An air-ground integrated UAV countermeasure precise positioning method

Abstract

The present invention relates to the field of image data processing technology, and in particular to an air-to-ground integrated UAV countermeasure precision positioning method, comprising: acquiring a panoramic image and wind speed of an active airspace in real time to determine the position information of the UAV at the time of acquisition; determining the UAV's heading angle at the current acquisition time, thereby obtaining the UAV's flight stability value at the current acquisition time; determining the UAV's flight reliability at the current acquisition time; selecting a fitting or motion model method based on the magnitude of the flight reliability to obtain the UAV's position information at the next acquisition time; transmitting the position information at the next acquisition time to a countermeasure device to complete the UAV countermeasure precision positioning. The present invention calculates the UAV's heading angle and flight stability value to determine the flight reliability, thereby selecting an appropriate method, effectively compensating for the speed fluctuation problem caused by wind speed changes that is not considered in traditional fitting, and improving the accuracy of UAV position prediction.

Description

Technical Field

[0001] The present invention relates to the field of image data processing technology. More specifically, the present invention relates to an air-to-ground integrated UAV countermeasure precision positioning method. Background Art

[0002] With the rapid development of drone technology, their applications are increasing across various fields, including aerial photography, logistics, agricultural spraying, and environmental monitoring. However, the widespread use of drones also presents a series of security risks and regulatory challenges. Therefore, how to effectively detect, track, and counter unauthorized drones has become a pressing issue.

[0003] The patent application document with publication number CN117316000A discloses a drone countermeasure method, system, equipment and storage medium. The detection terminal collects the current status information of the drone intrusion source and transmits the flight trajectory to the analysis terminal; after receiving the flight trajectory, the analysis terminal performs fitting processing on it and calculates the intrusion mission model; based on the intrusion mission model and the currently collected real-time flight data, a countermeasure instruction is generated and transmitted; the countermeasure terminal executes the countermeasure process on the drone according to the countermeasure instruction, thereby achieving timely and accurate strikes on the target.

[0004] However, in the process of drone countermeasures, accurately and in real time capturing the drone's future position information is crucial to ensuring that countermeasure equipment (such as jamming signal transmitters) can efficiently and accurately lock onto targets. Traditional fitting algorithms rely on a series of known drone motion position points for fitting to obtain future estimated position information; but in actual scenarios, since the drone's environment is susceptible to strong wind interference, the drone's actual speed will be affected by changes in wind speed, resulting in unpredictable fluctuations in the drone's speed, which in turn reduces the accuracy of the future position information predicted by the traditional fitting algorithm, severely weakening the positioning accuracy of the drone countermeasures, thereby affecting the subsequent strike effect. Summary of the Invention

[0005] In order to solve the problem that the traditional fitting algorithm does not take into account that the environment in which the drone is located is susceptible to strong wind interference, the actual speed of the drone will be affected by the change in wind speed, resulting in unpredictable fluctuations in the drone speed, which in turn reduces the accuracy of the future position information predicted by the traditional fitting algorithm, seriously weakens the positioning accuracy of the drone countermeasure, and thus affects the subsequent strike effect, the present invention proposes an air-ground integrated drone countermeasure precise positioning method, which includes the following steps:

[0006] The system acquires a panoramic image and wind speed of the active airspace in each frame in real time, determines the collection time in each frame based on a preset collection interval, and locates and identifies the UAV in the panoramic image at the collection time using a pre-trained target detection model to obtain the position information of the UAV at the collection time. The system determines the heading angle of the UAV at the current collection time based on the position information of the UAV at the previous collection time and the current collection time. The system determines the flight stability value of the UAV at the current collection time based on the difference between the heading angle of the UAV at the current collection time and the average of the heading angles of the UAV at the previous collection time and the standard deviation of the heading angles of the UAV at the current collection time and the previous collection time. The system corrects the flight stability value using the range of wind speeds of all frames in the interval from the previous collection time to the current collection time to obtain a corrected flight stability value of the UAV at the current collection time. The system determines the flight reliability of the UAV at the current collection time based on the flight stability value and the corrected flight stability value. The system selects a fitting or motion model method based on the magnitude of the flight reliability to obtain the position information of the UAV at the next collection time. The system transmits the position information of the UAV at the next collection time to the countermeasure device to complete the precise positioning of the UAV.

