represent the offset of hyperplane j from the origin. We can obtain a by calculating the cross product of two linearly independent vectors in the hyperplane. ij By calculating a ij b is obtained by taking the inner product with any point in the hyperplane. ij Each hyperplane can be calculated based on the user's location information and the building's location information. The value of j ranges from [1,2,3] to [1,2,3,4]. This represents a three-dimensional variable with the origin coordinates [0,0,0].

[0076] After obtaining the expression for the i-th obstacle region, its corresponding non-obstacle region can be expressed as:

[0077]

[0078] in, Indicates the total flight area of ​​the unmanned aerial vehicle (UAV). This represents a non-obstacle region. The above expression means that for the i-th obstacle region, the UAV is located outside at least one of the hyperplanes.

[0079] Therefore, based on the building information provided by the physical environment map, we can divide the flight area of ​​the UAV into obstructed and non-obstructed areas. By deploying the UAV in the non-obstructed area, the line-of-sight link between the UAV and the ground user can be guaranteed. Of course, as mentioned above, in a preferred embodiment of the present invention, a radio map can also be used to implement the present invention.

[0080] We consider using unmanned aerial vehicles (UAVs) as data collectors to gather information from ground users. By dividing the UAV's flight area into two categories, the corresponding ground-to-air (G2A) links can be divided into line-of-sight (LoS) and non-line-of-sight (NLoS) links. Deploying UAVs in unobstructed areas ensures that a line-of-sight link can be established between the UAV and ground users. We express the corresponding ground-to-air (G2A) link gain as:

[0081]

[0082] Where ||vu k || represents the distance from the UAV to user k, α 1,2 β represents the path loss coefficient under different occlusion conditions, i.e., line-of-sight links and non-line-of-sight links. 1,2 This is the reference link gain over a distance of 1m under different occlusion conditions, i.e., line-of-sight and non-line-of-sight links. Therefore, the upload rate between user k and the UAV is:

[0083]

[0084] Among them W kv It is the communication bandwidth, P k Where N is the transmission power of user k, N0 is the noise power spectral density, and g kv This refers to the ground-to-air link gain. It's important to note that in the method proposed in this application, the UAV will be deployed in an unobstructed area where a line-of-sight link can be established with the user, thus ensuring that the G2A in this scheme only has a line-of-sight link.

[0085] In a preferred embodiment of the invention, the invention uses Time Difference of Arrival (TDOA) positioning technology to locate users, whereby each user estimates their 3D position by calculating positioning reference signals emitted from four positioning anchor points. Figure 2In the preferred embodiment shown, the four positioning anchor points include three ground base stations BS1-BS3 and a UAV. It is assumed that the locations of the four positioning anchor points are known, i.e., b n =[x n ,y n ,h n ], u k =[x k ,y k ,h k ], v = [x v ,y v ,h v All are known and precisely synchronized in time. Using ground base station BS1 as the reference anchor point, the user calculates the corresponding arrival time difference based on the arrival times of the positioning signals from the other positioning anchor points and the reference anchor point, using the following expression:

[0086] τ n1 =(||b n -u k ||-||b1-u k ||) / c,n=2,3, (5)

[0087] τ v1 =(||vu k ||-||b1-u k ||) / c, (6)

[0088] Where c is the speed of light in free space, and τ n1 It is the time difference of arrival between the nth ground base station and the reference anchor point, while τ v1 It is the time difference between the arrival of the UAV and the reference anchor point, b n Here are the location coordinates of the nth base station, and b1 is the reference anchor point, i.e., the location coordinates of ground base station BS1; u k is the location coordinate of the kth user, and v is the location coordinate of the drone.

[0089] Therefore, the variance of the arrival time of the positioning signal between the drone and user k is:

[0090]

[0091] Where W is the positioning signal bandwidth, P u This represents the drone's positioning power, and ψ is a constant relating to the characteristics of the positioning signal. This is additive white Gaussian noise caused by propagation through non-line-of-sight links, where N0 is the noise power spectral density, and g... kv It is the ground-to-air link gain, α 1,2 β is the path loss coefficient for line-of-sight links and non-line-of-sight links.1,2 It is the reference link gain for a 1m distance between line-of-sight and non-line-of-sight links, u k is the location coordinate of the kth user, and v is the location coordinate of the drone.

