Main route extraction method and system for air target electronic activity analysis

Abstract

The application discloses a main route extraction method and system for air target electronic activity analysis, relates to the technical field of electronic reconnaissance and information processing, and discloses the main route extraction method and system for air target electronic activity analysis. Through a series of processing steps such as validity verification, coordinate conversion, direction correction, equi-arc length resampling, median statistical fusion, geometric simplification processing and statistical aggregation of electronic activity parameters of multi-source track point data, main route information can be accurately extracted from complex air target track data, the accuracy and reliability of electronic activity parameter analysis are enhanced while the main route extraction precision and robustness are improved.

Description

Methods and Systems for Main Flight Path Extraction in Electronic Activity Analysis of Airborne Targets Technical Field

[0001] This application relates to the field of electronic reconnaissance and intelligence processing technology, and in particular to a method and system for extracting main flight paths for electronic activity analysis of aerial targets. Background Technology

[0002] As a core component of situational awareness and intelligence processing, electronic activity analysis of airborne targets is crucial for accurately identifying and extracting the main flight path that characterizes the typical flight path of a target from continuous trajectory data collected by multi-source sensor systems (including radar detection systems, electronic reconnaissance systems, and communication reconnaissance systems). However, current technologies face multiple obstacles in achieving this goal: The fundamental differences in coordinate systems used by various sensors, uneven sampling frequency distribution, and frequent data gaps make spatial alignment and temporal synchronization of trajectory data extremely difficult, hindering effective integration and comparative analysis. Furthermore, the flight trajectories of airborne targets often exhibit directional inconsistencies due to sensor deployment deviations, dynamic adjustments to mission commands, or external environmental interference. Without precise correction, the target's flight characteristics cannot be accurately reconstructed. Traditional trajectory fusion methods lack robustness to measurement noise and abnormal trajectory points during processing, easily generating main flight paths that deviate from the target's actual flight path, resulting in distorted analysis results. Simultaneously, the spatial mapping relationship between electronic activity parameters (such as radar operating modes and communication operating modes) and the main flight path is difficult to establish accurately due to inconsistencies in trajectory data, leading to insufficient reliability of statistical aggregation results of electronic activity features. The aforementioned deficiencies result in limited accuracy and low computational efficiency in the main route extraction process, which restricts the ability to quantitatively analyze and visualize subsequent electronic activity characteristics, and fails to meet the real-time and accuracy requirements of modern intelligence processing systems.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for extracting the main flight path for electronic activity analysis of airborne targets, aiming to improve the accuracy and robustness of the main flight path extraction.

[0005] To achieve the above objectives, this application proposes a method for extracting the main flight path for electronic activity analysis of airborne targets, the method comprising:

[0006] Acquire multi-source trajectory point data of aerial targets, perform validity verification on the multi-source trajectory point data, remove invalid trajectory points with missing position information or missing time information, generate valid trajectory point data, and ensure that each trajectory contains at least a preset number of valid trajectory points;

[0007] The spherical latitude and longitude coordinates in the effective trajectory point data are converted into planar coordinate data in a Cartesian coordinate system through a local planar projection method based on the reference latitude of the trajectory set;

[0008] A reference trajectory is selected from the trajectory set corresponding to the plane coordinate data. The spatial matching degree between each of the remaining trajectories and the reference trajectory is calculated in both the forward and reverse directions. When the reverse matching degree is better than the forward matching degree, the plane coordinate data of the corresponding trajectory is reversed to generate plane coordinate data with corrected direction.

[0009] The plane coordinate data after the direction correction is subjected to equal arc length resampling processing. Based on the total arc length of each trajectory, a uniform number of sampling points are generated at preset arc length intervals. The plane coordinate components and height information of all sampling points are determined by interpolation to generate equal arc length sampling point data.

[0010] For the set of all trajectory points at the same sampling number position in the equal arc length sampling point data, the median statistics of the plane coordinate components and height information are performed respectively to generate the fusion point corresponding to each sampling number position. All fusion points are connected in the order of sampling number to form the main line point sequence data.

[0011] The main route point sequence data is geometrically simplified to generate simplified main route point sequence data, and spatial location information and attribute information are generated for each point in the simplified main route point sequence data to obtain the final main route data.

[0012] The radar operating mode data and communication operating mode data associated with the original trajectory point data are mapped to the final master flight path data. Statistical aggregation is then performed based on the spatial projection position of electronic activity parameters on the final master flight path data to generate electronic activity analysis data.

[0013] In one embodiment, the step of validating the multi-source trajectory point data, removing invalid trajectory points with missing location or time information, and generating valid trajectory point data includes:

[0014] For each trajectory point in the acquired multi-source trajectory point data, determine whether it simultaneously contains longitude information, latitude information, and timestamp information;

[0015] For trajectory points that lack any of the following information: longitude, latitude, or timestamp, mark them as invalid trajectory points.

[0016] For a trajectory point with complete information, calculate the time interval between the current trajectory point and the previous trajectory point. When the time interval exceeds a preset time threshold, mark the current trajectory point as an invalid trajectory point.

[0017] All invalid trajectory points are removed, and valid trajectory points are retained to generate the valid trajectory point data.

[0018] In one embodiment, the step of converting the spherical latitude and longitude coordinates in the effective trajectory point data into planar coordinate data in a Cartesian coordinate system through a local planar projection method based on the reference latitude of the trajectory set includes:

[0019] Based on the effective trajectory point data, the average latitude of all trajectory points in the effective trajectory point data is calculated as the reference latitude.

[0020] A local Cartesian coordinate system is established using the meridian corresponding to the reference latitude as the central meridian.

[0021] The latitude and longitude coordinates of each trajectory point in the effective trajectory point data are converted into planar coordinate components in the Cartesian coordinate system through projection calculation.

[0022] The converted planar coordinate components are combined with the height information in the effective trajectory point data to generate the planar coordinate data.

[0023] In one embodiment, the steps of selecting a reference trajectory from the trajectory set corresponding to the planar coordinate data, calculating the spatial matching degree between each of the remaining trajectories in both forward and reverse directions, and reversing the direction of the planar coordinate data of the corresponding trajectory when the reverse matching degree is better than the forward matching degree to generate the direction-corrected planar coordinate data include:

[0024] Select a trajectory from the trajectory set corresponding to the plane coordinate data as a reference trajectory, and obtain the plane coordinates of the starting point and the plane coordinates of the ending point of the reference trajectory;

[0025] For each of the remaining trajectories in the planar coordinate data, obtain the starting point planar coordinates and the ending point planar coordinates of the current trajectory;

[0026] The planar distance between the starting point of the current trajectory and the starting point of the reference trajectory is calculated as the first planar distance, and the planar distance between the ending point of the current trajectory and the ending point of the reference trajectory is calculated as the second planar distance. The first planar distance and the second planar distance are added together to obtain the positive matching distance.

[0027] The current trajectory's termination point is taken as the reverse starting point, and the starting point is taken as the reverse termination point. The planar distance between the reverse starting point and the reference trajectory's starting point is calculated as the third planar distance, and the planar distance between the reverse termination point and the reference trajectory's termination point is calculated as the fourth planar distance. The third planar distance and the fourth planar distance are added together to obtain the reverse matching distance.

[0028] The forward matching distance and the reverse matching distance are compared. When the reverse matching distance is less than the forward matching distance, the plane coordinate data of the current trajectory is reversed to generate the plane coordinate data after direction correction.

[0029] In one embodiment, the planar coordinate data after direction correction is subjected to equal arc length resampling processing. A uniform number of sampling points are generated according to a preset arc length interval based on the total arc length of each trajectory. The planar coordinate components and height information of all sampling points are determined by interpolation. The steps for generating the number of equal arc length sampling points include:

[0030] For each trajectory in the directionally corrected planar coordinate data, the total arc length of the trajectory is calculated based on the sum of the planar distances between all adjacent trajectory points in the trajectory;

[0031] Based on the preset number of sampling points, the total arc length is divided into equal arc segments of equal length (number of sampling points minus one), and the arc length interval between adjacent sampling points is calculated.

[0032] Sampling positions are generated on the trajectory according to the arc length intervals. Starting from the starting point of the trajectory, a sampling position is determined every arc length interval along the direction of trajectory movement.

[0033] For each sampling location, find two adjacent original trajectory points where the sampling location is located. Based on the distance ratio from the sampling location to these two original trajectory points, perform a weighted calculation on the planar coordinate components of the two original trajectory points to obtain the planar coordinate components of the sampling location.

[0034] For each sampling location, find two adjacent original trajectory points where the sampling location is located, and calculate the height information of the two original trajectory points by weighting them according to the distance ratio from the sampling location to the two original trajectory points to obtain the height information of the sampling location;

[0035] The planar coordinate components and height information of all sampling positions on the same trajectory are combined to form the equal arc length sampling data of the trajectory, and the equal arc length sampling data of all trajectories are combined to generate the equal arc length sampling point data.