[0007] By acquiring panoramic images and wind speed data in the active airspace in real time, combined with the calculation of the UAV's flight heading angle and flight stability value, it is possible to effectively compensate for the speed fluctuation problem caused by wind speed changes that is not considered in traditional fitting algorithms, thereby improving the accuracy of UAV position prediction; by correcting the flight stability value based on the extreme difference in wind speed, the flight stability during the countermeasure process can be dynamically adjusted to ensure that reliable position information can be obtained even in an environment with large wind speed changes, thereby improving the success rate of countermeasures; by analyzing the changes and stability of the UAV's heading angle, combined with the calculation of flight reliability, it is possible to select suitable fitting or motion models in different environments, thereby ensuring the positioning and strike effect of the UAV; based on the comparative analysis of the flight stability value and the corrected flight stability value, the flight status of the UAV can be accurately evaluated, thereby determining the appropriate positioning method and providing accurate position information for the countermeasure equipment, thereby enhancing the effect of the UAV countermeasure and optimizing the efficiency and success rate of subsequent strikes.

[0008] Furthermore, the target detection model adopts the YOLO series model.

[0009] Furthermore, the positioning identification is performed to obtain the position information of the drone at the time of collection, including: marking the minimum rectangular frame of the drone in the panoramic image at the time of collection, and generating the minimum rectangular frame position information of the drone, the minimum rectangular frame position information is the minimum rectangular frame of each position of the drone in the panoramic image at the time of collection. 、 The coordinate value of the axis; all the positions of the smallest rectangular frame of the drone in the panoramic image at the time of acquisition are 、 The mean value of the coordinate value of the axis is used as the collection time of the drone 、 The coordinate value of the axis is used to obtain the position information of the drone at the time of collection.

[0010] Furthermore, the driving heading angle satisfies:

[0011] Where, is the heading angle of the UAV at the current collection moment, and The current collection time of the UAV is Axis and The coordinate values ​​of the axis, and They are the previous collection time of the drone Axis and The coordinate values ​​of the axis, is the inverse tangent function, is the absolute value symbol.

[0012] By using position differences and the inverse tangent function, the heading angle calculation process is simple and efficient, reducing computational complexity. The heading angle can accurately reflect the UAV's flight direction and trajectory, improving the accuracy of position prediction. The absolute value sign effectively suppresses error fluctuations, ensuring accurate heading angle calculation. Analysis of the heading angle helps to more accurately assess the stability of the UAV's flight and assist in subsequent countermeasure decisions.

[0013] Furthermore, the flight stability value satisfies:

[0014] Where, is the flight stability value of the UAV at the current collection moment, is the heading angle of the UAV at the current collection moment, For the drone before The heading angle at the collection moment, is the number of previous collection moments, is the standard deviation of the UAV’s heading angle between the current acquisition moment and several previous acquisition moments, is the natural exponential function, is the absolute value symbol.

[0015] By calculating the deviation between the current heading angle and the historical heading angle, the flight stability of the drone can be evaluated in real time to help determine whether there are heading fluctuations or anomalies. When the flight stability value is low, it indicates that the drone's flight is unstable, and this information can be used to provide a basis for subsequent countermeasures and take timely intervention measures. Through historical data analysis, unstable trends in flight can be discovered in advance, providing early warnings for potential abnormal behaviors and enhancing the accuracy of countermeasures.

[0016] Furthermore, the flight reliability satisfies:

[0017] Where, is the flight reliability of the UAV at the current collection moment, is the flight stability value of the UAV at the current collection moment, and are the maximum and minimum wind speeds of all frames from the previous acquisition moment to the current acquisition moment, is the standard normalization function.

[0018] By monitoring flight stability values ​​and wind speed changes, it is possible to effectively identify fluctuations in the drone's flight stability; taking into account wind speed changes, it helps analyze the impact of the external environment on flight stability; and it monitors reliability in real time, accurately determines flight anomalies, and optimizes countermeasures.

[0019] Furthermore, the next collection time location information satisfies:

[0020] Where, The next collection moment location information of the drone, is the flight reliability of the UAV at the current collection moment, is the average of the UAV’s flight reliability at several previous acquisition moments, is the fitting value of the predicted position information of the UAV at the next collection moment, and The current collection time of the UAV is Axis and The coordinate values ​​of the axis, is the ratio of the Euclidean distance between the UAV’s location information from the previous acquisition moment to the current acquisition moment to the preset acquisition interval. is the heading angle of the UAV at the current collection moment, is the preset collection interval, is the cosine function, is a sine function.

