Unmanned aerial vehicle communication and sensing integrated trajectory design method and system based on radio map
Through the synesthesia integrated trajectory design method based on radio maps, the problem of traditional methods being unable to effectively plan the coordinated flight and no-fly areas of drone under complex flight conditions is solved, and more efficient drone trajectory design is achieved and the flight is improved. Security and communication capabilities.
Patent Information
- Application Number
- CN202411309627.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-19
- Publication Date
- 2024-12-27
AI Technical Summary
Traditional UAV flight path planning methods cannot effectively solve the problems of collaborative flight perception and flight path planning in complex flight conditions, especially when multiple UAVs fly at the same time, it is difficult to ensure the stability of air traffic planning and communication links. .
Using the synesthesia integrated trajectory design method based on radio maps, the channel gain map is initialized, the channel gain map is constructed, the grid map is preprocessed, and the minimum outsourcing circle of the no-fly zone is divided, and the drone position constraints are optimized based on this information to Maximize the amount of radar mutual information and communication information, and form appropriate flight trajectory and beam shape.
In complex perception communication scenarios, the operability and practicality of the drone's flight trajectory are improved, the constraints of the no-fly area and perception range are effectively handled, and the safety and communication capabilities of the drone's flight are ensured.
Smart Images
Figure CN119211842A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of unmanned aerial vehicle trajectory design, and in particular relates to a radio map-based unmanned aerial vehicle synaesthesia integrated trajectory design method and system. Background Art
[0002] With the rapid development of drone technology, drones are widely used in various fields, including logistics distribution, agricultural plant protection, disaster monitoring, etc. Especially when natural disasters and major accidents occur, drones can act as air base stations, quickly establish temporary communication links, and achieve timely and economical information transmission. In addition, drones have high maneuverability and operability, and can adjust flight trajectories according to the actual environment and terrain, thereby improving system performance.
[0003] When using drones to build temporary communication links, it is necessary to consider the no-fly zones or areas with special requirements that may be encountered during the flight of the drone, such as airports, military bases, government agencies, etc. At the same time, when multiple drones fly at the same time, it is also necessary to consider the issues of air traffic planning and coordinated flight. However, traditional drone flight path planning methods mainly focus on the shortest or most optimized trajectory, and cannot solve the problems of coordinated flight perception under complex flight conditions and flight path planning in no-fly zones. Summary of the invention
[0004] In order to solve the above problems existing in the prior art, the present invention provides a method and system for designing a UAV synaesthesia integrated trajectory based on a radio map. The technical problem to be solved by the present invention is achieved through the following technical solutions:
[0005] A method for designing a UAV synaesthesia integrated trajectory based on a radio map, comprising:
[0006] Initialize map parameters, wherein the map parameters include sensor target coordinates, the position of each time slot of the drone, the number of drone antennas, the maximum transmission power allowed by the drone, the flight altitude of the drone, and the flight speed of the drone;
[0007] Building a radio map based on the channel gain map of each ground base station, where the channel gain map represents the distribution of large-scale gain between the ground base station and the UAV;
[0008] Preprocessing the radio map to obtain a feasible grid map;
[0009] Dividing the minimum outer circle in the no-fly zone according to the feasible grid map, and obtaining the radius and center position information of the minimum outer circle;
[0010] Obtaining the channel gain between the UAV and the sensing target and the data collection center, and constructing the UAV position constraint condition based on the minimum outer circle;
[0011] The UAV position constraints are optimized according to the communication constraints to obtain the trajectory and beamforming under the condition of maximizing the radar mutual information for maximum perception.
[0012] In a specific embodiment, preprocessing the radio map to obtain a feasible grid map includes:
[0013] Discretize the spatial domain of the radio map into a space with finite granularity Δ D The grid is formed so that the channel gain in each grid unit is constant;
[0014] Determine each ground base station with a finite granularity Δ D Whether the signal between the user locations is blocked by buildings to determine whether the possible ray pairs connecting the drone and the ground base station are line-of-sight channels or non-line-of-sight channels;
[0015] The channels are modeled according to the channel parameters corresponding to the line-of-sight channel and the non-line-of-sight channel to obtain a feasible grid diagram according to the channel modeling.