[0092] Since this invention primarily studies scenarios in densely populated urban areas, it mainly considers the impact of adding a drone on system performance when the ground-to-ground (G2G) link is a non-line-of-sight link. Therefore, the ground-to-ground link gain can be expressed as:

[0093] β2 is the reference link gain for a non-line-of-sight link at a distance of 1m, α2 is the path loss coefficient for the non-line-of-sight link, ||b n -u k || is the distance from the nth ground base station to user k.

[0094] The measurement variance of the positioning signal ToA between the ground base station and user k is...

[0095]

[0096] Where P n Let W be the positioning power of the nth ground base station, W be the positioning signal bandwidth, and ψ be a constant relating to the characteristics of the positioning signal. This is additive white Gaussian noise caused by propagation through non-line-of-sight links, where N0 is the noise power spectral density, and g... nk β2 is the ground-to-ground link gain, β2 is the reference link gain for a 1m distance non-line-of-sight link, and α2 is the path loss coefficient for a non-line-of-sight link. k b represents the location coordinates of the k-th user. n These are the location coordinates of the nth base station. Therefore, the covariance matrix R of the TDoA positioning technology in this scheme can be derived. TDoA for

[0097]

[0098] The determinant of the Fisher information matrix (FIM), also known as the D-optimality criterion, is used as the positioning performance index, and its expression is:

[0099]

[0100] Where R = c 2 ·R TDoA The unit in TDoA positioning technology is meters. 2 The covariance matrix is ​​given by , where c is the speed of light in free space, and H is the Jacobian matrix of the TDoA equation, expressed as:

[0101]

[0102] The unit vector from the nth anchor point to user k is defined as follows: n∈v∪{1,2,3}. By maximizing the value of opt-D, we can optimize its positioning accuracy, where Where b n =[x n ,y n ,h n ], u k =[x k ,y k ,h k ], v = [x v ,y v ,h v As mentioned above, τ n1 It is the time difference of arrival between the nth ground base station and the reference anchor point, while τ v1 This is the arrival time difference between the UAV and the reference anchor point. In a preferred embodiment of the invention, the objective is to deploy the UAV in a non-obstruction area while meeting the constraints of positioning accuracy and communication rate requirements, thereby maximizing the communication rate between the UAV and ground users. Furthermore, we ensure that the UAV can establish line-of-sight links with all users, thus guaranteeing accurate link modeling and improved integrated communication and positioning performance. Therefore, based on the positioning accuracy constraints, the communication rate constraints, and the non-obstruction area, we transform the UAV's position in the non-obstruction area into solving the following problem:

[0103]

[0104]

[0105]

[0106]

[0107] Where R kv This represents the upload rate between user k and the UAV (User Aerial Vehicle). This represents the total communication rate between all users and the drone. u represents the given communication rate threshold for user k. k Let v represent the location coordinates of the k-th user, and v represent the location of the drone. kThe given positioning accuracy threshold for user k is represented; expressions (13b) and (13c) represent the positioning accuracy constraint and communication rate constraint for each user, respectively; expression (13d) represents the unobstructed area constraint for the UAV, which ensures that the UAV can establish line-of-sight links with all ground users.

[0108] Due to the non-convex objective function and constraints, problem (P1) is very difficult to solve. Therefore, we need to relax and approximate it first to reduce the computational complexity of the algorithm.