[0021] By determining whether to use fitting values ​​or predict the next position based on the motion model according to the current flight reliability, the position of the drone at the next collection moment can be predicted more accurately; when the reliability is high, the fitting value is used to ensure accuracy, and when the reliability is low, the kinematic model is relied upon to adapt to possible flight instability; based on the comparison between the flight reliability and the mean, the appropriate prediction method is flexibly selected, which can provide more appropriate position information under different flight environments and stabilities, thereby responding to environmental changes and flight fluctuations; by reasonably predicting the drone's position, accurate flight path information can be provided to the countermeasure system, potential flight anomalies can be identified in advance, and effective intervention and countermeasures can be facilitated.

[0022] Furthermore, the fitting value prediction method is: using the least squares method to fit the position information of the current collection moment of the drone with the position information of several previous collection moments, and obtain the fitting value of the position information of the next collection moment of the drone.

[0023] The present invention has the following beneficial effects:

[0024] The traditional fitting algorithm does not take into account the interference of strong winds, which reduces the accuracy of the prediction of the future position information of the drone. The present invention obtains panoramic images and wind speed in real time, comprehensively analyzes the impact of wind speed on the speed of the drone, and uses the drone's own heading angle information to determine the flight stability value, and then corrects it to obtain the corrected flight stability value, and finally determines the flight reliability. It comprehensively considers various influencing factors and selects a suitable method based on the flight reliability to obtain the position information at the next acquisition moment, which greatly improves the accuracy of the drone position prediction, thereby improving the drone's reverse positioning accuracy; taking into account the influence of wind speed changes, the flight reliability is determined according to the drone's flight stability value and the corrected flight stability value, and the fitting or motion model method is selected according to the size of the flight reliability to obtain the drone's position information at the next acquisition moment. The strategy of dynamically selecting the appropriate method according to the actual situation enables the positioning method to better adapt to the UAV positioning needs under different wind speed interference levels, enhancing the adaptability and flexibility of the method; accurate positioning is the key prerequisite for effectively combating UAVs. By improving the positioning accuracy of UAV countermeasures, subsequent strike operations can be more accurately carried out against target UAVs, thereby greatly improving the strike effect and ensuring the safety of the active airspace; real-time acquisition of panoramic images and wind speed data, and fusion processing of multi-source data such as position information and heading angle information at different acquisition times can more comprehensively reflect the flight status of the UAV. At the same time, real-time processing of these data ensures timely and accurate positioning of the UAV in complex and changing environments, providing strong support for rapid and effective countermeasures. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals indicate the same or corresponding parts, wherein:

[0026] Figure 1 This is a flowchart of the steps of an air-to-ground integrated UAV countermeasure precise positioning method according to an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the driving heading angle on a plane image of an air-to-ground integrated UAV countermeasure precise positioning method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] See also Figure 1 , which shows a flowchart of a method for precise positioning of an air-ground integrated UAV countermeasure provided by one embodiment of the present invention, the method comprising the following steps:

[0031] S1: Obtain panoramic images and wind speed of the active airspace in real time to determine the location information of the drone at the time of collection.

[0032] It should be noted that the optoelectronic radar equipment on the ground is used to conduct all-round monitoring of the active airspace and obtain a panoramic image of the active airspace in real time. The laser wind measuring radar is used to emit a laser beam into the active airspace and receive the reflected signal to obtain the wind speed in real time.

[0033] Acquire the panoramic image and wind speed of the active airspace in each frame in real time. Determine the acquisition time in each frame based on the preset acquisition interval. Use the pre-trained target detection model to locate and identify the drone in the panoramic image at the acquisition time to obtain the drone's position information at the acquisition time.

[0034] The implementer can set the acquisition frequency and acquisition interval according to the specific implementation situation. For example, if the acquisition frequency is 30 frames / second and the acquisition interval is 5 frames, the first acquisition time is Second.

[0035] Specifically, the target detection model adopts the YOLO series model.

[0036] Specifically, the positioning identification is performed to obtain the location information of the drone at the time of collection, including:

[0037] Mark the minimum rectangular frame of the drone in the panoramic image at the time of acquisition, and generate the minimum rectangular frame position information of the drone, the minimum rectangular frame position information is the minimum rectangular frame position of the drone in the 、 The coordinate values ​​of the axis;

[0038] All the positions of the smallest rectangular frame of the drone in the panoramic image at the time of acquisition are 、 The mean value of the coordinate value of the axis is used as the collection time of the drone 、 The coordinate value of the axis is used to obtain the position information of the drone at the time of collection.