[0016] In a specific implementation, the channel gain graph of the ground base station is expressed as:
[0017]
[0018] in represents the large-scale channel gain between the sth ground base station and each location in the discretized two-dimensional space, represents a set of grid coordinates, where The size of the two-dimensional space is determined by the finite particle size Δ D Determine, the grid cell at any position It is expressed as:
[0019]
[0020] in, Representing a collection The reference position in .
[0021] In a specific implementation, the channel model is:
[0022] in, 0 means that the ray pair p belongs to the non-line-of-sight channel, 1 means the line-of-sight channel, and the two-dimensional coordinate set of the building is expressed as s c Represents the coordinate c={x c ,y c}Height of the ray, z c Represents the height of the building at coordinate c.
[0023] In a specific implementation, dividing the minimum outer circle in the no-fly zone according to the feasible grid map, and obtaining the radius and center position information of the minimum outer circle includes:
[0024] Obtain the region from the feasible grid graph Large-scale channel gain maps between all locations in and all S ground base stations;
[0025] Calculating an expected SNR value at a corresponding position according to the large-scale channel gain map;
[0026] Convexly decomposing the radio map at the region notch to obtain a plurality of partitions according to the expected SNR value;
[0027] The radius and center position information of the minimum outer circle corresponding to each partition that can cover each partition to the greatest extent are found, and the cylindrical area corresponding to each minimum outer circle is determined as a no-fly zone.
[0028] In a specific implementation, performing convex decomposition at the region notch to obtain a plurality of partitions includes:
[0029] When the area of a region is smaller than the required area, scan and divide the straight line in this region until the required area is reached. If the divided straight line still cannot meet the requirement after coinciding with another boundary, move the straight line to the other boundary in parallel until the sum of the areas of the two divided regions is equal to the required area. If the area of this region is larger than the required area, use the concave point as the axis to scan and divide it counterclockwise in one of the convex polygons until the required area is obtained. The area that meets the area requirement will be retained, and the rest will continue to be divided as a whole until n-1 partitions are finally divided.
[0030] In a specific embodiment, the power gain of the communication link between the drone and the data collection center in time slot n can be expressed as:
[0031]
[0032] The power gain of the radar detection link between the UAV and the sensing node m in time slot n is expressed as:
[0033]
[0034] Where Gt and Gc represent the antenna gains of the UAV transmitter and communication receiver; represents the distance from the UAV to the data collection center. Gr represents the antenna gain of the UAV radar receiver, and η represents the radar cross section (RCS) of the target; Indicates the distance from the drone to the perceived target;
[0035] In a specific implementation, the drone position constraint is:
[0036]
[0037] q[1]=q I ,q[N]=q F
[0038]
[0039] Where V m is the maximum speed, q I and q F They represent the initial position and final position of the UAV, Q j The radius of the minimum outer circle of the no-fly zone.
[0040] In a specific implementation, the communication constraint condition is:
[0041]
[0042] Among them, R m，rad [] represents the radar estimation rate of the drone to the sensing node m in time slot n, R k，com [] represents the information rate from the UAV to the data collection center.
[0043] The present invention also provides a UAV synaesthesia integrated trajectory design system based on a radio map, comprising:
[0044] An initialization module, used to initialize map parameters, wherein the map parameters include sensor target coordinates, the position of each time slot of the drone, the number of drone antennas, the maximum transmission power allowed by the drone, the flight altitude of the drone, and the flight speed of the drone;
[0045] A radio map construction module, configured to construct a radio map based on a channel gain map of each ground base station, wherein the channel gain map represents a distribution of large-scale gain between the ground base station and the UAV;
[0046] A feasible grid map processing module, used for preprocessing the radio map to obtain a feasible grid map;
[0047] A no-fly zone division module, used to divide the minimum outer circle in the no-fly zone according to the feasible grid map, and obtain the radius and center position information of the minimum outer circle;
[0048] A constraint condition calculation module, used to obtain the channel gain between the UAV and the perception target and the data collection center, and to construct the UAV position constraint condition based on the minimum outer circle;
[0049] The trajectory determination module is used to optimize the UAV position constraints according to the communication constraints to obtain the trajectory and beamforming under the condition of maximizing the radar mutual information with maximum perception.
