Unmanned aerial vehicle flight path detection method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle remote identification, in particular to an unmanned aerial vehicle flight path detection method and system, and the method comprises the following steps: obtaining an airspace broadcast signal, detecting a newly added signal, correcting a terminal time difference, recognizing an information source position, extracting a position sequence, reconstructing an unmanned aerial vehicle flight path, comparing a declaration path, and recognizing the identity of an unmanned aerial vehicle. And detecting an abnormal unmanned aerial vehicle and an abnormal flight path, extracting a broadcast time sequence, identifying a time overlapping interval, detecting a remote control end, and obtaining a control identification result. According to the invention, by monitoring the airspace signal in real time and dynamically detecting the newly added signal source, the monitoring precision of the signal source is improved, the time difference correction and space cross reconstruction of a plurality of monitoring terminals are combined, the positioning precision of the unmanned aerial vehicle position is optimized, and a track reconstruction method is adopted, so that the real-time monitoring capability of abnormal behaviors is enhanced; by identifying the information source of the control end and extracting the position information, the airspace safety management and illegal flight prevention capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) remote identification, and particularly to a method and system for detecting the flight trajectory of a UAV. Background Art

[0002] The technical field of UAV remote identification includes a set of technologies for identifying the identity and tracking the trajectory of unmanned aerial vehicles through wireless communication means. The core content of this technical field is to use methods such as radio frequency broadcasting to transmit the unique identity code, spatial position information, speed, flight direction, and operator position information of the UAV in real time during flight, enabling the supervision platform and various receiving terminals to perceive the dynamic state of the UAV in the airspace. This includes an information broadcasting module, a positioning and measurement module, a flight controller docking protocol, and a communication interface with the ground supervision system, which are applied to the identity confirmation and behavior monitoring of UAVs, providing key support for urban low-altitude safety management, UAV cluster scheduling, and airspace traffic coordination.

[0003] Among them, a method for detecting the flight trajectory of a UAV refers to the specific technical means of obtaining the continuous spatial position information of the UAV during flight and performing tracking and analysis on it to identify the flight path. This patent theme includes the identification broadcasting, position information acquisition, and path reconstruction links involved in flight trajectory capture and recording. The methods used include using signal broadcasting that complies with the remote identification specification, transmitting the current coordinates and flight speed of the UAV through Bluetooth and WiFi, and then combining with the ground receiving terminal or platform to decode and analyze the signal. The flight trajectory is reconstructed according to the receiving time sequence, the obtained data is synchronously sorted to form a complete trajectory record sequence, and the trajectory data is matched and identified with the aircraft registration information through a communication protocol to achieve accurate extraction of the flight trajectory.

[0004] Traditional UAV flight trajectory detection technologies rely on traditional signal decoding and position acquisition methods, and there are obvious deficiencies in the monitoring accuracy and real-time performance of signal sources. They lack the ability to detect new signal sources in the airspace in real time, resulting in the possibility of missing the identification of some newly emerging UAV signals during dynamic flight, causing monitoring blind spots. Limited to simple coordinate point connection, they cannot effectively reflect the speed change and flight direction of the UAV in a complex flight environment, and cannot accurately describe the flight behavior. When dealing with multiple signal sources, the time synchronization and spatial position cross-reconstruction between signal sources are ignored, resulting in insufficient positioning accuracy and difficulty in providing stable and reliable real-time flight data. When identifying abnormal flight paths, they rely on the comparison between static declared paths and trajectories, and cannot effectively respond to the dynamic changes of trajectories, easily causing UAVs deviating from the declared path to be unable to be identified in time. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, an embodiment of the present invention provides a method and system for detecting the flight trajectory of an unmanned aerial vehicle. The technical solution is as follows: To achieve the above object, the present invention adopts the following technical solution. A method for detecting the flight trajectory of an unmanned aerial vehicle includes the following steps: S1: Obtain the broadcast signal in the target airspace, construct a signal source attribute group sequence by extracting the identification field, frequency band field, and channel structure field of the signal, identify the number of independent signal sources in the airspace and detect newly added signals, and obtain the airspace signal monitoring information; S2: According to the airspace signal monitoring information, extract the set of independent signal sources, correct the time difference between multiple listening terminals, perform spatial cross-reconstruction on the reception records between multiple listening nodes, identify the signal source location and detect the unmanned aerial vehicle, and obtain the unmanned aerial vehicle identification record; S3: According to the unmanned aerial vehicle identification record, call the spatial position sequence of the signal source coordinates of the unmanned aerial vehicle, extract the time interval value and coordinate difference value between each position point, construct a fitting trajectory point sequence with equal time intervals, and obtain the unmanned aerial vehicle trajectory reconstruction result; S4: According to the unmanned aerial vehicle trajectory reconstruction result, compare it with the trajectory sequences of multiple declared flight paths, calculate the trajectory similarity, identify the identity of the unmanned aerial vehicle and detect abnormal unmanned aerial vehicles, combine the deviations of the flight trajectory at multiple spatial positions, calculate the change trend of the trajectory deviation and detect abnormal flight paths, and obtain the airspace supervision record.

[0006] As a further solution of the present invention, the airspace signal monitoring information includes the number of independent signal sources, the signal source attribute group sequence, and the signal time distribution characteristics. The unmanned aerial vehicle identification record includes the set of unmanned aerial vehicle spatial coordinate points, the broadcast time sequence, and the signal concentration area identifier. The unmanned aerial vehicle trajectory reconstruction result includes the trajectory speed trend sequence, the direction change trajectory, the time synchronization trajectory point set, and the trajectory segment connection structure. The airspace supervision record includes the unmanned aerial vehicle identity recognition result, the abnormal flight recognition label, and the trajectory deviation cumulative index.

[0007] As a further solution of the present invention, the step of obtaining the broadcast signal in the target airspace, constructing a signal source attribute group sequence by extracting the identification field, frequency band field, and channel structure field of the signal, identifying the number of independent signal sources in the airspace and detecting newly added signals, and obtaining the airspace signal monitoring information is specifically as follows: S101: Obtain the broadcast signal in the target airspace, extract the identification field, frequency band field, and channel structure field of each piece of data in the broadcast signal, and construct a signal source attribute group sequence; S102: According to the combined value of the signal source attributes, perform uniqueness analysis on the signal source attribute group sequence, identify the number of independent signal sources in the airspace, and obtain the independent signal source quantity value; S103: According to the number value of the independent signal sources, detect the number of newly added signals in the airspace in real time, and obtain the airspace signal monitoring information.

[0008] As a further solution of the present invention, the steps of extracting the set of independent signal sources according to the airspace signal monitoring information, correcting the time difference between multiple listening terminals, performing spatial cross-reconstruction on the reception records between multiple listening nodes, identifying the signal source positions and detecting drones, and obtaining the drone identification records are specifically as follows: S201: According to the airspace signal monitoring information, extract the set of independent signal sources, call the reception time data recorded in each listening terminal and the corresponding local time records, compare the time records between each terminal, extract the time difference value and perform correction to obtain the time calibration reference value; S202: According to the time calibration reference value, extract the spatial distribution of signal reception nodes at the synchronous time, perform spatial cross-reconstruction on the reception records between multiple listening nodes, and obtain the signal spatial cross-distribution value; S203: According to the signal spatial cross-distribution value, extract the spatial centroid points of the signal concentration area, identify the positions of multiple signal sources, and according to the spatial coordinate information of the signal sources, identify the altitude information of the signal sources and detect drones to obtain the drone identification records.

