Three-dimensional meteorological-navigation fusion decision system for urban low-altitude traffic

Abstract

The present invention relates to the field of low-altitude traffic management systems, and specifically to a three-dimensional weather-navigation fusion decision-making system for urban low-altitude traffic, comprising: a region division module including an information collection unit and a region separation unit; wherein the information collection unit is used to obtain meteorological information, base station operating information and basic building information of the target area in real time; the region separation unit is used to divide the target area into several sub-areas based on the meteorological information, base station operating information and basic building information; the three-dimensional positioning module includes an initial positioning unit and a position correction unit; after calculating the current actual position of the drone, the present invention controls the drone to return to the set route, which can effectively prevent the drone from colliding with other buildings, and ensure that all drones in the target area can operate simultaneously and in an orderly manner, avoiding collisions between drones due to low positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of low-altitude traffic management systems, and in particular to a three-dimensional meteorological-navigation fusion decision-making system for urban low-altitude traffic. Background Art

[0002] Currently, common drone navigation systems determine the drone's location based on satellite positioning methods such as the Beidou Positioning System and guide the drone to fly along a set route. However, satellite positioning methods can only locate drones at the meter level, and their accuracy cannot meet the requirements for safe and stable operation of drones in low-altitude environments in cities. They are prone to colliding with surrounding buildings, and with the increasing number of drones, they are also prone to collisions between drones. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a three-dimensional meteorological-navigation fusion decision-making system for urban low-altitude traffic, which solves the technical problems in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The three-dimensional meteorological-navigation fusion decision-making system for urban low-altitude traffic includes:

[0006] A region division module, comprising an information collection unit and a region separation unit;

[0007] The information collection unit is used to obtain meteorological information, base station operation information and building infrastructure information of the target area in real time; the area segmentation unit is used to segment the target area into a plurality of sub-areas based on the meteorological information, base station operation information and building infrastructure information;

[0008] A three-dimensional positioning module, comprising an initial positioning unit and a position correction unit;

[0009] The initial positioning unit is used to calculate the initial position of the target UAV based on the information transmitted to the target UAV by the surrounding base stations; the position correction unit is used to obtain the motion speed vector of the target UAV. The initial position is corrected based on the initial position to obtain the corrected position;

[0010] The navigation control module is used to set a route and control the target UAV to return to the route according to the actual position.

[0011] Furthermore, meteorological information includes rainfall and fog concentration;

[0012] Base station operating information includes the three-dimensional location of the base station and the signal transmission frequency of the base station;

[0013] Basic building information includes the building's location, size, and materials.

[0014] Furthermore, the steps of dividing the target area into several sub-areas are as follows:

[0015] S121. Preset origin 0 and construct a test coordinate system based on basic building information. The test coordinate system is used to represent the three-dimensional position and transmission frequency of each base station in the target area, and the position, size, and material of each building;

[0016] S122, uniformly cutting the test coordinate system along the x-, y-, and z-axis directions to form a number of divided blocks, with a corner of each divided block being a test point;

[0017] S123. Select a test point and calculate the predicted signal receiving power of each base station. ;

[0018] S124, repeat the above steps until the predicted signal receiving power of each base station corresponding to all test points is obtained ;

[0019] S125: preset standard received signal power, select a test point and determine the predicted signal received power of each base station Whether it is higher than the standard receiving signal power;

[0020] If so, a connecting line is constructed to connect the test point and the base station;

[0021] If not, change other test points;

[0022] S126. Divide the target area into several sub-areas according to the connection line of each base station.

[0023] Furthermore, in step S123, the following steps are specifically included:

[0024] S1231. Calculate the free path loss of each base station transmitting signal to the target UAV. , and its calculation formula is:

[0025]

[0026] Where, represents the distance between the i-th base station and the test point; represents the signal transmission frequency of the i-th base station;

[0027] S1232. Calculate the comprehensive building loss from the test point to each base station ;

[0028] S1233. Calculate meteorological attenuation loss based on meteorological information ;

[0029] S1234, based on free path loss , meteorological attenuation loss and comprehensive building losses Calculate the predicted signal received power , and its calculation formula is:

[0030]

[0031] Where, represents the signal transmission power of the i-th base station.

