Unmanned aerial vehicle integrated deployment method based on user distribution probability

Abstract

The application provides a user distribution probability-based unmanned aerial vehicle (UAV) integrated deployment method and device, electronic equipment and storage medium, and belongs to the technical field of data processing.The method comprises the following steps: determining the Cramer-Rao lower bound of each user; discretizing a target area into multiple grids, and determining the distribution probability of each user in different grids according to prior information about the position of the user; determining the prior Cramer-Rao lower bound of each user based on the distribution probability; taking the UAV deployment set as an optimization variable, taking the weighted sum of the Cramer-Rao lower bounds of all users and the communication rate as the target, and establishing an optimization problem based on a preset constraint condition; the preset constraint condition comprises that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold; and the optimization problem is solved to obtain an optimal UAV deployment set.The target area is divided into multiple grids, and the distribution probability of the user in different areas is used to guide the rapid deployment of the UAV.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for the integrated deployment of communication and navigation for unmanned aerial vehicles (UAVs) based on user distribution probability. Background Technology

[0002] Major natural disasters such as earthquakes and fires often cause changes in the terrain of the affected area, making it difficult for affected people to report their location and disaster situation in a timely manner. Command centers also struggle to accurately dispatch rescue forces, easily leading to delays in crucial rescue time due to information silos. In emergency rescue scenarios, ensuring both communication and positioning capabilities for ground personnel simultaneously becomes a major challenge: on the one hand, traditional ground base stations may be paralyzed due to infrastructure damage (such as power outages), and satellite signals may be attenuated or even rejected due to terrain obstruction, leading to problems such as "communication interruption and inaccurate positioning," severely hindering effective disaster relief and rescue work; on the other hand, the inability to accurately determine the location of affected users results in "low rescue efficiency," necessitating an emergency communication and positioning system (referred to as an emergency communication and navigation system) to ensure the uploading of disaster information, the issuance of command orders, and real-time tracking of personnel locations.

[0003] Unmanned aerial vehicles (UAVs), with their advantages of low cost, high mobility, and ability to ignore ground obstacles, have become one of the best carriers for emergency communication mobile nodes. They can carry emergency communication and positioning equipment to provide emergency communication and positioning services to ground personnel. However, UAV deployment relies on the precise location of ground users, which is often difficult to obtain in real-world scenarios. Existing solutions typically simplify the user distribution model or assume that user locations are known. For example, they assume that user locations are continuous variables following a Gaussian or uniform distribution. But in real-world scenarios, due to the influence of obstacles, users are distributed with different discrete probabilities in certain areas of the scene. Therefore, the traditional assumptions of Gaussian and uniform distributions do not hold, preventing UAVs from providing users with high-quality positioning and communication services.

[0004] Therefore, in emergency scenarios with harsh environments and limited resources, how to quickly deploy drone networks and ensure communication and positioning coverage for ground users has become an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a method for the integrated deployment of communication and navigation for unmanned aerial vehicles (UAVs) based on user distribution probability, in order to address the shortcomings of existing technologies in emergency scenarios with harsh environments and limited resources, which cannot quickly achieve ground user communication and positioning coverage for UAV deployment.

[0006] This invention provides a method for the integrated deployment of communication and navigation for unmanned aerial vehicles (UAVs) based on user distribution probability, comprising the following steps:

[0007] Determine the lower bound of Clamello for each user;

[0008] The target area is discretized into multiple grids, and the distribution probability of each user in different grids is determined.

[0009] Based on the distribution probability, a prior Cramer-Rao lower bound is determined for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the distribution probability of the user.

[0010] Using the drone deployment set as the optimization variable, and aiming to minimize the Cramer-Rao lower bound of all users and the weighted sum of communication rates, an optimization problem is established based on preset constraints. The preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0011] Solving the optimization problem yields the optimal set of drone deployments.

[0012] According to the present invention, a method for integrated UAV communication and navigation deployment based on user distribution probability is provided, wherein solving the optimization problem to obtain the optimal UAV deployment set includes:

[0013] With the goal of minimizing the sum of the prior Cramerlow lower bounds for all users, a first sub-optimization problem is established based on the first sub-constraints. The first sub-constraints include the distance between any two drones being greater than or equal to the safe distance, and the deployment range constraint of the drones.

[0014] Solving the first sub-optimization problem yields the initial UAV deployment set;

[0015] Based on the initial drone deployment set, the location information of each user is determined;

[0016] With the goal of minimizing the Cramerlow lower bound of all users and the weighted sum of communication rates, a second sub-optimization problem is established based on the second sub-constraints. The second sub-constraints include at least one drone providing communication services for each user, the number of services provided by each drone not exceeding its maximum number of services, the distance between any two drones being greater than or equal to the safe distance, and the deployment range constraint of the drones.

[0017] Based on the location information of each user, the second sub-optimization problem is solved to obtain the optimal set of drone deployments.