[0009] As a further solution of the present invention, the steps of calling the spatial position sequence of the drone signal source coordinates according to the drone identification records, extracting the time interval value and coordinate difference between each position point, constructing a fitting trajectory point sequence with equal time intervals, and obtaining the drone trajectory reconstruction result are specifically as follows: S301: According to the drone identification records, call the spatial position sequence of the drone signal source coordinates, extract the time interval value and coordinate difference between each adjacent position point to obtain the position change parameter value; S302: According to the position change parameter value, extract the velocity change direction between consecutive coordinate points, and establish a sequence trend according to the velocity direction change to obtain the velocity direction trend value; S303: According to the velocity direction trend value, construct a fitting trajectory point sequence with equal time intervals, reconstruct the moving trajectory sequence of the target drone signal source, including the position information of multiple time points, and obtain the drone trajectory reconstruction result.

[0010] As a further solution of the present invention, the steps of comparing with the trajectory sequences of multiple declared flight paths according to the drone trajectory reconstruction result, calculating the trajectory similarity, identifying the drone identity and detecting abnormal drones, combining the deviations of the flight trajectory at multiple spatial positions, calculating the change trend of the trajectory deviation and detecting abnormal flight paths, and obtaining the airspace supervision records are specifically as follows: S401: According to the reconstructed UAV trajectory result, extract the trajectory sequences of multiple declared flight paths, calculate the trajectory similarity between the UAV trajectory and each declared trajectory, identify the UAV identity based on the UAV information corresponding to the declared path, and detect abnormal UAVs to obtain the trajectory similarity recognition result; S402: According to the trajectory similarity recognition result, combined with the deviation of the flight trajectory at multiple spatial positions, extract the spatial distance value between each reconstructed trajectory point and the corresponding declared path point to obtain the trajectory offset sequence value; S403: According to the trajectory offset sequence value, identify abnormal flight paths by analyzing the change trend of the offset value along the trajectory time sequence, and obtain the airspace supervision record.

[0011] As a further solution of the present invention, the specific formula for calculating the trajectory similarity between the UAV trajectory and each declared trajectory is: ; Calculate the similarity index value of the trajectory spatial point set; Wherein, is the similarity index value of the trajectory spatial point set between the UAV trajectory p and the declared trajectory q, p is the number index of the current UAV reconstructed trajectory, q is the number index of the current declared flight path, k is the position index of the trajectory point in the point set sequence, m is the total number of valid trajectory points after the trajectories p and q are aligned on the time axis, is the X-axis coordinate value of the kth point in the trajectory p, is the Y-axis coordinate value of the kth point in the trajectory p, is the Z-axis coordinate value of the kth point in the trajectory p, is the X-axis coordinate value of the kth point in the trajectory q, is the Y-axis coordinate value of the kth point in the trajectory q, is the Z-axis coordinate value of the kth point in the trajectory q, is the spatial distance between the reference segments corresponding to the kth point in the trajectory q, indicating the length of the ideal path segment near the kth point of the declared trajectory, is the direction weight coefficient of the kth point in the trajectory.

[0012] As a further solution of the present invention, the method further includes: S5: According to the airspace signal monitoring information, UAV identification record and airspace supervision record, extract the spatial coordinates and broadcast time series of the identified information sources, call the broadcast time series of multiple UAV information sources, compare with the broadcast times of multiple non-UAV information sources, extract the time overlap interval, identify the remote control information source corresponding to each UAV and extract the position information to obtain the control information source recognition result; The control source association result includes the remote control end source identification number, the spatial position of the remote control signal, and the remote control source synchronization control label.

[0013] As a further solution of the present invention, according to the airspace signal monitoring information, the UAV identification record, and the airspace supervision record, the spatial coordinates and the broadcast time series of the identified sources are extracted, the broadcast time series of multiple UAV sources are called, and by comparing with the broadcast times of multiple non-UAV sources, the time overlap interval is extracted, and the steps of identifying the remote control end source corresponding to each UAV and extracting the position information to obtain the control source identification result are specifically as follows: S501: According to the airspace signal monitoring information, the UAV identification record, and the airspace supervision record, extract the spatial coordinates and the broadcast time series of each identified source to obtain the source spatio-temporal data; S502: According to the source spatio-temporal data, compare the broadcast time series of multiple identified UAV sources with the broadcast times of multiple non-UAV sources, extract the time overlap interval, and obtain the time overlap interval data; The specific formula for extracting the time overlap interval is: ; Calculate the time intersection offset index value; Wherein, represents the time intersection offset index value between the UAV source i and the non-UAV source j, represents the start point of the broadcast time of the UAV source i, represents the end point of the broadcast time of the UAV source i, represents the start point of the broadcast time of the non-UAV source j, represents the end point of the broadcast time of the non-UAV source j, represents the central time point of the broadcast time period of the UAV source i, represents the central time point of the broadcast time period of the non-UAV source j, i is used to identify the UAV source number, indicating the i-th UAV source being processed currently, j is used to identify the non-UAV source number, indicating the j-th non-UAV source being processed currently, u represents the UAV source, n represents the non-UAV source, start represents the start time of the corresponding broadcast time period, end represents the end time of the corresponding broadcast time period, and center represents the central time point of the broadcast time period; S503: According to the time overlap interval data, identify the remote control end source corresponding to each UAV, and extract the position information of the target remote control end source to obtain the control source identification result.

[0014] On the other hand, a UAV flight trajectory detection system is provided. This system is applied to the UAV flight trajectory detection method. This system includes: The signal monitoring module extracts the identification field, frequency band field, and channel structure field from the signals based on the broadcast signals in the target airspace, analyzes the attributes of each signal, constructs a signal source attribute group sequence, identifies the number of independent signal sources in the airspace, detects new signals, and generates airspace signal monitoring information; The signal source positioning module extracts the set of independent signal sources based on the airspace signal monitoring information, corrects the time difference between multiple listening terminals, and through spatial cross-reconstruction, identifies the signal source location and detects drones, generating drone identification records; The trajectory reconstruction module, based on the drone identification records, calls the spatial position sequence of the drone signal source coordinates, extracts the time interval value and coordinate difference between each position point, constructs a fitting trajectory point sequence with equal time intervals, and generates the drone trajectory reconstruction result; The anomaly detection module, based on the drone trajectory reconstruction result, calculates the trajectory similarity by comparing with the trajectory sequences of multiple declared flight paths, identifies the drone identity and detects abnormal drones, combines the deviations of the flight trajectory at multiple spatial positions, calculates the change trend of the trajectory deviation and detects abnormal flight paths, generating airspace supervision records; The control source identification module, based on the airspace signal monitoring information, drone identification records, and airspace supervision records, extracts the spatial coordinates and broadcast time sequence of the identified signal sources, compares with the broadcast times of multiple non-drone signal sources, extracts the time overlap interval, identifies the remote control source corresponding to each drone and extracts the location information, generating the control source identification result.

