Air-ground integrated unmanned aerial vehicle countering precise positioning method

Abstract

The invention relates to the technical field of image data processing, in particular to an air-ground integrated unmanned aerial vehicle countering precise positioning method, which comprises the following steps: acquiring a panoramic image and a wind speed of an active airspace in real time, and determining position information of an unmanned aerial vehicle at an acquisition moment; determining a driving course angle of the unmanned aerial vehicle at the current acquisition moment, and further obtaining a flight stable value of the unmanned aerial vehicle at the current acquisition moment; determining the flight reliability of the unmanned aerial vehicle at the current acquisition moment; according to the size of the flight reliability, selecting a fitting or motion model method to obtain the position information of the unmanned aerial vehicle at the next acquisition moment; and the position information at the next acquisition moment is transmitted to countering equipment, and unmanned aerial vehicle countering precise positioning is completed. The flight reliability is determined by calculating the driving course angle and the flight stable value of the unmanned aerial vehicle, so that a proper mode is selected, the problem of speed fluctuation caused by the fact that wind speed changes are not considered in traditional fitting is effectively solved, and the accuracy of unmanned aerial vehicle position prediction is improved.

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, drones are increasingly used in various fields, including aerial photography, logistics, agricultural spraying, environmental monitoring, etc. However, the popularity of drones has also brought a series of security risks and regulatory challenges. Therefore, how to effectively detect, track and counter unauthorized drones has become an urgent problem to be solved.

[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, so as to achieve timely and accurate strikes on the target.

[0004] However, in the process of drone countermeasures, accurately and real-time capture of the drone's future position information is crucial to ensure that countermeasure equipment (such as jammers) 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 future position information predicted by traditional fitting algorithms, severely weakening the positioning accuracy of drone countermeasures, thereby affecting subsequent strike effects. 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 speed of the drone, thereby reducing the accuracy of the future position information predicted by the traditional fitting algorithm, seriously weakening the positioning accuracy of the drone countermeasure, and thus affecting the subsequent strike effect, the present invention proposes an air-to-ground integrated drone countermeasure precise positioning method, which includes the following steps: The panoramic image and wind speed of the active airspace in each frame are acquired in real time, the acquisition time in each frame is determined according to the preset acquisition interval, and the UAV in the panoramic image at the acquisition time is located and identified by using the pre-trained target detection model to obtain the position information of the UAV at the acquisition time; the driving heading angle of the UAV at the current acquisition time is determined according to the position information of the UAV at the previous acquisition time and the current acquisition time; the flight stability value of the UAV at the current acquisition time is determined according to the difference between the driving heading angle of the UAV at the current acquisition time and the mean of the driving heading angles of several previous acquisition times, and the standard deviation of the driving heading angles of the UAV at the current acquisition time and several previous acquisition times; the flight stability value is corrected by using the extreme difference in wind speed of all frames in the interval from the previous acquisition time to the current acquisition time to obtain the corrected flight stability value of the UAV at the current acquisition time, and the flight reliability of the UAV at the current acquisition time is determined according to the flight stability value and the corrected flight stability value; according to the size of the flight reliability, the fitting or motion model method is selected to obtain the position information of the UAV at the next acquisition time; the position information of the next acquisition time is transmitted to the countermeasure device to complete the precise positioning of the UAV countermeasure.

[0006] 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 the traditional fitting algorithm, thereby improving the accuracy of the UAV position prediction; by correcting the flight stability value through 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 the countermeasure; 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.

[0007] Furthermore, the target detection model adopts the YOLO series model.

[0008] 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, wherein 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 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 coordinates of the axis is taken 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.

[0009] Furthermore, the driving heading angle satisfies: ; In the formula, 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.

[0010] By using the position difference and the inverse tangent function, the process of calculating the heading angle is simple and efficient, reducing the computational complexity; the heading angle can accurately reflect the flight direction and trajectory of the drone, improving the accuracy of position prediction; the absolute value sign effectively suppresses error fluctuations and ensures accurate calculation of the heading angle; the analysis of the heading angle helps to more accurately evaluate the stability of the drone's flight and assist in subsequent countermeasure decisions.