[0032] Furthermore, the comprehensive building loss The specific calculation steps are as follows:

[0033] S12321. Calculate the signal attenuation constant for each building based on the material of each building. , and its calculation formula is:

[0034]

[0035] Where, represents the magnetic permeability in vacuum; represents the electrical conductivity of the jth building material;

[0036] S12322, according to the attenuation constant Calculate the single penetration loss of the signal through each building , and its calculation formula is:

[0037]

[0038] Where, represents the base of natural logarithms;

[0039] S12323, based on single penetration loss Calculate comprehensive building losses , and its calculation formula is:

[0040]

[0041] Where, Represents the total number of buildings from the test point to the i-th base station.

[0042] Furthermore, the meteorological attenuation loss The specific calculation steps are as follows:

[0043] S12331. Calculate rainfall loss based on rainfall , and its calculation formula is:

[0044]

[0045] Where, Indicates rainfall; and Respectively express about The first and second empirical coefficients of ;

[0046] S12332. Calculate fog loss based on fog concentration , and its calculation formula is:

[0047]

[0048] Where, Indicates the density of liquid water in the fog; and Respectively express about The first and second empirical coefficients of ;

[0049] S12333, based on rainfall losses and fog losses Calculating meteorological attenuation losses , and its calculation formula is:

[0050]

[0051] Where, Represents the meteorological attenuation loss from the test point to the i-th base station.

[0052] Furthermore, the specific calculation steps of the initial position of the target UAV are as follows:

[0053] S211. Obtain satellite positioning information of the target UAV;

[0054] S212, selecting a sub-area including satellite positioning information, and using the corresponding base station as a target base station;

[0055] S213. Calculate the initial position of the target UAV based on the 5G signal transmitted by each target base station.

[0056] Furthermore, the specific calculation steps of the initial position of the target UAV are as follows:

[0057] S2131. Calculate the arrival time difference of the target UAV signal to each base station , and its calculation formula is:

[0058]

[0059] Where, and They represent the time when the target UAV sends the signal to the i-th and j-th target base stations respectively;

[0060] S2132, based on arrival time difference Calculate base station distance difference , and its calculation formula is:

[0061]

[0062] Where, represents the speed of light;

[0063] S2133. Assume that the three-dimensional coordinates of the target UAV in the test coordinate system are , and construct the distance equation, which is expressed as:

[0064]

[0065] Where, and Respectively represent the three-dimensional coordinates of the i-th and j-th target base stations in the test coordinate system;

[0066] S2134, construct a function to minimize the error , whose expression is:

[0067]

[0068] S2135, according to the minimization error function Solve the distance equation to get the initial position.

[0069] Furthermore, in the position correction unit, the following steps are specifically included:

[0070] S221. Obtain the delay time required for the target UAV to calculate the initial position ;

[0071] S222: Delay time is advanced from the current moment ;

[0072] S223, according to the delay time and the velocity vector Calculate the current corrected position of the target drone using the following formula:

[0073]

[0074] Where, represents the initial position of the target UAV; Indicates the corrected position of the target drone.

[0075] Compared with the existing technology, the present invention provides a three-dimensional weather-navigation fusion decision-making system for urban low-altitude traffic, which has the following beneficial effects:

[0076] 1. After calculating the current actual position of the drone, the present invention controls the drone to return to the set route, which can effectively prevent the drone from colliding with other buildings and ensure that all drones in the target area can operate in an orderly manner at the same time, avoiding collisions between drones due to low positioning accuracy.

[0077] 2. In the position correction unit of the present invention, the influence of the movement speed of the UAV on the actual signal reception time is taken into consideration, and the position of the UAV is corrected accordingly. Compared with other calculation methods, the position of the UAV obtained by the present invention is more accurate, so that the actual position of the UAV can be adjusted accordingly in the later stage.

[0078] 3. In the initial positioning unit of the present invention, base stations with qualified surrounding signal power can be selected according to the satellite positioning information of the drone, and the precise position of the drone can be calculated based on these base stations. Compared with other calculation methods, base stations with unaffected signals can be automatically selected, and the three-dimensional coordinates of the drone calculated using the 5G signals transmitted by these base stations are more accurate.