[0018] According to the present invention, a method for the integrated deployment of communication and navigation of unmanned aerial vehicles (UAVs) based on user distribution probability determines whether a specified UAV can provide communication services to a specified user based on the following steps:

[0019] Using the location information of the specified user as the center and the error range of the location information as the radius, the uncertain area of ​​the specified user's location is modeled as a circle;

[0020] Based on the location information of the designated drone, determine the distance between the designated drone and the most distant possible location of the user in the circle;

[0021] Based on the distance, determine the signal-to-noise ratio between the designated drone and the designated user;

[0022] If the signal-to-noise ratio is greater than or equal to the signal-to-noise ratio threshold, then it is determined that the designated drone can provide communication services to the designated user.

[0023] According to the present invention, a method for integrated UAV communication and navigation deployment based on user distribution probability is provided, wherein determining the Cramero lower bound for each user includes:

[0024] Fisher's information matrix to determine the location of each user;

[0025] Based on the Fisher information matrices of each user, the lower bound of Clamello is determined for each user.

[0026] According to the present invention, a method for integrated communication and navigation deployment of unmanned aerial vehicles (UAVs) based on user distribution probabilities is provided, wherein determining the prior Cramero lower bound for each user based on the distribution probabilities includes:

[0027] For any given grid, a preset smoothing function is used to smooth the horizontal and vertical coordinates of the grid respectively, resulting in a horizontal coordinate smoothing function and a vertical coordinate smoothing function.

[0028] Based on the smoothing functions of the horizontal and vertical axes of each grid, the probability density function of the user's location is determined.

[0029] Based on the probability density function, the prior Bayesian Fisher information matrix of each user is determined; the prior Bayesian Fisher information matrix is ​​a Bayesian Fisher information matrix based on the user's probability distribution.

[0030] Based on the prior Bayesian Fisher information matrix of each user, the prior Cramerlow lower bound of each user is determined.

[0031] According to the present invention, a method for integrated communication and navigation deployment of unmanned aerial vehicles based on user distribution probability is provided, wherein the preset constraint conditions further include:

[0032] For each user, there must be at least one drone providing communication services to that user;

[0033] Each drone may provide no more than its maximum number of services.

[0034] The distance between any two drones is greater than or equal to the safe distance;

[0035] Deployment range constraints for drones.

[0036] The present invention also provides an integrated communication and navigation deployment device for unmanned aerial vehicles (UAVs) based on user distribution probability, comprising the following modules:

[0037] The first lower bound determination module is used to: determine the Clamerlow lower bound for each user;

[0038] The probability distribution determination module is used to: discretize the target area into multiple grids and determine the probability distribution of each user in different grids;

[0039] The second lower bound determination module is used to: determine the prior Cramer-Rao lower bound for each user based on the distribution probability; the prior Cramer-Rao lower bound is the Cramer-Rao lower bound based on the distribution probability of the user;

[0040] The optimization problem establishment module is used to: take the UAV deployment set as the optimization variable, and aim to minimize the weighted sum of the Cramer-Rao lower bounds of all users and the communication rates, based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0041] The optimization problem-solving module is used to: solve the optimization problem to obtain the optimal set of drone deployments.

[0042] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the UAV communication and navigation integrated deployment method based on user distribution probability as described above.

[0043] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the UAV communication and navigation integrated deployment method based on user distribution probability as described above.

[0044] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the UAV integrated communication and navigation deployment method based on user distribution probability as described above.

[0045] This invention provides a method for integrated UAV communication and navigation deployment based on user distribution probability. The method determines the Cramer-Rao lower bound for each user; discretizes the target area into multiple grids and determines the distribution probability of each user within different grids; based on the distribution probability, it determines the prior Cramer-Rao lower bound for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the user's distribution probability; using the UAV deployment set as the optimization variable, and aiming to minimize the weighted sum of the Cramer-Rao lower bounds of all users and the communication rate, an optimization problem is established based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold; the optimization problem is solved to obtain the optimal UAV deployment set. This invention divides the target area into multiple grids, determines the distribution probability of users within different grids, and further obtains the prior Cramer-Rao lower bound based on user distribution, guiding the rapid deployment of UAVs and effectively ensuring the communication and positioning needs of disaster-stricken personnel on the ground. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the UAV integrated communication and navigation deployment method based on user distribution probability provided by the present invention.

[0048] Figure 2 This is a schematic diagram of the integrated communication and navigation deployment of unmanned aerial vehicles (UAVs) in emergency rescue scenarios provided by the present invention.

[0049] Figure 3 This is a schematic diagram of the grid provided by the present invention;

[0050] Figure 4 This is a line graph showing the variation of the sum of the Craméro lower bounds of the UAV after initial deployment with the number of users in the simulation experiment provided by this invention;

[0051] Figure 5 The different weighting factors in the simulation experiment provided by this invention A diagram illustrating the lower bound of Clameros;

[0052] Figure 6 The different weighting factors in the simulation experiment provided by this invention A schematic diagram of the communication rate below;

[0053] Figure 7This is a schematic diagram illustrating the change in communication coverage with the number of users in a simulation experiment provided by the present invention;

[0054] Figure 8 This is a schematic diagram illustrating the change of fitness function value with the number of drones in the simulation experiment provided by the present invention;

[0055] Figure 9 This is a schematic diagram of the structure of the UAV integrated communication and navigation deployment device based on user distribution probability provided by the present invention;

[0056] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0058] It should be noted that in the description of the embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly, for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0059] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0060] The following is combined with Figures 1-10 This invention describes the UAV integrated communication and navigation deployment method, apparatus, electronic device, and storage medium based on user distribution probability provided in embodiments of the present invention.