[0050] Beneficial effects of the present invention:
[0051] The method for designing the integrated trajectory of UAV synaesthesia based on radio map of the present invention converts the irregular non-convex area into the minimum outer circle form of the convex polygonal area by dividing the no-fly zone based on the radio map, so as to constrain the problem. The scheme proposed by the present invention is more operational and practical in complex perception and communication scenarios. In addition, the flight trajectory of the UAV is designed by comprehensively considering the no-fly zone of the radio map, the perception range of the UAV, the constraints of the radar mutual information and the communication information perceived by the UAV, and the perception and communication capabilities.
[0052] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0053] Figure 1 It is a flow chart of a method for designing a trajectory of a UAV synaesthesia integration based on a radio map provided by an embodiment of the present invention;
[0054] Figure 2 The invention provides a radio map-based unmanned aerial vehicle synaesthesia integrated trajectory design system module. DETAILED DESCRIPTION
[0055] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.
[0056] Embodiment 1
[0057] See also Figure 1 , Figure 1 The present invention provides a flow chart of a method for designing a UAV synaesthesia integrated trajectory based on a radio map, including:
[0058] S1. Initialize map parameters, wherein the map parameters include sensor target coordinates, the position of each time slot of the drone, the number of drone antennas, the maximum transmission power allowed by the drone, the flight altitude of the drone, and the flight speed of the drone;
[0059] Specifically, the drone needs to start from the designated starting point and sense M ground sensor targets, while ensuring that the communication information in the next time slot is greater than the radar perception mutual information in the previous time slot, and finally fly to the designated end point without entering the no-fly zone. Our goal is to find the flight path with the maximum radar mutual information while maintaining the minimum outer circle radius between the drone and the circular no-fly zone while meeting the drone's perception accuracy.
[0060] When initializing map parameters, some parameters need to be assigned values. Input ground sensor targets m = {1, 2, ..., M}, the coordinates of the mth ground sensor target are (u mx ,u my ,0), where u m =(u mx ,u my ) is its horizontal coordinate; the position of each time slot of the UAV is (x[n],y[n],H), where q[n]=(x[n],y[n]) is its horizontal position, H is the flight altitude of the UAV, and the time slot n∈N, N={1,2,…,Ν} is the discretization of the perception task cycle T into N time slots, and each time slot must be small enough to ensure that the flight state of the UAV remains approximately unchanged within the time slot.
[0061] In a specific example, for example, assuming that the number of antennas of the drone is M t =16, the maximum transmission power allowed by the drone is P max =1W, the UAV's flight altitude is 100m, and the flight speed is constant at 35m / s.
[0062] S2. construct a radio map based on a channel gain map of each ground base station, wherein the channel gain map represents the distribution of large-scale gain between the ground base station and the UAV;
[0063] In this step, channel gain is introduced to construct a radio map. This map can accurately reflect the large-scale gain distribution between the UAV and the ground base station at a specific flight altitude. The channel gain map of the sth ground base station represents the large-scale gain distribution between the ground base station and the UAV.
[0064] Distribution, use represents the large-scale gain distribution between the UAV at position u and the sth ground base station. Based on this, a method for constructing the SNR map is proposed.
[0065] S3. Preprocess the radio map to obtain a feasible grid map.
[0066] Specifically, the spatial domain of the radio map is discretized into a finite granularity Δ DThe grid is formed to make the channel gain in each grid unit constant; each ground base station is judged to have a finite granularity Δ D Whether the signal between the user positions is blocked by the building determines whether the possible ray pair connecting the UAV and the ground base station is a line-of-sight channel or a non-line-of-sight channel; the channel is modeled according to the channel parameters corresponding to the line-of-sight channel and the non-line-of-sight channel to obtain a feasible grid diagram based on the channel modeling.