[0079] 4. In the area segmentation unit of the present invention, the influence of buildings in the city on the signal power of each base station is taken into account, and the signal receiving power of each position around each base station relative to the base station is calculated, so as to divide the target area into several sub-areas. When the corresponding base station is selected to determine the position of the drone in the later stage, the positioning accuracy of the drone can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0081] Figure 1 This is a block diagram of the three-dimensional weather-navigation fusion decision-making system for urban low-altitude traffic of the present invention.

[0082] Figure 2 It is a simple logic diagram of the present invention. DETAILED DESCRIPTION

[0083] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0084] Those skilled in the art will appreciate that all or part of the steps in the following embodiments can be accomplished by instructing related hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] Drones are being used more and more widely. Currently, some drones with automatic navigation and autonomous driving functions have appeared. Their navigation and positioning components usually include positioning components, inertial measurement components, barometers, magnetometers and MCUs. These components work together to achieve precise positioning and automatic navigation of drones. However, these components may cause errors due to weather and surrounding environment. The environment in urban low-altitude areas is more complex, and there may be a large number of buildings, telephone poles and other drones. If the error is large, it may cause the drone to collide with the above objects. For this reason, Figure 1-Figure 2 As shown, the present invention proposes a three-dimensional meteorological-navigation fusion decision system for urban low-altitude traffic, including: an area division module, a three-dimensional positioning module and a navigation control module;

[0086] The area division module includes an information collection unit and an area separation unit;

[0087] Among them, the information collection unit is used to obtain meteorological information, base station working information and building basic information of the target area in real time; specifically, since the satellite signal is too far away from the low-altitude drone, the signal will be delayed and easily interfered by various factors such as weather and atmospheric conditions, so the accuracy of using satellite to locate the three-dimensional coordinates of the drone is low, and its horizontal positioning error is usually around 3-10 meters, and the vertical positioning error is usually around 10-30 meters. In the complex urban low-altitude environment, such accuracy may cause the drone to collide with other objects. With the popularization of 5G signals in cities, it is more convenient and faster to locate drones through 5G signals. However, 5G signals are easily affected by weather and buildings. For this reason, meteorological information includes rainfall and fog concentration;

[0088] Base station operating information includes the three-dimensional location of the base station and the signal transmission frequency of the base station;

[0089] Basic building information includes the building's location, size, and material. Specifically, rainfall, precipitation, temperature, and humidity can be obtained directly through the target area's meteorological platform. The three-dimensional coordinates of the base station can be obtained through operator data. The signal transmission frequency is measured by the spectrum analyzer in the base station. The building's location, size, and material can be obtained through the geographic information system (GIS).

[0090] The region segmentation unit is used to divide the target area into several sub-regions based on meteorological information, base station operating information, and building infrastructure information. Specifically, since the distance between adjacent base stations in cities is generally around 500-1000 meters, the tall buildings in cities are relatively dense and complex, and the weather is also changeable, these factors will interfere with 5G signals. Therefore, the steps for dividing the target area into several sub-regions are as follows:

[0091] S121. Preset origin 0 and construct a test coordinate system based on basic building information. The test coordinate system is used to represent the three-dimensional position and transmission frequency of each base station in the target area, and the position, size, and material of each building;

[0092] S122, uniformly cutting the test coordinate system along the x-, y-, and z-axis directions to form a number of divided blocks, with a corner of each divided block being a test point;

[0093] S123. Select a test point and calculate the predicted signal receiving power of each base station. Specifically, since signals will be lost when passing through buildings, and 5G signals have weaker penetration than other lower-frequency signals, it is necessary to calculate the received signal power of the drone corresponding to each base station based on the distribution of buildings in the city. In addition, weather conditions will also affect 5G signals. Therefore, in step S123, the following steps are specifically included:

[0094] S1231. Calculate the free path loss of each base station transmitting signal to the target UAV. , and its calculation formula is:

[0095]