[0061] Figure 1 This is a flowchart illustrating the UAV integrated communication and navigation deployment method based on user distribution probability provided by the present invention, as shown below. Figure 1 As shown, the method includes the following:

[0062] S110, determine the lower bound of Clameros for each user;

[0063] S120, Discretize the target area into multiple grids, and determine the distribution probability of each user in different grids;

[0064] S130, Based on the distribution probability, determine the prior Cramer lower bound for each user; the prior Cramer lower bound is a Cramer lower bound based on the distribution probability of the user;

[0065] S140, taking the drone deployment set as the optimization variable, and aiming to minimize the Cramer-Rao lower bound of all users and the weighted sum of communication rates, an optimization problem is established based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0066] S150, Solve the optimization problem to obtain the optimal set of drone deployments.

[0067] In this embodiment of the invention, the executing entity of the UAV integrated communication and navigation deployment method based on user distribution probability can be a UAV deployment device. This UAV deployment device may include, but is not limited to, servers, computer equipment such as mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs). The executing entity of this UAV integrated communication and navigation deployment method based on user distribution probability can also be a UAV deployment system, which belongs to the UAV deployment device.

[0068] Figure 2 This is a schematic diagram of the integrated communication and navigation deployment of unmanned aerial vehicles (UAVs) in emergency rescue scenarios provided by the present invention, as shown in the figure. Figure 2 As shown, assuming the ground emergency network consists of... drones and Composed of 1 user group, each using and This indicates that the drone provides communication services to ground users via downlink, while simultaneously transmitting location signals. Users are distributed with varying probabilities in several regions of interest within the scene. The location of the drone can be represented as ,in This indicates the drone's deployment altitude. All users are at altitude 0, located on the same horizontal plane. Users are located in .

[0069] Assume the propagation channel is controlled by a line-of-sight (LoS) link, where channel quality primarily depends on the distance between the UAV and the user. Assume there are a total of... A user simultaneously requests communication and location services, and the drone uses Frequency Division Multiple Access (FDMA) technology to transmit signals to the user on orthogonal channels. Therefore, the first... The drone to the The air-to-ground channel power gain for an individual user can be modeled as follows:

[0070] ,(1);

[0071] in, It is the first The drone to the Air-to-ground channel power gain for individual users The reference distance is Channel gain at that time It is the path loss index.

[0072] Furthermore, the first The drone and the first The signal-to-noise ratio (SNR) between individual users can be calculated using the following formula:

[0073] (2);

[0074] in, It is the first The drone and the first Signal-to-noise ratio between individual users It is the first The drone and the first Transmission power between users This is the noise power. The normalized communication rate between the UAV and the user is then expressed as follows:

[0075] (3);

[0076] in, It is the first The drone and the first Normalized communication rate between users.

[0077] In emergency scenarios, drones must provide reliable communication for each user; therefore, a minimum signal-to-noise ratio (SNR) requirement must be met between the drone and the user. Define a binary variable. Characterizing drones Is it a user? Provides communication services. Each user can connect to a maximum of one drone.

[0078] (4);

[0079] Furthermore, this embodiment of the invention employs a TOA (Time of Arrival) positioning algorithm using a drone as a base station. The user calculates their own position based on the TOA positioning method, assuming that... The drone was used to locate the target user, excluding the user's location. Drones that provide communication services. (Definition) Indicates the arrival delay of the measurement, then the first... From drone to user The actual distance is:

[0080] (5);

[0081] in, It is the first From drone to user The actual distance It's the speed of light. (Using vectors) This represents the distance between the user and the drone. The TOA positioning measurement model can be expressed as:

[0082] (6);

[0083] in, Indicates the first From drone to user The measured distance, The measured values ​​are represented by Gaussian noise with a mean of zero and a variance of . For simplicity, use express.

[0084] In practical applications, the noise variance of UAV time of arrival measurements It is related to the signal-to-noise ratio (SNR) of the signal and can be written as:

[0085] (7);

[0086] in, It is a constant and depends on the received signal. It is a drone To users The power of the transmitted positioning signal. This represents the channel power gain. Therefore, the measurement vector... It follows the distribution as follows:

[0087] (8);

[0088] in, It is the true value vector The covariance matrix can be written as follows according to formula (7):

[0089] , (9).

[0090] In an optional embodiment, determining the lower bound of Cramerro for each user includes:

[0091] Fisher's information matrix to determine the location of each user;

[0092] Based on the Fisher information matrices of each user, the lower bound of Clamello is determined for each user.