[0109] First, the positioning accuracy constraint (13b) is a non-convex function of the UAV's position; approximating it makes the original problem easier to solve. We express the determinant of the covariance matrix, det(R), as:

[0110]

[0111] in in and These represent the measurement errors between the user and the three ground base stations and the drone, respectively. Due to the limitations of G2G links, Right now Far greater than Therefore, based on the actual situation and experimental simulation results, it can be found that D2 is much smaller than D1. Therefore, we can approximate det(R) with D1, and obtain the corresponding opt-D1 as follows:

[0112]

[0113] As for det(H), we express it as

[0114]

[0115] Where the coefficient e is:

[0116] e1=(q 22 -q 12 )(q 33 -q 13 )-(q 23 -q 13 )(q 32 -q 12 ), (17)

[0117] e2=(q 23 -q 13 )(q 31 -q 11 )-(q 21 -q 11 )(q 33 -q 13), (18)

[0118] e3=(q 21 -q 11 )(q 32 -q 12 )-(q 22 -q 12 )(q 31 -q 11 (19) and the positioning accuracy constraint in the original problem is also approximately:

[0119]

[0120] Here, det(H) is the Jacobian determinant of the UAV's position variable. The value of this determinant changes with the UAV's position, and based on its sign, we classify it into three categories: (1) det(H) = 0, (2) det(H) > 0, and (3) det(H) < 0. As mentioned earlier, the unit vector from the nth anchor point to user k is defined as... n∈v∪{1,2,3}. Therefore, q n It is a three-dimensional variable, q 11 q 12 q 13 Let q represent the variable value of each dimension of q1, q 21 q 22 q 23 Let q represent the variable value of each dimension of q2, q 31 q 32 and q 33 Let each dimension of q3 represent a specific user. ∈ indicates the analysis for a particular user, as described below: det(H) > 0 is described as ∈1, and det(H) < 0 is described as ∈2. Case 1: When det(H) = 0, det(H) will only be zero if the drone, ground base station, and user are on the same plane, or if the drone and base station or user are in the same location. However, in this invention, the drone has flight altitude constraints, so none of these situations will occur. Therefore, det(H) will not be zero in the drone's flight area, and we can exclude this case.

[0121] Case 2: When det(H)>0, (20) is equivalent to

[0122] e1(x v -x k )+e2(y v -y k )+e3(h v -h k )≥∈1||vu||,(21) in When ∈ satisfies the following conditions

[0123]

[0124] in, c2 = e1q 11 +e2q 12 +e3q 13 Then, the UAV flight area that satisfies the positioning accuracy constraint is the following second-order cone region:

[0125] e1q v1 +e2q v2 +e3q v3 ≥∈1, (23)

[0126] Where q v1 q v2 q v3 These represent the unit vectors from the drone to the user.

[0127] Case 3: When det(H) < 0, (20) is equivalent to

[0128] e1(x v -x k )+e2(y v -y k )+e3(h v -h k )≤∈2||vu||, (24)

[0129] in When ∈ satisfies the following conditions

[0130]

[0131] The UAV flight area that satisfies the positioning accuracy constraint is the following second-order cone region:

[0132] e1q v1 +e2q v2 +e3q v3 ≤∈2, (26)

[0133] Generally, in the flight area of ​​a UAV, det(H) is either always positive or always negative, meaning that the UAV flight area that meets the positioning accuracy requirements corresponds to one of (21) and (24). Therefore, we can determine which positioning area (21) or (24) to choose as the feasible positioning accuracy area for the UAV by calculating the det(H) value of any point in the UAV flight area. We represent this feasible positioning accuracy area as:

[0134]

[0135] in Indicates based on a given ∈ k User k satisfies the second-order cone region of the positioning accuracy constraint; ∈ k This represents the given positioning accuracy threshold for user k.

[0136] Then, analyzing the communication rate constraint (13c), for user k, the communication rate constraint (13c) is equivalent to...

[0137]

[0138] The drone can only meet user k's positioning accuracy requirements if it is located in the following areas.

[0139]

[0140] This is a sphere centered on user k. The given communication rate constraint can only be satisfied when the drone is located inside this sphere. We represent this feasible communication region as...

[0141]

[0142] in Indicates based on a given The 3D region where user k satisfies the communication rate constraint; This represents the given communication rate threshold for user k.