[0039] S2: Determine the driving heading angle of the UAV at the current collection moment, and then obtain the flight stability value of the UAV at the current collection moment.

[0040] It should be noted that drones often fly in straight lines, turn, or adjust their positions in airspace. In real-world scenarios, if a drone is turning or encountering external interference, the drastic adjustment of its flight direction can cause an offset between its position information at adjacent acquisition times (in other words, the flight direction at the current acquisition time is subject to a certain heading angle). This can significantly change the heading angle at the current acquisition time, leading to unstable flight attitude at the current acquisition time. However, when a drone is flying in a straight line, its position information at adjacent acquisition times will maintain a consistent flight direction in a certain dimension (e.g., straight up, downward, or horizontal). In this case, the heading angle is consistent, indicating a stable flight attitude at the current acquisition time. Therefore, the current acquisition time's heading angle is calculated by combining the offsets between the current acquisition time and the previous acquisition time, along with the triangular relationship. The difference between the current acquisition time's heading angle and the heading angles at the previous acquisition time is then analyzed to determine the stability coefficient of the drone's flight attitude at the current acquisition time, or the flight stability value.

[0041] The heading angle of the drone at the current collection moment is determined based on the position information of the drone at the previous collection moment and the current collection moment.

[0042] Specifically, the driving heading angle satisfies:

[0043] ;

[0044] Where, is the heading angle of the UAV at the current collection moment, and The current collection time of the UAV is Axis and The coordinate values ​​of the axis, and They are the previous collection time of the drone Axis and The coordinate values ​​of the axis, is the inverse tangent function, is the absolute value symbol.

[0045] Among them, the heading angle can reflect the flight direction of the UAV at a certain collection moment, that is, the deviation from the previous collection moment to the current collection moment. Figure 2 As shown in the figure, the angle between the previous point and the current point relative to the horizontal direction is represented on the plane image. Therefore, the heading angle is determined by the difference in displacement of the drone in different axis directions between the two times, using the definition of trigonometric functions.

[0046] The flight stability value of the drone at the current collection moment is determined based on the difference between the drone's heading angle at the current collection moment and the average of the heading angles at several previous collection moments, as well as the standard deviation of the drone's heading angles at the current collection moment and several previous collection moments.

[0047] Specifically, the flight stability value satisfies:

[0048] ;

[0049] Where, is the flight stability value of the UAV at the current collection moment, is the heading angle of the UAV at the current collection moment, For the drone before The heading angle at the collection moment, is the number of previous collection moments, is the standard deviation of the UAV’s heading angle between the current acquisition moment and several previous acquisition moments, is the natural exponential function, is the absolute value symbol.

[0050] in, The smaller the value, the more the current collection time is different from the previous one. The closer the heading angles at each collection moment are, the more straight and stable the drone's flight is during this period. Indicates the difference between the heading angle of the drone at the current collection moment and the average of the heading angles of the previous collection moments. The larger the value, the higher the heading angle of the drone at the current collection moment compared to the previous The greater the difference in the average driving heading angles at the current collection moment, the greater the difference in the average driving heading angles at the previous collection moment. The more stable the change of the heading angle at each collection moment, and The presence of significant mutations may be due to the drone turning or encountering external interference, causing the drone's posture at the current collection moment to suddenly change, making the drone's flight posture at the current collection moment more unstable.

[0051] S3: Determine the flight reliability of the drone at the current collection moment.

[0052] It should be noted that the environment in which the drone is located is susceptible to interference from airspace wind speed, and the change in wind speed is uncontrollable. If the flight stability value of the drone at the current acquisition moment is lower, it is not only due to normal behavior such as turning, but may also be due to the increase in wind speed interference, causing the drone to deviate from the original actual route direction; for the panoramic image at the acquisition moment, the drone is traveling from the location of its previous image to the location of the current image. If the wind speed changes significantly during this process, it indicates that there is a greater possibility of instability caused by wind speed interference; therefore, by analyzing the degree of change in wind speed during the drone's driving process from its previous acquisition moment to the current acquisition moment, the flight stability value of the drone at the current acquisition moment is corrected to accurately distinguish whether the current behavior is normal or wind interference.