[0093] In this embodiment of the invention, the Cramer-Rao Lower Bound (CRLB) is selected as the metric for measuring positioning accuracy. First, a TOA-based CRLB without prior information is given, and then the Bayesian Cramer-Rao Lower Bound (BCRLB) is derived when the user's prior distribution information is available; that is, the prior Cramer-Rao Lower Bound.

[0094] Based on the above model, the parameter to be estimated is defined as follows: According to the chain rule, the Fisher Information Matrix (FIM) can be written as:

[0095] (10);

[0096] in, It's about distance FIM, It is a Jacobian matrix, in the form of:

[0097] ,(11)

[0098] According to equations (7) and (9) The Line number The elements of a column can be written as:

[0099] (12);

[0100] in, .

[0101] when When, the first term of formula (12) can be written as:

[0102] (13);

[0103] The second item can be written as:

[0104] (14);

[0105] Where tr[⋅] represents the trace of the matrix.

[0106] when hour, Substituting equations (13) and (14) into formula (12), we can obtain the Fisher information matrix regarding the user's location:

[0107] (15);

[0108] in,

[0109] ,(16)

[0110] (17);

[0111] , (18).

[0112] Then regarding users coordinates and The lower bound of the coordinate system can be written as:

[0113] , (19).

[0114] The UAV integrated communication and navigation deployment method based on user distribution probability provided in this invention uses the Cramer-Rao lower bound as an indicator to measure positioning accuracy, providing a theoretical minimum error limit. By calculating the Fisher information matrix to obtain the Cramer-Rao lower bound, the best possible estimation accuracy under a given data model can be clearly known, providing a basis for solving subsequent optimization problems and achieving a performance balance between communication and positioning.

[0115] In an optional embodiment, determining the prior Cramero lower bound for each user based on the probability distribution includes:

[0116] For any given grid, a preset smoothing function is used to smooth the horizontal and vertical coordinates of the grid respectively, resulting in a horizontal coordinate smoothing function and a vertical coordinate smoothing function.

[0117] Based on the smoothing functions of the horizontal and vertical axes of each grid, the probability density function of the user's location is determined.

[0118] Based on the probability density function, the prior Bayesian Fisher information matrix of each user is determined; the prior Bayesian Fisher information matrix is ​​a Bayesian Fisher information matrix based on the user's probability distribution.

[0119] Based on the prior Bayesian Fisher information matrix of each user, the prior Cramerlow lower bound of each user is determined.

[0120] Here, when priori distribution information (PDI) about user distribution is available, the Fisher information matrix can be generalized to the Bayesian Fisher Information Matrix (BFIM):

[0121] (20);

[0122] Define parameters The prior FIM is:

[0123] ,(twenty one);

[0124] The FIM derived from the observation vector is defined as:

[0125] ,(twenty two).

[0126] To calculate the CRLB value based on prior user location, a typical approach is to treat the user location as a Gaussian 3D random variable. However, in a more general case, user locations do not follow a continuous probability distribution, but rather are distributed with discrete probabilities across certain regions of the scene. It should be noted that no other assumptions are made regarding the probability distribution of user locations here.

[0127] For ease of representation, subscripts are omitted. And the target area (i.e., the disaster-stricken area) is discretized into grid Therefore, based on prior information, In different supports (grids) The probability distribution within the range can be written in the following form:

[0128] ,(twenty three).

[0129] Figure 3 This is a schematic diagram of the grid provided by the present invention, such as... Figure 3 As shown, the definition They represent the first Each support and the horizontal and vertical axes represent the horizontal... Line, vertical The length of the connected components of a line. Definition Representing line segments respectively The midpoint.

[0130] Since the probability density function corresponding to formula (23) is discontinuous and cannot satisfy the regularity condition, a smoothing function is introduced. Solution. Assume along and If the smoothing is independent, then it is a support for smoothing a two-dimensional map. Its area is defined as Its probability density function can be expressed as:

[0131] ,(twenty four).

[0132] Therefore, the prior Bayesian Fisher information matrix can be written as:

[0133] , (25).

[0134] neglect Coordinate smoothing is approximated as follows:

[0135] , (26).

[0136] To simplify the calculation, the Fisher Information (FI) is calculated separately for each support. Therefore, FI can be written as the sum of the FI contributions of each support:

[0137] , (27).

[0138] Substituting equation (27) into equation (25) yields:

[0139] , (28).

[0140] Define each support middle Axis (horizontal axis direction) and The side lengths of the axis (vertical axis direction) are respectively , Then by Easy to obtain:

[0141] , (29).

[0142] Similarly, the derivation yields... .

[0143] Therefore, the FIM regarding the user's two-dimensional location prior information can be written as:

[0144] (30);

[0145] in, ;

[0146] In formula (22) It can be written as:

[0147] , (31).

[0148] Then, the prior Cramero lower bound based on the user's prior distribution information can be written as: .

[0149] The UAV communication and navigation integrated deployment method based on user distribution probability provided in this embodiment of the invention introduces a smoothing function to smooth the x and y directions, thereby approximating the probability density function of the user distribution and making it satisfy the regularization condition, so that it can be further derived to the Fisher information matrix and the lower bound of Cramérault.