[0036] In one embodiment, the steps of performing median statistics on the planar coordinate components and height information for all trajectory points at the same sampling index position in the equal arc length sampling point data, generating a fusion point corresponding to each sampling index position, and connecting all fusion points in the order of sampling index to form the main line point sequence data include:

[0037] For each sampling sequence number position in the equal arc length sampling point data, collect all the sampling point data corresponding to the sampling sequence number position of the trajectory to form the sampling point set corresponding to the current sampling sequence number position;

[0038] For all sampling points in the sampling point set, extract the horizontal and vertical component values ​​of the planar coordinates respectively;

[0039] Sort all the horizontal component values ​​by size, and select the horizontal component value located in the middle position as the horizontal component of the main route fusion point at that sampling number position;

[0040] Sort all the vertical component values ​​by size, and select the vertical component value in the middle position as the vertical component of the main flight path fusion point at that sampling number position;

[0041] Sort all the height information values ​​by size, and select the height information value in the middle position as the height information of the main route fusion point at that sampling number position;

[0042] The horizontal component, the vertical component, and the height information are combined to form the main line fusion point at the sampling sequence number position. All the main line fusion points at the sampling sequence number positions are connected in order to form the main line point sequence data.

[0043] In one embodiment, the step of performing geometric simplification on the main waypoint sequence data to generate simplified main waypoint sequence data includes:

[0044] Traverse all points in the main route point sequence data to obtain the planar coordinate components of each point;

[0045] Starting from the starting point of the main route point sequence data, add the starting point to the simplified point set;

[0046] Using the starting point as the reference point, calculate the distance between the reference point and each subsequent point;

[0047] Determine whether the distance of the connecting line exceeds a preset simplification threshold. When the distance of the connecting line exceeds the simplification threshold, add the nearest point before the current point to the set of simplified points and use that point as the new reference point.

[0048] Repeat the steps of using the starting point as the reference point, calculating the distance between the reference point and each subsequent point, and determining whether the distance exceeds a preset simplification threshold. When the distance exceeds the simplification threshold, add the nearest point before the current point to the simplified point set and use that point as the new reference point, until the termination point of the main route point sequence data is reached, and add the termination point to the simplified point set.

[0049] Connect the points in the simplified point set in their original order to generate the simplified main route point sequence data.

[0050] In one embodiment, the step of generating spatial location information and attribute information for each point in the simplified main route point sequence data to obtain the final main route data includes:

[0051] Based on the simplified main route point sequence data, the sum of the planar distances between all adjacent points from the starting point of the main route to the current point is calculated as the cumulative voyage of the current point;

[0052] Based on the simplified main route point sequence data, the reference time value of each main route point is calculated by interpolation according to the time information of the original trajectory points corresponding to the main route points.

[0053] Based on the simplified main route point sequence data, the planar distance between the current point and the adjacent previous point is calculated, and the speed of the current point is obtained by dividing it by the time difference between the two points.

[0054] Based on the simplified main waypoint sequence data, calculate the plane vector between the current point and the next adjacent point, and calculate the heading angle of the current point according to the direction of the plane vector.

[0055] The cumulative voyage, the reference time value, the speed, and the heading angle are combined with the simplified main route point sequence data as attribute information to obtain the final main route data.

[0056] In one embodiment, the steps of mapping radar operating mode data and communication operating mode data associated with the original trajectory point data to the final master flight path data, and performing statistical aggregation based on the spatial projection position of electronic activity parameters on the final master flight path data to generate electronic activity analysis data include:

[0057] Based on the final main flight path data and the original trajectory point data, radar operating mode data and communication operating mode data associated with the original trajectory point data are obtained, wherein each electronic activity parameter data includes at least timestamp information, spatial location information and parameter identification information;

[0058] For each of the electronic activity parameter data, calculate the planar distance between the spatial location of the electronic activity parameter and each main route point in the final main route data;

[0059] Record the sampling sequence number of the main route point in the final main route data that is closest to the spatial location plane distance of the electronic activity parameters;

[0060] The electronic activity parameter data is associated with the corresponding sampling sequence number of the main route point to establish a parameter mapping relationship;

[0061] Based on the parameter mapping relationship, all the electronic activity parameter data with the same main route point sampling number are grouped together to form a parameter group set;

[0062] For each numerical parameter in the parameter group set, sort all parameter values ​​by size and select the parameter value in the middle position as the representative parameter value for that sampling sequence position.

[0063] For each enumerated parameter in the parameter group set, count the number of times each enumerated value appears, and select the enumerated value with the most occurrences as the working state of that sampling sequence position;

[0064] For each parameter group set, the number of parameter samples participating in the statistics is recorded as a statistical reliability indicator;

[0065] The representative parameter values, the operating status, and the statistical reliability index are combined to form the electronic activity analysis data.

[0066] Furthermore, to achieve the above objectives, this application also proposes a main flight path extraction system for electronic activity analysis of airborne targets. The main flight path extraction system for electronic activity analysis of airborne targets includes: a memory, a processor, and a main flight path extraction program for electronic activity analysis of airborne targets stored in the memory and executable on the processor. The main flight path extraction program for electronic activity analysis of airborne targets is configured to implement the steps of the main flight path extraction method for electronic activity analysis of airborne targets.

[0067] The main flight path extraction method and system proposed in this application for electronic activity analysis of airborne targets can accurately extract main flight path information from complex airborne target trajectory data through a series of processing steps, including validity verification of multi-source trajectory point data, coordinate transformation, direction correction, equal arc length resampling, median statistical fusion, geometric simplification processing, and statistical aggregation of electronic activity parameters. This improves the accuracy and robustness of main flight path extraction while enhancing the accuracy and reliability of electronic activity parameter analysis. Attached Figure Description

[0068] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0069] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 is a flowchart illustrating an embodiment of the main flight path extraction method for electronic activity analysis of airborne targets in this application.

[0071] Figure 2 is a schematic diagram of an embodiment of the main flight path extraction system for electronic activity analysis of airborne targets provided in this application.

[0072] Explanation of icon numbers:

[0073] 10. Memory; 20. Processor.

[0074] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0075] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0076] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0077] In existing technologies, airborne target electronic activity analysis methods face challenges such as data integration difficulties, inconsistent trajectory directions, sensitivity of fusion methods to noise, and difficulty in accurately establishing the mapping relationship between electronic activity parameters and the main flight path when processing multi-source trajectory data. These problems result in insufficient accuracy in extracting the main flight path, low computational efficiency, and an inability to effectively support subsequent quantitative analysis and visualization of electronic activity characteristics.

[0078] Based on this, this application provides a method for extracting the main flight path for electronic activity analysis of airborne targets. Referring to Figure 1, the method for extracting the main flight path for electronic activity analysis of airborne targets includes steps S100 to S700, wherein:

[0079] Step S100: Obtain multi-source trajectory point data of aerial targets, perform validity verification on the multi-source trajectory point data, remove invalid trajectory points with missing position information or missing time information, generate valid trajectory point data, and ensure that each trajectory contains at least a preset number of valid trajectory points.

[0080] Step S200: The spherical latitude and longitude coordinates in the effective trajectory point data are converted into plane coordinate data in a plane rectangular coordinate system by a local plane projection method based on the reference latitude of the trajectory set.

[0081] Step S300: Select a reference trajectory from the trajectory set corresponding to the plane coordinate data, and calculate the spatial matching degree between each of the remaining trajectories in the forward and reverse directions and the reference trajectory. When the reverse matching degree is better than the forward matching degree, reverse the direction of the plane coordinate data of the corresponding trajectory to generate plane coordinate data after direction correction.

[0082] Step S400: Perform equal arc length resampling processing on the directional corrected planar coordinate data. Generate a uniform number of sampling points according to the total arc length of each trajectory at a preset arc length interval. Determine the planar coordinate components and height information of all sampling points through interpolation to generate equal arc length sampling point data.

[0083] Step S500: For all trajectory points at the same sampling number position in the equal arc length sampling point data, perform median statistics on the plane coordinate components and height information respectively, generate the fusion point corresponding to each sampling number position, and connect all the fusion points in the order of sampling number to form the main line point sequence data;

[0084] Step S600: Perform geometric simplification on the main route point sequence data to generate simplified main route point sequence data, and generate spatial location information and attribute information for each point in the simplified main route point sequence data to obtain the final main route data.

[0085] Step S700: Map the radar operating mode data and communication operating mode data associated with the original trajectory point data to the final main flight path data, and perform statistical aggregation based on the spatial projection position of the electronic activity parameters on the final main flight path data to generate electronic activity analysis data.