[0096] Where, represents the distance between the i-th base station and the test point; represents the signal transmission frequency of the i-th base station;

[0097] S1232. Calculate the comprehensive building loss from the test point to each base station ; Specifically, comprehensive building losses The specific calculation steps are as follows:

[0098] S12321. Calculate the signal attenuation constant for each building based on the material of each building. , and its calculation formula is:

[0099]

[0100] Where, represents the magnetic permeability in vacuum; represents the electrical conductivity of the j-th building material; in the present invention, ;

[0101] S12322, according to the attenuation constant Calculate the single penetration loss of the signal through each building , and its calculation formula is:

[0102]

[0103] Where, represents the base of natural logarithms;

[0104] S12323, based on single penetration loss Calculate comprehensive building losses , and its calculation formula is:

[0105]

[0106] Where, Represents the total number of buildings from the test point to the i-th base station.

[0107] S1233. Calculate meteorological attenuation loss based on meteorological information ; Specifically, meteorological attenuation loss The specific calculation steps are as follows:

[0108] S12331. Calculate rainfall loss based on rainfall , and its calculation formula is:

[0109]

[0110] Where, Indicates rainfall; and Respectively express about The first and second empirical coefficients; In the present invention, and 0.075 and 0.86 respectively;

[0111] S12332. Calculate fog loss based on fog concentration , and its calculation formula is:

[0112]

[0113] Where, Indicates the density of liquid water in the fog; and Respectively express about The first and second empirical coefficients; In the present invention, and 0.001 and 2.4 respectively;

[0114] S12333, based on rainfall losses and fog losses Calculating meteorological attenuation losses , and its calculation formula is:

[0115]

[0116] Where, Represents the meteorological attenuation loss from the test point to the i-th base station.

[0117] S1234, based on free path loss , meteorological attenuation loss and comprehensive building losses Calculate the predicted signal received power , and its calculation formula is:

[0118]

[0119] Where, represents the signal transmission power of the i-th base station.

[0120] S124, repeat the above steps until the predicted signal receiving power of each base station corresponding to all test points is obtained ;

[0121] S125: preset standard received signal power, select a test point and determine the predicted signal received power of each base station Whether it is higher than the standard receiving signal power;

[0122] If so, a connecting line is constructed to connect the test point and the base station;

[0123] If not, change other test points;

[0124] S126. Divide the target area into several sub-areas based on the connection lines of each base station. Specifically, the target area can be divided into several sub-areas using the Occupancy Networks model. First, select some base stations and calculate the signal receiving power of each test point around them, and manually draw the corresponding sub-areas. Then, use this as training data to train the Occupancy Networks model to obtain an Occupancy Networks model that can be put into use. Finally, use the Occupancy Networks model to divide the sub-areas. In addition, each sub-area represents the effective signal strength range of a base station.

[0125] In the area segmentation unit of the present invention, the influence of buildings in the city on the signal power of each base station is taken into account, and the signal receiving power of each position around each base station relative to the base station is calculated, so as to divide the target area into several sub-areas. When the corresponding base station is selected based on it in the later stage to determine the position of the drone, the positioning accuracy of the drone can be effectively improved.

[0126] The three-dimensional positioning module includes an initial positioning unit and a position correction unit;

[0127] Among them, the initial positioning unit is used to calculate the initial position of the target drone based on the information transmitted to the target drone by the surrounding base stations. Specifically, since the direct use of satellites to locate the drone is affected by a variety of factors due to the long distance, the positioning accuracy of the drone is low in this case, making it difficult to adapt to the complex urban low-altitude environment. It can only be used for auxiliary positioning. Therefore, the specific steps for calculating the initial position of the target drone are as follows:

[0128] S211. Obtain satellite positioning information of the target UAV. Specifically, the satellite positioning information uses satellite positioning technology to obtain the three-dimensional coordinate position of the target UAV in the city. Although its accuracy is relatively low, it can be used to select a base station in the corresponding sub-area.