[0150] In an optional embodiment, the preset constraint further includes:

[0151] For each user, there must be at least one drone providing communication services to that user;

[0152] Each drone may provide no more than its maximum number of services.

[0153] The distance between any two drones is greater than or equal to the safe distance;

[0154] Deployment range constraints for drones.

[0155] In this embodiment of the invention, a drone deployment optimization problem is constructed, with the objective of minimizing the weighted sum of the Cramero lower bound and communication rates for all users. The optimization problem is as follows:

[0156] (32);

[0157] (33);

[0158] (34);

[0159] (35);

[0160] ,(36;

[0161] (37);

[0162] The objective function for optimization is a weighted sum of the Cramer-Rao lower bound and the communication rate. This represents a weighting factor to achieve a tractable tradeoff between communication and positioning performance. The optimization variable is the set of drone deployments. This refers to the set of drone locations. Constraint C1 specifies the CRLB positioning error requirement when the user's prior probability distribution information is available; C2 constrains the connection relationship between drones and communication users, meaning that at least one drone must provide services to the communication user; constraint C3 states that the number of services provided by each drone cannot exceed its maximum service count. Constraint C4 indicates that the distance between any two drones cannot be less than the safe distance. To prevent collisions; the C5 constraint limits the deployment range of the drone.

[0163] The UAV integrated communication and navigation deployment method based on user distribution probability provided in this invention ensures the accuracy of user positioning by constraining positioning error requirements through a priori Cramero lower bound; constrains the connection relationship between UAVs and users to ensure that at least one UAV can provide reliable communication services to users; and constrains the distance between UAVs to prevent UAV collisions.

[0164] In an optional embodiment, solving the optimization problem to obtain the optimal drone deployment set includes:

[0165] With the goal of minimizing the sum of the prior Cramerlow lower bounds for all users, a first sub-optimization problem is established based on the first sub-constraints. The first sub-constraints include the distance between any two drones being greater than or equal to the safe distance, and the deployment range constraint of the drones.

[0166] Solving the first sub-optimization problem yields the initial UAV deployment set;

[0167] Based on the initial drone deployment set, the location information of each user is determined;

[0168] With the goal of minimizing the Cramerlow lower bound of all users and the weighted sum of communication rates, a second sub-optimization problem is established based on the second sub-constraints. The second sub-constraints include at least one drone providing communication services for each user, the number of services provided by each drone not exceeding its maximum number of services, the distance between any two drones being greater than or equal to the safe distance, and the deployment range constraint of the drones.

[0169] Based on the location information of each user, the second sub-optimization problem is solved to obtain the optimal set of drone deployments.

[0170] In real-world scenarios, user locations are unknown, while drones must target precise user locations for communication coverage. Therefore, it's difficult to directly solve this problem to obtain the drone deployment results. Thus, this embodiment of the invention divides drone deployment into two stages: initial positioning deployment and communication / navigation deployment, based on the positioning and search and rescue phases of the rescue process. In the first stage, prior information about the user's location can be used to optimize drone placement, thereby calculating the user's precise location. In the second stage, based on the user's location, the communication and positioning deployment of the drone is jointly optimized to meet the communication and navigation needs of the ground user.

[0171] First sub-optimization problem It can be represented as:

[0172] (38);

[0173] ;

[0174] in, This represents the initial deployment result of the drone, which can be used to calculate the location information of each user using the least squares (LS) algorithm, defined as... And as input for the second stage.

[0175] In an optional embodiment, it is determined whether a specified drone can provide communication services to a specified user based on the following steps:

[0176] Considering the existence of positioning errors, the calculated user Location Not accurate. The error range is defined as follows: Then the first The uncertain region of a user's location can be modeled as a circle centered at... , radius is The circle is defined, and the distance between the drone and the farthest user location in the uncertain area is:

[0177] (39);

[0178] in, Indicates the first Two-dimensional coordinates of the drone For the first The altitude of the drone. At this time, the... The drone and the first The signal-to-noise ratio between individual users can be written as:

[0179] (40);

[0180] At this point, the signal-to-noise ratio requirement in formula (4) can be rewritten as:

[0181] ,(41)

[0182] If the Individual users within the error range Inside, with the If the minimum requirements are met even at the furthest point of the drone, then the drone will provide communication coverage for the user.

[0183] The UAV integrated communication and navigation deployment method based on user distribution probability provided in this embodiment of the invention models the uncertain area of ​​user location as a circle. If the UAV can provide services to the farthest location in the uncertain area, it is determined that the UAV can provide communication services to the user. Even if the user's location is inaccurate, it can still ensure that the UAV provides services to the user.

[0184] Considering that drones provide users with both location and communication services, the second sub-optimization problem... It can be written as:

[0185] (42);

[0186] .

[0187] Furthermore, the problem and As this is a non-convex nonlinear optimization problem, it cannot be solved directly using standard convex optimization tools. However, it has few optimization variables and a limited solution space. Therefore, the improved Gray Wolf Optimizer (IGWO) algorithm can be used to optimize the three-dimensional position of the UAV. The process of optimizing UAV deployment using the improved Gray Wolf algorithm is shown in Table 1 below.