[0086] In this embodiment, multi-source trajectory point data refers to a discrete set of points representing the motion trajectories of aerial targets acquired from different sensors or systems. This data may contain information such as longitude, latitude, altitude, and timestamps, but due to the diverse sources, there may be differences in format, accuracy, or completeness. Valid trajectory point data refers to a set of trajectory points that, after verification and filtering, meet preset quality standards. This data eliminates invalid points with missing position or time information, ensuring the accuracy and reliability of subsequent processing. Local planar projection is a method of converting the spherical latitude and longitude coordinates of the Earth's surface into local planar rectangular coordinates. By selecting a reference latitude as a benchmark, the spherical coordinates of this region are approximately projected onto a plane, facilitating planar geometric calculations. Planar coordinate data refers to two-dimensional rectangular coordinate data obtained after converting spherical latitude and longitude coordinates through local planar projection. It typically contains X and Y components and can be combined with the original altitude information. The reference trajectory refers to a trajectory selected as a reference in the trajectory set. Other trajectories are compared with this reference trajectory to determine their spatial relationships and orientations.

[0087] In this embodiment, spatial matching degree is an indicator that measures the degree of similarity or proximity between two trajectories in space. This indicator is usually determined by calculating the distance or shape similarity between trajectory points. Direction-corrected planar coordinate data refers to the planar coordinate data obtained after direction judgment and adjustment of the original trajectory data. When the trajectory direction is inconsistent with the reference trajectory, the direction is unified by reversing the order of the trajectory points. Equal arc length resampling is a method of generating new sampling points on the trajectory at fixed arc length intervals. This process ensures that all trajectories have a uniform sampling density in space, facilitating subsequent alignment and fusion. Equal arc length sampling point data refers to the set of sampling points that all trajectories have the same number of and are distributed at equal arc length intervals after equal arc length resampling. Median statistics are used to determine the middle value of a set of values. Compared with the mean, the median is less sensitive to outliers and can more accurately reflect the central tendency of the data.

[0088] In this embodiment, the fusion point refers to a representative point obtained by statistically analyzing the median of all trajectory sampling points at the same sampling sequence number position. It integrates the planar coordinates and altitude information of multiple trajectories at that position. The main flight path point sequence data refers to the trajectory sequence formed by connecting all fusion points in the order of their sampling sequence numbers. This sequence represents the typical flight path of an aerial target group. Geometric simplification is a method to reduce the number of trajectory points while maintaining the geometric characteristics of the trajectory. This process can remove redundant points and improve data storage and processing efficiency. The simplified main flight path point sequence data refers to the trajectory sequence that, after geometric simplification, has a reduced number of points but still accurately reflects the shape of the main flight path. The final main flight path data refers to the complete main flight path data containing spatial location information and attribute information (such as cumulative range, reference time, airspeed, and heading angle) after all processing steps. Electronic activity analysis data refers to the data generated by mapping electronic activity parameters such as radar operating mode data and communication operating mode data onto the final main flight path data and then statistically aggregating them. This data is used to analyze the electronic activity characteristics of aerial targets in different flight segments.

[0089] In this embodiment, the main flight path extraction method for electronic activity analysis of aerial targets first requires acquiring multi-source trajectory point data of aerial targets. This data can come from different sensors, such as radar, electronic reconnaissance equipment, or communication reconnaissance equipment. For example, trajectory information of the same aerial target can be received from multiple radar stations, and this information may include fields such as longitude, latitude, altitude, and timestamp.

[0090] In this embodiment, after acquiring multi-source trajectory point data, it is necessary to perform validity verification on the multi-source trajectory point data to remove invalid trajectory points with missing location or time information, thereby generating valid trajectory point data. Simultaneously, it is necessary to ensure that each trajectory contains at least a preset number of valid trajectory points. For example, each trajectory point can be manually checked to determine if its latitude, longitude, or timestamp is empty, and incomplete points can be manually deleted. Alternatively, a simple filter can be set to automatically delete any trajectory points lacking key fields.

[0091] In this embodiment, the spherical latitude and longitude coordinates in the effective trajectory point data are then converted into planar coordinate data in a Cartesian coordinate system using a local planar projection method based on the reference latitude of the trajectory set. For example, a fixed reference latitude can be preset, and Mercator projection can be performed using this reference latitude to convert the latitude and longitude coordinates of all trajectory points into Cartesian coordinates. This planar coordinate data will contain X and Y components and be combined with the original height information.

[0092] In this embodiment, a reference trajectory is then selected from the trajectory set corresponding to the planar coordinate data. The spatial matching degree between each of the remaining trajectories and the reference trajectory is calculated for both forward and reverse orientations. When the reverse matching degree is better than the forward matching degree, the planar coordinate data of the corresponding trajectory is reversed to generate directional-corrected planar coordinate data. For example, a trajectory can be randomly selected as the reference trajectory, and then the direction of the other trajectories can be visually judged to see if it matches the reference trajectory. If not, their point order can be manually adjusted. Alternatively, the distance between the starting point of each trajectory and the starting point of the reference trajectory, as well as the distance between the ending point and the ending point of the reference trajectory, can be simply compared. If the reverse distance is smaller, the direction is considered reversed and the trajectory is reversed.

[0093] In this embodiment, the directionally corrected planar coordinate data undergoes equal-arc-length resampling processing. Based on the total arc length of each trajectory, a uniform number of sampling points are generated at preset arc-length intervals. The planar coordinate components and height information of all sampling points are determined through interpolation, generating equal-arc-length sampling point data. For example, a fixed number of sampling points can be set for each trajectory, and then these sampling points are uniformly inserted between the start and end points of the trajectory using linear interpolation, regardless of the actual arc length.

[0094] In this embodiment, for all trajectory points at the same sampling index position in the equal arc length sampling point data, the median of the planar coordinate components and altitude information is calculated to generate a fusion point corresponding to each sampling index position. All fusion points are then connected in order of sampling index to form the main line point sequence data. For example, for each sampling index position, the arithmetic mean of the planar coordinate components and altitude information of all trajectory points at that position can be simply calculated as the fusion point for that position.

[0095] In this embodiment, the main route point sequence data is geometrically simplified to generate simplified main route point sequence data. Spatial location information and attribute information are then generated for each point in the simplified main route point sequence data to obtain the final main route data. For example, a point can be selected from the main route point sequence data at fixed intervals (e.g., every 5 points) to form the simplified sequence. For each simplified point, its planar coordinates and altitude information can be recorded as its spatial location information. The attribute information may only contain a timestamp.

[0096] In this embodiment, the radar operating mode data and communication operating mode data associated with the original trajectory point data are finally mapped to the final master flight path data. Statistical aggregation is then performed based on the spatial projection positions of the electronic activity parameters onto the final master flight path data to generate electronic activity analysis data. For example, each electronic activity parameter data can be simply associated with the closest master flight path point in time, and then a simple count is performed on all electronic activity parameters associated with each master flight path point to understand how much electronic activity occurs near that point.

[0097] In this embodiment, by performing validity verification, local planar projection transformation, trajectory direction correction, equal arc length resampling, and median fusion on multi-source trajectory data, the challenges of traditional methods in data integration, direction inconsistency, and noise sensitivity can be effectively overcome. As a result, the generated master flight path can more accurately reflect the typical flight path of aerial targets and provide a reliable basis for the spatial mapping of electronic activity parameters and the master flight path, thereby supporting the quantitative analysis and visualization of electronic activity characteristics.

[0098] In one feasible implementation, the step of validating the multi-source trajectory point data and removing invalid trajectory points with missing location or time information to generate valid trajectory point data includes: for each trajectory point in the acquired multi-source trajectory point data, determining whether it simultaneously contains longitude, latitude, and timestamp information; marking a trajectory point lacking any one of longitude, latitude, or timestamp information as an invalid trajectory point; for trajectory points with complete information, calculating the time interval between the current trajectory point and the previous trajectory point, and marking the current trajectory point as an invalid trajectory point when the time interval exceeds a preset time threshold; removing all invalid trajectory points and retaining valid trajectory points to generate the valid trajectory point data.

[0099] In this embodiment, for each trajectory point in the acquired multi-source trajectory point data, it is determined whether it simultaneously contains longitude, latitude, and timestamp information. This step aims to verify the basic integrity of each trajectory point. After receiving the multi-source trajectory point data, the system performs a structured check on each trajectory point in the data stream to confirm whether it completely contains its geographic location information (longitude and latitude) and its occurrence time information (timestamp). This information is the most basic element constituting a valid trajectory point; the absence of any one of these elements will prevent the trajectory point from being accurately located and sorted. For example, the fields of the data packet can be parsed to check whether the corresponding fields have non-empty values ​​or conform to a preset data format.

[0100] In this embodiment, trajectory points lacking any of the following information—longitude, latitude, or timestamp—are marked as invalid trajectory points. For trajectory points found to lack critical information during the above checks, the system explicitly marks them as invalid. This marking can be achieved by setting an internal status flag or by temporarily storing them in a separate invalid dataset for later unified processing. This ensures that the data processing flow can clearly distinguish between usable and unusable data.

