A method and system suitable for multi-scenario unmanned aerial vehicle tracking countermeasure

Abstract

The application relates to the technical field of unmanned aerial vehicle tracking countermeasures, in particular to a method and system suitable for unmanned aerial vehicle tracking countermeasures in multiple scenes, which comprises the following steps: acquiring real-time position data of an invading unmanned aerial vehicle and real-time position data of a tracked target; when the real-time position data signal of the invading unmanned aerial vehicle disappears, increasing the signal transmission power for positioning of the tracked target, continuing to acquire the real-time position data of the tracked target; calculating the correlation between the real-time position data of the invading unmanned aerial vehicle and the real-time position data of the tracked target, deducing the real-time position data of the invading unmanned aerial vehicle after the real-time position data signal of the invading unmanned aerial vehicle disappears, and identifying the real-time position data of the invading unmanned aerial vehicle. The application deduces the real-time position data of the invading unmanned aerial vehicle after the real-time position data signal of the invading unmanned aerial vehicle disappears, and carries out targeted scanning and identification, so that the identification effect of the invading unmanned aerial vehicle under complex geographical shielding conditions is improved.

Description

Technical Field

[0001] This invention relates to the field of drone tracking and countermeasures technology, specifically a drone tracking and countermeasures method and system applicable to multiple scenarios. Background Technology

[0002] Drone tracking and countermeasures technology has important applications in modern security. Specifically, in certain attack and defense scenarios, intruding drones often use pattern recognition and other tracking methods to identify and track targets belonging to our side. To counter these intruding drones, our technical means often involve using signal scanning devices to detect the location of the intruding drone, and then using drone capture devices to capture it.

[0003] However, in complex terrain environments such as forests, high-rise buildings, and mountains where signal obstruction is common, unidirectional scanning and detection signals often lose connection due to terrain blockage, making it impossible to accurately obtain real-time location data. Existing technologies lack effective continuous tracking methods after signal loss, making it difficult to re-acquire targets in complex and multi-scenario environments, resulting in broken tracking chains and failed countermeasures. Therefore, it is necessary to propose a method that can continue to deduce the location of intruding drones and guide the scanning device to re-identify them after the drone's signal disappears, in order to achieve continuous tracking and countermeasures against intruding drones in complex terrain environments.

[0004] With the development of data analysis technology, using correlated data for inference and simulation has become a technical means to solve many real-world problems. By analyzing historical time-series data to build correlation models and predicting future states based on real-time input data, this provides a technical feasibility for continuously acquiring real-time location data of intruding drones even when information is incomplete. Summary of the Invention

[0005] (1) Technical problems to be solved The purpose of this invention is to provide a method and system for tracking and countering drones in various scenarios, so as to improve the identification effect of intruding drones under complex geographical cover conditions.

[0006] (2) Technical solution To achieve the above objectives, the present invention provides a method for tracking and countering drones in multiple scenarios, the method comprising the following steps: S1, acquire the real-time location data of the intruding drone identified by the signal scanning device, and record it as the first location data sequence; acquire the real-time location data of the tracked target identified by the signal receiving device, and record it as the second location data sequence; the tracked target transmits a signal for positioning so that the signal receiving device can receive the real-time location data signal of the tracked target in complex terrain.

[0007] S2, after the real-time location data signal of the intruding drone disappears, the signal transmission power of the tracked target used for positioning is increased, and the real-time location data of the tracked target is continued to be acquired through the signal receiving device, which is recorded as the third location data sequence.

[0008] S3, calculate the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target based on the first location data sequence and the second location data sequence, and record it as the tracking correlation relationship; based on the tracking correlation relationship and the third location data sequence, deduce the real-time location data of the intruding drone after the real-time location data signal of the intruding drone disappears, and record it as the fourth location data sequence.

[0009] S4, set the scanning center of the signal scanning device to the spatial position indicated by the fourth position data sequence, and identify the real-time position data of the intruding drone.

[0010] Further, the method for calculating the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target based on the first location data sequence and the second location data sequence, denoted as the tracking correlation relationship, includes: The first and second position data sequences are converted into three-dimensional spatial coordinate points and time-aligned to obtain the X-axis coordinate values ​​of the first position data sequence as follows: to The Y-axis coordinates of the first position data sequence are as follows: to The Z-axis coordinates of the first position data sequence are as follows: to The X-axis coordinates of the second position data sequence are as follows: to The Y-axis coordinates of the second position data sequence are as follows: to The Z-axis coordinates of the second position data sequence are as follows: to ;in, This indicates the number of data items contained in the first position data sequence, and also the number of data items contained in the second position data sequence.

[0011] The differences between the X-axis and Y-axis coordinates of the second and first position data sequences are calculated separately. The differences between the Y-axis and Z-axis coordinates of the second and first position data sequences are also calculated separately. These differences are then fitted to obtain the relationship between the X-axis and Z-axis coordinate differences of the second and first position data sequences over time, denoted as the first function. The relationships between the Y-axis and Z-axis coordinate differences of the second and first position data sequences over time are also fitted, denoted as the second function. Finally, the relationships between the Z-axis and Z-axis coordinate differences of the second and first position data sequences over time are fitted, denoted as the third function. All three functions are collectively referred to as the tracking correlation relationship.

[0012] Further, the method of subtracting the X-axis coordinates of the second position data sequence from the X-axis coordinates of the first position data sequence, subtracting the Y-axis coordinates of the second position data sequence from the Y-axis coordinates of the first position data sequence, subtracting the Z-axis coordinates of the second position data sequence from the Z-axis coordinates of the first position data sequence, and fitting these subtractions to obtain the relationship between the difference between the X-axis coordinates of the second position data sequence and the X-axis coordinates of the first position data sequence over time, denoted as a first function; fitting the relationship between the difference between the Y-axis coordinates of the second position data sequence and the Y-axis coordinates of the first position data sequence over time, denoted as a second function; and fitting the relationship between the difference between the Z-axis coordinates of the second position data sequence and the Z-axis coordinates of the first position data sequence over time, denoted as a third function, includes: The X-axis displacement data sequence is obtained by subtracting the X-axis coordinate values ​​of the second position data sequence from the X-axis coordinate values ​​of the first position data sequence. to The calculation formula for the X-axis displacement data sequence is as follows: ; in, This represents the first [number] in the X-axis displacement data sequence. One element; This represents the first position in the X-axis coordinate value of the second position data sequence. One element; This represents the first position in the X-axis coordinate value of the data sequence. One element; The value is 1 to Integer variables.

[0013] The difference between the Y-axis coordinates of the second position data sequence and the Y-axis coordinates of the first position data sequence is used to obtain the Y-axis displacement data sequence. to The calculation formula for the Y-axis displacement data sequence is as follows: ; in, This represents the first [number] in the Y-axis displacement data sequence. One element; This represents the first position in the Y-axis coordinate value of the second position data sequence. One element; This represents the first position data sequence's Y-axis coordinate value. Each element.