[0188] Table 1 Improved Gray Wolf Algorithm IGWO

[0189]

[0190] The Grey Wolf Algorithm is defined as containing [a total of] [parts]. K Sekiro: Shadows Die Twice is defined as In the first During the nth iteration, the 1st Only the gray wolf can be represented as ,in Indicates the first The three-dimensional position of each drone. The quality of each gray wolf will be evaluated by a fitness value associated with the objective function, defined as follows: .

[0191] The three gray wolves with the lowest fitness values ​​are denoted as follows: , , The remaining gray wolves were in the The position update in the next iteration is as follows:

[0192] (43);

[0193] in,

[0194] ,(44;

[0195] (45);

[0196] ,(46;

[0197] in, and These are control parameters, expressed as:

[0198] (47);

[0199] (48);

[0200] in, and yes Random parameters between It decreases as the number of iterations increases:

[0201] , (49).

[0202] In the later stages of the gray wolf algorithm, all gray wolves approach the region of the optimal gray wolf, leading to a loss of population diversity. Therefore, a reverse learning strategy is incorporated in the later stages of each iteration to further enhance the diversity of the gray wolf population and avoid getting trapped in local optima. Given The reverse individual:

[0203] (50);

[0204] in, and These represent the upper and lower boundaries of the search space, respectively.

[0205] Define the reverse population set as Calculate the reverse population Using the fitness function, select the three gray wolves with the best fitness in the current population and the reverse population as... The wolf, thus guiding the remaining gray wolves to update their positions.

[0206] In summary, the UAV deployment method provided by this invention first uses the IGWO algorithm to solve the subproblem. To obtain the initial positioning and deployment of the drone The results were obtained using the classical least squares algorithm, and the user's location was then calculated. Using user location Simultaneously considering the inaccuracy of the position calculation, subproblems are formed. .by Using this as initial input, the optimal deployment of the drones is solved again using IGWO, thereby determining the drone deployment result. Together, we can improve the communication and positioning performance of ground users.

[0207] This invention provides a method for integrated UAV communication and navigation deployment based on user distribution probability. The method determines the Cramer-Rao lower bound for each user; discretizes the target area into multiple grids and determines the distribution probability of each user within different grids; based on the distribution probability, it determines the prior Cramer-Rao lower bound for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the user's distribution probability; using the UAV deployment set as the optimization variable, and aiming to minimize the weighted sum of the Cramer-Rao lower bounds of all users and the communication rate, an optimization problem is established based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold; the optimization problem is solved to obtain the optimal UAV deployment set. This invention divides the target area into multiple grids and determines the probability distribution of users within different grids, thereby accurately determining the probability distribution of users in different areas, and further obtaining the prior Cramer-Rao lower bound based on the user probability distribution, guiding the rapid deployment of UAVs and effectively ensuring the communication and positioning needs of disaster-stricken personnel on the ground.

[0208] The following experimental simulation results demonstrate the advantages of the proposed UAV communication and positioning deployment scheme based on user prior information and the improved Grey Wolf algorithm.

[0209] Considering the size of the disaster area is The path loss exponent and channel gain are set as follows in the scenario: The noise power is Set the drone's flight altitude to Each drone is assigned the same positioning and communication transmission power to the user, that is... , The minimum signal-to-noise ratio required to ensure communication for disaster victims is To ensure the maximum positioning error required for location services is The drone's collision avoidance distance is... The maximum number of services is Divide the region into a grid of equal size, where each grid cell is of length . , width is .

[0210] The initialization method considering prior information provided by this invention is labeled "IGWO with PDI" and is compared with the following two schemes:

[0211] 1) Fuzzy c-means clustering deployment scheme based on prior information (FCM with PDI).

[0212] 2) Random deployment scheme without prior information (RD without PDI).

[0213] Figure 4 This is a line graph showing the variation of the sum of the Cramérod lower bounds of the UAVs after initial deployment with the number of users in the simulation experiment provided by this invention, as shown in the figure. Figure 4 As shown, the IGWO and FCM schemes, which utilize prior distribution information, can significantly improve positioning accuracy. In contrast, since the generation results of random deployment are random, prior information cannot be utilized, leading to larger fluctuations in CRLB values ​​and a much higher positioning error than the proposed schemes. Overall, the IGWO scheme remains the best among all other schemes, indicating that it is more reliable than other algorithms.

[0214] Figure 5 The different weighting factors in the simulation experiment provided by this invention A diagram of the lower bound of Clameros. Figure 6 The different weighting factors in the simulation experiment provided by this invention The following is a diagram illustrating the communication rate, as shown below. Figure 5 and Figure 6 As shown, the user's overall CRLB and communication rate change with the weighting factor. The increase leads to a decrease. This observation can be theoretically explained as follows: The larger the value, the more the system tends to optimize positioning performance, which in turn leads to a gradual degradation in communication performance. Therefore, a trade-off emerges between communication rate and positioning error. It is worth noting that when... When set to 0.5, compared with random initialization, the communication rate of the scheme provided by this invention is increased by 16.46% and the positioning error is reduced by 22.33%, verifying the improvement of the proposed scheme in system performance.