[0011] Furthermore, the flight stability value satisfies: ; In the formula, is the flight stability value of the drone at the current collection moment, is the heading angle of the UAV at the current collection moment, For the drone The heading angle at the time of collection, is the number of previous collection moments, is the standard deviation of the heading angle of the drone at the current collection time and several previous collection times, is the natural exponential function, is the absolute value symbol.

[0012] 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 is heading fluctuation or abnormality. When the flight stability value is low, it indicates that the drone's flight is unstable, which can provide a basis for subsequent countermeasures based on this information 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.

[0013] Furthermore, the flight reliability satisfies: ; In the formula, is the flight reliability of the drone at the current collection moment, is the flight stability value of the drone at the current collection moment, and are the maximum and minimum wind speeds of all frames from the previous acquisition time to the current acquisition time, respectively. is the standard normalization function.

[0014] By monitoring the flight stability value and wind speed changes, the flight stability fluctuations of the UAV can be effectively identified; considering the wind speed changes, it helps to analyze the impact of the external environment on flight stability; real-time monitoring of reliability, accurate judgment of flight anomalies, and optimization of countermeasure operations.

[0015] Furthermore, the next collection time location information satisfies: ; In the formula, The next collection time location information of the drone, is the flight reliability of the drone at the current collection moment, is the average of the flight reliability of the drone at several previous acquisition moments, is the fitting value of the predicted position information of the UAV at the next collection time. 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 drone’s previous collection time and the current collection time to the preset collection 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.

[0016] By judging 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; using fitting values ​​when the reliability is high to ensure accuracy, relying on kinematic models when the reliability is low to adapt to possible flight instability; based on the comparison between the flight reliability and the mean, flexible selection of appropriate prediction methods can provide more appropriate position information under different flight environments and stabilities, thereby coping with 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.

[0017] Furthermore, the prediction method of the fitting value is: using the least squares method to fit the position information of the current collection moment of the drone with the previous collection moments, and obtain the fitting value of the position information of the next collection moment of the drone.

[0018] The present invention has the following beneficial effects: The traditional fitting algorithm does not take into account the interference of strong winds, which reduces the accuracy of predicting the future position information of the UAV. The present invention obtains panoramic images and wind speed in real time, comprehensively analyzes the impact of wind speed on the speed of the UAV, and uses the UAV'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, selects a suitable method based on the flight reliability to obtain the position information at the next collection moment, greatly improves the accuracy of the UAV position prediction, and thus improves the UAV's reverse positioning accuracy; considering the impact of wind speed changes, the flight reliability is determined according to the UAV'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 UAV's position information at the next collection moment. The strategy of dynamically selecting the appropriate method according to the actual situation makes the positioning method 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 collection 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 changeable environments, providing strong support for rapid and effective countermeasures. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. 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 are the same or corresponding parts, wherein: Figure 1 It 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.

[0020] Figure 2 It is a schematic diagram of the driving heading angle of an air-to-ground integrated UAV countermeasure precise positioning method on a plane image according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] See also Figure 1 , which shows a flow chart of the steps of an air-to-ground integrated UAV countermeasure precise positioning method provided by an embodiment of the present invention, the method comprising the following steps: 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.

[0024] 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.

[0025] Acquire the panoramic image and wind speed of the active airspace in each frame in real time, determine the collection time in each frame according to the preset collection interval, and use the pre-trained target detection model to locate and identify the drone in the panoramic image at the collection time to obtain the position information of the drone at the collection time.

[0026] 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.

[0027] Specifically, the target detection model adopts the YOLO series model.

[0028] Specifically, 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 of the drone at each position in , The coordinate values ​​of the axis; All positions of the smallest rectangular frame of the drone in the panoramic image at the time of acquisition are , The mean value of the coordinates of the axis is taken 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.