[0053] The flight stability value is corrected by using the extreme difference in wind speed of all frames in the interval from the previous acquisition moment to the current acquisition moment to obtain the corrected flight stability value of the UAV at the current acquisition moment. The flight reliability of the UAV at the current acquisition moment is determined based on the flight stability value and the corrected flight stability value.

[0054] Specifically, the flight reliability meets the following requirements:

[0055] ;

[0056] Where, is the flight reliability of the UAV at the current collection moment, is the flight stability value of the UAV at the current collection moment, and are the maximum and minimum wind speeds of all frames from the previous acquisition moment to the current acquisition moment, is the standard normalization function.

[0057] in, It represents the difference between the maximum and minimum wind speeds of all frames in the interval from the previous acquisition moment to the current acquisition moment. The larger the value, the greater the change in wind speed during the process of the drone traveling from the location of the previous acquisition moment to the location of the current acquisition moment, which means that the process is greatly affected by wind interference. Since the unstable flight posture of the drone may be in normal behavior such as turning or the drone is disturbed by wind speed, if the wind speed interference is ignored, it will be impossible to accurately distinguish whether the current behavior is normal or wind interference. Therefore, by right Make corrections, The larger the value is, the greater the wind speed interference is on the UAV’s flight process at the current collection time, which means the UAV’s flight process at the current collection time is more unstable. The larger the value, the more corrected the The smaller it is, the more likely it is that the unstable change frame caused by wind interference will be highlighted; The smaller the The closer it is to 1, the more corrected The closer it is to itself, the more stable the flight attitude it obtains when there is no wind interference or the interference is small, so there is no need to If the difference between the stability coefficients of the drone's flight attitude at the current acquisition time before and after the correction is large, it indicates that the current acquisition time is more likely to be affected by wind speed interference, resulting in unstable attitude changes. In this case, the drone's flight attitude changes at the current acquisition time are considered less reliable.

[0058] S4: According to the magnitude of the flight reliability, a fitting or motion model method is selected to obtain the position information of the UAV at the next collection moment.

[0059] It should be noted that after the wind speed in the actual scene changes significantly, it will not change much in a short period of time; due to the characteristics of the drone itself, the running speed will not be too fast and the time between adjacent collection moments is relatively close, which means that the change of the drone's position information between consecutive collection moments is relatively smooth, which means that the reliability of the flight attitude at the current collection moment, that is, when the flight reliability is high, the position information of the next collection moment can be directly obtained by smooth fitting; when the reliability of the flight attitude at the current collection moment is low, the position information at the current collection moment destroys the original smooth feature, resulting in inaccurate fitting results. Since the wind speed does not change much or even remains the same in a short period of time after the mutation, the position information of the current and previous collection moments and the heading angle under the influence of the current wind speed are used to predict the position information of the next collection moment.

[0060] Specifically, the next collection time location information satisfies:

[0061] ;

[0062] Where, The next collection moment location information of the drone, is the flight reliability of the UAV at the current collection moment, is the average of the UAV’s flight reliability at several previous acquisition moments, is the fitting value of the predicted position information of the UAV at the next collection moment, and The current collection time of the UAV is Axis and The coordinate values ​​of the axis, is the ratio of the Euclidean distance between the UAV's position information from the previous collection moment to the current collection moment to the preset collection interval, that is, the UAV's speed at the current collection moment, is the heading angle of the UAV at the current collection moment, is the preset collection interval, is the cosine function, is a sine function.

[0063] Among them, when , indicating that the flight reliability of the drone at the current collection moment is no less than its previous The average flight reliability of each collection moment means that the UAV is in a normal driving process at the current collection moment and is not disturbed by the outside world. Therefore, the position information of the UAV at the current collection moment is smooth. Therefore, the fitting method is used to fit the recent position information and obtain the fitting value of the UAV's position information at the next collection moment as the UAV's position information at the next collection moment. , indicating that the flight reliability of the UAV at the current collection moment is low, that is, the UAV's flight attitude is unstable due to the sudden change in wind force at the current collection moment. Since this sudden change destroys the smoothness of the original path, the fitting result will be biased. Therefore, when the wind speed does not change much, the UAV will continue to travel along the heading angle direction at the speed at the current collection moment. Therefore, the kinematic theory formula can be used to estimate the UAV's position information at the next collection moment to compensate for the error caused by the sudden change.