[0215] Figure 7 This is a schematic diagram illustrating the change in communication coverage with the number of users in a simulation experiment provided by the present invention, as shown below. Figure 7 As shown, the proposed IGWO scheme is compared with the traditional Gray Wolf Optimizer (GWO) algorithm as a benchmark. With the increase in the number of users, the satisfaction rate of the GWO scheme is significantly lower than that of the IGWO scheme, indicating that the deployment strategy provided by this invention has better flexibility and effectiveness. Secondly, in scenarios where positioning errors are ignored, the coverage of both the IGWO and GWO schemes decreases significantly, but the IGWO scheme is still superior to the GWO scheme.

[0216] Figure 8 This is a schematic diagram illustrating the change of the fitness function value with the number of drones in the simulation experiment provided by this invention, as shown below. Figure 8As shown, the GWO algorithm and Particle Swarm Optimization (PSO) algorithm are compared with the IGWO algorithm provided in this invention. With the increase in the number of UAVs, the fitness function values ​​obtained by different algorithms gradually increase, indicating that more UAVs contribute to improved communication and positioning performance. Furthermore, it can be observed that regardless of the number of UAVs, the proposed IGWO consistently exhibits better system performance than the other two algorithms.

[0217] In summary, the UAV integrated communication and navigation deployment method based on user distribution probability provided by this invention fully utilizes prior user distribution information to directly guide UAV deployment, avoiding the limitations of previous studies that made specific assumptions about user distribution, thus better meeting the application needs of real-world scenarios. Simulation results show that the proposed scheme significantly improves communication speed and reduces positioning errors compared to other methods.

[0218] The UAV communication and navigation integrated deployment device based on user distribution probability provided in the embodiments of this application is described below. The UAV communication and navigation integrated deployment device based on user distribution probability described below and the UAV communication and navigation integrated deployment method based on user distribution probability described above can be referred to each other.

[0219] Figure 9 This is a schematic diagram of the structure of the UAV integrated communication and navigation deployment device based on user distribution probability provided by the present invention, as shown below. Figure 9 As shown, the UAV communication and navigation integrated deployment device based on user distribution probability may include, but is not limited to;

[0220] The first lower bound determination module 910 is used to: determine the Clamerlow lower bound for each user;

[0221] The probability distribution determination module 920 is used to: discretize the target area into multiple grids and determine the probability distribution of each user in different grids;

[0222] The second lower bound determination module 930 is used to: determine the prior Cramer lower bound for each user based on the distribution probability; the prior Cramer lower bound is the Cramer lower bound based on the distribution probability of the user;

[0223] The optimization problem establishment module 940 is used to: take the UAV deployment set as the optimization variable, and aim to minimize the weighted sum of the Cramer-Rao lower bounds of all users and the communication rates, based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0224] The optimization problem-solving module 950 is used to: solve the optimization problem to obtain the optimal set of drone deployments.

[0225] It should be noted that the UAV communication and navigation integrated deployment device based on user distribution probability provided in this embodiment of the invention can execute the UAV communication and navigation integrated deployment method based on user distribution probability described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0226] Figure 10 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 10 As shown, the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030, and a communication bus 1040, wherein the processor 1010, the communications interface 1020, and the memory 1030 communicate with each other via the communication bus 1040. The processor 1010 can call logical instructions in the memory 1030 to execute a UAV integrated communication and navigation deployment method based on user distribution probability. This method includes:

[0227] Determine the lower bound of Clamello for each user;

[0228] The target area is discretized into multiple grids, and the distribution probability of each user in different grids is determined.

[0229] Based on the distribution probability, a prior Cramer-Rao lower bound is determined for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the distribution probability of the user.

[0230] Using the drone deployment set as the optimization variable, and aiming to minimize the Cramer-Rao lower bound of all users and the weighted sum of communication rates, an optimization problem is established based on preset constraints. The preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0231] Solving the optimization problem yields the optimal set of drone deployments.

[0232] Furthermore, the logical instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0233] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the UAV integrated communication and navigation deployment method based on user distribution probability provided by the above methods, the method comprising:

[0234] Determine the lower bound of Clamello for each user;

[0235] The target area is discretized into multiple grids, and the distribution probability of each user in different grids is determined.

[0236] Based on the distribution probability, a prior Cramer-Rao lower bound is determined for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the distribution probability of the user.

[0237] Using the drone deployment set as the optimization variable, and aiming to minimize the Cramer-Rao lower bound of all users and the weighted sum of communication rates, an optimization problem is established based on preset constraints. The preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0238] Solving the optimization problem yields the optimal set of drone deployments.

[0239] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the UAV integrated communication and navigation deployment method based on user distribution probability provided by the above methods, the method comprising:

[0240] Determine the lower bound of Clamello for each user;

[0241] The target area is discretized into multiple grids, and the distribution probability of each user in different grids is determined.

[0242] Based on the distribution probability, a prior Cramer-Rao lower bound is determined for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the distribution probability of the user.