[0101] In this embodiment, for trajectory points with complete information, the time interval between the current trajectory point and the previous trajectory point is calculated. When the time interval exceeds a preset time threshold, the current trajectory point is marked as an invalid trajectory point. To further ensure the continuity and rationality of trajectory data, for trajectory points that have been confirmed to contain complete longitude, latitude, and timestamp information, the system also performs time series verification. Specifically, the time difference between the timestamp of the current trajectory point and the timestamp of the immediately preceding trajectory point in the trajectory is calculated. If this time difference exceeds the preset time threshold, it indicates that there is an abnormally large time jump between the trajectory point and the previous point, which may mean data acquisition interruption, transmission delay, or data loss, resulting in trajectory discontinuity. In this case, the current trajectory point will also be marked as an invalid trajectory point. The preset time threshold can be flexibly configured according to the actual application scenario and data source characteristics. For example, for high-frequency acquired radar data, the threshold may be set to several seconds; for low-frequency acquired ADS-B data, the threshold may be set to tens of seconds.

[0102] In this embodiment, finally, all invalid trajectory points are removed, and valid trajectory points are retained to generate the valid trajectory point data. After completing the above verification and marking of all trajectory points, the system will perform a data cleaning operation. All trajectory points marked as invalid will be removed from the dataset, and only those valid trajectory points that pass all verifications will be retained. These rigorously filtered and cleaned trajectory points will constitute the final valid trajectory point data, providing high-quality input for the subsequent main route extraction algorithm.

[0103] In this embodiment, through the above technical solution, this application can effectively identify and eliminate data quality problems caused by missing information or temporal anomalies in multi-source trajectory point data. First, by performing a basic information integrity check on each trajectory point, the reliability of the core spatial and temporal information upon which subsequent processing depends is ensured. Second, by introducing a time interval threshold judgment, trajectory discontinuities caused by data acquisition or transmission interruptions are further eliminated, avoiding interference from abnormal data on trajectory morphology. This multi-layered validity verification mechanism ensures the purity and continuity of input data from the source, thus laying a solid foundation for a series of complex processing steps such as local plane projection, direction correction, equal arc length resampling, median fusion, and geometric simplification, significantly improving the accuracy, stability, and representativeness of the final main route extraction.

[0104] In one feasible implementation, the step of converting the spherical latitude and longitude coordinates in the effective trajectory point data into planar coordinate data in a Cartesian coordinate system through a local planar projection method based on the reference latitude of the trajectory set includes: calculating the average latitude of all trajectory points in the effective trajectory point data as a reference latitude; establishing a local Cartesian coordinate system using the meridian corresponding to the reference latitude as the central meridian; converting the latitude and longitude coordinates of each trajectory point in the effective trajectory point data into planar coordinate components in the Cartesian coordinate system through projection calculation; and combining the converted planar coordinate components with the height information in the effective trajectory point data to generate the planar coordinate data.

[0105] In this embodiment, to ensure local optimality of the projection, the average latitude of all trajectory points is first calculated as a reference latitude based on the effective trajectory point data. This reference latitude represents the central position of the entire trajectory set in the north-south direction, providing a crucial geographical reference for the subsequent establishment of a local Cartesian coordinate system. This method allows the projection plane to more closely resemble the actual trajectory distribution area, effectively reducing projection distortion. After determining the reference latitude, the meridian corresponding to that latitude is used as the central meridian to establish a local Cartesian coordinate system. This local coordinate system is typically parameterized using projection methods suitable for small areas, such as the Gauss-Kruger projection or the Universal Transverse Mercator (UTM) projection. By setting the projection origin or central axis near the geographical center of the trajectory set, projection errors can be minimized, ensuring the accuracy and consistency of the planar coordinates within the local area. Subsequently, the latitude and longitude coordinates of each trajectory point in the effective trajectory point data are converted into planar coordinate components in the local Cartesian coordinate system through projection calculations. This transformation process is based on the mathematical formulas of the selected projection method, mapping the spherical coordinates on the Earth's ellipsoid to a two-dimensional plane. This simplifies the originally complex calculations of spherical distances and directions into planar geometric calculations. Finally, the transformed planar coordinate components are combined with the existing height information in the valid trajectory point data to generate complete planar coordinate data. In this way, each trajectory point possesses three-dimensional coordinates in a local Cartesian coordinate system, where Z represents height. This combination method provides a unified and accurate three-dimensional spatial data foundation for subsequent operations such as trajectory matching, resampling, and fusion.

[0106] In this embodiment, a local Cartesian coordinate system is established based on the average latitude of the calculated trajectory set, achieving precise local projection of the aerial target trajectory data. This method avoids the significant distortion that may result from using a universal global projection, ensuring the accuracy of the relative spatial relationships between trajectory points within a specific airspace. The generation of planar coordinate data allows subsequent operations such as trajectory direction correction, equal arc length resampling, main flight path fusion, and statistical aggregation of electronic activity parameters to be performed within a local Cartesian coordinate system with minimal distortion. This greatly simplifies computational complexity, improves the accuracy and efficiency of spatial analysis, and provides a reliable spatial foundation for accurately extracting the main flight path and analyzing electronic activity.

[0107] In one feasible implementation, the steps of selecting a reference trajectory from the trajectory set corresponding to the planar coordinate data, calculating the spatial matching degree between each of the remaining trajectories in both forward and reverse directions, and reversing the direction of the planar coordinate data of the corresponding trajectory when the reverse matching degree is better than the forward matching degree to generate directional corrected planar coordinate data include: selecting a trajectory from the trajectory set corresponding to the planar coordinate data as a reference trajectory, and obtaining the planar coordinates of the starting point and the ending point of the reference trajectory; for each of the remaining trajectories in the planar coordinate data, obtaining the planar coordinates of the starting point and the ending point of the current trajectory; calculating the planar distance between the starting point of the current trajectory and the starting point of the reference trajectory as a first planar distance, and calculating the current trajectory... The planar distance between the current trajectory's termination point and the reference trajectory's termination point is used as the second planar distance. The first and second planar distances are added together to obtain the forward matching distance. The termination point of the current trajectory is used as the reverse starting point, and the starting point is used as the reverse termination point. The planar distance between the reverse starting point and the reference trajectory's starting point is calculated as the third planar distance, and the planar distance between the reverse termination point and the reference trajectory's termination point is calculated as the fourth planar distance. The third and fourth planar distances are added together to obtain the reverse matching distance. The forward matching distance and the reverse matching distance are compared. When the reverse matching distance is less than the forward matching distance, the planar coordinate data of the current trajectory is reversed to generate the direction-corrected planar coordinate data.

[0108] In this embodiment, to unify the direction of all trajectories in the trajectory set, a trajectory is first selected from the trajectory set corresponding to the planar coordinate data as a reference trajectory. This reference trajectory serves as the reference for correcting the direction of all other trajectories. There are various ways to select the reference trajectory; for example, a trajectory can be randomly selected, or the trajectory with the most trajectory points, the longest total arc length, and the best data quality can be selected as the reference trajectory to ensure its representativeness and stability. Once the reference trajectory is determined, its starting point planar coordinates and ending point planar coordinates need to be obtained. These two points define the overall spatial orientation of the reference trajectory. Subsequently, for each of the remaining trajectories in the planar coordinate data, the starting point planar coordinates and ending point planar coordinates of the current trajectory also need to be obtained. These points are the basis for calculating the direction matching degree. The acquisition method is usually to directly read the planar coordinates of the first and last points in the trajectory data sequence.

[0109] In this embodiment, to evaluate the degree of matching between the current trajectory and the reference trajectory in the "positive" case, it is necessary to calculate the planar distance between the starting point of the current trajectory and the starting point of the reference trajectory as the first planar distance, and calculate the planar distance between the ending point of the current trajectory and the ending point of the reference trajectory as the second planar distance. Adding these two distances yields the positive matching distance. The planar distance can be calculated using the Euclidean distance formula, which is the straight-line distance between two points in a Cartesian coordinate system. The smaller the positive matching distance, the closer the current trajectory is to the starting and ending points of the reference trajectory in the current direction, i.e., the more consistent the direction.

[0110] In this embodiment, to evaluate the matching degree of the current trajectory in the "reverse" case, it is necessary to simulate the state after the current trajectory direction is reversed. Specifically, the end point of the current trajectory is taken as the reverse start point, and the start point is taken as the reverse end point. Then, the planar distance between the reverse start point (i.e., the original end point of the current trajectory) and the start point of the reference trajectory is calculated as the third planar distance, and the planar distance between the reverse end point (i.e., the original start point of the current trajectory) and the end point of the reference trajectory is calculated as the fourth planar distance. Adding the third and fourth planar distances yields the reverse matching distance. The reverse matching distance also reflects the closeness of the current trajectory to the start and end points of the reference trajectory after reversal.

[0111] In this embodiment, by comparing the forward matching distance and the reverse matching distance, the matching degree between the actual direction of the current trajectory and the direction of the reference trajectory can be determined. When the reverse matching distance is less than the forward matching distance, it indicates that the current trajectory has a higher degree of matching with the reference trajectory in the reverse direction, that is, the original direction of the current trajectory is opposite to the direction of the reference trajectory. At this time, it is necessary to perform direction reversal processing on the planar coordinate data of the current trajectory, for example, by reversing the sequence of trajectory points to generate the direction-corrected planar coordinate data.