[0014] The Z-axis displacement data sequence is obtained by subtracting the Z-axis coordinate values ​​of the second position data sequence from the Z-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Z-axis displacement data sequence is as follows: ; in, This represents the first Z-axis displacement data sequence. One element; This represents the Z-axis coordinate value of the second position data sequence. One element; This represents the Z-axis coordinate value of the first position data sequence. Each element.

[0015] Construct X-axis displacement data sample points, Y-axis displacement data sample points, and Z-axis displacement data sample points, which are respectively represented as... , , ;in, This indicates the pre-set time interval for scanning the real-time location data of the intruding drone.

[0016] make Traverse 1 to The value of , in As the independent variable, with Using the variable as the dependent variable, a nonlinear least squares fitting algorithm is used to fit the first function. The sum of squared residuals during the process of fitting the first function using the nonlinear least squares fitting algorithm is recorded and denoted as the first residual sum of squares. As the independent variable, with Using the variable as the dependent variable, a second function is obtained by fitting it using a nonlinear least squares fitting algorithm. The sum of squared residuals during the process of fitting the second function using the nonlinear least squares fitting algorithm is recorded and denoted as the second residual sum of squares. As the independent variable, with The third function is obtained by fitting a nonlinear least squares fitting algorithm with as the dependent variable. The sum of squared residuals in the process of fitting the third function with the nonlinear least squares fitting algorithm is recorded and denoted as the third residual sum of squares.

[0017] Furthermore, the method of deriving the real-time location data of the intruding drone after the disappearance of the real-time location data signal based on the tracking correlation and the third location data sequence, and denoting it as the fourth location data sequence, includes: The third position data sequence is converted into three-dimensional spatial coordinate points, and time-stamped with the starting points of the first and second position data sequences as the time origin for the three-dimensional spatial coordinate points obtained from the conversion of the third position data sequence, denoted as . The time corresponding to the starting point of the third position data sequence is denoted as... ; This represents the time difference between the moment corresponding to the data in the third position data sequence and the time origin.

[0018] The spatial three-dimensional coordinates of the fourth position data sequence were calculated. The formula for calculating the spatial three-dimensional coordinates of the fourth position data sequence is as follows: ; in, Represents the first function, Indicates the second function, This represents the third function.

[0019] The spatial three-dimensional coordinate points corresponding to the fourth location data sequence are mapped back to geographic space to obtain the fourth location data sequence.

[0020] Furthermore, the method for identifying the real-time location data of an intruding drone by setting the scanning center of the signal scanning device to the spatial location indicated by the fourth location data sequence includes: Calculate the average of the first residual sum of squares, the second residual sum of squares, and the third residual sum of squares to obtain the mean value of the residual sum of squares; calculate the scan concentration value based on the mean value of the residual sum of squares; set the half-power beamwidth of the signal scanning device to the scan concentration value, set the scanning center of the signal scanning device to the spatial position indicated by the fourth position data sequence, and identify the real-time position data of the intruding UAV; the scanning range of the signal scanning device is a spatial cone.

[0021] The scan concentration is taken as the minimum value between the preset upper limit of half-power beamwidth and the linear correlation value of scan concentration; wherein, the formula for calculating the linear correlation value of scan concentration is: ; in, This indicates the linear correlation value of the scan concentration. This represents the mean of the sum of squared residuals. This represents the pre-set correlation coefficient. This indicates the pre-set lower limit of the half-power beamwidth.

[0022] Based on the same inventive concept, this invention also provides a drone tracking and countermeasure system applicable to multiple scenarios, the system comprising: The data reading module is used to acquire the real-time location data of the intruding drone identified by the signal scanning device, which is recorded as the first location data sequence; acquire the real-time location data of the tracked target identified by the signal receiving device, which is recorded as the second location data sequence; the tracked target transmits a positioning signal so that the signal receiving device can receive the real-time location data signal of the tracked target in complex terrain.

[0023] The third position data sequence calculation module, connected to the data reading module, is used to increase the signal transmission power of the tracked target for positioning after the real-time position data signal of the intruding drone disappears, and continue to acquire the real-time position data of the tracked target through the signal receiving device, which is recorded as the third position data sequence.

[0024] The fourth position data sequence calculation module, connected to the third position data sequence calculation module, is used to calculate the correlation between the real-time position data of the intruding drone and the real-time position data of the tracked target based on the first position data sequence and the second position data sequence, and is denoted as the tracking correlation relationship; based on the tracking correlation relationship and the third position data sequence, the real-time position data of the intruding drone after the real-time position data signal of the intruding drone disappears is deduced and is denoted as the fourth position data sequence.

[0025] The real-time location data identification module for intrusion drones is connected to the fourth location data sequence calculation module. It is used to set the scanning center of the signal scanning device to the spatial location indicated by the fourth location data sequence, and to identify the real-time location data of the intrusion drones.

[0026] Furthermore, the fourth position data sequence calculation module includes: The coordinate transformation module is used to convert the first position data sequence and the second position data sequence into three-dimensional spatial coordinate points, and perform time alignment to obtain the X-axis coordinate values ​​of the first position data sequence as follows: to The Y-axis coordinates of the first position data sequence are as follows: to The Z-axis coordinates of the first position data sequence are as follows: to The X-axis coordinates of the second position data sequence are as follows: to The Y-axis coordinates of the second position data sequence are as follows: to The Z-axis coordinates of the second position data sequence are as follows: to ;in, This indicates the number of data items contained in the first position data sequence, and also the number of data items contained in the second position data sequence.

[0027] The data fitting module, connected to the coordinate transformation module, is used to calculate the difference between the X-axis coordinates of the second position data sequence and the X-axis coordinates of the first position data sequence, the difference between the Y-axis coordinates of the second position data sequence and the Y-axis coordinates of the first position data sequence, and the difference between the Z-axis coordinates of the second position data sequence and the Z-axis coordinates of the first position data sequence, respectively. The module then fits these differences over time to obtain the relationship between the X-axis and Y-axis coordinates of the second position data sequence and the first position data sequence, denoted as the first function. Similarly, it fits the difference between the Y-axis and Y-axis coordinates of the second position data sequence and the first position data sequence, denoted as the second function, and fits the difference between the Z-axis and Z-axis coordinates of the second position data sequence and the first position data sequence, denoted as the third function. All three functions are collectively referred to as the tracking correlation relationship.