[0243] Using the drone deployment set as the optimization variable, and aiming to minimize the Cramer-Rao lower bound of all users and the weighted sum of communication rates, an optimization problem is established based on preset constraints. The preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold.

[0244] Solving the optimization problem yields the optimal set of drone deployments.

[0245] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0246] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0247] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for integrated communication and navigation deployment of unmanned aerial vehicles (UAVs) based on user distribution probability, characterized in that, include: Determine the lower bound of Clamello for each user; The target area is discretized into multiple grids, and the distribution probability of each user in different grids is determined. Based on the distribution probability, a prior Cramer-Rao lower bound is determined for each user; the prior Cramer-Rao lower bound is a Cramer-Rao lower bound based on the distribution probability of the user. Using the drone deployment set as the optimization variable, and aiming to minimize the Cramer-Rao lower bound of all users and the weighted sum of communication rates, an optimization problem is established based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold. Solving the optimization problem yields the optimal set of drone deployments. Based on the aforementioned probability distribution, the prior Cramero lower bound for each user is determined, including: For any given grid, a preset smoothing function is used to smooth the horizontal and vertical coordinates of the grid respectively, resulting in a horizontal coordinate smoothing function and a vertical coordinate smoothing function. Based on the smoothing functions of the horizontal and vertical axes of each grid, the probability density function of the user's location is determined. Based on the probability density function, the prior Bayesian Fisher information matrix of each user is determined; the prior Bayesian Fisher information matrix is ​​a Bayesian Fisher information matrix based on the user distribution probability. Based on the prior Bayesian Fisher information matrix of each user, the prior Cramerlow lower bound of each user is determined.

2. The UAV integrated communication and navigation deployment method based on user distribution probability according to claim 1, characterized in that, Solving the optimization problem to obtain the optimal drone deployment set includes: With the goal of minimizing the sum of the prior Cramerlow lower bounds for all users, a first sub-optimization problem is established based on the first sub-constraints. The first sub-constraints include the distance between any two drones being greater than or equal to the safe distance, and the deployment range constraints of the drones. Solving the first sub-optimization problem yields the initial UAV deployment set; Based on the initial drone deployment set, the location information of each user is determined; With the goal of minimizing the Cramerlow lower bound of all users and the weighted sum of communication rates, a second sub-optimization problem is established based on the second sub-constraints. The second sub-constraints include at least one drone providing communication services for each user, the number of services provided by each drone not exceeding its maximum number of services, the distance between any two drones being greater than or equal to the safe distance, and the deployment range constraint of the drones. Based on the location information of each user, the second sub-optimization problem is solved to obtain the optimal set of drone deployments.

3. The UAV integrated communication and navigation deployment method based on user distribution probability according to claim 2, characterized in that, The following steps are used to determine whether a specified drone can provide communication services to a specified user: Using the location information of the specified user as the center and the error range of the location information as the radius, the uncertain area of ​​the specified user's location is modeled as a circle; Based on the location information of the designated drone, determine the distance between the designated drone and the most distant possible location of the user in the circle; Based on the distance, determine the signal-to-noise ratio between the designated drone and the designated user; If the signal-to-noise ratio is greater than or equal to the signal-to-noise ratio threshold, then it is determined that the designated drone can provide communication services to the designated user.

4. The UAV integrated communication and navigation deployment method based on user distribution probability according to claim 1, characterized in that, Determine the lower bound of Cramerlow for each user, including: Fisher's information matrix to determine the location of each user; Based on the Fisher information matrices of each user, the lower bound of Clamello is determined for each user.

5. The UAV integrated communication and navigation deployment method based on user distribution probability according to claim 1, characterized in that, The preset constraints also include: For each user, there must be at least one drone providing communication services to that user; Each drone may provide no more than its maximum number of services. The distance between any two drones is greater than or equal to the safe distance; Deployment range constraints for drones.

6. A drone communication and navigation integrated deployment device based on user distribution probability, characterized in that, The application of the UAV integrated communication and navigation deployment method based on user distribution probability as described in claim 1 includes: The first lower bound determination module is used to: determine the Clamerlow lower bound for each user; The probability distribution determination module is used to: discretize the target area into multiple grids and determine the probability distribution of each user in different grids; The second lower bound determination module is used to: determine the prior Cramer-Rao lower bound for each user based on the distribution probability; the prior Cramer-Rao lower bound is the Cramer-Rao lower bound based on the user distribution probability; The optimization problem establishment module is used to: take the UAV deployment set as the optimization variable, and aim to minimize the weighted sum of the Cramer-Rao lower bounds of all users and the communication rates, based on preset constraints; the preset constraints include that the sum of the prior Cramer-Rao lower bounds of all users is less than or equal to a preset threshold. The optimization problem-solving module is used to: solve the optimization problem to obtain the optimal set of drone deployments.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the UAV communication and navigation integrated deployment method based on user distribution probability as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the UAV communication and navigation integrated deployment method based on user distribution probability as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the UAV communication and navigation integrated deployment method based on user distribution probability as described in any one of claims 1 to 5.

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