[0015] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include: By real-time monitoring airspace signals and dynamically detecting new signal sources, the monitoring accuracy of signal sources is improved, ensuring the accurate identification of drones in the airspace. Combining the time difference correction and spatial cross-reconstruction technologies of multiple listening terminals, the positioning accuracy of drone positions is optimized. Using the trajectory reconstruction method and detecting abnormal flight paths through comparison with declared paths, the real-time monitoring ability of abnormal behaviors is enhanced. By identifying the control source and extracting location information, the accurate identification of the controller's identity is ensured, improving the airspace safety management and the prevention ability of illegal flights. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of the working process of the present invention; Figure 2 This is the system flow chart of the present invention. Specific embodiments

[0018] The following will describe the technical solutions in the present invention with reference to the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0021] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0022] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] Please refer to Figure 1 , the present invention provides a technical solution, a method for detecting the flight trajectory of an unmanned aerial vehicle, including the following steps: S1: Obtain the broadcast signals in the target airspace, construct a sequence of signal source attribute groups by extracting the identification field, frequency band field, and channel structure field of each broadcast signal, identify the number of independent signal sources in the airspace, and detect newly added signals in the airspace in real time to obtain airspace signal monitoring information; Identification field: Refers to the unique device identification code that conforms to the Remote Identification (RemoteID) standard, such as FAA UAS ID, the unique serial number of EU Class C unmanned aerial vehicles, and GB / T 41300-2022; Frequency band field: Refers to the physical transmission frequency range used by the broadcast signal, commonly 2.4 GHz or 5.8 GHz, which belongs to the international standard radio spectrum classification; Channel structure field: Refers to the protocol format structure marker adopted by the signal, which can be based on the Wi-Fi NAN frame structure, BLE broadcast structure, or custom remote control protocol header; S2: According to the airspace signal monitoring information, extract the set of independent signal sources, call the reception time data of each listening terminal for the signal source and the local time record, correct the time difference between multiple listening terminals, construct a unified time reference sequence, extract the spatial distribution of signal reception nodes at the synchronous time, perform spatial cross-reconstruction on the reception records between multiple listening nodes, extract the spatial centroid point of the signal concentration area, and detect the drone based on the spatial coordinate information of the signal source to obtain the drone identification record; Time difference correction: Refers to the unified processing of the clock offset of the local reception timestamps recorded by multiple terminals. The common method is the GPS clock reference or network synchronization; Spatial centroid point: Refers to the geometric mean center of multiple listening coordinates in the spatial distribution of the signal, which is used to estimate the position of the target emission source; Drone identification record: Refers to the structured identification information set established for the target with stable broadcast trajectory and spatial movement characteristics; S3: According to the drone identification record, call the spatial position sequence of the drone signal source coordinates, extract the time interval value and coordinate difference between each position point, perform trend extraction processing on the velocity change direction between the coordinate points, construct a sequence of fitting trajectory points at equal time intervals, and obtain the drone trajectory reconstruction result; Time interval value: The time stamp difference between adjacent broadcast data; Coordinate difference: The longitude and latitude difference between consecutive position points; Velocity change direction: The velocity vector sequence calculated from the coordinate difference and time interval, which reflects the flight path direction; S4: According to the drone trajectory reconstruction result, by comparing with the trajectory sequences of multiple declared flight paths, calculate the trajectory similarity, identify the drone identity and detect abnormal drones, combine the deviations of the flight trajectory at multiple spatial positions, calculate the change trend of the trajectory deviation and detect abnormal flight paths, and obtain the airspace supervision record; Trajectory similarity: Based on the principle of the Euclidean distance sequence of point sets, calculate the shape coincidence degree between any two trajectories; Trajectory deviation change trend: The continuous change sequence of the offset distance between the actual trajectory and the declared trajectory at different spatial points over time or path length; Airspace supervision record: The event status data set generated by the system according to the mapping result of the offset behavior and identity; S5: According to the airspace signal monitoring information, UAV identification records, and airspace supervision records, extract the spatial coordinates and broadcast time series of the identified signal sources, call the broadcast time series of multiple UAV signal sources, compare with the broadcast times of multiple non-UAV signal sources, extract the time overlap intervals, identify the remote control signal sources corresponding to each UAV and extract the location information to obtain the identification result of the control signal source; Broadcast time series: refers to the time stamp series of the continuous broadcast records of a specific signal source by the listening terminal; Time overlap interval: refers to the intersection period of two groups of broadcast events in the time dimension, representing the possibility of synchronous behavior; Identification result of the control signal source: based on the corresponding relationship between time overlap and spatial relative position, establish the logical control attribution between the UAV and its remote control terminal.

[0024] The airspace signal monitoring information includes the number of independent signal sources, the signal source attribute group sequence, and the signal time distribution characteristics. The UAV identification records include the UAV spatial coordinate point set, the broadcast time series, and the signal concentration area identifier. The UAV trajectory reconstruction result includes the trajectory speed trend sequence, the direction change trajectory, the time synchronization trajectory point set, and the trajectory segment connection structure. The airspace supervision records include the UAV identity identification result, the abnormal flight identification label, and the trajectory deviation cumulative index. The control signal source association result includes the remote control signal source identification number, the remote control signal spatial position, and the remote control source synchronization control label.

[0025] S101: Obtain the broadcast signals in the target airspace, extract the identification field, frequency band field, and channel structure field of each piece of data in the broadcast signals, and construct the signal source attribute group sequence; When obtaining the broadcast signal in the target airspace, the monitoring terminal needs to continuously receive broadcast data within the set communication frequency band and convert the original signal data into a structured data stream. When parsing each piece of broadcast data item by item, it is necessary to extract the identification field, frequency band field, and channel structure field in sequence. The identification field is usually represented by the MAC address. The frequency band field represents the operating frequency, such as 2.4 GHz or 5.8 GHz. The channel structure field is used to characterize the channel bandwidth and its frequency hopping characteristics. After extracting the above fields, a sequence of attribute groups composed of the three fields is constructed, and each attribute group serves as the basis for subsequent identification of the signal source. In practical applications, if the monitoring terminal receives 500 broadcast frames, 500 attribute groups are obtained after field extraction and combination. The system judges the uniqueness of the attribute groups. First, convert each group into a string format and then compare whether they are exactly the same. If there are duplicates, it indicates the same signal source. The number of unique attribute groups obtained after deduplication can be used to judge the distribution of signal source types. In addition, it is necessary to compare the channel structure field with the historical signal congestion record of the current frequency band to judge whether there is frequency band overlap between signal sources. Calculate the interval of the broadcast time corresponding to each attribute group to judge whether adjacent broadcasts are continuous transmissions, and use the set broadcast interval value as the judgment criterion. If the proportion of the number of times the adjacent time interval is less than the standard value in this signal source is too high, it is marked as a high-frequency broadcast signal source. During this process, combined with the broadcast frame number and time tag, draw the time distribution sequence of each field, and evaluate its stability according to whether there are regular repetitions. The sequence of signal source attribute groups constructed in the above way will be used for subsequent independent signal source identification and airspace signal monitoring and processing.

[0026] S102: According to the signal source attribute combination value, perform uniqueness analysis on the signal source attribute group sequence, identify the number of independent signal sources in the airspace, and obtain the independent signal source quantity value; According to the constructed sequence of signal source attribute groups, the unique identification field values in each group need to be extracted and uniformly converted into the standard string format to form a set of identification value sets. The system performs a repeatability comparison on each value in this set to determine which combinations are unique combinations. The comparison process needs to follow three criteria, that is, the identification fields should be exactly the same, the difference between the frequency band fields does not exceed the set bandwidth error range, and the channel structure fields need to maintain the same structure. After removing the duplicates, the number of remaining combinations is the number of identified independent signal sources. In an actual scenario, if a total of 800 groups of broadcast data are processed, and after the above judgment, it is found that 520 groups are unique combinations, then the current number of valid independent signal sources in this airspace is 520. The system also needs to perform a frequency band distribution statistics on these unique signal sources, calculate the number of signal sources in different frequency bands respectively, and then compare with the capacity upper limit values of each frequency band. For example, when the capacity upper limit of the 2.4 GHz frequency band is set to 300 and the actual statistical value is 340, the system should issue a warning of exceeding the frequency band capacity. In addition, to prevent repeated signal sources from repeatedly entering the judgment process, the uniquely identified combinations are cached using an efficient data structure to support rapid filtering of redundant data in subsequent signal source identification.