[0143] Finally, we analyze the non-obstacle region constraint (13d). First, to transform (13d) into a more easily solvable form, we introduce a binary variable l. ij It is equivalent to replacing it with the following mixed-integer linear form

[0144]

[0145]

[0146]

[0147]

[0148] in Let v = [x] represent the set of hyperplanes representing the i-th obstacle region. v ,y v ,h v The ] indicates the location of the drone. Let represent the normal vector of the j-th hyperplane in the i-th obstacle region. Represents a three-dimensional variable, with constant b. ijLet C represent the offset of hyperplane j relative to the origin, where the coordinates of the origin are [0,0,0]. C is a sufficiently large constant. It is the number of hyperplanes in the i-th obstacle region. This represents the total flight area of ​​the UAV (Unmanned Aerial Vehicle). For example, C can take values ​​greater than... The absolute value of the minimum value in the range.

[0149] When position v is inside hyperplane j, i.e. At that time, we set the corresponding l ij =1. (31b) indicates that there is at least one l. ij =0, to ensure that position v is at least outside a hyperplane. Then, we set the binary variable l to 0. ij Equivalent replacement

[0150]

[0151]

[0152] Therefore, solving problem P1 is simplified to solving problem P2:

[0153]

[0154] st(27),(30),(31a)-(31c),(32),(33).

[0155] Next, we use Lagrange relaxation to solve the above problem. First, we use the Lagrange multiplier λ constraint equation (33) to obtain a partial Lagrange problem:

[0156]

[0157] Among them, R kv This represents the upload rate between user k and the UAV (User Aerial Vehicle). Its corresponding dual function is expressed as

[0158]

[0159] Its dual problem is

[0160]

[0161] Given the Lagrange multipliers, we first solve the dual problem (35). We first express the Lagrange multiplier of the nth outer loop as... Because the second term in (35) is concave, we use the continuous convex approximation to obtain its lower bound, such as...

[0162]

[0163] in, This is obtained in the t-th inner loop. Therefore, the dual problem (35) is transformed into a convex problem:

[0164]

[0165] st(27),(30),(31a)-(31c),(32),

[0166] in It is R kv The lower bound can be calculated by examining its position in ||v. (t) -u k The first-order Taylor expansion at || yields:

[0167]

[0168] in,

[0169]

[0170]

[0171] and v (t) Let represent the drone position obtained in the t-th inner loop. Since problem P2.1 is a convex problem, iterative optimization can be performed using SCA until the obtained target value is less than a threshold. This is achieved by providing the Lagrange multiplier λ. i After obtaining the value of and approximating the objective function, the UAV deployment optimization problem can be solved iteratively within t loops.

[0172] Given the Lagrange multiplier λ i After obtaining the position of the UAV by calculating the value of λ, we need to update the Lagrange multiplier λ. i We update the value of λ to reduce the duality gap. We use the following equation to update the Lagrange multiplier λ. i value

[0173]

[0174] in The l obtained when solving the drone deployment problem earlier ij The value of γ, and (n) for

[0175]

[0176] Where μ (n) >0 is an adjustment parameter; if the drone's position obtained in the drone deployment optimization problem is located in a non-obstacle area, and Let P2 and P2 be the objective function values ​​for the La Langrangian problem P2.1 and the original problem P2, respectively. If the drone's position obtained in the drone deployment optimization problem is located in the obstacle region, then... The value is set to zero. The value is still the objective function value of the La Langrangian problem P2.1.

[0177] The aforementioned Lagrange relaxation algorithm, such as Figure 4 As shown. Of course, in other preferred embodiments of the present invention, semi-positive definite relaxation can also be used to solve the original problem.

[0178] Since the solution obtained by the Lagrange relaxation algorithm is not necessarily a feasible solution, meaning the obtained UAV position may not be located in a non-obstacle area, in some rare cases, the UAV position obtained from the above may be infeasible because some G2A links are obstructed by buildings. In this case, we apply the following remedy to recover a feasible and effective solution. The key idea is to first locate a closed and convex non-obstacle area, and then optimize the deployment of UAVs within it.