[0129] S212, selecting a sub-area including satellite positioning information, and using the corresponding base station as a target base station;

[0130] S213. Calculate the initial position of the target UAV based on the 5G signal transmitted by each target base station. Specifically, the specific steps for calculating the initial position of the target UAV are as follows:

[0131] S2131. Calculate the arrival time difference of the target UAV signal to each base station , and its calculation formula is:

[0132]

[0133] Where, and They represent the time when the target UAV sends the signal to the i-th and j-th target base stations respectively;

[0134] S2132, based on arrival time difference Calculate base station distance difference , and its calculation formula is:

[0135]

[0136] Where, represents the speed of light; in the present invention, represents the distance difference between the target UAV and the i-th and j-th target base stations;

[0137] S2133. Assume that the three-dimensional coordinates of the target UAV in the test coordinate system are , and construct the distance equation, which is expressed as:

[0138]

[0139] Where, and Respectively represent the three-dimensional coordinates of the i-th and j-th target base stations in the test coordinate system;

[0140] S2134, construct a function to minimize the error , whose expression is:

[0141]

[0142] S2135, according to the minimization error function Solve the distance equation to get the initial position.

[0143] In the initial positioning unit of the present invention, base stations with qualified surrounding signal power can be selected according to the satellite positioning information of the drone, and the precise position of the drone can be calculated based on these base stations. Compared with other calculation methods, base stations whose signals are not affected can be automatically selected, and the three-dimensional coordinates of the drone calculated using the 5G signals transmitted by these base stations have higher accuracy.

[0144] The position correction unit is used to obtain the motion velocity vector of the target UAV The initial position is corrected based on the time delay to obtain the corrected position. Specifically, if the drone is moving at high speed or accelerating, when the base station receives the request signal from the drone and sends the 5G signal, the drone is likely to have moved a certain distance. In this case, even a small time delay will lead to a significant position estimation error. Therefore, the position correction unit specifically includes the following steps:

[0145] S221. Obtain the delay time required for the target UAV to calculate the initial position Specifically, it is obtained based on the technical statistics of the drone manufacturer;

[0146] S222: Delay time is advanced from the current moment ;

[0147] S223, according to the delay time and the velocity vector Calculate the current corrected position of the target drone using the following formula:

[0148]

[0149] Where, represents the initial position of the target UAV; Indicates the corrected position of the target drone.

[0150] In the position correction unit of the present invention, the influence of the movement speed of the UAV on the actual signal reception time is taken into consideration, and the position of the UAV is corrected accordingly. Compared with other calculation methods, the position of the UAV obtained by the present invention is more accurate, so that the actual position of the UAV can be adjusted accordingly in the later stage.

[0151] The navigation control module is used to set a route and control the target drone to return to the route based on its actual position. Specifically, after calculating the drone's current actual position, the present invention controls the drone to return to the set route, which can effectively prevent the drone from colliding with other buildings and ensure that all drones in the target area can operate in an orderly manner at the same time, avoiding collisions between drones due to low positioning accuracy.

[0152] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A three-dimensional meteorological-navigation fusion decision-making system for urban low-altitude traffic, characterized by: include: A region division module, comprising an information collection unit and a region separation unit; The information collection unit is used to obtain meteorological information, base station operation information and building infrastructure information of the target area in real time; Meteorological information includes rainfall and fog concentration; Base station operating information includes the three-dimensional location of the base station and the signal transmission frequency of the base station; Basic building information includes the building's location, size, and materials; The region segmentation unit is used to segment the target area into a plurality of sub-regions according to meteorological information, base station operation information and building infrastructure information; The steps for dividing the target area into several sub-areas are as follows: S121. Preset origin 0 and construct a test coordinate system based on basic building information. The test coordinate system is used to represent the three-dimensional position and transmission frequency of each base station in the target area, and the position, size, and material of each building; S122, uniformly cutting the test coordinate system along the x-, y-, and z-axis directions to form a number of divided blocks, with a corner of each divided block being a test point; S123. Select a test point and calculate the predicted signal receiving power of each base station. ; S124, repeat the above steps until the predicted signal receiving power of each base station corresponding to all test points is obtained ; S125: preset standard received signal power, select a test point and determine the predicted signal received power of each base station Whether it is higher than the standard receiving signal power; If so, a connecting line is constructed to connect the test point and the base station; If not, change other test points; S126, dividing the target area into a plurality of sub-areas according to the connection line of each base station; A three-dimensional positioning module, comprising an initial positioning unit and a position correction unit; The initial positioning unit is used to calculate the initial position of the target UAV based on the information transmitted to the target UAV by the surrounding base stations; The specific calculation steps of the initial position of the target UAV are as follows: S211. Obtain satellite positioning information of the target UAV; S212, selecting a sub-area including satellite positioning information, and using the corresponding base station as a target base station; S213. Calculate the initial position of the target UAV based on the 5G signal transmitted by each target base station; The position correction unit is used to obtain the motion speed vector of the target UAV The initial position is corrected based on the initial position to obtain the corrected position; The navigation control module is used to set a route and control the target UAV to return to the route according to the actual position.