[0027] S103: According to the value of the number of independent signal sources, detect the number of newly added signals in the airspace in real time and obtain the airspace signal monitoring information; Based on the value of the number of independent signal sources calculated in the previous stage, the system needs to call the number of independent signal sources saved in the previous round of data and perform a comparison, calculate the difference between the current identified number and the previous one to judge the change trend of the number of newly added signal sources. If this difference is positive, it means that there are new signal sources in the current airspace. The system sets a time monitoring window. If the increase in the number of signal sources is detected in multiple consecutive cycles within this window, it can be determined as a stable new addition trend. Subsequently, the newly identified signal sources need to be separated from the attribute group, and the difference between the time tag of their first appearance and the current system time is analyzed. If this difference is less than the set time threshold, these signal sources can be identified as real newly added signal sources. In addition, the broadcast frequencies of the newly added signal sources also need to be monitored, and the difference between this frequency and the global average broadcast frequency is calculated to judge whether their broadcast behavior is stable. In actual operation, if 15 newly identified signal sources are identified, and the broadcast frequencies of 12 of them are within the set range of the average value, then these 12 signal sources are classified as frequency-stable signal sources. Next, the system needs to statistically calculate the distribution center value of the frequency bands where these signal sources are located, and perform a linear fitting on the change of the center value on the time axis to construct the trend sequence of the current airspace frequency band usage. This trend information will be used as the basis for subsequent airspace density analysis, and finally obtain the airspace signal monitoring information.

[0028] According to the airspace signal monitoring information, extract the set of independent signal sources, correct the time difference between multiple listening terminals, perform spatial cross-reconstruction on the reception records among multiple listening nodes, identify the signal source location and detect the UAV. The specific steps for obtaining the UAV identification record are as follows: S201: According to the airspace signal monitoring information, extract the set of independent signal sources, call the reception time data recorded in each listening terminal and the corresponding local time record, compare the time records between each terminal, extract the time difference and perform correction to obtain the time calibration reference value; To extract the set of independent signal sources according to the airspace signal monitoring information, it is necessary to summarize the signal identification fields in the monitoring information and construct a set, which respectively correspond to the broadcast sources with unique identifiers in the data frames recorded by the listening terminals. In the listening network, multiple listening terminals respectively record the broadcast time of the same signal source received by them and their local system time. To obtain the accurate broadcast reception moment, it is necessary to extract the reception time tag and the local time tag from the data records of each listening terminal, pair the two item by item, perform difference operation on the paired data and record the time difference sequence. This time difference represents the synchronization error between the local clock of the terminal and the unified standard clock. The system calculates the error mean value between different terminals according to the time difference sequence, and uses this mean value as the initial calibration value to correct the original reception time of each terminal. For example, listening terminal A records the reception time as 10:00:05.120, the local time as 10:00:05.000, and the time difference is 0.120 seconds. Similarly, the time difference of terminal B is -0.080 seconds, and the average time difference between the two terminals is 0.02 seconds. The system uses this value to adjust the time tags of all terminals. By subtracting the time difference of each terminal and unifying the reference standard time, the corrected time stamps of each reception record are regenerated. During the correction process, the system sets the time difference adjustment threshold to 0.5 seconds. If the average difference between a certain terminal and other terminals exceeds this threshold, it is marked as a time abnormal terminal and its data record is excluded to ensure the unity and continuity of the time tags for subsequent processing. The standardized corrected time set formed on this basis is the time calibration reference value.

[0029] S202: According to the time calibration reference value, extract the spatial distribution of signal reception nodes at the synchronized time, perform spatial cross-reconstruction on the reception records among multiple listening nodes to obtain the signal spatial cross-distribution value; According to the time calibration reference value, the spatial distribution of signal receiving nodes under synchronous time is extracted. It is necessary to match the location information of each monitoring terminal with its corrected reception broadcast record under a unified time reference, and construct a set of spatial receiving points. The spatial coordinates are preset and solidified in the system database when the monitoring terminal is deployed. The one-to-one correspondence is achieved by matching the terminal identifier with the source identifier field in the reception record. In actual operation, if at the time point 10:00:05, terminals A, B, and C are located at coordinates (20,30), (25,35), and (22,32), respectively, and all three receive the broadcast of signal source X, the system associates these three coordinates with their corresponding reception records to form a spatial reception matrix at the same time. The matrix contains all the received The terminal location information of the same signal. On this basis, the system needs to perform spatial cross-processing. The steps are to first classify all receiving records according to the signal source identification field, extract all receiving node positions of the same signal source, and then measure the distances between the node positions in pairs, calculate their geometric centers, and gradually reduce the intersection area of ​​the straight line segments between adjacent nodes and compare it with the actual coordinate boundaries to screen out the area with the maximum receiving density in spatial distribution. If the number of receiving points in this area accounts for more than 75% of the total receiving points, then the area is defined as a signal source receiving concentration area. The boundary points of the concentration area are used to construct a signal space coverage contour map, which can be visualized in the system GIS interface to form a signal space cross-distribution value.

[0030] S203: extracting the spatial center of gravity of the signal concentration area according to the signal spatial cross-distribution value, identifying multiple signal source locations, and identifying the altitude information of the signal source according to the spatial coordinate information of the signal source, detecting the drone, and obtaining the drone identification record; Extract the spatial centroid point of the signal concentration area according to the signal space cross-distribution value. The system needs to perform three-dimensional weighted calculations on the coordinates of all receiving nodes that make up the spatial cross-region, extract the arithmetic mean values in the X and Y directions as the plane centroid coordinates, and the value in the Z-axis direction needs to be calibrated by combining the surface elevation of the corresponding coordinates in the terrain database. Use the digital elevation model DEM to obtain the surface elevation of this plane centroid point, and then perform spatial attenuation correction on the broadcast signal strength received by the monitoring terminal. According to the signal path loss model, inversely deduce the relative height of the signal source emission point. If the signal emission intensity is set to 20 dBm and the actual received intensity of the monitoring terminal is -60 dBm, estimate the propagation distance to be 100 meters according to the free space path loss formula. If the vertical distance from the ground point to the monitoring point is 80 meters, the signal source should be 20 meters above the monitoring point. Thus, estimate the altitude of the signal source as the surface elevation plus the propagation height, and finally judge its three-dimensional coordinates. The system judges its position within the low-altitude activity range according to this coordinate, and combines its broadcast frequency band field and broadcast frequency value to further judge whether it is a drone type broadcast source. If the frequency band field is 5.8 GHz, the frequency value is above 50 frames per second, and the signal source position height is between 30 meters and 150 meters above the ground surface, record it as a drone signal source and generate the corresponding identifier, and finally obtain the drone identification record.

[0031] According to the drone identification record, call the spatial position sequence of the drone signal source coordinates, extract the time interval value and coordinate difference between each position point, and the steps to obtain the drone trajectory reconstruction result are specifically as follows: S301: According to the drone identification record, call the spatial position sequence of the drone signal source coordinates, extract the time interval value and coordinate difference between each adjacent position point, and obtain the position change parameter value; Based on the UAV recognition records, the system extracts the set of signal source coordinate information corresponding to it, constructs a spatial position sequence in chronological order, extracts the corresponding time tags for every two adjacent position points and calculates the time interval, calculates the absolute values of the differences between the two coordinate values on the three axes respectively and calculates the straight-line distance between the coordinates. At the same time, divides this distance by the time interval to obtain the position change rate value, which is used as the basic position change parameter. Then synthesizes the differences in the three directions into a unit vector of spatial direction change to form the change direction parameter. The system performs such a calculation process on all coordinate points in this sequence to construct the time and space change sequence of the entire path. In the example, if the time tags of two adjacent points are 12:00:01.500 and 12:00:02.000 respectively, and the spatial coordinates are (10.0, 15.0, 5.0) and (12.0, 16.5, 6.0), then the time interval is 0.5 seconds, the differences in the three axes are 2.0, 1.5 and 1.0, the path length is approximately 2.69 meters under the square root of 7.25. Then the position change rate of this section is 2.69 divided by 0.5 equal to 5.38 meters per second. Uses the difference sequence as data input to construct a complete change parameter sequence. When there are multiple data with inconsistent time intervals in the same trajectory, the system segments the time interval values according to a fixed time window, extracts three indicators of the maximum rate, minimum rate, and average rate in each segment, and then compares them with the set moving rate interval of the system respectively. The set interval is 0.5 to 30 meters per second. If the rate value is lower than 0.5 or higher than 30, the corresponding paragraph is marked as a speed abnormal section, and further analyzes whether there are mutation values in the data of this section. Marks and eliminates the abnormal fluctuation section by means of exceeding the speed difference limit to form a stable position change parameter value.