[0028] Furthermore, the data fitting module includes: The displacement data sequence calculation module is used to calculate the difference between the X-axis coordinate values ​​of the second position data sequence and the X-axis coordinate values ​​of the first position data sequence to obtain the X-axis displacement data sequence. to The calculation formula for the X-axis displacement data sequence is as follows: ; in, This represents the first [number] in the X-axis displacement data sequence. One element; This represents the first position in the X-axis coordinate value of the second position data sequence. One element; This represents the first position in the X-axis coordinate value of the data sequence. One element; The value is 1 to Integer variables.

[0029] The Y-axis displacement data sequence is obtained by subtracting the Y-axis coordinate values ​​of the second position data sequence from the Y-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Y-axis displacement data sequence is as follows: ; in, This represents the first [number] in the Y-axis displacement data sequence. One element; This indicates the first position in the Y-axis coordinate value of the second position data sequence. One element; This represents the first position data sequence's Y-axis coordinate value. Each element.

[0030] The Z-axis displacement data sequence is obtained by subtracting the Z-axis coordinate values ​​of the second position data sequence from the Z-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Z-axis displacement data sequence is as follows: ; in, This represents the first Z-axis displacement data sequence. One element; This represents the Z-axis coordinate value of the second position data sequence. One element; This represents the Z-axis coordinate value of the first position data sequence. Each element.

[0031] The sample point construction module, connected to the displacement data sequence calculation module, is used to construct X-axis displacement data sample points, Y-axis displacement data sample points, and Z-axis displacement data sample points. These X-axis, Y-axis, and Z-axis displacement data sample points are respectively represented as... , , ;in, This indicates the pre-set time interval for scanning the real-time location data of the intruding drone.

[0032] The fitting calculation module, connected to the sample point construction module, is used to enable... Traverse 1 to The value of , in As the independent variable, with Using the variable as the dependent variable, a nonlinear least squares fitting algorithm is used to fit the first function. The sum of squared residuals during the process of fitting the first function using the nonlinear least squares fitting algorithm is recorded and denoted as the first residual sum of squares. As the independent variable, with Using the variable as the dependent variable, a second function is obtained by fitting it using a nonlinear least squares fitting algorithm. The sum of squared residuals during the process of fitting the second function using the nonlinear least squares fitting algorithm is recorded and denoted as the second residual sum of squares. As the independent variable, with The third function is obtained by fitting a nonlinear least squares fitting algorithm with as the dependent variable. The sum of squared residuals in the process of fitting the third function with the nonlinear least squares fitting algorithm is recorded and denoted as the third residual sum of squares.

[0033] Furthermore, the fourth position data sequence calculation module also includes: The data extrapolation module is used to convert the third position data sequence into spatial three-dimensional coordinate points, and to add time labels to the spatial three-dimensional coordinate points obtained from the conversion of the third position data sequence, using the starting points of the first and second position data sequences as the time origin, denoted as... The time corresponding to the starting point of the third position data sequence is denoted as... ; This represents the time difference between the moment corresponding to the data in the third position data sequence and the time origin.

[0034] The spatial three-dimensional coordinates of the fourth position data sequence were calculated. The formula for calculating the spatial three-dimensional coordinates of the fourth position data sequence is as follows: ; in, Represents the first function, Indicates the second function, This represents the third function.

[0035] The spatial three-dimensional coordinate points corresponding to the fourth location data sequence are mapped back to geographic space to obtain the fourth location data sequence.

[0036] Furthermore, the real-time location data identification module for the intruding drone includes: The scanning range calculation module is used to calculate the average of the first residual sum of squares, the second residual sum of squares, and the third residual sum of squares to obtain the mean value of the residual sum of squares; the scanning concentration value is calculated based on the mean value of the residual sum of squares; the half-power beamwidth of the signal scanning device is set as the scanning concentration value, and the scanning center of the signal scanning device is set as the spatial position indicated by the fourth position data sequence to identify the real-time position data of the intruding UAV; the scanning range of the signal scanning device is a spatial cone.

[0037] The scan concentration is taken as the minimum value between the preset upper limit of half-power beamwidth and the linear correlation value of scan concentration; wherein, the formula for calculating the linear correlation value of scan concentration is: ; in, This indicates the linear correlation value of the scan concentration. This represents the mean of the sum of squared residuals. This represents the pre-set correlation coefficient. This indicates the pre-set lower limit of the half-power beamwidth.

[0038] (3) Beneficial effects Compared with the prior art, the beneficial effects of the present invention are: By extrapolating the real-time location data of intrusive drones after their real-time location data signals disappear, and then performing targeted scanning and identification, the identification effect of intrusive drones under complex geographical cover conditions can be improved. Attached Figure Description

[0039] Figure 1 This is a flowchart of a drone tracking and countermeasure method applicable to multiple scenarios according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the module composition of a drone tracking and countermeasure system applicable to multiple scenarios according to Embodiment 2 of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Before providing examples, it is necessary to describe the application scenario of this invention. This invention is applied to the process of an intruding drone tracking a target being tracked. The intruding drone uses a signal scanning device to identify its real-time location data. When the intruding drone's real-time location data signal disappears, data analysis methods are used to extrapolate the drone's real-time location, and a targeted, concentrated scan is performed on the area surrounding the extrapolated location. The tracking method used by the intruding drone against the target employs a fixed pattern recognition method.

[0042] Example 1: As Figure 1 As shown, this embodiment provides a method for tracking and countering drones in multiple scenarios. The method includes the following steps: S1, acquire the real-time location data of the intruding drone identified by the signal scanning device, and record it as the first location data sequence; acquire the real-time location data of the tracked target identified by the signal receiving device, and record it as the second location data sequence; the tracked target transmits a signal for positioning so that the signal receiving device can receive the real-time location data signal of the tracked target in complex terrain.

[0043] S2, after the real-time location data signal of the intruding drone disappears, the signal transmission power of the tracked target used for positioning is increased, and the real-time location data of the tracked target is continued to be acquired through the signal receiving device, which is recorded as the third location data sequence.

[0044] S3, calculate the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target based on the first location data sequence and the second location data sequence, and record it as the tracking correlation relationship; based on the tracking correlation relationship and the third location data sequence, deduce the real-time location data of the intruding drone after the real-time location data signal of the intruding drone disappears, and record it as the fourth location data sequence.

[0045] S4, set the scanning center of the signal scanning device to the spatial position indicated by the fourth position data sequence, and identify the real-time position data of the intruding drone.

[0046] For example, real-time location data of the intruding drone identified by a signal scanning device is acquired and recorded as the first location data sequence; simultaneously, real-time location data of the tracked target identified by a signal receiving device is acquired and recorded as the second location data sequence. The tracked target continuously emits a dedicated signal for positioning. This signal has a specific encoding format and transmission frequency band, enabling the signal receiving device to effectively receive and parse the real-time location data signal of the tracked target even in complex terrain environments, such as areas with building obstructions or vegetation cover. This step establishes two independent location data sources for the intruding drone and our tracked target.