2. The three-dimensional weather-navigation fusion decision system according to claim 1, characterized in that: In step S123, the following steps are specifically included: S1231. Calculate the free path loss of each base station transmitting signal to the target UAV. , and its calculation formula is: ； Where, represents the distance between the i-th base station and the test point; represents the signal transmission frequency of the i-th base station; S1232. Calculate the comprehensive building loss from the test point to each base station ; S1233. Calculate meteorological attenuation loss based on meteorological information ; S1234, based on free path loss , meteorological attenuation loss and comprehensive building losses Calculate the predicted signal received power , and its calculation formula is: ； Where, represents the signal transmission power of the i-th base station.

3. The three-dimensional weather-navigation fusion decision system according to claim 2, characterized in that: Comprehensive building losses The specific calculation steps are as follows: S12321. Calculate the signal attenuation constant for each building based on the material of each building. , and its calculation formula is: ； Where, represents the magnetic permeability in vacuum; represents the electrical conductivity of the jth building material; S12322, according to the attenuation constant Calculate the single penetration loss of the signal through each building , and its calculation formula is: ； Where, represents the base of natural logarithms; S12323, based on single penetration loss Calculate comprehensive building losses , and its calculation formula is: ； Where, Represents the total number of buildings from the test point to the i-th base station.

4. The three-dimensional weather-navigation fusion decision system according to claim 3, characterized in that: Weather attenuation loss The specific calculation steps are as follows: S12331. Calculate rainfall loss based on rainfall , and its calculation formula is: ； Where, Indicates rainfall; and Respectively express about The first and second empirical coefficients; In the present invention, and They are S12332. Calculate fog loss based on fog concentration , and its calculation formula is: ； Where, Indicates the density of liquid water in the fog; and Respectively express about The first and second empirical coefficients; In the present invention, and They are S12333, based on rainfall losses and fog losses Calculating meteorological attenuation losses , and its calculation formula is: ； Where, Represents the meteorological attenuation loss from the test point to the i-th base station.

5. The three-dimensional weather-navigation fusion decision system according to claim 1, characterized in that: The specific calculation steps of the initial position of the target UAV are as follows: S2131. Calculate the arrival time difference of the target UAV signal to each base station , and its calculation formula is: ； Where, and They represent the time when the target UAV sends the signal to the i-th and j-th target base stations respectively; S2132, based on arrival time difference Calculate base station distance difference , and its calculation formula is: ； Where, represents the speed of light; S2133. Assume that the three-dimensional coordinates of the target UAV in the test coordinate system are , and construct the distance equation, which is expressed as: ； Where, and Respectively represent the three-dimensional coordinates of the i-th and j-th target base stations in the test coordinate system; S2134, construct a function to minimize the error , whose expression is: ； S2135, according to the minimization error function Solve the distance equation to get the initial position.

6. The three-dimensional weather-navigation fusion decision system according to claim 1, characterized in that: In the position correction unit, the following steps are specifically included: S221. Obtain the delay time required for the target UAV to calculate the initial position ; S222: Delay time is advanced from the current moment ; S223, according to the delay time and the velocity vector Calculate the current corrected position of the target drone using the following formula: ； Where, represents the initial position of the target UAV; Indicates the corrected position of the target drone.

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