[0032] S302: Extract the velocity change direction between consecutive coordinate points according to the position change parameter value, and establish a sequence trend based on the velocity direction change to obtain the velocity direction trend value; Based on the obtained position change parameter values, extract the direction vector differences for each group of consecutive coordinate points in the sequence, and use the vector angle calculation as the judgment basis. The system sets the direction change sensitivity threshold to 15 degrees. That is, when the angle between two consecutive velocity direction vectors exceeds this value, it is considered that the direction has changed. The calculation method is to perform a dot product operation on adjacent velocity vectors, and use the ratio of the dot product to the product of their respective magnitudes for inverse cosine processing to obtain the angle value. For example, if the first velocity vector is (2, 2, 0) and the second velocity vector is (3, 0, 0), then their dot product is 6, the magnitudes are 2.83 and 3.0 respectively, the cosine value of the angle is 0.707, and after inverse cosine it is 45 degrees, which is greater than the set threshold, so this point is marked as a direction mutation point. The system sorts all mutation points, numbers each stable direction segment in chronological order, and records the average direction value vector within this numbered segment. Subsequently, perform an angle increment judgment on all average direction vectors. If the direction changes always show a similar direction offset in three consecutive numbers, construct a trend vector sequence. The trend vector is composed of the difference vectors between each average direction segment, which is used to reflect the continuous change trend of the trajectory. The system requires that when the angle between the trend vectors is less than 10 degrees, it is considered that the directions are the same. If the angle is between 10 and 30 degrees, it is regarded as a weak trend. If the angle exceeds 30 degrees, it is regarded as a mutation or turning segment. Finally, generate the velocity direction trend value.

[0033] S303: According to the velocity direction trend value, construct a sequence of fitting trajectory points at equal time intervals, reconstruct the moving trajectory sequence of the target UAV source, including the position information at multiple time points, and obtain the UAV trajectory reconstruction result; According to the velocity direction trend value, the system needs to construct a sequence of trajectory fitting points at equal time intervals. First, extract the time label range to determine the total time span, and then perform equal interval division according to the set time resolution. The time resolution can be set at the level of 0.5 seconds or 1 second. After determining the time label of each interpolation point, it is necessary to perform superposition and translation processing on the direction vector of the trend segment where it is located and the coordinates of the previous trajectory point to generate a new point. The superposition value is calculated by multiplying the unit direction vector by the product of the local average velocity and the time interval. In the example, if the direction vector of the trend segment is (0.6, 0.8, 0), the average velocity is 4 meters per second, and the time interval is 1 second, then the position increment is (2.4, 3.2, 0). Add this increment to the coordinates of the previous point to generate a new trajectory point, and then perform superposition in sequence to form a complete sequence of trajectory points. All fitting points constitute the final trajectory fitting sequence. The system requires comparing the offset between the fitting points and the actual coordinate points. If it exceeds the set fitting deviation threshold of 5 meters, then this fitting point is marked as a deviation point and corrected. Finally, output a set of reconstructed path data including time labels and spatial coordinates to obtain the UAV trajectory reconstruction result.

[0034] According to the UAV trajectory reconstruction result, by comparing with the trajectory sequences of multiple declared flight paths, calculating the trajectory similarity, identifying the UAV identity and detecting abnormal UAVs, and combining the deviations of the flight trajectory at multiple spatial positions, calculating the change trend of the trajectory deviation and detecting abnormal flight paths, the steps of obtaining the airspace supervision record are specifically as follows: S401: According to the UAV trajectory reconstruction result, extract the trajectory sequences of multiple declared flight paths, calculate the trajectory similarity between the UAV trajectory and each declared trajectory, identify the UAV identity according to the UAV information corresponding to the declared path, and detect abnormal UAVs to obtain the trajectory similarity recognition result; The specific formula for calculating the trajectory similarity between the UAV trajectory and each declared trajectory is: ; Calculate the similarity index value of the trajectory spatial point set; Among them, is the similarity index value of the trajectory spatial point set between the UAV trajectory p and the declared trajectory q, p is the number index of the current UAV reconstruction trajectory, q is the number index of the current declared flight path, k is the position index of the trajectory point in the point set sequence, m is the total number of valid trajectory points after the trajectories p and q are aligned on the time axis, is the X-axis coordinate value of the kth point in the trajectory p, is the Y-axis coordinate value of the kth point in the trajectory p, is the Z-axis coordinate value of the kth point in the trajectory p, is the X-axis coordinate value of the kth point in the trajectory q, is the Y-axis coordinate value of the kth point in the trajectory q, is the Z-axis coordinate value of the kth point in the trajectory q, is the spatial distance between the reference segments corresponding to the kth point in the trajectory q, indicating the length of the ideal path segment near the kth point of the declared trajectory, is the direction weight coefficient of the kth point in the trajectory.

[0035] Formula: ; Detailed explanation of the formula and the derivation process of the formula calculation: The formula is used to calculate the similarity value between the UAV trajectory and the declared trajectory at the spatial point set level. By accumulating the Euclidean distance differences point by point for the multi-moment three-dimensional point positions after alignment, and combining with the local segment reference distance for normalization processing, the calculation result is used to judge the structural fitting degree between the trajectories. The formula is based on the spatial distance differences between the trajectory reconstruction points and the declared path points after pairing on the time axis. By calculating the absolute error between the three-dimensional position deviation of each pair of corresponding points and its due reference distance, and assigning different weights according to the key degree of each point in the trajectory structure, the weighted sum of all point errors is calculated and normalized, so as to construct a similarity index for measuring the consistency of the spatial trajectory shape. If the spatial distance differences of all key points are close to their ideal inter-segment distances in the declared trajectory and no structural mutations occur, the index value approaches 1, indicating a high degree of trajectory structure matching. If the relative errors show continuous offsets or the matching of key point segments is unbalanced, the index value approaches 0 or even becomes negative, indicating abnormal flight characteristics such as overall deviation of the actual trajectory from the declared path, local turning misalignment, or unaligned key points; Parameter meaning and setting value: It is the reconstructed trajectory number, numbered UAV_07, which comes from the analysis of UAV telemetry broadcast data and trajectory reconstruction processing; It is the declared trajectory number, numbered REG_09, corresponding to the pre-registered flight path on the supervision platform; It is the index value of the k-th point in the trajectory. The number of aligned trajectory points m is set to 5; It is the three-dimensional coordinate value of the k-th point of the UAV trajectory. Through three-dimensional positioning solution, the unit is meters, and it is set as (120.0, 50.0, 35.0), (122.5, 51.0, 36.0), (125.0, 52.0, 36.5), (127.0, 53.5, 36.7), (129.0, 55.0, 37.0); It is the three-dimensional coordinate value of the k-th point of the declared trajectory, with the unit of meters, and it is set as (120.5, 50.2, 35.0), (122.8, 51.1, 36.0), (125.1, 52.2, 36.5), (127.1, 53.4, 36.8), (129.2, 55.2, 37.2); It is the distance between adjacent points of the k-th point in the declared trajectory, ; is the weight value of the trajectory point, which is weighted and set according to the direction change rate. The direction change angle is calculated by the arccosine of the included angle formed by three adjacent points. If the angle exceeds 15 degrees, the weight is set to 1.5; if it is between 10 and 15 degrees, it is set to 1.2; if it is between 5 and 10 degrees, it is set to 1.0; and if it is less than 5 degrees, it is set to 0.8. The weight values of the current five points are set to 1.0, 1.2, 1.5, 1.2, and 1.0 respectively; Substitute the parameters into the formula for calculation: Calculate the difference between the spatial distance of each point and the reference distance multiplied by the weight term as follows: The first point: ; ; The error term is: ; Similarly, we get: For the second point: the spatial difference is 0.3317, the reference distance is 2.5295, and the error term is 2.1978×1.2 = 2.6374; for the third point: the spatial difference is 0.2236, the reference distance is 2.5090, and the error term is 2.2854×1.5 = 3.4281; for the fourth point: the spatial difference is 0.2449, the reference distance is 2.4832, and the error term is 2.2383×1.2 = 2.6859; for the fifth point: the spatial difference is 0.3606, the reference distance is 2.5170, and the error term is 2.1564×1.0 = 2.1564; The weighted sum of errors is: 2.0082 + 2.6374 + 3.4281 + 2.6859 + 2.1564 = 12.9160; The sum of reference distances is: 2.5467 + 2.5295 + 2.5090 + 2.4832 + 2.5170 = 12.5854; ; The result of 0.0263 indicates that there is an excessive cumulative deviation in the spatial structure between the UAV trajectory and the declared trajectory, and they cannot be matched, indicating that the trajectories do not have synchrony or consistency. This value can be used to screen out suspected disguised or unreported flight individuals on the path, and output an index for judging the result of trajectory similarity recognition.