[0047] When the real-time location data signal of the intruding drone disappears, the system automatically triggers the signal transmission power enhancement mechanism of the tracked target. By increasing the transmission power of the signal used for positioning, it overcomes signal attenuation caused by terrain obstruction or increased distance, ensuring that the signal receiving device can continue to stably acquire the real-time location data of the tracked target, and records this data as the third location data sequence. This step ensures that we still have critical motion reference information even after the intruding drone's signal is lost.

[0048] Based on the first and second location data sequences, the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target is calculated by analyzing their correspondence in the time dimension. This correlation is recorded as the tracking correlation. Then, based on the tracking correlation and the subsequently acquired third location data sequence, a data extrapolation method is used to reverse-engineer the possible location of the intruding drone after its real-time location data signal disappears, i.e., the fourth location data sequence. This step utilizes the correlation of historical data to predict the currently missing information.

[0049] The scanning center of the signal scanning device is set to the spatial location indicated by the fourth position data sequence. Specifically, the scanning center is set to the spatial coordinates corresponding to the latest data point in the fourth position data sequence. Simultaneously, based on the sum of squared residuals fitted during the calculation of the tracking correlation, the half-power beamwidth of the signal scanning device is dynamically adjusted to determine the spatial cone scanning range. Within the obtained scanning center and scanning range, the real-time position data of the intruding UAV is identified, enabling focused area searches.

[0050] Further, the method for calculating the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target based on the first location data sequence and the second location data sequence, denoted as the tracking correlation relationship, includes: The first and second position data sequences are converted into three-dimensional spatial coordinate points and time-aligned to obtain the X-axis coordinate values ​​of the first position data sequence as follows: to The Y-axis coordinates of the first position data sequence are as follows: to The Z-axis coordinates of the first position data sequence are as follows: to The X-axis coordinates of the second position data sequence are as follows: to The Y-axis coordinates of the second position data sequence are as follows: to The Z-axis coordinates of the second position data sequence are as follows: to ;in, This indicates the number of data items contained in the first position data sequence, and also the number of data items contained in the second position data sequence.

[0051] The differences between the X-axis and Y-axis coordinates of the second and first position data sequences are calculated separately. The differences between the Y-axis and Z-axis coordinates of the second and first position data sequences are also calculated separately. These differences are then fitted to obtain the relationship between the X-axis and Z-axis coordinate differences of the second and first position data sequences over time, denoted as the first function. The relationships between the Y-axis and Z-axis coordinate differences of the second and first position data sequences over time are also fitted, denoted as the second function. Finally, the relationships between the Z-axis and Z-axis coordinate differences of the second and first position data sequences over time are fitted, denoted as the third function. All three functions are collectively referred to as the tracking correlation relationship.

[0052] For example, the first and second location data sequences are converted into spatial three-dimensional coordinate points. Specifically, each location data point is parsed into a spatial point with X-axis, Y-axis, and Z-axis coordinate values ​​according to a preset geographic coordinate system transformation rule. The units for the X-axis, Y-axis, and Z-axis coordinates are all meters. Based on this, time alignment processing is performed on the data points in the two sequences to ensure that each first location data point has a corresponding second location data point with its timestamp, thereby forming two sets of three-dimensional coordinate sequences that are strictly synchronized in time.

[0053] The X, Y, and Z axis coordinates of the second position data sequence are subtracted from the corresponding axial coordinates of the first position data sequence to obtain displacement difference data sequences in three axes. These data sequences reflect the changes in the relative positions of the intruding drone and the tracked target in various spatial dimensions.

[0054] The obtained displacement difference data sequences for the three axes were processed using function fitting methods. For each axial displacement difference sequence, with the corresponding time point as the independent variable and the displacement difference value as the dependent variable, curve fitting was performed to obtain the relationship between the differences in X-axis, Y-axis, and Z-axis coordinates between the second position data sequence and the first position data sequence and time. These three relationships were denoted as the first function, the second function, and the third function, respectively.

[0055] The first, second, and third functions are collectively referred to as the tracking correlation. This tracking correlation characterizes the motion following pattern of the intruding drone relative to the tracked target in three-dimensional space before the signal disappears, providing a core mathematical model for subsequently deducing the position of the intruding drone after the signal disappears. It is worth noting that existing drone intrusion technologies typically use pattern recognition to track the target. During the tracking process, the following pattern is fixed; therefore, the tracking correlation identified in this embodiment is a quantitative analysis of this following pattern.

[0056] Further, the method of subtracting the X-axis coordinates of the second position data sequence from the X-axis coordinates of the first position data sequence, subtracting the Y-axis coordinates of the second position data sequence from the Y-axis coordinates of the first position data sequence, subtracting the Z-axis coordinates of the second position data sequence from the Z-axis coordinates of the first position data sequence, and fitting these subtractions to obtain the relationship between the difference between the X-axis coordinates of the second position data sequence and the X-axis coordinates of the first position data sequence over time, denoted as a first function; fitting the relationship between the difference between the Y-axis coordinates of the second position data sequence and the Y-axis coordinates of the first position data sequence over time, denoted as a second function; and fitting the relationship between the difference between the Z-axis coordinates of the second position data sequence and the Z-axis coordinates of the first position data sequence over time, denoted as a third function, includes: The X-axis displacement data sequence is obtained by subtracting the X-axis coordinate values ​​of the second position data sequence from the X-axis coordinate values ​​of the first position data sequence. to The calculation formula for the X-axis displacement data sequence is as follows: ; in, This represents the first [number] in the X-axis displacement data sequence. One element; This represents the first position in the X-axis coordinate value of the second position data sequence. One element; This represents the first position in the X-axis coordinate value of the data sequence. One element; The value is 1 to Integer variables.

[0057] The Y-axis displacement data sequence is obtained by subtracting the Y-axis coordinate values ​​of the second position data sequence from the Y-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Y-axis displacement data sequence is as follows: ; in, This represents the first [number] in the Y-axis displacement data sequence. One element; This indicates the first position in the Y-axis coordinate value of the second position data sequence. One element; This represents the first position data sequence's Y-axis coordinate value. Each element.

[0058] The Z-axis displacement data sequence is obtained by subtracting the Z-axis coordinate values ​​of the second position data sequence from the Z-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Z-axis displacement data sequence is as follows: ; in, This represents the first Z-axis displacement data sequence. One element; This represents the Z-axis coordinate value of the second position data sequence. One element; This represents the Z-axis coordinate value of the first position data sequence. Each element.

[0059] Construct X-axis displacement data sample points, Y-axis displacement data sample points, and Z-axis displacement data sample points, which are respectively represented as... , , ;in, This indicates the pre-set time interval for scanning the real-time location data of the intruding drone.