[0029] S2: Determine the driving heading angle of the drone at the current collection moment, and then obtain the flight stability value of the drone at the current collection moment.

[0030] It should be noted that when the drone is in the airspace, it often flies straight, turns or adjusts. In actual scenarios, if the current drone is turning or encountering external interference, the position information of the drone corresponding to the adjacent collection time will be offset due to the need to adjust the flight direction (that is, for the current collection time, there is a certain heading angle in the flight process). At this time, the heading angle of the drone's flight process at the current collection time may have a significant mutation compared with the previous one, resulting in an unstable flight attitude of the drone at the current collection time; while when the current drone is flying in a straight line, the position information of the drone corresponding to the adjacent collection time will remain unchanged in a certain dimension, that is, straight up, down or horizontal, etc. At this time, the heading angle is consistent, which means that the flight attitude of the drone is stable at the current collection time. Therefore, the offset of the drone in different directions at the current collection time and the previous collection time, as well as the triangle relationship, is combined to obtain the driving heading angle of the drone at the current collection time, and the change difference between the driving heading angle at the current collection time and the driving heading angle at the previous collection times is further analyzed as a stability coefficient to measure the flight attitude of the drone at the current collection time, that is, the flight stability value.

[0031] According to the position information of the drone at the previous collection time and the current collection time, the driving heading angle of the drone at the current collection time is determined.

[0032] Specifically, the driving heading angle satisfies: ; In the formula, 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.

[0033] 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 compared to the horizontal direction is represented on the plane image. Therefore, the heading angle is determined by the displacement difference of the drone in different axis directions twice, using the definition of trigonometric functions.

[0034] The flight stability value of the drone at the current collection moment is determined based on the difference between the driving heading angle of the drone at the current collection moment and the average of the driving heading angles at the previous collection moments, as well as the standard deviation of the driving heading angles at the current collection moment and the previous collection moments.

[0035] Specifically, the flight stability value satisfies: ; In the formula, is the flight stability value of the drone at the current collection moment, is the heading angle of the UAV at the current collection moment, For the drone The heading angle at the time of collection, is the number of previous collection moments, is the standard deviation of the heading angle of the drone at the current collection time and several previous collection times, is the natural exponential function, is the absolute value symbol.

[0036] 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 at 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 heading angles at the current collection time, the greater the difference in the average heading angles at the previous collection time. The more stable the change of the heading angle at each collection moment, and There is a significant mutation, which may be due to the drone is turning or encountering external interference, causing the drone's posture at the current collection time to suddenly change, making the drone's flight posture at the current collection time more unstable.

[0037] S3: Determine the flight reliability of the drone at the current collection moment.

[0038] It should be noted that the environment in which the drone is located is susceptible to wind speed interference in the airspace, 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 behaviors 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 behavior.

[0039] 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.

[0040] Specifically, the flight reliability satisfies: ; In the formula, is the flight reliability of the drone at the current collection moment, is the flight stability value of the drone at the current collection moment, and are the maximum and minimum wind speeds of all frames from the previous acquisition time to the current acquisition time, respectively. is the standard normalization function.

[0041] 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 disturbed by wind. Since the unstable flight attitude 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 flight process of the drone at the current collection time, which means that the flight process of the drone at the current collection time is more unstable. The larger the value, the more corrected The smaller it is, the more unstable the change frame caused by wind disturbance is 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 UAV flight attitude at the current acquisition time before and after the correction is relatively large, it indicates that the current acquisition time is more likely to be disturbed by wind speed, resulting in unstable attitude changes. In this case, the flight attitude change of the UAV at the current acquisition time is considered to be less reliable.

[0042] S4: 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.

[0043] It should be noted that after the wind speed in the actual scene changes significantly, it will not change much in a short 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 close, which means that the change of the drone's position information between consecutive collection moments is relatively smooth, which means that when the flight attitude reliability at the current collection moment, that is, the flight reliability, is high, the position information at the next collection moment can be directly obtained by smooth fitting; when the flight attitude reliability at the current collection moment is low, the position information at the current collection moment destroys the original smooth characteristics, resulting in inaccurate fitting results, and because the wind speed does not change much or even remains the same in a short period of time after the mutation, the position information at 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 at the next collection moment.