[0179] First, we find a drone location V. L =(x L ,y L ,h L It satisfies all positioning performance constraints with a minimum height limit, and can be minimized by substituting constraint equation (27) into the minimum h. v The solution is obtained by solving the convex problem.

[0180] If V L If the non-obstacle zone constraint (13d) is satisfied, then V L =V F Otherwise, fix the horizontal coordinate x. L ,y L Gradually increase the height coordinate h L Until at a height coordinate h F The non-obstacle zone constraint (13d) is satisfied. Increasing this altitude ensures the UAV remains within the cone-shaped area V that satisfies the positioning accuracy constraint. F =(x L ,y L ,h F It satisfies constraint (27).

[0181] When the drone is positioned in an unobstructed location V F =(x L ,y L ,h F When ), we calculate the corresponding l. ij The value of . That is At that time, we set the corresponding lij =1, otherwise l ij =0. The obtained {l ij} describes a point including V F A closed, non-barrier region can be denoted as l. ij (F) , will be l ij (F) Substituting this into the original problem P2, we can make all constraints convex.

[0182] If the simplified convex problem P2 is feasible, we again use the Lagrange relaxation algorithm to solve problem P2, thereby obtaining the optimized UAV deployment location. Otherwise (P2), it indicates that the positioning accuracy constraint or the communication rate constraint is too large, and the parameters can be adjusted to expand the area where UAVs can be deployed, for example, by reducing the given positioning accuracy threshold ∈ based on expression (22) or (25). k The value of is then taken. The aforementioned calculation is then performed again until the optimized drone deployment location is finally obtained. The specific algorithm is as follows: Figure 4 As shown.

[0183] The map-assisted UAV communication and positioning integrated deployment method of the present invention employs a dual-function UAV-assisted ground base station to provide communication and positioning services to users. This network not only reduces network deployment costs and improves resource utilization efficiency, but also significantly improves positioning accuracy and communication speed by optimizing the 3D position of the UAV with the assistance of a map. In a preferred embodiment of the present invention, the flight area of ​​the UAV is divided using a physical environment map, further improving the performance of the integrated communication and positioning network and overcoming the shortcomings of traditional channel models, thus significantly improving the practicality of the network. In a preferred embodiment of the present invention, the D-optimal criterion is used as the positioning accuracy constraint to derive its 3D feasible region, and techniques such as Lagrange relaxation are used to solve the problem, significantly reducing the computational complexity of the algorithm.

[0184] Although the present invention has been described through specific embodiments, those skilled in the art will understand that various modifications and equivalent substitutions can be made to the invention without departing from its scope. Furthermore, various modifications can be made to the invention for specific situations or materials without departing from its scope. Therefore, the present invention is not limited to the specific embodiments disclosed, but should include all embodiments falling within the scope of the claims.

[0185] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A map-assisted UAV communication and positioning integrated deployment method, characterized in that, include: S1. Based on the building location information given in the map, the flight area of ​​the UAV is divided into obstacle area and non-obstacle area; S2. Deploy the drone based on the non-obstacle area; Step S1 further includes: S11. Obtain the first [location] based on the building location information and user location information. i The expression for each obstacle region (1) in Indicates the first One obstacle area, Indicates the first A set of hyperplanes representing obstacle regions. Indicates the location of the drone. x , y and h Representing three-dimensional coordinates respectively, Indicates the first The first obstacle area Normal vectors of the hyperplane Represents three-dimensional variables and constants. Indicates the first The offset of each hyperplane from the origin, where the coordinates of the origin are [0,0, 0]. S12. Based on the expression (1), obtain the expression (2) for the corresponding non-obstacle region. in This indicates the total flight area of ​​the drone. Indicates the non-barrier area; Step S2 further includes: S2b: Deploy the UAV based on positioning accuracy constraints, communication rate constraints, and the non-obstacle area; step S2b specifically includes: Based on the positioning accuracy constraint, the communication rate constraint, and the non-obstacle region, the position of the UAV in the non-obstacle region is transformed into solving the following problem P1: in Indicates the first k The upload rate between the user and the drone; This represents the total communication rate between all users and the drone. Indicates the first k A given communication rate threshold for a user Indicates the first k The location coordinates of each user Indicates the location of the drone. Indicates the first k A given location accuracy threshold for a user; expression and expression Represent the positioning accuracy constraints and communication rate constraints for each user, respectively; Expression This indicates the non-obstacle area constraint for the drone. Indicates the number of users.