[0112] In this embodiment, by calculating the spatial matching degree between each trajectory and the baseline trajectory in both forward and reverse directions, and selecting the direction with the better matching degree as the final direction of the trajectory, the spatial directional uniformity of all trajectories is ensured. This avoids errors or inaccuracies in the main flight path caused by inconsistent trajectory directions in subsequent fusion processing (such as equal arc length resampling, median statistics, etc.), thereby significantly improving the accuracy and reliability of main flight path extraction and providing high-quality basic data for subsequent electronic activity analysis.

[0113] In one feasible implementation, the directionally corrected planar coordinate data undergoes equal-arc-length resampling processing. A uniform number of sampling points are generated based on the total arc length of each trajectory at preset arc-length intervals. The planar coordinate components and height information of all sampling points are determined through interpolation. The steps for generating equal-arc-length sampling points include: for each trajectory in the directionally corrected planar coordinate data, calculating the total arc length of the trajectory based on the sum of the planar distances between all adjacent trajectory points; dividing the total arc length into equal-length arc segments (number of sampling points minus one segment) according to the preset number of sampling points, and calculating the arc-length interval between adjacent sampling points; generating sampling positions on the trajectory according to the arc-length intervals, starting from the trajectory's starting point and determining a sampling point every arc-length interval along the trajectory's forward direction. Location; For each sampling location, find two adjacent original trajectory points where the sampling location is located, and calculate the weighted planar coordinate components of the two original trajectory points according to the distance ratio from the sampling location to the two original trajectory points to obtain the planar coordinate components of the sampling location; For each sampling location, find two adjacent original trajectory points where the sampling location is located, and calculate the weighted height information of the two original trajectory points according to the distance ratio from the sampling location to the two original trajectory points to obtain the height information of the sampling location; Combine the planar coordinate components and height information of all sampling locations of the same trajectory to form the equal arc length sampling data of the trajectory, and combine the equal arc length sampling data of all trajectories to generate the equal arc length sampling point data.

[0114] In this embodiment, for each trajectory in the directionally corrected planar coordinate data, the total arc length of the trajectory is calculated based on the sum of the planar distances between all adjacent trajectory points. This step aims to accurately quantify the actual spatial length of each trajectory, providing basic data for subsequent standardized sampling. By traversing all points on the trajectory and accumulating the Euclidean distances between adjacent points, the precise arc length of the trajectory can be obtained, thereby avoiding errors caused by uneven point density or approximate straight-line distances.

[0115] In this embodiment, based on a preset number of sampling points, the total arc length is divided into equal arc segments of the number of sampling points minus one segment, and the arc length interval between adjacent sampling points is calculated. This preset number of sampling points can be configured according to actual application requirements; for example, it can be set to 100 or 200. By evenly distributing the total arc length of the trajectory to a preset number of sampling points, it ensures that all trajectories will have the same number of sampling points after resampling, thereby achieving standardization of trajectory representation.

[0116] In this embodiment, sampling positions are generated on the trajectory according to the stated arc length intervals. Starting from the trajectory's starting point, a sampling position is determined every arc length interval along the trajectory's forward direction. This step ensures a uniform distribution of sampling points by systematically advancing along the trajectory path and marking new sampling point positions at fixed arc length intervals. For example, starting from the first point on the trajectory, the distance can be gradually accumulated along the connecting line segments of the trajectory, and a sampling position is determined whenever the accumulated distance reaches an arc length interval.

[0117] In this embodiment, for each sampling location, two adjacent original trajectory points are found. Based on the distance ratio from the sampling location to these two original trajectory points, the planar coordinate components of the two original trajectory points are weighted and calculated to obtain the planar coordinate components of the sampling location. Specifically, if the sampling location is located between original trajectory points Pj and Pj+1, and its arc distance to Pj is d1 and its arc distance to Pj+1 is d2, then the planar coordinate components of the sampling location can be calculated using a linear interpolation formula to ensure that the newly generated sampling point is accurately located on the path of the original trajectory and maintains its spatial continuity.

[0118] In this embodiment, for each sampling location, two adjacent original trajectory points are found. Based on the distance ratio from the sampling location to these two original trajectory points, the height information of the two original trajectory points is weighted and calculated to obtain the height information of the sampling location. Similar to the processing of planar coordinate components, the height information is also obtained using the same weighted interpolation method, thereby ensuring that the height information of the resampled point remains consistent with the height change trend of the original trajectory.

[0119] In this embodiment, the planar coordinate components and height information of all sampling positions on the same trajectory are combined to form the equal-arc length sampling data of that trajectory. The equal-arc length sampling data of all trajectories are then combined to generate the equal-arc length sampling point data. By organizing the resampling points of each trajectory sequentially and finally aggregating the resampling data of all trajectories, a unified and standardized dataset is formed, providing high-quality input for subsequent trajectory fusion and main route extraction.

[0120] In this embodiment, the above-described technical solution performs equal-arc-length resampling on the directional-corrected planar coordinate data, effectively solving the problems of uneven distribution of original trajectory points and large differences in trajectory length. By calculating the total arc length of each trajectory and dividing it equally according to a preset number of sampling points, it is ensured that all trajectories have a uniform number of equally spaced sampling points after resampling. This standardization process allows different trajectories to be better aligned in space, providing a uniform and representative data foundation for subsequent fusion statistics. Specifically, by weighted interpolation of the planar coordinate components and height information, it is ensured that the resampling points accurately reflect the spatial position and height information of the original trajectory, avoiding fusion deviations caused by uneven sampling, thereby significantly improving the accuracy and stability of the main route extraction and laying a solid foundation for the generation of main route point sequence data.

[0121] In one feasible implementation, the steps of performing median statistics on the planar coordinate components and height information for all trajectory points at the same sampling index position in the equal arc length sampling point data, generating a fusion point corresponding to each sampling index position, and connecting all fusion points in sampling index order to form the main line point sequence data include: for each sampling index position in the equal arc length sampling point data, collecting all trajectory sampling point data corresponding to that sampling index position to form a sampling point set corresponding to the current sampling index position; extracting the horizontal and vertical component values ​​of the planar coordinates for all sampling points in the sampling point set; and sorting all the horizontal component values ​​by size. The process involves: selecting the horizontal component value located in the middle position as the horizontal component of the main route fusion point at that sampling sequence position; sorting all the vertical component values ​​by size and selecting the vertical component value located in the middle position as the vertical component of the main route fusion point at that sampling sequence position; sorting all the height information values ​​by size and selecting the height information value located in the middle position as the height information of the main route fusion point at that sampling sequence position; combining the horizontal component, the vertical component, and the height information to form the main route fusion point at that sampling sequence position; and connecting all the main route fusion points at each sampling sequence position in order of sampling sequence number to form the main route point sequence data.

[0122] In this embodiment, for each sampling sequence position in the aforementioned equal-arc-length sampling point data, the system collects all sampling point data corresponding to that sampling sequence position for all trajectories, thereby forming a set of sampling points corresponding to the current sampling sequence position. This step aims to align multiple trajectories after preprocessing and resampling in time or space. Since the previous equal-arc-length resampling process ensures that all trajectories are comparable at the same sampling sequence position, by collecting all trajectory points at the same sampling sequence position, a set of points that are close to each other in space can be formed, laying the foundation for subsequent fusion processing. This ensures that subsequent statistical operations are performed on a meaningful "cross-section".

[0123] In this embodiment, the horizontal and vertical components of the planar coordinates are extracted from all sampling points in the sampling point set. This step decomposes the two-dimensional planar coordinates (e.g., X and Y coordinates) of each sampling point into independent horizontal and vertical components. This is to allow for independent median statistics of these components later, since the two components of the planar coordinates are usually independent, and processing them separately can more accurately capture their distribution characteristics.

[0124] In this embodiment, all horizontal component values ​​are sorted by size, and the horizontal component value located in the middle position is selected as the horizontal component of the main flight path fusion point at that sampling sequence position. This step determines the horizontal coordinates of the main flight path fusion point by performing median statistics on the horizontal components of all trajectory points at the same sampling sequence position. Median statistics are a robust statistical method that is not affected by extreme outliers and can more accurately reflect the central trend of the dataset. In specific implementation, all horizontal component values ​​can be arranged from smallest to largest (or from largest to smallest), and then the value located in the middle can be selected. If the number of data points is even, the average of the two middle values ​​is usually taken, or one of them is chosen by convention.

[0125] In this embodiment, all the vertical component values ​​are sorted by size, and the vertical component value located in the middle position is selected as the vertical component of the main route fusion point at that sampling sequence position. Similar to the horizontal component, this step determines the vertical coordinates of the main route fusion point by performing median statistics on the vertical components of all trajectory points at the same sampling sequence position. This also utilizes the median's resistance to outliers, ensuring that the fusion point accurately represents the center position of the trajectory set in the vertical direction.