[0060] make Traverse 1 to The value of , in As the independent variable, with Using the variable as the dependent variable, a nonlinear least squares fitting algorithm is used to fit the first function. The sum of squared residuals during the process of fitting the first function using the nonlinear least squares fitting algorithm is recorded and denoted as the first residual sum of squares. As the independent variable, with Using the variable as the dependent variable, a second function is obtained by fitting it using a nonlinear least squares fitting algorithm. The sum of squared residuals during the process of fitting the second function using the nonlinear least squares fitting algorithm is recorded and denoted as the second residual sum of squares. As the independent variable, with The third function is obtained by fitting a nonlinear least squares fitting algorithm with as the dependent variable. The sum of squared residuals in the process of fitting the third function with the nonlinear least squares fitting algorithm is recorded and denoted as the third residual sum of squares.

[0061] Furthermore, the method of deriving the real-time location data of the intruding drone after the disappearance of the real-time location data signal based on the tracking correlation and the third location data sequence, and denoting it as the fourth location data sequence, includes: The third position data sequence is converted into three-dimensional spatial coordinate points, and time-stamped with the starting points of the first and second position data sequences as the time origin for the three-dimensional spatial coordinate points obtained from the conversion of the third position data sequence, denoted as . The time corresponding to the starting point of the third position data sequence is denoted as... ; This represents the time difference between the moment corresponding to the data in the third position data sequence and the time origin.

[0062] The spatial three-dimensional coordinates of the fourth position data sequence were calculated. The formula for calculating the spatial three-dimensional coordinates of the fourth position data sequence is as follows: ; in, Represents the first function, Indicates the second function, This represents the third function.

[0063] The spatial three-dimensional coordinate points corresponding to the fourth location data sequence are mapped back to geographic space to obtain the fourth location data sequence.

[0064] Furthermore, the method for identifying the real-time location data of an intruding drone by setting the scanning center of the signal scanning device to the spatial location indicated by the fourth location data sequence includes: Calculate the average of the first residual sum of squares, the second residual sum of squares, and the third residual sum of squares to obtain the mean value of the residual sum of squares; calculate the scan concentration value based on the mean value of the residual sum of squares; set the half-power beamwidth of the signal scanning device to the scan concentration value, set the scanning center of the signal scanning device to the spatial position indicated by the fourth position data sequence, and identify the real-time position data of the intruding UAV; the scanning range of the signal scanning device is a spatial cone.

[0065] The scan concentration is taken as the minimum value between the preset upper limit of half-power beamwidth and the linear correlation value of scan concentration; wherein, the formula for calculating the linear correlation value of scan concentration is: ; in, This indicates the linear correlation value of the scan concentration. This represents the mean of the sum of squared residuals. This represents the pre-set correlation coefficient. This indicates the pre-set lower limit of the half-power beamwidth.

[0066] For example, the average of the first, second, and third residual sums of squares is calculated to obtain the mean of the residual sums of squares. This mean of the residual sums of squares serves as a comprehensive quantitative indicator, reflecting the overall accuracy of the function fitting to the three axial displacement differences. A larger mean of the residual sums of squares indicates a greater deviation in the description of historical data by the fitted function, and consequently, a higher uncertainty in its prediction.

[0067] The scan concentration value is calculated based on the mean of the residual sum of squares. Specifically, the scan concentration value is the minimum of the preset upper limit of half-power beamwidth (i.e., 30 degrees) and the linear correlation value of scan concentration. The linear correlation value of scan concentration is obtained by multiplying the mean of the residual sum of squares by a preset correlation coefficient (i.e., 0.025 degrees per meter squared) and adding it to a preset lower limit of half-power beamwidth (i.e., 5 degrees). This calculation logic ensures that the final scan concentration value is limited to the preset upper and lower limits, and the larger the fitting error, the more the scan concentration value tends towards the upper limit, and vice versa.

[0068] The half-power beamwidth of the signal scanning device is set to the scanning concentration value determined in the preceding steps, thereby determining the angular size of the spatial cone-shaped region formed by the signal scanning device during scanning. A smaller scanning concentration value results in a narrower beamwidth and more concentrated energy; a larger value results in a wider beamwidth and a larger coverage area. The scanning center of the signal scanning device is set to the spatial position indicated by the fourth position data sequence. Simultaneously, based on the set scanning center and half-power beamwidth, the signal scanning device identifies the real-time position data of the intruding UAV within its defined spatial cone-shaped region. Through this dynamically adaptive scanning method, the width of the search range can be intelligently adjusted according to the uncertainty of the fitted model.

[0069] Example 2: Based on the same inventive concept, such as Figure 2 As shown, this embodiment also provides a drone tracking and countermeasure system suitable for multiple scenarios, the system comprising: The data reading module is used to acquire the real-time location data of the intruding drone identified by the signal scanning device, which is recorded as the first location data sequence; acquire the real-time location data of the tracked target identified by the signal receiving device, which is recorded as the second location data sequence; the tracked target transmits a positioning signal so that the signal receiving device can receive the real-time location data signal of the tracked target in complex terrain.

[0070] The third position data sequence calculation module, connected to the data reading module, is used to increase the signal transmission power of the tracked target for positioning after the real-time position data signal of the intruding drone disappears, and continue to acquire the real-time position data of the tracked target through the signal receiving device, which is recorded as the third position data sequence.

[0071] The fourth position data sequence calculation module, connected to the third position data sequence calculation module, is used to calculate the correlation between the real-time position data of the intruding drone and the real-time position data of the tracked target based on the first position data sequence and the second position data sequence, and is denoted as the tracking correlation relationship; based on the tracking correlation relationship and the third position data sequence, the real-time position data of the intruding drone after the real-time position data signal of the intruding drone disappears is deduced and is denoted as the fourth position data sequence.

[0072] The real-time location data identification module for intrusion drones is connected to the fourth location data sequence calculation module. It is used to set the scanning center of the signal scanning device to the spatial location indicated by the fourth location data sequence, and to identify the real-time location data of the intrusion drones.

[0073] Furthermore, the fourth position data sequence calculation module includes: The coordinate transformation module is used to convert the first position data sequence and the second position data sequence into three-dimensional spatial coordinate points, and perform time alignment to obtain the X-axis coordinate values ​​of the first position data sequence as follows: to The Y-axis coordinates of the first position data sequence are as follows: to The Z-axis coordinates of the first position data sequence are as follows: to The X-axis coordinates of the second position data sequence are as follows: to The Y-axis coordinates of the second position data sequence are as follows: to The Z-axis coordinates of the second position data sequence are as follows: to ;in, This indicates the number of data items contained in the first position data sequence, and also the number of data items contained in the second position data sequence.