[0067] Given the continuous nature of wireless propagation in two-dimensional space, if we try to store large-scale gain information for all possible UAV positions, we will face huge storage challenges, which is impractical in practice. To address this limitation, this step adopts a grid-based approach to discretize the spatial domain into a grid with finite granularity Δ D By choosing an infinitesimally small Δ D To keep the channel gain within each grid cell nearly constant, this discretization method helps to efficiently store and operate large-scale gain information, which is crucial for drone path planning. In such a setting, the channel gain map of the sth ground base station can be expressed as follows:
[0068]
[0069] in represents the large-scale channel gain between the sth ground base station and each position in the discretized two-dimensional space. To represent the set of grid coordinates, where The size of the two-dimensional space is determined by the finite particle size Δ D Therefore, the grid cell at any position It can be expressed as follows:
[0070]
[0071] The symbol Representing a collection Therefore, by storing only X for each ground base station s ×Y s Floating point data can be used to efficiently derive large-scale channel gain values corresponding to different UAV positions.
[0072] Specifically, a ray tracing modeling method is used to determine the relationship between each ground base station and the granularity Δ D The signal between the user's location and the ground base station is blocked by buildings, thereby determining whether the drone is in LoS or non-line-of-sight (NLoS) between this location and the ground base station. Mathematically, this embodiment defines a set to represent the possible ray pairs connecting the drone and the ground base station. Each ray pair p is associated with a specific LoS or NLoS condition, determined by the presence or absence of obstacles on the ray path. The set of 2D coordinates of the buildings is represented as s c Indicates coordinate c = x c ,y c}Height of the ray, z c Represents the height of the building at coordinate c.
[0073] In order to model the channel characteristics, a binary array is introduced Where 0 means that the ray pair p belongs to a non-line-of-sight channel, and 1 means a line-of-sight channel. LoS and NLoS channels are processed using different channel parameters. Therefore, the channel can be modeled as:
[0074]
[0075] In obtaining After that, the channel gain diagram of S base stations can be obtained. When the grid size Δ D When it is small enough, the channel gain in each grid can be considered to remain unchanged, that is,
[0076] S4. Divide the minimum outer circle in the no-fly zone according to the feasible grid map, and obtain the radius and center position information of the minimum outer circle.
[0077] Specifically, the area is obtained from the feasible grid map A large-scale channel gain map between all positions in and all S ground base stations is obtained; the expected SNR value of the corresponding position is calculated according to the large-scale channel gain map; the radio map is convexly decomposed at the regional notch to obtain a number of partitions according to the expected SNR value; the radius and center position information of the minimum outer circle corresponding to each partition that can cover each partition to the greatest extent are found, and the cylindrical area corresponding to each minimum outer circle is determined as a no-fly zone.
[0078] In this step, the goal is to use the defined channel gain map associated with S ground base stations To construct the SNR map, we can estimate the expected SNR value of the UAV at any position. In addition, we assume that the UAV flies at a fixed height H during the mission. u Fly, where the flight altitude satisfies the constraint H min ≤H u ≤H max To avoid collisions with urban buildings, H u Cannot be lower than the minimum height H min, and must not exceed the maximum flight altitude H specified by government regulations. max At the same time, it is assumed that the service range of the drone is a square area with a side length of L, and the SNR map will use the same granularity Δ as the channel gain map D Therefore, the drone has a square service area with a side length of L. The discrete positions on the grid form a D×D grid, where D = L / Δ D The grid can be represented as In this embodiment, D (i,j) represents the (i,j)th position in the grid, which is represented by the following equation:
[0079]
[0080] Due to the discretization of the map, it is possible to construct a service area The expected SNR at each discrete position in the image is obtained to obtain the final SNR map. The SNR map can be represented as a two-dimensional matrix The (i,j)th element of the matrix is represented by the following equation:
[0081]
[0082] To calculate The specific value of needs to obtain the area The large-scale channel gain map between all locations in and all S ground base stations is expressed as Using the channel gain map storage method in the previous step, calculate each
[0083] If the position u D (i, j) is outside the effective coverage of S ground base stations, then its large-scale channel gain will be set to zero. D (i, j) is in the coverage area of a ground base station, and its large-scale channel gain will be determined by the large-scale gain value of the ground base station associated with it. For each location in , the large-scale gains from all ground base stations are compared to determine the ground base station with which it is most strongly associated. The large-scale gain values of the associated ground base stations are used to calculate the expected SNR for that location.