[0126] Furthermore, all the altitude information values ​​are sorted by size, and the altitude information value in the middle position is selected as the altitude information of the main route fusion point at that sampling sequence number. This step performs median statistics on the altitude information of all trajectory points at the same sampling sequence number to determine the altitude of the main route fusion point. Altitude information may also be affected by measurement errors or local disturbances; using median statistics can effectively filter out these noises, making the altitude of the fusion point more representative.

[0127] In this embodiment, the horizontal component, the vertical component, and the height information are finally combined to form the main flight path fusion point at the sampling sequence number position. All the main flight path fusion points at each sampling sequence number position are then connected in order of their sampling sequence numbers to form the main flight path point sequence data. This step involves recombining the horizontal component, vertical component, and height information obtained through median statistics to form a complete three-dimensional spatial point, i.e., the main flight path fusion point. By connecting all the fusion points at each sampling sequence number position in order of their sampling sequence numbers, a smooth and representative main flight path point sequence data can be constructed. This connection method ensures the continuity and logical order of the main flight path.

[0128] In this embodiment, by employing the aforementioned technical solution, median statistics are performed on the horizontal, vertical, and altitude components of the planar coordinates for all trajectory points at the same sampling index position in the equal arc length sampling point data. This effectively avoids the problem of traditional averaging methods being susceptible to outliers. As a non-parametric statistical method, median statistics are robust to extreme values ​​in the data. Even if some trajectory points have large measurement errors or local deviations, they will not significantly affect the calculation results of the fusion points. This allows the fusion points corresponding to each sampling index position to more accurately reflect the central trend of multiple trajectories at that position, thereby constructing a smoother, more stable, and representative main flight path sequence. This main flight path can better represent the core flight path of an aerial target group, providing a more reliable spatial benchmark for subsequent electronic activity analysis and improving the accuracy and reliability of the analysis results.

[0129] In one feasible implementation, the step of geometrically simplifying the main route point sequence data to generate simplified main route point sequence data includes: traversing all points in the main route point sequence data and obtaining the planar coordinate components of each point; starting from the starting point of the main route point sequence data, adding the starting point to the simplified point set; using the starting point as a reference point, calculating the distance between the reference point and each subsequent point; determining whether the distance exceeds a preset simplification threshold, and when the distance exceeds the simplification threshold, adding the nearest point before the current point to the simplified point set. The simplified point set is used as a new reference point. The steps of calculating the distance between the reference point and each subsequent point and determining whether the distance exceeds a preset simplification threshold are repeated. When the distance exceeds the simplification threshold, the nearest point before the current point is added to the simplified point set and used as a new reference point. This process continues until the termination point of the main route point sequence data is reached, and the termination point is added to the simplified point set. The points in the simplified point set are then connected in their original order to generate the simplified main route point sequence data.

[0130] In this embodiment, all points in the main route point sequence data are traversed to obtain the planar coordinate components of each point. This step aims to provide basic data for subsequent geometric simplification operations. By accessing each point in the main route point sequence data one by one, its X and Y coordinate values ​​in the local Cartesian coordinate system can be extracted. These coordinate values ​​are necessary inputs for calculating the distance between points and judging geometric features. Subsequently, starting from the starting point of the main route point sequence data, the starting point is added to the simplified point set. Taking the starting point of the main route point sequence data as the first key point in the simplification process and directly including it in the simplified point set ensures that the simplified main route can accurately start from the starting point of the original route, providing a clear starting point for subsequent simplification algorithms. Then, using the starting point as the reference point, the distance between the reference point and each subsequent point is calculated. Using the currently determined simplified point (i.e., the reference point) as a reference, the Euclidean distance between the reference point and each subsequent point in the main route point sequence is calculated. This distance is used to measure the degree of deviation of the subsequent point from the reference point and is the core basis for determining whether to add new simplified points.

[0131] Based on this, it is determined whether the distance between the connecting lines exceeds a preset simplification threshold. This simplification threshold is a key parameter that determines the degree of simplification. When the distance exceeds the simplification threshold, it indicates that the path deviation between the current point and the reference point is significant, and the nearest point before the current point needs to be added to the simplified point set and used as the new reference point. This mechanism ensures that sufficient detail is preserved when the route undergoes significant directional or positional changes. The process of calculating the connecting line distance, determining the threshold, and updating the simplified points is repeated until the termination point of the main route point sequence data is reached, and the termination point is added to the simplified point set. Finally, regardless of whether the last point meets the simplification threshold condition, it is added to the simplified point set to ensure that the simplified main route completely covers the range of the original route. Finally, the points in the simplified point set are connected in the original order to generate the simplified main route point sequence data. In this way, a new simplified main route point sequence data with fewer points but still representing the geometric characteristics of the original main route is formed.

[0132] In this embodiment, the above-described technical solution performs geometric simplification on the main flight path point sequence data, effectively removing redundant points that have little impact on the overall geometric shape, thereby significantly reducing the amount of data on the main flight path. This not only reduces the overhead of data storage and transmission, but more importantly, it improves the computational efficiency of subsequent electronic activity analysis and makes the main flight path clearer and simpler when visualized, facilitating analysts to quickly identify the flight paths and key turning points of aerial targets. By preserving the geometric features of the flight path while eliminating minor fluctuations, it ensures that the electronic activity parameters, when statistically aggregated on the simplified main flight path, still accurately reflect their spatial distribution patterns, enhancing the intuitiveness and effectiveness of the analysis results.

[0133] In one feasible implementation, the steps of generating spatial location information and attribute information for each point in the simplified main route point sequence data to obtain the final main route data include: based on the simplified main route point sequence data, calculating the sum of the planar distances between all adjacent points from the main route starting point to the current point as the cumulative distance of the current point; based on the simplified main route point sequence data, calculating the reference time value of each main route point by interpolation according to the time information of the original trajectory points corresponding to the main route points; based on the simplified main route point sequence data, calculating the planar distance between the current point and the adjacent previous point, dividing it by the time difference between the two points to obtain the speed of the current point; based on the simplified main route point sequence data, calculating the planar vector between the current point and the adjacent next point, and calculating the heading angle of the current point according to the direction of the planar vector; combining the cumulative distance, the reference time value, the speed, and the heading angle as attribute information with the simplified main route point sequence data to obtain the final main route data.

[0134] In this embodiment, the cumulative flight distance refers to the total planar distance from the starting point of the main flight path to the current point. It is calculated as follows: traverse all points in the simplified main flight path point sequence data; for each point, calculate the planar distance between it and its previous adjacent point, and sum these distances. For example, the cumulative flight distance of the first point is zero, the cumulative flight distance of the second point is the planar distance between the first and second points, the cumulative flight distance of the third point is the sum of the planar distance between the first and second points, and so on. This cumulative flight distance provides a quantitative indicator of the target's flight distance along the main flight path.

[0135] In this embodiment, the reference time value refers to the estimated time corresponding to a point on the main flight path. Since the main flight path points are the result of fusion and simplification, they do not directly carry the original timestamps. Therefore, it is necessary to determine the reference time value through interpolation based on the time information of the original trajectory points corresponding to the main flight path points. Specifically, for any point on the main flight path, linear or nonlinear interpolation can be performed based on its position in the simplified main flight path point sequence data, combined with the time information of the original trajectory points or their interpolation points corresponding to it in the equal arc length sampling point data stage. For example, if a main flight path point is located between two sampling points with known time information, the reference time of that main flight path point can be calculated proportionally based on their relative positions.

[0136] In this embodiment, speed refers to the instantaneous or average speed of the target at a point on the main route. It is calculated as follows: for each point in the simplified main route point sequence data, the planar distance between that point and its immediate preceding point is calculated, and then this planar distance is divided by the reference time difference between the two points obtained through interpolation. For example, if the current point is P_i and the previous point is P_{i-1}, then the speed can be approximated as the planar distance (P_{i-1}, P_i) / (reference time value (P_i) - reference time value (P_{i-1})). This provides dynamic speed information of the target at various segments of the main route.

[0137] In this embodiment, the heading angle refers to the direction of movement of the target at a certain point on the main route. It is calculated as follows: for each point in the simplified main route point sequence data, determine the plane vector between that point and its immediately following point. Then, calculate the heading angle based on the angle of this plane vector relative to a preset reference direction (e.g., true north or the positive X-axis in a local Cartesian coordinate system). This provides information on the target's direction of movement at various segments of the main route.

[0138] In this embodiment, the final combination operation involves associating the calculated cumulative distance, reference time value, speed, and heading angle with each point in the simplified main waypoint sequence data. This means that each main waypoint no longer only contains spatial coordinates (such as planar coordinate components and altitude information), but is endowed with richer contextual information, forming a complete data structure that includes spatial location and dynamic attributes.