[0074] The data fitting module, connected to the coordinate transformation module, is used to calculate the difference between the X-axis coordinates of the second position data sequence and the X-axis coordinates of the first position data sequence, the difference between the Y-axis coordinates of the second position data sequence and the Y-axis coordinates of the first position data sequence, and the difference between the Z-axis coordinates of the second position data sequence and the Z-axis coordinates of the first position data sequence, respectively. The module then fits these differences over time to obtain the relationship between the X-axis and Y-axis coordinates of the second position data sequence and the first position data sequence, denoted as the first function. Similarly, it fits the difference between the Y-axis and Y-axis coordinates of the second position data sequence and the first position data sequence, denoted as the second function, and fits the difference between the Z-axis and Z-axis coordinates of the second position data sequence and the first position data sequence, denoted as the third function. All three functions are collectively referred to as the tracking correlation relationship.

[0075] Furthermore, the data fitting module includes: The displacement data sequence calculation module is used to calculate the difference between the X-axis coordinate values ​​of the second position data sequence and the X-axis coordinate values ​​of the first position data sequence to obtain the X-axis displacement data sequence. to The calculation formula for the X-axis displacement data sequence is as follows: ; in, This represents the first [number] in the X-axis displacement data sequence. One element; This represents the first position in the X-axis coordinate value of the second position data sequence. One element; This represents the first position in the X-axis coordinate value of the data sequence. One element; The value is 1 to Integer variables.

[0076] The Y-axis displacement data sequence is obtained by subtracting the Y-axis coordinate values ​​of the second position data sequence from the Y-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Y-axis displacement data sequence is as follows: ; in, This represents the first [number] in the Y-axis displacement data sequence. One element; This indicates the first position in the Y-axis coordinate value of the second position data sequence. One element; This represents the first position data sequence's Y-axis coordinate value. Each element.

[0077] The Z-axis displacement data sequence is obtained by subtracting the Z-axis coordinate values ​​of the second position data sequence from the Z-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Z-axis displacement data sequence is as follows: ; in, This represents the first Z-axis displacement data sequence. One element; This represents the Z-axis coordinate value of the second position data sequence. One element; This represents the Z-axis coordinate value of the first position data sequence. Each element.

[0078] The sample point construction module, connected to the displacement data sequence calculation module, is used to construct X-axis displacement data sample points, Y-axis displacement data sample points, and Z-axis displacement data sample points. These X-axis, Y-axis, and Z-axis displacement data sample points are respectively represented as... , , ;in, This indicates the pre-set time interval for scanning the real-time location data of the intruding drone.

[0079] The fitting calculation module, connected to the sample point construction module, is used to enable... Traverse 1 to The value of , in As the independent variable, with Using the variable as the dependent variable, a nonlinear least squares fitting algorithm is used to fit the first function. The sum of squared residuals during the process of fitting the first function using the nonlinear least squares fitting algorithm is recorded and denoted as the first residual sum of squares. As the independent variable, with Using the variable as the dependent variable, a second function is obtained by fitting it using a nonlinear least squares fitting algorithm. The sum of squared residuals during the process of fitting the second function using the nonlinear least squares fitting algorithm is recorded and denoted as the second residual sum of squares. As the independent variable, with The third function is obtained by fitting a nonlinear least squares fitting algorithm with as the dependent variable. The sum of squared residuals in the process of fitting the third function with the nonlinear least squares fitting algorithm is recorded and denoted as the third residual sum of squares.

[0080] Furthermore, the fourth position data sequence calculation module also includes: The data extrapolation module is used to convert the third position data sequence into spatial three-dimensional coordinate points, and to add time labels to the spatial three-dimensional coordinate points obtained from the conversion of the third position data sequence, using the starting points of the first and second position data sequences as the time origin, denoted as... The time corresponding to the starting point of the third position data sequence is denoted as... ; This represents the time difference between the moment corresponding to the data in the third position data sequence and the time origin.

[0081] The spatial three-dimensional coordinates of the fourth position data sequence were calculated. The formula for calculating the spatial three-dimensional coordinates of the fourth position data sequence is as follows: ; in, Represents the first function, Indicates the second function, This represents the third function.

[0082] The spatial three-dimensional coordinate points corresponding to the fourth location data sequence are mapped back to geographic space to obtain the fourth location data sequence.

[0083] Furthermore, the real-time location data identification module for the intruding drone includes: The scanning range calculation module is used to calculate the average of the first residual sum of squares, the second residual sum of squares, and the third residual sum of squares to obtain the mean value of the residual sum of squares; the scanning concentration value is calculated based on the mean value of the residual sum of squares; the half-power beamwidth of the signal scanning device is set as the scanning concentration value, and the scanning center of the signal scanning device is set as the spatial position indicated by the fourth position data sequence to identify the real-time position data of the intruding UAV; the scanning range of the signal scanning device is a spatial cone.

[0084] The scan concentration is taken as the minimum value between the preset upper limit of half-power beamwidth and the linear correlation value of scan concentration; wherein, the formula for calculating the linear correlation value of scan concentration is: ; in, This indicates the linear correlation value of the scan concentration. This represents the mean of the sum of squared residuals. This represents the pre-set correlation coefficient. This indicates the pre-set lower limit of the half-power beamwidth.

[0085] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0086] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for tracking and countering drones applicable to multiple scenarios, characterized in that, The method includes the following steps: S1, acquire the real-time location data of the intruding drone identified by the signal scanning device, and record it as the first location data sequence; acquire the real-time location data of the tracked target identified by the signal receiving device, and record it as the second location data sequence; the tracked target transmits a signal for positioning so that the signal receiving device can receive the real-time location data signal of the tracked target in complex terrain. S2, When the real-time location data signal of the intruding drone disappears, the signal transmission power of the tracked target used for positioning is increased, and the real-time location data of the tracked target is continued to be acquired through the signal receiving device, which is recorded as the third location data sequence; S3, calculate the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target based on the first location data sequence and the second location data sequence, and record it as the tracking correlation; based on the tracking correlation and the third location data sequence, deduce the real-time location data of the intruding drone after the real-time location data signal of the intruding drone disappears, and record it as the fourth location data sequence; S4, set the scanning center of the signal scanning device to the spatial position indicated by the fourth position data sequence, and identify the real-time position data of the intruding drone.