[0084] The radio map no-fly zones are divided according to different SNR values. Considering the scenario where the no-fly zones in the radio map are non-convex, convex decomposition is performed at the notch of the zone, and the non-convex area corresponding to the no-fly zone is represented as a combination of one or several convex polygonal areas.
[0085] The specific method is: when the area of a region is smaller than the required area, scan and divide the straight line in this region until the required area is reached. If the divided straight line still cannot meet the requirement after coinciding with another boundary, move the straight line to the other boundary in parallel until the sum of the areas of the two divided regions is equal to the required area. If the area of this region is larger than the required area, use the concave point as the axis to scan and divide it counterclockwise in one of the convex polygons until the required area is obtained. The area that meets the area requirement will be retained, and the rest will continue to be divided as a whole until n-1 partitions are finally divided.
[0086] After convexly dividing the non-convex no-fly zone of the radio map, it is also necessary to find the radius and center position information of the minimum outer circle that can cover each sub-area to the greatest extent, and define the cylindrical area corresponding to each minimum outer circle as the no-fly zone to replace the real no-fly zone. and Q j , Respectively represent the horizontal coordinate center and radius of each no-fly zone j.
[0087] S5, obtaining the channel gain between the UAV and the sensing target and the data collection center, and constructing the UAV position constraint condition based on the minimum outer circle;
[0088] This embodiment considers a vertically placed uniform linear array (ULA) scenario, and the communication link between the UAV and the data collection center is mainly a LoS link. Therefore, the power gain of the communication link from the UAV to the data collection center in time slot n can be expressed as:
[0089]
[0090] Considering the transmission link and echo link of the radar signal, we can express the radar detection link power gain between the UAV and the sensing node m in time slot n as:
[0091]
[0092] Where Gt and Gc represent the antenna gains of the UAV transmitter and communication receiver; represents the distance from the UAV to the data collection center. Gr represents the antenna gain of the UAV radar receiver, and η represents the radar cross section (RCS) of the target. Indicates the distance from the drone to the perceived target.
[0093] In this embodiment, a H (q[n],u k ) represents the specific steering vector associated with the data collection center in time slot n. The steering vector can be expressed as follows:
[0094] in, and It depends on the signal zenith deviation angle (AoD) from the UAV to the target and the corresponding AoD azimuth. dx and dy are the antenna spacings along the x-axis and y-axis respectively.
[0095] In order to measure the performance of the ISAC system using the same dimension, we decided to use the information rate and radar estimation rate as the performance evaluation indicators of the ISAC system. Due to the dual nature of the ISAC system, it is necessary to take into account the interference within the system. When communication and radar detection are performed simultaneously, the communication receiver may be interfered by the radar signal, and the radar receiver may be affected by the communication signal reflected by the radar target. Therefore, when the UAV performs a mission, the signal-to-noise ratio (SINR) of the communication channel and the radar channel can be expressed as:
[0096]
[0097] Among them, σ 2 is the noise power from the UAV to the data collection center and the perception target. and They represent radar waveform precoding vector and communication beamforming respectively.
[0098] According to Shannon's theorem, the information rate from the UAV to the data collection center and the radar estimation rate of the UAV to the sensing node m in time slot n can be described as:
[0099] R k,com [n] = log2(1 + γ k,com [n])
[0100] R m,rad [n] = log2(1 + γ m,rad [n])
[0101] It should be noted that the target result is to maximize the radar mutual information of the drone in the presence of a no-fly zone based on the radio map and derive its actual trajectory. During the flight of the drone, as long as the drone maintains a certain distance from the equivalent no-fly zone, it can be considered that it has not passed through the no-fly zone. The irregular no-fly zone can be first equivalent to a polygon, and then the no-fly zone can be divided into convex polygons according to the polygon division rules. Finally, the minimum outer bounding distance of each convex polygon is found, and the circle and radius of the outer bounding circle are determined, and then optimization can be performed based on the information obtained.