[0064] Specifically, the prediction method of the fitting value is:

[0065] The least squares method is used to fit the current acquisition moment of the UAV with the position information of several previous acquisition moments to obtain the fitting value of the position information of the UAV at the next acquisition moment.

[0066] S5: Transmitting the position information at the next acquisition moment to the countermeasure device to complete the precise positioning of the UAV countermeasure.

[0067] Using the acquired location information of the next acquisition moment and its corresponding time, the drone countermeasure device can perform corresponding countermeasure operations, such as interfering with or shooting down the drone.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for precise positioning of air-ground integrated UAV countermeasures, characterized in that: include: Acquire the panoramic image and wind speed of the active airspace in each frame in real time. Determine the acquisition time in each frame based on the preset acquisition interval. Use the pre-trained target detection model to locate and identify the drone in the panoramic image at the acquisition time to obtain the drone's position information at the acquisition time. Determine the heading angle of the drone at the current collection moment based on the drone's position information at the previous collection moment and the current collection moment; Determine the flight stability value of the drone at the current collection moment based on the difference between the drone's heading angle at the current collection moment and the average of the heading angles at several previous collection moments, as well as the standard deviation of the drone's heading angles at the current collection moment and several previous collection moments; The flight stability value is corrected using the extreme difference in wind speed of all frames in the interval from the previous acquisition moment to the current acquisition moment to obtain a corrected flight stability value of the UAV at the current acquisition moment; and the flight reliability of the UAV at the current acquisition moment is determined based on the flight stability value and the corrected flight stability value; According to the flight reliability, a fitting or motion model method is selected to obtain the position information of the UAV at the next collection moment; Transmitting the next acquisition moment position information to the countermeasure device to complete the precise positioning of the drone countermeasure; The flight reliability meets the following requirements: ； Where, is the flight reliability of the UAV at the current collection moment, is the flight stability value of the UAV at the current collection moment, and are the maximum and minimum wind speeds of all frames from the previous acquisition moment to the current acquisition moment, is the standard normalization function.

2. The air-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The target detection model adopts the YOLO series model.

3. The air-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The positioning identification is performed to obtain the location information of the drone at the time of collection, including: Mark the minimum rectangular frame of the drone in the panoramic image at the time of acquisition, and generate the minimum rectangular frame position information of the drone, the minimum rectangular frame position information is the minimum rectangular frame position of the drone in the 、 The coordinate values ​​of the axis; All the positions of the smallest rectangular frame of the drone in the panoramic image at the time of acquisition are 、 The mean value of the coordinate value of the axis is used as the collection time of the drone 、 The coordinate value of the axis is used to obtain the position information of the drone at the time of collection.

4. The air-ground integrated UAV countermeasure precise positioning method according to claim 3 is characterized in that: The driving heading angle satisfies: ； Where, is the heading angle of the UAV at the current collection moment, and The current collection time of the UAV is Axis and The coordinate values ​​of the axis, and They are the previous collection time of the drone Axis and The coordinate values ​​of the axis, is the inverse tangent function, is the absolute value symbol.

5. The air-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The flight stability value satisfies: ； Where, is the flight stability value of the UAV at the current collection moment, is the heading angle of the UAV at the current collection moment, For the drone before The heading angle at the collection moment, is the number of previous collection moments, is the standard deviation of the UAV’s heading angle between the current acquisition moment and several previous acquisition moments, is the natural exponential function, is the absolute value symbol.

6. The air-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The next collection time location information satisfies: ； Where, The next collection moment location information of the drone, is the flight reliability of the UAV at the current collection moment, is the average of the UAV’s flight reliability at several previous acquisition moments, is the fitting value of the predicted position information of the UAV at the next collection moment, and The current collection time of the UAV is Axis and The coordinate values ​​of the axis, is the ratio of the Euclidean distance between the UAV’s location information from the previous acquisition moment to the current acquisition moment to the preset acquisition interval. is the heading angle of the UAV at the current collection moment, is the preset collection interval, is the cosine function, is a sine function.

7. The air-ground integrated UAV countermeasure precise positioning method according to claim 6 is characterized in that: The prediction method of the fitting value is: The least squares method is used to fit the current acquisition moment of the UAV with the position information of several previous acquisition moments to obtain the fitting value of the position information of the UAV at the next acquisition moment.

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