2. The method for tracking and countering drones in multiple scenarios as described in claim 1, characterized in that, The method for calculating the correlation between the real-time location data of the intruding drone and the real-time location data of the tracked target based on the first location data sequence and the second location data sequence, denoted as the tracking correlation relationship, includes: The first and second position data sequences are converted into three-dimensional spatial coordinate points and time-aligned to obtain the X-axis coordinate values ​​of the first position data sequence as follows: to The Y-axis coordinates of the first position data sequence are as follows: to The Z-axis coordinates of the first position data sequence are as follows: to The X-axis coordinates of the second position data sequence are as follows: to The Y-axis coordinates of the second position data sequence are as follows: to The Z-axis coordinates of the second position data sequence are as follows: to ;in, This indicates the number of data items contained in the first position data sequence, and also the number of data items contained in the second position data sequence; The differences between the X-axis and Y-axis coordinates of the second and first position data sequences are calculated separately. The differences between the Y-axis and Z-axis coordinates of the second and first position data sequences are also calculated separately. These differences are then fitted to obtain the relationship between the X-axis and Z-axis coordinate differences of the second and first position data sequences over time, denoted as the first function. The relationships between the Y-axis and Z-axis coordinate differences of the second and first position data sequences over time are also fitted, denoted as the second function. Finally, the relationships between the Z-axis and Z-axis coordinate differences of the second and first position data sequences over time are fitted, denoted as the third function. All three functions are collectively referred to as the tracking correlation relationship.

3. The method for tracking and countering drones in multiple scenarios as described in claim 2, characterized in that, The method of subtracting the X-axis coordinates of the second position data sequence from the X-axis coordinates of the first position data sequence, subtracting the Y-axis coordinates of the second position data sequence from the Y-axis coordinates of the first position data sequence, subtracting the Z-axis coordinates of the second position data sequence from the Z-axis coordinates of the first position data sequence, and fitting these subtractions to obtain the relationship between the difference of the X-axis coordinates of the second position data sequence and the first position data sequence over time, denoted as a first function; fitting the relationship between the difference of the Y-axis coordinates of the second position data sequence and the first position data sequence over time, denoted as a second function; and fitting the relationship between the difference of the Z-axis coordinates of the second position data sequence and the first position data sequence over time, denoted as a third function, includes: The X-axis displacement data sequence is obtained by subtracting the X-axis coordinate values ​​of the second position data sequence from the X-axis coordinate values ​​of the first position data sequence. to The calculation formula for the X-axis displacement data sequence is as follows: ； in, This represents the first [number] in the X-axis displacement data sequence. One element; This represents the first position in the X-axis coordinate value of the second position data sequence. One element; This represents the first position in the X-axis coordinate value of the data sequence. One element; The value is 1 to Integer variables; The Y-axis displacement data sequence is obtained by subtracting the Y-axis coordinate values ​​of the second position data sequence from the Y-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Y-axis displacement data sequence is as follows: ； in, This represents the first [number] in the Y-axis displacement data sequence. One element; This indicates the first position in the Y-axis coordinate value of the second position data sequence. One element; This represents the first position data sequence's Y-axis coordinate value. One element; The Z-axis displacement data sequence is obtained by subtracting the Z-axis coordinate values ​​of the second position data sequence from the Z-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Z-axis displacement data sequence is as follows: ； in, This represents the first Z-axis displacement data sequence. One element; This represents the Z-axis coordinate value of the second position data sequence. One element; This represents the Z-axis coordinate value of the first position data sequence. One element; Construct X-axis displacement data sample points, Y-axis displacement data sample points, and Z-axis displacement data sample points, which are respectively represented as... , , ;in, This indicates the pre-set real-time location data scanning time interval for intrusive drones; make Traverse 1 to The value of , in As the independent variable, with Using the variable as the dependent variable, a nonlinear least squares fitting algorithm is used to fit the first function. The sum of squared residuals during the process of fitting the first function using the nonlinear least squares fitting algorithm is recorded and denoted as the first residual sum of squares. As the independent variable, with Using the variable as the dependent variable, a second function is obtained by fitting it using a nonlinear least squares fitting algorithm. The sum of squared residuals during the process of fitting the second function using the nonlinear least squares fitting algorithm is recorded and denoted as the second residual sum of squares. As the independent variable, with The third function is obtained by fitting a nonlinear least squares fitting algorithm with as the dependent variable. The sum of squared residuals in the process of fitting the third function with the nonlinear least squares fitting algorithm is recorded and denoted as the third residual sum of squares.

4. The method for tracking and countering drones in multiple scenarios as described in claim 3, characterized in that, The method of deriving the real-time location data of the intruding drone after the disappearance of the real-time location data signal based on the tracking correlation and the third location data sequence, and denoting it as the fourth location data sequence, includes: The third position data sequence is converted into three-dimensional spatial coordinate points, and time-stamped with the starting points of the first and second position data sequences as the time origin for the three-dimensional spatial coordinate points obtained from the conversion of the third position data sequence, denoted as . The time corresponding to the starting point of the third position data sequence is denoted as... ; This represents the time difference between the moment corresponding to the data in the third position data sequence and the time origin; The spatial three-dimensional coordinates of the fourth position data sequence were calculated. The formula for calculating the spatial three-dimensional coordinates of the fourth position data sequence is as follows: ； in, Represents the first function, Indicates the second function, Indicates the third function; The spatial three-dimensional coordinate points corresponding to the fourth location data sequence are mapped back to geographic space to obtain the fourth location data sequence.

5. The method for tracking and countering drones in multiple scenarios as described in claim 4, characterized in that, The method for identifying the real-time location data of an intruding drone by setting the scanning center of the signal scanning device to the spatial location indicated by the fourth location data sequence includes: The average values ​​of the first residual sum of squares, the second residual sum of squares, and the third residual sum of squares are calculated to obtain the mean value of the residual sum of squares; the scan concentration value is calculated based on the mean value of the residual sum of squares; the half-power beamwidth of the signal scanning device is set as the scan concentration value, and the scan center of the signal scanning device is set as the spatial position indicated by the fourth position data sequence to identify the real-time position data of the intruding UAV; the scanning range of the signal scanning device is a spatial cone. The scan concentration is taken as the minimum value between the preset upper limit of half-power beamwidth and the linear correlation value of scan concentration; wherein, the formula for calculating the linear correlation value of scan concentration is: ； in, This indicates the linear correlation value of the scan concentration. This represents the mean of the sum of squared residuals. This represents the pre-set correlation coefficient. This indicates the pre-set lower limit of the half-power beamwidth.

6. A drone tracking and countermeasure system applicable to multiple scenarios, characterized in that, The system includes: The data reading module is used to acquire the real-time location data of the intruding drone identified by the signal scanning device, which is recorded as the first location data sequence; acquire the real-time location data of the tracked target identified by the signal receiving device, which is recorded as the second location data sequence; the tracked target transmits a signal for positioning so that the signal receiving device can receive the real-time location data signal of the tracked target in complex terrain. The third position data sequence calculation module is connected to the data reading module. It is used to increase the signal transmission power of the tracked target for positioning after the real-time position data signal of the intruding drone disappears, and continue to acquire the real-time position data of the tracked target through the signal receiving device, which is recorded as the third position data sequence. The fourth position data sequence calculation module, connected to the third position data sequence calculation module, is used to calculate the correlation between the real-time position data of the intruding drone and the real-time position data of the tracked target based on the first position data sequence and the second position data sequence, which is denoted as the tracking correlation; and to deduce the real-time position data of the intruding drone after the real-time position data signal of the intruding drone disappears based on the tracking correlation and the third position data sequence, which is denoted as the fourth position data sequence. The real-time location data identification module for intrusion drones is connected to the fourth location data sequence calculation module. It is used to set the scanning center of the signal scanning device to the spatial location indicated by the fourth location data sequence, and to identify the real-time location data of the intrusion drones.