[0036] S402: According to the result of trajectory similarity recognition, combined with the deviations of the flight trajectory at multiple spatial positions, extract the spatial distance values between each reconstructed trajectory point and the corresponding declared path point to obtain the trajectory offset sequence value; According to the trajectory similarity recognition results, the system needs to call the recognized trajectory and its corresponding declared path trajectory point sequence, calculate the difference between the coordinate values of each pair of corresponding time points in the trajectory. The difference is the offset distance between the reconstructed trajectory point and the declared trajectory point in the x, y, and z directions. Use the spatial distance formula to extract the Euclidean distance of the three-dimensional coordinates and store it in the trajectory offset record. Suppose the reconstructed point is (15.0, 20.0, 5.0) and the declared point is (14.0, 19.5, 5.0) at a certain time point. The differences in the three axes are 1.0, 0.5, and 0 respectively. The sum of squares is 1.25, and the offset distance after taking the square root is approximately 1.12 meters. Take this value as the offset value at this time point. After processing all corresponding points in the trajectory in turn, the system will form an offset sequence with a length equal to the number of trajectory points. Each element in the sequence represents the spatial difference between the reconstructed trajectory point and the declared path at this moment. After the sequence is generated, the system uses the time tag as the horizontal axis and the offset value as the vertical axis to construct a trajectory offset curve graph, and marks the maximum offset value, minimum offset value, and average offset value in the graph. The system sets the offset value interval division standard into four levels: 0 to 1 meter, 1 to 3 meters, 3 to 5 meters, and more than 5 meters. If the offset value in a continuous time period of a certain section of the trajectory is in the interval of 3 to 5 meters and exceeds 30% of the total duration, then mark this section as a moderately offset section. If the continuous time with an offset value above 5 meters exceeds 10 seconds, then mark it as a severely offset section. The offset sequence and the offset level information are combined to form the final trajectory offset sequence value.

[0037] S403: According to the trajectory offset sequence value, by analyzing the change trend of the offset value with the trajectory time sequence, identify the abnormal flight path and obtain the airspace supervision record; According to the trajectory offset sequence value, the system constructs an offset trend curve with the time tag as the horizontal axis and the offset value as the vertical axis, performs slope analysis on this curve, calculates the slope by extracting the change in offset increment between adjacent three time points, and sets the offset trend judgment criteria as follows: if the slope is positive and the offset increases gradually in three consecutive time periods, it is determined as a continuous offset trend; if the slope is negative and its absolute value increases, it is regarded as a rapidly converging offset; if the slope fluctuates near zero and the offset value changes between 1 meter and 3 meters, it is an intermittent disturbance trajectory. In a specific example, if the offset sequence is 1.1, 1.5, 2.2, 2.9, 3.6, the adjacent increments are 0.4, 0.7, 0.7, 0.7 respectively, forming a stable upward trend, which is determined as the rising section of the trajectory offset. The system performs tagging processing on the entire trajectory trend section, marks the continuous sections of the same trend type as a trend interval, and calculates its average slope and duration within the trend interval. If the duration exceeds 30 seconds and the average slope exceeds 0.1, it is determined that this trend section is an abnormal path section. At the same time, combined with the geographical location information, the airspace position where the offset occurs is judged. If it occurs within 30 meters of the boundary of the restricted airspace or no-fly zone, a risk level tag is added. After structuring and recording all abnormal path sections, they are stored in the supervision database, and finally the airspace supervision record is obtained.

[0038] According to the airspace signal monitoring information, UAV identification records and airspace supervision records, extract the spatial coordinates and broadcast time series of the identified information sources, call the broadcast time series of multiple UAV information sources, compare them with the broadcast times of multiple non-UAV information sources, extract the time overlap intervals, and identify the remote control information sources corresponding to each UAV and extract the location information. The specific steps to obtain the control information source identification result are as follows: S501: According to the airspace signal monitoring information, UAV identification records and airspace supervision records, extract the spatial coordinates and broadcast time series of each identified information source to obtain the information source spatio-temporal data; Based on the airspace signal monitoring information, UAV identification records, and airspace supervision records, the system calls the unique identification field of the identified information source from each dataset, extracts the source number, its corresponding spatial coordinate data, and the broadcast time series record respectively, and establishes a mapping index. The spatial coordinate is the average position where the listening terminal receives the signal of the information source or the positioning point after cross-reconstruction. The broadcast time series consists of the timestamps of continuously received broadcast frames. The extraction method is to sort the broadcast records by the source number and count its start time, end time, and the number of consecutive frames to form a time series segment. In an example, a certain information source sends 20 broadcast frames within the time period from 10:00:01 to 10:00:06. The system takes this segment as its continuous broadcast time period, and at the same time marks its spatial coordinate as the average position (120.5, 85.2, 30.0) calculated when listening nodes A, B, and C receive the signal together. Then, the system binds and stores this position information with the time period label. The system forms a source data table for all information sources, and the fields include the source number, coordinate point, start and end broadcast times, number of frames, frequency band field, and identification field. The system requires the time label accuracy to reach the millisecond level. If the interval between frames is greater than 1 second, it is considered that there is an interruption and segmented recording is performed, and finally, the source spatio-temporal data for traceability analysis is formed.

[0039] S502: According to the source spatio-temporal data, compare the broadcast time series of multiple identified UAV information sources with the broadcast times of multiple non-UAV information sources, extract the time overlap interval, and obtain the time overlap interval data; The specific formula for extracting the time overlap interval is: ; Calculate the time intersection offset index value; Among them, represents the time intersection offset index value between UAV information source i and non-UAV information source j, represents the start point of the broadcast time of UAV information source i, represents the end point of the broadcast time of UAV information source i, represents the start point of the broadcast time of non-UAV information source j, represents the end point of the broadcast time of non-UAV information source j, represents the central time point of the broadcast time period of UAV information source i, represents the central time point of the broadcast time period of non-UAV information source j. i is used to identify the UAV information source number, indicating the i-th UAV information source being processed currently. j is used to identify the non-UAV information source number, indicating the j-th non-UAV information source being processed currently. u represents the UAV information source, n represents the non-UAV information source, start represents the start time of the corresponding broadcast time period, end represents the end time of the corresponding broadcast time period, and center represents the central time point of the broadcast time period.