[0102] Specifically, the no-fly zone is constructed with is the center of the circle, Q j The minimum outer circle of the no-fly zone with a radius of 10000 meters is the position of the drone, which must meet the following conditions:
[0103]
[0104] q[1]=q I ,q[N]=q F
[0105]
[0106] Where V m is the maximum speed, q I and q F They represent the initial position and final position of the UAV respectively.
[0107] To ensure that the perception nodes and data collection center can fully obtain the perception information of the drone, the amount of communication information transmitted to the data collection center in each time slot should be no less than the amount of radar perception information in the previous time slot. In addition, in the entire flight cycle, it is stipulated that the first time slot only performs the task of the perception node, and the last time slot only performs the task of communicating with the data center. Therefore, the communication constraint can be expressed as:
[0108]
[0109] According to the above communication constraints, the UAV position constraints are optimized to obtain the trajectory and beamforming under the condition of maximizing the radar mutual information of maximum perception. For example, the cvx toolbox of Matlab can be used to iteratively optimize the above problem until the value of the radar mutual information reaches the maximum and the operation ends.
[0110] The method for designing the integrated trajectory of UAV synaesthesia based on radio map of the present invention converts the irregular non-convex area into the minimum outer circle form of the convex polygonal area by dividing the no-fly zone based on the radio map, so as to constrain the problem. The scheme proposed by the present invention is more operational and practical in complex perception and communication scenarios. In addition, the flight trajectory of the UAV is designed by comprehensively considering the no-fly zone of the radio map, the perception range of the UAV, the constraints of the radar mutual information and the communication information perceived by the UAV, and the perception and communication capabilities.
[0111] The present invention also provides a UAV synaesthesia integrated trajectory design system based on radio maps, see Figure 2 ,include:
[0112] An initialization module, used to initialize map parameters, wherein the map parameters include sensor target coordinates, the position of each time slot of the drone, the number of drone antennas, the maximum transmission power allowed by the drone, the flight altitude of the drone, and the flight speed of the drone;
[0113] A radio map construction module, configured to construct a radio map based on a channel gain map of each ground base station, wherein the channel gain map represents a distribution of large-scale gain between the ground base station and the UAV;
[0114] A feasible grid map processing module, used for preprocessing the radio map to obtain a feasible grid map;
[0115] A no-fly zone division module, used to divide the minimum outer circle in the no-fly zone according to the feasible grid map, and obtain the radius and center position information of the minimum outer circle;
[0116] A constraint condition calculation module, used to obtain the channel gain between the UAV and the perception target and the data collection center, and to construct the UAV position constraint condition based on the minimum outer circle;
[0117] The trajectory determination module is used to optimize the UAV position constraints according to the communication constraints to obtain the trajectory and beamforming under the condition of maximizing the radar mutual information with maximum perception.
[0118] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification.
[0119] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.
[0120] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.
Claims
1. A method for designing a UAV synaesthesia integrated trajectory based on a radio map, characterized in that: include: Initialize map parameters, wherein the map parameters include sensor target coordinates, the position of each time slot of the drone, the number of drone antennas, the maximum transmission power allowed by the drone, the flight altitude of the drone, and the flight speed of the drone; Building a radio map based on the channel gain map of each ground base station, where the channel gain map represents the distribution of large-scale gain between the ground base station and the UAV; Preprocessing the radio map to obtain a feasible grid map; Dividing the minimum outer circle in the no-fly zone according to the feasible grid map, and obtaining the radius and center position information of the minimum outer circle; Obtaining the channel gain between the UAV and the sensing target and the data collection center, and constructing the UAV position constraint condition based on the minimum outer circle; The UAV position constraints are optimized according to the communication constraints to obtain the trajectory and beamforming under the condition of maximizing the radar mutual information for maximum perception.
2. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 1 is characterized in that: Preprocessing the radio map to obtain a feasible grid map includes: Discretize the spatial domain of the radio map into a space with finite granularity Δ D The grid is formed so that the channel gain in each grid unit is constant; Determine each ground base station with a finite granularity Δ D Whether the signal between the user locations is blocked by buildings to determine whether the possible ray pairs connecting the drone and the ground base station are line-of-sight channels or non-line-of-sight channels; The channels are modeled according to the channel parameters corresponding to the line-of-sight channel and the non-line-of-sight channel to obtain a feasible grid diagram according to the channel modeling.
3. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 2, characterized in that: The channel gain diagram of the ground base station is expressed as: in represents the large-scale channel gain between the sth ground base station and each location in the discretized two-dimensional space, represents a set of grid coordinates, where The size of the two-dimensional space is determined by the finite particle size Δ D Determine, the grid cell at any position It is expressed as: in, Representing a collection The reference position in .
4. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 2, characterized in that: The channel model is: in, 0 means that the ray pair p belongs to the non-line-of-sight channel, 1 means the line-of-sight channel, and the two-dimensional coordinate set of the building is expressed as s c Represents the coordinate c={x c ,y c }Height of the ray, z c Represents the height of the building at coordinate c.
5. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 1, characterized in that: Dividing the minimum outer circle in the no-fly zone according to the feasible grid map, and obtaining the radius and center position information of the minimum outer circle, including: Obtain the region from the feasible grid graph Large-scale channel gain maps between all locations in and all S ground base stations; Calculating an expected SNR value at a corresponding position according to the large-scale channel gain map; Convexly decomposing the radio map at the region notch to obtain a plurality of partitions according to the expected SNR value; The radius and center position information of the minimum outer circle corresponding to each partition that can cover each partition to the greatest extent are found, and the cylindrical area corresponding to each minimum outer circle is determined as a no-fly zone.
6. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 5, characterized in that: Convex decomposition is performed at the notch of the region to obtain several partitions including: When the area of a region is smaller than the required area, scan and divide the straight line in this region until the required area is reached. If the divided straight line still cannot meet the requirement after coinciding with another boundary, move the straight line to the other boundary in parallel until the sum of the areas of the two divided regions is equal to the required area. If the area of this region is larger than the required area, use the concave point as the axis to scan and divide it counterclockwise in one of the convex polygons until the required area is obtained. The area that meets the area requirement will be retained, and the rest will continue to be divided as a whole until n-1 partitions are finally divided.
7. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 1, characterized in that: The power gain of the communication link between the UAV and the data collection center in time slot n can be expressed as: The power gain of the radar detection link between the UAV and the sensing node m in time slot n is expressed as: Where Gt and Gc represent the antenna gains of the UAV transmitter and communication receiver; represents the distance from the UAV to the data collection center. Gr represents the antenna gain of the UAV radar receiver, and η represents the radar cross section (RCS) of the target; Indicates the distance from the drone to the perceived target.
8. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 1, characterized in that: The UAV position constraints are: q[1]=q I ,q[N]=q F Where V m is the maximum speed, q I and q F They represent the initial position and final position of the UAV, Q j The radius of the no-fly zone is the minimum outer circle.
9. The method for designing a UAV synaesthesia integrated trajectory based on a radio map according to claim 1, characterized in that: The communication constraints are: Among them, R m，rad [] represents the radar estimation rate of the drone to the sensing node m in time slot n, R k，com [] represents the information rate from the UAV to the data collection center.
10. A UAV synaesthesia integrated trajectory design system based on radio maps, characterized in that: include: An initialization module, used to initialize map parameters, wherein the map parameters include sensor target coordinates, the position of each time slot of the drone, the number of drone antennas, the maximum transmission power allowed by the drone, the flight altitude of the drone, and the flight speed of the drone; A radio map construction module, configured to construct a radio map based on a channel gain map of each ground base station, wherein the channel gain map represents a distribution of large-scale gain between the ground base station and the UAV; A feasible grid map processing module, used for preprocessing the radio map to obtain a feasible grid map; A no-fly zone division module, used to divide the minimum outer circle in the no-fly zone according to the feasible grid map, and obtain the radius and center position information of the minimum outer circle; A constraint condition calculation module is used to obtain the channel gain between the UAV and the perception target and the data collection center, and to construct the UAV position constraint condition based on the minimum outer circle; The trajectory determination module is used to optimize the UAV position constraints according to the communication constraints to obtain the trajectory and beamforming under the condition of maximizing the radar mutual information with maximum perception.