[0044] Specifically, the next collection time location information satisfies: ; In the formula, The next collection time location information of the drone, is the flight reliability of the drone at the current collection moment, is the average of the flight reliability of the drone at several previous acquisition moments, is the fitting value of the predicted position information of the UAV at the next collection time. 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 position information of the drone from the previous collection time to the current collection time and the preset collection interval, that is, the speed of the drone at the current collection time, 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.

[0045] Among them, when , indicating that the flight reliability of the drone at the current collection time is no less than its previous The average flight reliability of the collection time means that the UAV is in a normal driving process at the current collection time and is not disturbed by the outside world. Therefore, the position information of the UAV at the current collection time 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 time as the UAV's position information at the next collection time. , indicating that the flight reliability of the UAV at the current collection moment is low, that is, the UAV at the current collection moment is unstable due to the sudden change in wind force. 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 of the current collection moment. Therefore, the kinematic theory formula can be used to estimate the position information of the UAV at the next collection moment to compensate for the error caused by the sudden change.

[0046] Specifically, 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.

[0047] S5: Transmitting the position information at the next acquisition time to the countermeasure device to complete the precise positioning of the drone countermeasure.

[0048] Using the acquired next acquisition moment location information and its corresponding time, the drone countermeasure device can perform corresponding countermeasure operations, such as interfering with or shooting down the drone.

[0049] 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 protection scope of the present invention.

Claims

1. An air-to-ground integrated UAV countermeasure precise positioning method, characterized in that: include: Obtain the panoramic image and wind speed of the active airspace in each frame in real time, determine the collection time in each frame according to the preset collection interval, and use the pre-trained target detection model to locate and identify the drone in the panoramic image at the collection time to obtain the location information of the drone at the collection time; Determine the heading angle of the drone at the current collection time based on the location information of the drone at the previous collection time and the current collection time; Determine the flight stability value of the drone at the current collection moment according to the difference between the driving heading angle of the drone at the current collection moment and the average of the driving heading angles at several previous collection moments, and the standard deviation of the driving heading angles at the current collection moment and several previous collection moments; 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, and the flight reliability of the UAV at the current acquisition moment is determined according to 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; The position information at the next acquisition time is transmitted to the countermeasure device to complete the precise positioning of the drone countermeasure.

2. The air-to-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-to-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 of the drone at each position in , The coordinate values ​​of the axis; All positions of the smallest rectangular frame of the drone in the panoramic image at the time of acquisition are , The mean value of the coordinates of the axis is taken 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-to-ground integrated UAV countermeasure precise positioning method according to claim 3 is characterized in that: The driving heading angle satisfies: ； In the formula, 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-to-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The flight stability value satisfies: ； In the formula, is the flight stability value of the drone at the current collection moment, is the heading angle of the UAV at the current collection moment, For the drone The heading angle at the time of collection, is the number of previous collection moments, is the standard deviation of the heading angle of the drone at the current collection time and several previous collection times, is the natural exponential function, is the absolute value symbol.

6. The air-to-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The flight reliability meets the following requirements: ； In the formula, is the flight reliability of the drone at the current collection moment, is the flight stability value of the drone at the current collection moment, and are the maximum and minimum wind speeds of all frames from the previous acquisition time to the current acquisition time, respectively. is the standard normalization function.

7. The air-to-ground integrated UAV countermeasure precise positioning method according to claim 1 is characterized in that: The next collection time location information satisfies: ； In the formula, The next collection time location information of the drone, is the flight reliability of the UAV at the current collection moment, is the average of the flight reliability of the drone at several previous acquisition moments, is the fitting value of the predicted position information of the UAV at the next collection time. 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 drone’s previous collection time and the current collection time to the preset collection 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.

8. The air-to-ground integrated UAV countermeasure precise positioning method according to claim 7 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.

Images