[0040] Formula: ; Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the degree of intersection offset of the broadcast time series between the drone source and the non-drone source on the time axis. The result is used to measure whether the two types of sources have overlapping or synchronous behavior characteristics, providing a preliminary time logic correlation index for remote control signal recognition. Based on the time coincidence judgment logic, the sum of the broadcast durations of the drone and non-drone sources is averaged to reflect the typical broadcast cycle lengths of the two types of sources. The average length is then subtracted from the offset distance of the center point of each time period to measure the overlapping symmetry of the two broadcasts on the time axis. If the broadcast duration is long and the center offset is small, the intersection offset index value is large, indicating that the two broadcasts are highly close in time range. On the contrary, if the center offset is large or the broadcast duration is extremely short, the index value tends to decrease, and there is no temporal linkage characteristic. Through the combination of duration and offset difference, a quantitative judgment method for dynamic synchronization trend is constructed. Parameter meaning and setting value: : The start time of the i-th broadcast of the drone source is set to 10:00:05.200; : The end time of the i-th broadcast of the drone source is determined by the timestamp of the last frame and is set to 10:00:15.200. The broadcast duration is calculated by the sequence number of the 20 broadcast frames, and the interval between each frame is 0.5 seconds; : The start time of the jth broadcast of the non-UAV source is set to 10:00:08.000; : The end time of the jth broadcast of the non-UAV source is set to 10:00:18.000, 2 frames are sent per second, and the broadcast period is 10 seconds; : The center time point of the drone broadcast period, ; : The central time point of the non-UAV source broadcast period, ; Substitute the parameters into the formula for calculation: ; ; ; ; ; The result of 7.200 seconds indicates that the pair of signal sources are relatively close in terms of the broadcast time length. However, the difference in the center of the time axis is close to 3 seconds, and there is an obvious interval in their center overlap degree. This result is used to judge whether there is synchronization or remote control end linkage behavior, and will be used as a priority sorting factor when combined with the spatial distance judgment in the follow-up. The larger the formula calculation result, the higher the coincidence degree of the two signal sources' broadcasts, and the higher the sorting position.

[0041] S503: According to the time overlap interval data, identify the remote control end signal source corresponding to each drone, extract the position information of the target remote control end signal source, and obtain the recognition result of the control signal source; According to the time overlap interval data, the system extracts all non-drone signal source numbers that have continuous overlap records with a certain drone signal source, and queries the spatial coordinate field in the signal source spatio-temporal data table as its broadcast source position. Then, calculate the spatial distance between these positions and the starting point coordinates of the drone trajectory. If the distance is less than the system-set control radius threshold of 100 meters and the broadcast overlap time covers more than 80% of the starting time period of the trajectory, then it is determined that this non-drone signal source is the corresponding remote control end signal source. In the example, if a non-drone signal source number is R123, its broadcast time overlaps continuously with the drone U456 for 15 seconds, accounting for 90% of the total broadcast time of U456, and the spatial position distance is 85 meters, then R123 is identified as the remote control signal source of U456. The system records the corresponding number relationship and matching parameters for all matching relationships, generates a remote control association record, and finally extracts the spatial position field and signal source number field of this signal source and writes them into the control information recognition table to obtain the recognition result of the control signal source.

[0042] Please refer to Figure 2 , a drone flight trajectory detection system. The drone flight trajectory detection system is used to execute the above-mentioned drone flight trajectory detection method. The system includes: The signal monitoring module extracts the identification field, frequency band field, and channel structure field in the signal based on the broadcast signals in the target airspace, analyzes the attributes of each signal and constructs a signal source attribute group sequence, identifies the number of independent signal sources in the airspace, detects new signals, and generates airspace signal monitoring information; The signal source positioning module extracts the set of independent signal sources based on the airspace signal monitoring information, corrects the time difference between multiple listening terminals, and through spatial cross-reconstruction, identifies the signal source position and detects drones, generating drone identification records; The trajectory reconstruction module, based on the drone identification records, calls the spatial position sequence of the drone signal source coordinates, extracts the time interval value and coordinate difference between each position point, constructs a fitting trajectory point sequence with equal time intervals, and generates a drone trajectory reconstruction result; Based on the UAV trajectory reconstruction results, the anomaly detection module calculates the trajectory similarity by comparing with the trajectory sequences of multiple declared flight paths, identifies the UAV identity and detects abnormal UAVs. Combining the deviations of the flight trajectory at multiple spatial positions, it calculates the change trend of the trajectory deviation and detects abnormal flight paths, and generates airspace supervision records. Based on the airspace signal monitoring information, UAV identification records and airspace supervision records, the control source identification module extracts the spatial coordinates and broadcast time series of the identified signal sources. By comparing with the broadcast times of multiple non-UAV signal sources, it extracts the time overlap intervals, identifies the remote control signal sources corresponding to each UAV and extracts the location information, and generates control signal source identification results.

[0043] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be solid-state drives.

[0044] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0045] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural item(s). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0046] It should be understood that in various embodiments of the present invention, the magnitude of the serial numbers of the above - mentioned processes does not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0047] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0048] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0049] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0050] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0052] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0053] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for detecting the flight trajectory of an unmanned aerial vehicle, characterized in that: The method comprises: S1: Obtain the target airspace broadcast signal, extract the signal identification field, frequency band field, and channel structure field, construct a signal source attribute group sequence, identify the number of independent signal sources in the airspace, detect new signals, and obtain airspace signal monitoring information; S2: extracting a set of independent signal sources based on the airspace signal monitoring information, correcting the time difference between multiple monitoring terminals, spatially cross-reconstructing the reception records between multiple monitoring nodes, identifying the signal source location and detecting the drone, and obtaining the drone identification record; S3: According to the drone identification record, the spatial position sequence of the drone signal source coordinates is called, the time interval value and the coordinate difference between each position point are extracted, a fitting trajectory point sequence with equal time intervals is constructed, and the drone trajectory reconstruction result is obtained; S4: Based on the drone trajectory reconstruction result, by comparing it with the trajectory sequences of multiple declared flight paths, the trajectory similarity is calculated, the drone identity is identified and abnormal drones are detected. Combined with the deviation of the flight trajectory at multiple spatial positions, the changing trend of the trajectory deviation is calculated and abnormal flight paths are detected to obtain airspace supervision records.

2. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 1, characterized in that: The airspace signal monitoring information includes the number of independent signal sources, the sequence of signal source attribute groups, and the signal time distribution characteristics. The drone identification record includes the drone spatial coordinate point set, the broadcast time sequence, and the signal concentration area identifier. The drone trajectory reconstruction result includes the trajectory speed trend sequence, the direction change trajectory, the time synchronization trajectory point set, and the trajectory segment connection structure. The airspace supervision record includes the drone identity recognition result, the abnormal flight identification label, and the trajectory deviation cumulative index.

3. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 1, characterized in that: The target airspace broadcast signal is obtained, and the signal source attribute group sequence is constructed by extracting the signal identification field, frequency band field, and channel structure field, identifying the number of independent signal sources in the airspace and detecting new signals. The specific steps for obtaining airspace signal monitoring information are as follows: S101: Acquire a target airspace broadcast signal, extract the identification field, frequency band field, and channel structure field of each piece of data in the broadcast signal, and construct a signal source attribute group sequence; S102: performing uniqueness analysis on a signal source attribute group sequence according to the signal source attribute combination value, identifying the number of independent signal sources in the airspace, and obtaining an independent signal source quantity value; S103: According to the value of the number of independent signal sources, the number of new signals in the airspace is detected in real time to obtain airspace signal monitoring information.

4. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 3, characterized in that: According to the airspace signal monitoring information, a set of independent signal sources is extracted, the time difference between multiple monitoring terminals is corrected, the receiving records between multiple monitoring nodes are spatially cross-reconstructed, the signal source position is identified and the drone is detected, and the steps of obtaining the drone identification record are specifically as follows: S201: extracting a set of independent signal sources according to the airspace signal monitoring information, calling the receiving time data and the corresponding local time record recorded in each monitoring terminal, comparing the time records between each terminal, extracting the time difference and correcting it, and obtaining a time calibration reference value; S202: extracting the spatial distribution of signal receiving nodes at the synchronization time according to the time calibration reference value, and performing spatial cross-reconstruction on the receiving records between multiple monitoring nodes to obtain a signal spatial cross-distribution value; S203: Extract the spatial center of gravity of the signal concentration area according to the signal spatial cross-distribution value, identify multiple signal source positions, and identify the altitude information of the signal source and detect the drone according to the spatial coordinate information of the signal source to obtain the drone identification record.

5. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 4, characterized in that: According to the drone identification record, the spatial position sequence of the drone signal source coordinates is called, the time interval value and coordinate difference between each position point are extracted, and a fitting trajectory point sequence with equal time intervals is constructed. The steps of obtaining the drone trajectory reconstruction result are as follows: S301: According to the drone identification record, call the spatial position sequence of the drone signal source coordinates, extract the time interval value and coordinate difference between each adjacent position point, and obtain the position change parameter value; S302: extracting the speed change direction between consecutive coordinate points according to the position change parameter value, and establishing a sequence trend according to the speed direction change to obtain a speed direction trend value; S303: construct a fitting trajectory point sequence with equal time intervals according to the speed direction trend value, reconstruct the moving trajectory sequence of the target UAV source, including the position information of multiple time points, and obtain the UAV trajectory reconstruction result.

6. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 5, characterized in that: According to the reconstructed drone trajectory results, by comparing with the trajectory sequences of multiple declared flight paths, calculating the trajectory similarity, identifying the drone identity and detecting abnormal drones, combining the deviations of the flight trajectory at multiple spatial positions, calculating the trend of trajectory deviation changes and detecting abnormal flight paths, the specific steps for obtaining airspace supervision records are as follows: S401: extracting trajectory sequences of multiple reported flight paths according to the drone trajectory reconstruction result, calculating the trajectory similarity between the drone trajectory and each reported trajectory, identifying the drone identity according to the drone information corresponding to the reported path, detecting abnormal drones, and obtaining trajectory similarity recognition results; S402: extracting the spatial distance value between each reconstructed trajectory point and the corresponding declared path point according to the trajectory similarity recognition result and combining the deviation of the flight trajectory at multiple spatial positions to obtain a trajectory offset sequence value; S403: According to the trajectory offset sequence value, by analyzing the change trend of the offset value along the trajectory time sequence, the abnormal flight path is identified and the airspace supervision record is obtained.

7. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 6, characterized in that: The specific formula for calculating the trajectory similarity between the drone trajectory and each reported trajectory is: ； Calculate the similarity index value of trajectory space point set; in, is the trajectory space point set similarity index value between the drone trajectory p and the declared trajectory q, p is the number index of the current drone reconstructed trajectory, q is the number index of the current declared flight path, k is the position index of the trajectory point in the point set sequence, m is the total number of valid trajectory points after the trajectory p and trajectory q are aligned on the time axis, is the X-axis coordinate value of the kth point in trajectory p, is the Y-axis coordinate value of the kth point in trajectory p, is the Z-axis coordinate value of the kth point in trajectory p, is the X-axis coordinate value of the kth point in trajectory q, is the Y-axis coordinate value of the kth point in trajectory q, is the Z-axis coordinate value of the kth point in trajectory q, is the spatial distance between reference segments corresponding to the kth point in trajectory q, indicating the ideal path segment length of the declared trajectory near the kth point, is the directional weight coefficient of the kth point in the trajectory.

8. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 1, characterized in that: The method further comprises: S5: extracting the spatial coordinates and broadcast time sequence of the identified signal source according to the airspace signal monitoring information, drone identification record and airspace supervision record, calling the broadcast time sequence of multiple drone signal sources, extracting the time overlapping interval by comparing with the broadcast time of multiple non-drone signal sources, identifying the remote control terminal signal source corresponding to each drone and extracting the position information, and obtaining the control signal source identification result; The control signal source identification result includes a remote control terminal signal source identification number, a remote control signal spatial position, and a remote control source synchronous control tag.

9. The method for detecting the flight trajectory of an unmanned aerial vehicle according to claim 8, characterized in that: According to the airspace signal monitoring information, drone identification records and airspace supervision records, the spatial coordinates and broadcast time sequence of the identified source are extracted, the broadcast time sequence of multiple drone sources is called, and the time overlapping interval is extracted by comparing with the broadcast time of multiple non-drone sources. The remote control source corresponding to each drone is identified and the location information is extracted. The specific steps of obtaining the control source identification result are as follows: S501: extracting the spatial coordinates and broadcast time sequence of each identified signal source according to the airspace signal monitoring information, drone identification records and airspace supervision records, and obtaining the signal source spatiotemporal data; S502: According to the information source spatiotemporal data, the broadcast time series of multiple identified drone information sources are compared with the broadcast time of multiple non-drone information sources, and the time overlapping intervals are extracted to obtain the time overlapping interval data; The specific formula for extracting the time overlap interval is: ； Calculate the time intersection offset index value; in, Represents the time intersection offset index value between drone source i and non-drone source j, represents the starting time of the broadcast of drone source i, represents the end point of the broadcast time of drone source i, represents the broadcast time starting point of non-UAV source j, represents the end point of the broadcast time of non-UAV source j, Represents the central time point of the broadcast time period of drone source i, Represents the central time point of the broadcast time period of non-UAV source j, i is used to identify the UAV source number, indicating the i-th UAV source currently being processed, j is used to identify the non-UAV source number, indicating the j-th non-UAV source currently being processed, u represents the UAV source, n represents the non-UAV source, start represents the start time of the corresponding broadcast time period, end represents the end time of the corresponding broadcast time period, and center represents the central time point of the broadcast time period; S503: Identify the remote control end signal source corresponding to each UAV according to the time overlap interval data, extract the position information of the target remote control end signal source, and obtain the control signal source identification result.

10. A UAV flight trajectory detection system, characterized in that: The system is used to implement the UAV flight trajectory detection method according to any one of claims 1 to 9, and the system includes: The signal monitoring module extracts the identification field, frequency band field and channel structure field in the target airspace broadcast signal, performs attribute analysis on each signal and constructs a signal source attribute group sequence, identifies the number of independent signal sources in the airspace, detects new signals, and generates airspace signal monitoring information; The signal source positioning module extracts a set of independent signal sources based on the airspace signal monitoring information, corrects the time difference between multiple monitoring terminals, identifies the signal source location and detects the drone through spatial cross reconstruction, and generates a drone identification record; The trajectory reconstruction module calls the spatial position sequence of the drone signal source coordinates based on the drone identification record, extracts the time interval value and coordinate difference between each position point, constructs a fitting trajectory point sequence with equal time intervals, and generates a drone trajectory reconstruction result; The anomaly detection module is based on the reconstruction result of the UAV trajectory, and by comparing it with the trajectory sequences of multiple declared flight paths, calculates the trajectory similarity, identifies the identity of the UAV and detects abnormal UAVs. In combination with the deviation of the flight trajectory at multiple spatial positions, it calculates the change trend of the trajectory deviation and detects abnormal flight paths, and generates airspace supervision records; The control source identification module extracts the spatial coordinates and broadcast time sequence of the identified signal source based on the airspace signal monitoring information, drone identification records and airspace supervision records, extracts the time overlapping interval by comparing with the broadcast time of multiple non-drone signal sources, identifies the remote control source corresponding to each drone and extracts the location information, and generates a control source identification result.

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