2. The map-assisted UAV communication and positioning integrated deployment method according to claim 1, characterized in that, Step S2b further includes: S2b1. The feasible positioning accuracy region of the UAV is represented by approximately restoring the positioning accuracy constraint as follows: in Indicates based on a given No. k 3D area that meets positioning accuracy constraints for each user Indicates the first k A given location accuracy threshold for each user; S2b2. Based on the communication rate constraint, the feasible communication accuracy region of the UAV is represented as follows: in Indicates based on a given No. k 3D region where a user satisfies communication rate constraints Indicates the first k A given communication rate threshold for each user; S2b3, Introduce a binary variable The non-obstacle region constraint is represented as: in Indicates the first A set of hyperplanes representing obstacle regions. Indicates the location of the drone. Indicates the first The first obstacle area Normal vectors of the hyperplane Represents three-dimensional variables and constants. Indicates the first The offsets of each hyperplane from the origin, whose coordinates are [0, 0, 0]. The value is greater than The absolute value of the minimum value in. It is the first The number of hyperplanes in each obstacle region This indicates the total flight area of ​​the UAV; When position Located in the Within each hyperplane, a corresponding... This will transform binary variables Equivalent replacement S2b4. Simplify the problem P1 into the problem P2: S2b5, using Lagrange relaxation to solve problem P2 to obtain the drone deployment location and then deploying the drone at the drone deployment location; .

3. The map-assisted UAV communication and positioning integrated deployment method according to claim 2, characterized in that, Step S2b5 further includes: S2b51, using Lagrange multipliers Restricting equation (33) yields a partial Lagrange duality problem (35): in, Indicates the first The upload rate between the drone and the drone ; S2b52, The partial Lagrangian dual problem (35) is solved by continuous convex approximation cyclic solution to transform it into a convex problem to solve for the deployment location of the UAV.

4. The map-assisted UAV communication and positioning integrated deployment method according to claim 3, characterized in that, When the drone deployment location obtained in step S2b52 is not located in the non-obstacle area, search for a drone location that satisfies all positioning accuracy constraints and has a minimum height limit. If the drone location satisfies the non-obstacle area constraint formula (13d), the drone location is used as the preset deployment location. Otherwise, fix its horizontal coordinates and increase its height coordinates until its height coordinates satisfy the non-obstacle area constraint formula (13d), and then use the drone location obtained at this time as the preset deployment location. Calculate the corresponding binary variable based on the preset deployment location. The value is used to obtain a closed, non-obstructive area including the preset deployment location, and the closed, non-obstructive area is used in problem P2 to solve for the deployment location of the UAV.

5. The map-assisted UAV communication and positioning integrated deployment method according to claim 4, characterized in that, Step S2 further includes: S2c, when the deployment location of the UAV cannot be determined, reducing the given positioning accuracy threshold. ; Step S2b5 further includes: after obtaining the deployment location of the UAV, updating the Lagrange multipliers. The value is used to reduce the duality gap.

6. The map-assisted UAV communication and positioning integrated deployment method according to claim 1, characterized in that, The maps include urban environment maps or radio maps.

7. A drone communication and positioning system, comprising multiple ground base stations, multiple users, and at least one drone, characterized in that, The UAV is deployed based on the map-assisted UAV communication and positioning integrated deployment method described in any one of claims 1-6.

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