[0139] In this embodiment, the simplified main flight path sequence data is expanded from a simple geometric path representation to final main flight path data containing rich dynamic and temporal attributes through the aforementioned technical solution. Specifically, by calculating and correlating cumulative flight distance, the flight distance of the target along the main flight path can be quantified, providing a foundation for flight distance analysis; by interpolating reference time values, each point on the main flight path is given a precise timestamp, enabling subsequent electronic activity data to be accurately matched and correlated with specific spatiotemporal locations on the main flight path; by calculating airspeed and heading angle, key dynamic information is provided for each point on the main flight path, helping to understand the target's flight status and intentions at different stages. The addition of these attribute information greatly enriches the connotation of the main flight path data, enabling it to more comprehensively and accurately reflect the flight characteristics and behavioral patterns of aerial targets. The final main flight path data thus becomes a powerful analytical foundation, supporting deeper and more refined spatial projection and statistical aggregation of electronic activity parameters such as radar operating mode data and communication operating mode data, thereby achieving comprehensive and multi-dimensional analysis of aerial target electronic activity and effectively solving the problem that geometric path information alone is insufficient to support complex electronic activity analysis.

[0140] In one feasible implementation, the steps of mapping radar operating mode data and communication operating mode data associated with the original trajectory point data to the final master flight path data, and performing statistical aggregation based on the spatial projection position of electronic activity parameters on the final master flight path data to generate electronic activity analysis data include: acquiring radar operating mode data and communication operating mode data associated with the original trajectory point data based on the final master flight path data and the original trajectory point data, wherein each electronic activity parameter data includes at least timestamp information, spatial location information, and parameter identification information; for each electronic activity parameter data, calculating the planar distance between the spatial location of the electronic activity parameter and each master flight path point in the final master flight path data; and recording the sampling sequence number of the master flight path point with the closest planar distance to the spatial location of the electronic activity parameter in the final master flight path data. The electronic activity parameter data is associated with the corresponding main route point sampling number to establish a parameter mapping relationship. Based on the parameter mapping relationship, all electronic activity parameter data with the same main route point sampling number are grouped together to form a parameter group set. For numerical parameters in each parameter group set, all parameter values ​​are sorted by size, and the parameter value in the middle position is selected as the representative parameter value for that sampling number position. For enumerated parameters in each parameter group set, the number of times each enumerated value appears is counted, and the enumerated value with the most occurrences is selected as the working state for that sampling number position. For each parameter group set, the number of parameter samples participating in the statistics is recorded as a statistical reliability index. The representative parameter value, the working state, and the statistical reliability index constitute the electronic activity analysis data.

[0141] In this embodiment, based on the final main flight path data and the original trajectory point data, radar operating mode data and communication operating mode data associated with the original trajectory point data are obtained. These electronic activity parameter data are key information for analyzing the behavior of aerial targets. Each piece of electronic activity parameter data includes at least timestamp information, spatial location information, and parameter identification information, ensuring the integrity and traceability of the data.

[0142] In this embodiment, for each piece of electronic activity parameter data, the planar distance between the spatial location of the electronic activity parameter and each master route point in the final master route data is calculated. This step aims to determine the spatial correlation between each electronic activity event and the master route points. By calculating the planar distance, their spatial proximity can be efficiently evaluated. The sampling sequence number of the master route point with the closest planar distance to the spatial location of the electronic activity parameter in the final master route data is recorded. By recording the sampling sequence number instead of direct coordinates, a unified and ordered index can be provided for subsequent aggregation operations, simplifying the complexity of data management and processing.

[0143] In this embodiment, the electronic activity parameter data is associated with the corresponding sampling sequence number of the main route point, establishing a parameter mapping relationship. This mapping relationship establishes a one-to-many connection between discrete electronic activity parameter data and specific locations on the main route, laying the foundation for subsequent statistical aggregation. Based on the parameter mapping relationship, all electronic activity parameter data with the same sampling sequence number of the main route point are grouped together to form a parameter group set. This grouping operation gathers all electronic activity parameter data spatially associated with the same main route point, creating conditions for performing local statistical analysis.

[0144] In this embodiment, for each numerical parameter in the parameter group set, all parameter values ​​are sorted by size, and the parameter value in the middle position is selected as the representative parameter value for that sampling sequence position. Using the median as the representative parameter value can effectively avoid the influence of outliers on the statistical results, providing a more robust and representative value. For each enumerated parameter in the parameter group set, the frequency of each enumerated value is counted, and the enumerated value with the most frequent occurrence is selected as the working state for that sampling sequence position. By statistically analyzing the mode, the most frequently occurring electronic activity pattern or working state at that main line point position can be accurately reflected.

[0145] In this embodiment, for each parameter group set, the number of parameter samples participating in the statistics is recorded as a statistical reliability index. This index provides a quantitative assessment of the confidence level of the aggregation results. The larger the sample size, the higher the reliability of the representative parameter values ​​and operating status, which helps subsequent analysts interpret the data and make decisions. The representative parameter values, the operating status, and the statistical reliability index are combined to form the electronic activity analysis data. The final generated electronic activity analysis data not only includes typical electronic activity parameters and operating statuses at each point on the main route, but also includes statistical reliability information, forming a comprehensive, reliable, and easy-to-analyze dataset.

[0146] In this embodiment, through the above technical solution, this application can effectively and accurately map and aggregate raw, discrete, and potentially noisy radar and communication operating mode data onto simplified final main flight path data. Using the median and mode for statistical aggregation significantly enhances the robustness of the analysis results and reduces the impact of outlier data on the analysis conclusions. Simultaneously, the introduction of statistical reliability indicators provides analysts with a basis for evaluating data quality and confidence levels, making the analysis of airborne target electronic activity more accurate and reliable. This allows analysts to intuitively understand the electronic activity characteristics of airborne targets along their main flight path, thereby supporting deeper situational awareness and threat assessment.

[0147] In the embodiments of this application, the main flight path extraction method for electronic activity analysis of airborne targets, through a series of processing steps such as validity verification of multi-source trajectory point data, coordinate transformation, direction correction, equal arc length resampling, median statistical fusion, geometric simplification processing, and statistical aggregation of electronic activity parameters, can accurately extract main flight path information from complex airborne target trajectory data. While improving the accuracy and robustness of main flight path extraction, it also enhances the accuracy and reliability of electronic activity parameter analysis.

[0148] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the main flight path extraction method for electronic activity analysis of airborne targets in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0149] This application also provides a main flight path extraction system for electronic activity analysis of airborne targets. Referring to FIG2, the main flight path extraction system for electronic activity analysis of airborne targets includes: a memory 10, a processor 20, and a main flight path extraction program for electronic activity analysis of airborne targets stored on the memory 10 and executable on the processor 20. The main flight path extraction program for electronic activity analysis of airborne targets is configured to implement the steps of the main flight path extraction method for electronic activity analysis of airborne targets.

[0150] The main flight path extraction system for electronic activity analysis of airborne targets provided in this application employs the main flight path extraction method for electronic activity analysis of airborne targets in the above embodiments, which can improve the accuracy and robustness of main flight path extraction. Compared with the prior art, the beneficial effects of the main flight path extraction system for electronic activity analysis of airborne targets provided in this application are the same as those of the main flight path extraction method for electronic activity analysis of airborne targets provided in the above embodiments, and other technical features of the main flight path extraction system for electronic activity analysis of airborne targets are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0151] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0152] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method for extracting the main flight path for electronic activity analysis of airborne targets, characterized in that, The method includes: acquiring multi-source trajectory point data of aerial targets; validating the multi-source trajectory point data; removing invalid trajectory points with missing position or time information; generating valid trajectory point data; and ensuring that each trajectory contains at least a preset number of valid trajectory points; converting the spherical latitude and longitude coordinates in the valid trajectory point data into planar coordinate data in a Cartesian coordinate system through a local planar projection method based on the reference latitude of the trajectory set; selecting a reference trajectory from the trajectory set corresponding to the planar coordinate data; calculating the spatial matching degree between each of the remaining trajectories and the reference trajectory in both forward and reverse directions; and when the reverse matching degree is better than the forward matching degree, processing the planar coordinate data of the corresponding trajectory... The direction is reversed to generate directional-corrected planar coordinate data. The directional-corrected planar coordinate data is then resampled using equal arc lengths. A uniform number of sampling points are generated at preset arc length intervals based on the total arc length of each trajectory. The planar coordinate components and height information of all sampling points are determined through interpolation to generate equal arc length sampling point data. For all trajectory points at the same sampling index position in the equal arc length sampling point data, median statistics are performed on the planar coordinate components and height information to generate a fusion point corresponding to each sampling index position. All fusion points are connected in order of sampling index to form the main line point sequence data. The main line point sequence data is then geometrically simplified to generate simplified main line point sequence data, which is then used for the... The simplified main line point sequence data generates spatial location information and attribute information for each point to obtain the final main line data. Radar operating mode data and communication operating mode data associated with the original trajectory point data are mapped to the final main line data. Statistical aggregation is performed based on the spatial projection position of electronic activity parameters on the final main line data to generate electronic activity analysis data. The steps of mapping radar operating mode data and communication operating mode data associated with the original trajectory point data to the final main line data and performing statistical aggregation based on the spatial projection position of electronic activity parameters on the final main line data to generate electronic activity analysis data include: based on the final main line data and the original trajectory point data, obtaining the spatial location information and attribute information of each point in the original trajectory point data. The radar operating mode data and communication operating mode data associated with the trace data are included, wherein each electronic activity parameter data includes at least timestamp information, spatial location information, and parameter identification information; for each electronic activity parameter data, the planar distance between the spatial location of the electronic activity parameter and each main line point in the final main line data is calculated; the sampling sequence number of the main line point with the closest planar distance to the spatial location of the electronic activity parameter in the final main line data is recorded; the electronic activity parameter data is associated with the corresponding sampling sequence number of the main line point to establish a parameter mapping relationship; based on the parameter mapping relationship, all electronic activity parameter data with the same sampling sequence number of the main line point are grouped together to form a parameter group set;For each set of parameters containing numerical parameters, all parameter values ​​are sorted by size, and the parameter value in the middle position is selected as the representative parameter value for that sampling position. For each set of parameters containing enumerated parameters, the frequency of each enumerated value is counted, and the enumerated value with the highest frequency is selected as the working state for that sampling position. For each set of parameters, the number of parameter samples participating in the statistics is recorded as a statistical reliability index. The representative parameter value, the working state, and the statistical reliability index are combined to form the electronic activity analysis data.

2. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 1, characterized in that, The steps for validating the multi-source trajectory point data and removing invalid trajectory points with missing location or time information to generate valid trajectory point data include: for each trajectory point in the acquired multi-source trajectory point data, determining whether it simultaneously contains longitude, latitude, and timestamp information; marking a trajectory point lacking any of these three information as an invalid trajectory point; for trajectory points with complete information, calculating the time interval between the current trajectory point and the previous trajectory point, and marking the current trajectory point as an invalid trajectory point when the time interval exceeds a preset time threshold; removing all invalid trajectory points and retaining valid trajectory points to generate the valid trajectory point data.

3. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 1, characterized in that, The steps of converting the spherical latitude and longitude coordinates in the effective trajectory point data into planar coordinate data in a Cartesian coordinate system through a local planar projection method based on the reference latitude of the trajectory set include: calculating the average latitude of all trajectory points in the effective trajectory point data as a reference latitude; establishing a local Cartesian coordinate system with the meridian corresponding to the reference latitude as the central meridian; converting the latitude and longitude coordinates of each trajectory point in the effective trajectory point data into planar coordinate components in the Cartesian coordinate system through projection calculation; and combining the converted planar coordinate components with the height information in the effective trajectory point data to generate the planar coordinate data.

4. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 1, characterized in that, The steps of selecting a reference trajectory from the trajectory set corresponding to the plane coordinate data, calculating the spatial matching degree between each of the remaining trajectories in both forward and reverse directions, and reversing the direction of the plane coordinate data of the corresponding trajectory when the reverse matching degree is better than the forward matching degree, to generate plane coordinate data with the direction corrected include: selecting a trajectory from the trajectory set corresponding to the plane coordinate data as a reference trajectory, and obtaining the plane coordinates of the starting point and ending point of the reference trajectory; for each of the remaining trajectories in the plane coordinate data, obtaining the plane coordinates of the starting point and ending point of the current trajectory; calculating the plane distance between the starting point of the current trajectory and the starting point of the reference trajectory as a first plane distance, and calculating the plane distance between the ending point of the current trajectory and the starting point of the reference trajectory. The planar distance between the endpoints of the reference trajectory is used as the second planar distance. The first and second planar distances are added together to obtain the forward matching distance. The endpoint of the current trajectory is used as the reverse starting point, and the starting point is used as the reverse endpoint. The planar distance between the reverse starting point and the starting point of the reference trajectory is calculated as the third planar distance. The planar distance between the reverse endpoint and the endpoint of the reference trajectory is calculated as the fourth planar distance. The third and fourth planar distances are added together to obtain the reverse matching distance. The forward matching distance and the reverse matching distance are compared. When the reverse matching distance is less than the forward matching distance, the planar coordinate data of the current trajectory is reversed to generate the direction-corrected planar coordinate data.

5. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 1, characterized in that, The process of resampling the directionally corrected planar coordinate data using equal-arc-length sampling, generating a uniform number of sampling points at preset arc-length intervals based on the total arc length of each trajectory, and determining the planar coordinate components and height information of all sampling points through interpolation, includes the following steps: For each trajectory in the directionally corrected planar coordinate data, calculate the total arc length of the trajectory based on the sum of the planar distances between all adjacent trajectory points; divide the total arc length into equal-length arc segments (number of sampling points minus one segment) according to the preset number of sampling points, and calculate the arc-length interval between adjacent sampling points; generate sampling positions on the trajectory according to the arc-length intervals, starting from the trajectory starting point and determining a sampling position every arc-length interval along the trajectory's forward direction; for each For each sampling location, two adjacent original trajectory points are found. The planar coordinate components of the two original trajectory points are weighted according to the distance ratio from the sampling location to these two original trajectory points to obtain the planar coordinate components of the sampling location. For each sampling location, two adjacent original trajectory points are found. The height information of the two original trajectory points is weighted according to the distance ratio from the sampling location to these two original trajectory points to obtain the height information of the sampling location. The planar coordinate components and height information of all sampling locations on the same trajectory are combined to form the equal-arc length sampling data of the trajectory. The equal-arc length sampling data of all trajectories are combined to generate the equal-arc length sampling point data.

6. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 1, characterized in that, For the set of all trajectory points at the same sampling index position in the equal arc length sampling point data, the median statistics of the plane coordinate components and height information are performed respectively to generate a fusion point corresponding to each sampling index position. Connecting all fusion points in the sampling index order to form the main line point sequence data includes: for each sampling index position in the equal arc length sampling point data, collecting all trajectory sampling point data corresponding to that sampling index position to form a sampling point set corresponding to the current sampling index position; extracting the horizontal and vertical component values ​​of the plane coordinates from all sampling points in the sampling point set; sorting all the horizontal component values ​​by size and selecting those located at... The horizontal component value at the middle position is used as the horizontal component of the main route fusion point at that sampling sequence position; all the vertical component values ​​are sorted by size, and the vertical component value at the middle position is selected as the vertical component of the main route fusion point at that sampling sequence position; all the height information values ​​are sorted by size, and the height information value at the middle position is selected as the height information of the main route fusion point at that sampling sequence position; the horizontal component, the vertical component, and the height information are combined to form the main route fusion point at that sampling sequence position; all the main route fusion points at the sampling sequence positions are connected in order of sampling sequence number to form the main route point sequence data.

7. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 1, characterized in that, The steps of geometrically simplifying the main route point sequence data to generate simplified main route point sequence data include: traversing all points in the main route point sequence data and obtaining the planar coordinate components of each point; starting from the starting point of the main route point sequence data, adding the starting point to the simplified point set; using the starting point as a reference point, calculating the distance between the reference point and each subsequent point; determining whether the distance exceeds a preset simplification threshold; when the distance exceeds the simplification threshold, adding the nearest point before the current point to the simplified point set and using that point as a new reference point; repeating the steps of calculating the distance between the reference point and each subsequent point and determining whether the distance exceeds the preset simplification threshold; when the distance exceeds the simplification threshold, adding the nearest point before the current point to the simplified point set and using that point as a new reference point, until the end point of the main route point sequence data is reached and the end point is added to the simplified point set; connecting the points in the simplified point set in the original order to generate the simplified main route point sequence data.

8. The main flight path extraction method for electronic activity analysis of airborne targets as described in claim 7, characterized in that, The steps for generating spatial location information and attribute information for each point in the simplified main route point sequence data to obtain the final main route data include: based on the simplified main route point sequence data, calculating the sum of the planar distances between all adjacent points from the starting point of the main route to the current point, as the cumulative distance of the current point; based on the simplified main route point sequence data, calculating the reference time value of each main route point by interpolation according to the time information of the original trajectory points corresponding to the main route points; based on the simplified main route point sequence data, calculating the planar distance between the current point and the adjacent previous point, dividing it by the time difference between the two points to obtain the speed of the current point; based on the simplified main route point sequence data, calculating the planar vector between the current point and the adjacent next point, and calculating the heading angle of the current point according to the direction of the planar vector; combining the cumulative distance, the reference time value, the speed, and the heading angle as attribute information with the simplified main route point sequence data to obtain the final main route data.

9. A main flight path extraction system for electronic activity analysis of airborne targets, characterized in that, The main flight path extraction system for airborne target electronic activity analysis includes: a memory, a processor, and a main flight path extraction program for airborne target electronic activity analysis stored in the memory and executable on the processor, wherein the main flight path extraction program for airborne target electronic activity analysis is configured to implement the steps of the main flight path extraction method for airborne target electronic activity analysis as described in any one of claims 1 to 8.

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