7. A drone tracking and countermeasure system applicable to multiple scenarios as described in claim 6, characterized in that, The fourth position data sequence calculation module includes: The coordinate transformation module is used to convert the first position data sequence and the second position data sequence into three-dimensional spatial coordinate points, and perform time alignment to obtain the X-axis coordinate values ​​of the first position data sequence as follows: to The Y-axis coordinates of the first position data sequence are as follows: to The Z-axis coordinates of the first position data sequence are as follows: to The X-axis coordinates of the second position data sequence are as follows: to The Y-axis coordinates of the second position data sequence are as follows: to The Z-axis coordinates of the second position data sequence are as follows: to ;in, This indicates the number of data items contained in the first position data sequence, and also the number of data items contained in the second position data sequence; The data fitting module, connected to the coordinate transformation module, is used to calculate the difference between the X-axis coordinates of the second position data sequence and the X-axis coordinates of the first position data sequence, the difference between the Y-axis coordinates of the second position data sequence and the Y-axis coordinates of the first position data sequence, and the difference between the Z-axis coordinates of the second position data sequence and the Z-axis coordinates of the first position data sequence, respectively. The module then fits these differences over time to obtain the relationship between the X-axis and Y-axis coordinates of the second position data sequence and the first position data sequence, denoted as the first function. Similarly, it fits the difference between the Y-axis and Y-axis coordinates of the second position data sequence and the first position data sequence, denoted as the second function, and fits the difference between the Z-axis and Z-axis coordinates of the second position data sequence and the first position data sequence, denoted as the third function. All three functions are collectively referred to as the tracking correlation relationship.

8. A drone tracking and countermeasure system applicable to multiple scenarios as described in claim 7, characterized in that, The data fitting module includes: The displacement data sequence calculation module is used to calculate the difference between the X-axis coordinate values ​​of the second position data sequence and the X-axis coordinate values ​​of the first position data sequence to obtain the X-axis displacement data sequence. to The calculation formula for the X-axis displacement data sequence is as follows: ； in, This represents the first [number] in the X-axis displacement data sequence. One element; This represents the first position in the X-axis coordinate value of the second position data sequence. One element; This represents the first position in the X-axis coordinate value of the data sequence. One element; The value is 1 to Integer variables; The Y-axis displacement data sequence is obtained by subtracting the Y-axis coordinate values ​​of the second position data sequence from the Y-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Y-axis displacement data sequence is as follows: ； in, This represents the first [number] in the Y-axis displacement data sequence. One element; This indicates the first position in the Y-axis coordinate value of the second position data sequence. One element; This represents the first position data sequence's Y-axis coordinate value. One element; The Z-axis displacement data sequence is obtained by subtracting the Z-axis coordinate values ​​of the second position data sequence from the Z-axis coordinate values ​​of the first position data sequence. to The calculation formula for the Z-axis displacement data sequence is as follows: ； in, This represents the first Z-axis displacement data sequence. One element; This represents the Z-axis coordinate value of the second position data sequence. One element; This represents the Z-axis coordinate value of the first position data sequence. One element; The sample point construction module, connected to the displacement data sequence calculation module, is used to construct X-axis displacement data sample points, Y-axis displacement data sample points, and Z-axis displacement data sample points. These X-axis, Y-axis, and Z-axis displacement data sample points are respectively represented as... , , ;in, This indicates the pre-set real-time location data scanning time interval for intrusive drones; The fitting calculation module, connected to the sample point construction module, is used to enable... Traverse 1 to The value of , in As the independent variable, with Using the variable as the dependent variable, a nonlinear least squares fitting algorithm is used to fit the first function. The sum of squared residuals during the process of fitting the first function using the nonlinear least squares fitting algorithm is recorded and denoted as the first residual sum of squares. As the independent variable, with Using the variable as the dependent variable, a second function is obtained by fitting it using a nonlinear least squares fitting algorithm. The sum of squared residuals during the process of fitting the second function using the nonlinear least squares fitting algorithm is recorded and denoted as the second residual sum of squares. As the independent variable, with The third function is obtained by fitting a nonlinear least squares fitting algorithm with as the dependent variable. The sum of squared residuals in the process of fitting the third function with the nonlinear least squares fitting algorithm is recorded and denoted as the third residual sum of squares.

9. A drone tracking and countermeasure system applicable to multiple scenarios as described in claim 8, characterized in that, The fourth position data sequence calculation module also includes: The data extrapolation module is used to convert the third position data sequence into spatial three-dimensional coordinate points, and to add time labels to the spatial three-dimensional coordinate points obtained from the conversion of the third position data sequence, using the starting points of the first and second position data sequences as the time origin, denoted as... The time corresponding to the starting point of the third position data sequence is denoted as... ; This represents the time difference between the moment corresponding to the data in the third position data sequence and the time origin; The spatial three-dimensional coordinates of the fourth position data sequence were calculated. The formula for calculating the spatial three-dimensional coordinates of the fourth position data sequence is as follows: ； in, Represents the first function, Indicates the second function, Indicates the third function; The spatial three-dimensional coordinate points corresponding to the fourth location data sequence are mapped back to geographic space to obtain the fourth location data sequence.

10. A drone tracking and countermeasure system applicable to multiple scenarios as described in claim 9, characterized in that, The real-time location data identification module for the intrusion drone includes: The scanning range calculation module is used to calculate the average of the first residual sum of squares, the second residual sum of squares, and the third residual sum of squares to obtain the mean value of the residual sum of squares; the scanning concentration value is calculated based on the mean value of the residual sum of squares; the half-power beamwidth of the signal scanning device is set as the scanning concentration value, and the scanning center of the signal scanning device is set as the spatial position indicated by the fourth position data sequence to identify the real-time position data of the intruding drone; the scanning range of the signal scanning device is a spatial cone. The scan concentration is taken as the minimum value between the preset upper limit of half-power beamwidth and the linear correlation value of scan concentration; wherein, the formula for calculating the linear correlation value of scan concentration is: ； in, This indicates the linear correlation value of the scan concentration. This represents the mean of the sum of squared residuals. This represents the pre-set correlation coefficient. This indicates the pre-set lower limit of the half-power beamwidth.

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