A method and system for detecting the flight trajectory of an unmanned aerial vehicle

Abstract

The present invention relates to the field of drone remote identification technology, specifically a drone flight trajectory detection method and system, comprising the following steps: acquiring airspace broadcast signals to detect new signals, correcting terminal time differences and identifying signal source locations, extracting position sequences to reconstruct drone flight trajectories, comparing declared paths, identifying drone identities, detecting abnormal drones and abnormal flight paths, extracting broadcast time sequences, identifying time-overlapping intervals and detecting remote control terminals, and obtaining control identification results. The present invention improves signal source monitoring accuracy by real-time monitoring of airspace signals and dynamically detecting new signal sources. Time difference correction and spatial cross-reconstruction of multiple monitoring terminals are combined to optimize the positioning accuracy of drone positions. A trajectory reconstruction method is employed to enhance real-time monitoring capabilities for abnormal behavior. By identifying the control terminal signal source and extracting location information, airspace safety management and the prevention of illegal flights are enhanced.

Description

Technical Field

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

[0002] The field of drone remote identification technology includes a collection of technologies that achieve identity recognition and trajectory tracking of unmanned aerial vehicles through wireless communication means. The core content of this technology field is to use radio frequency broadcasting and other methods to transmit the drone's unique identity code, spatial position information, speed, flight direction, and operator location information in real time during flight, so that the supervision platform and various receiving terminals can perceive the dynamic state of the drone in the airspace. It includes information broadcast modules, positioning and measurement modules, flight controller docking protocols, and communication interfaces with ground supervision systems. It is used for drone identity confirmation and behavior monitoring, providing key support for urban low-altitude safety management, drone cluster scheduling, and airspace traffic coordination.

[0003] Among them, a method for detecting the flight trajectory of a UAV refers to a specific technical means of identifying the flight path by obtaining the continuous spatial position information of the UAV during flight and tracking and analyzing it. The patent subject includes the identification broadcast, position information acquisition, and path reconstruction links involved in flight trajectory capture and recording. The method adopted includes using signal broadcasts that comply with remote identification specifications, transmitting the current coordinates and flight speed of the UAV via Bluetooth and WiFi, and then decoding and analyzing the signals in combination with the ground receiving terminal or platform, reconstructing the flight trajectory according to the receiving timing, synchronously sorting the acquired data to form a complete trajectory record sequence, and matching and identifying the trajectory data with the aircraft registration information through the communication protocol to achieve accurate extraction of the flight trajectory.

[0004] Traditional UAV flight trajectory detection technology relies on traditional signal decoding and position acquisition methods, which have obvious deficiencies in the monitoring accuracy and real-time performance of signal sources. It lacks 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. It is limited to simple coordinate point connections and cannot effectively reflect the speed changes and flight direction of UAVs in complex flight environments, and cannot accurately describe flight behavior. When processing multiple signal sources, it ignores the time synchronization and spatial position cross-reconstruction between signal sources, resulting in insufficient positioning accuracy and difficulty in providing stable and reliable real-time flight data. When identifying abnormal flight paths, it relies on static comparison of reported paths and trajectories, and cannot effectively respond to dynamic changes in trajectories, which easily leads to the inability to timely identify UAVs that deviate from the reported path. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present invention provide a method and system for detecting the flight trajectory of a UAV. The technical solution is as follows:

[0006] In order to achieve the above objectives, the present invention adopts the following technical solution, a method for detecting the flight trajectory of a UAV, comprising the following steps:

[0007] S1: Obtain the target airspace broadcast signal, extract the signal's 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;

[0008] S2: Extracting a set of independent signal sources based on the airspace signal monitoring information, correcting the time difference between multiple monitoring terminals, performing spatial cross-reconstruction of reception records between multiple monitoring nodes, identifying the signal source location and detecting drones, and obtaining drone identification records;

[0009] S3: Based on 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 to obtain the drone trajectory reconstruction result;

[0010] S4: Based on the drone trajectory reconstruction results, 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 changing trend of the trajectory deviation and detect abnormal flight paths, and obtain airspace supervision records.

[0011] 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 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.

[0012] As a further solution of the present invention, the steps of obtaining the target airspace broadcast signal, extracting the signal's identification field, frequency band field, and channel structure field, constructing a signal source attribute group sequence, identifying the number of independent signal sources in the airspace, and detecting newly added signals to obtain airspace signal monitoring information are as follows:

[0013] S101: Acquire the target airspace broadcast signal, extract the identification field, frequency band field, and channel structure field of each data in the broadcast signal, and construct a signal source attribute group sequence;

[0014] S102: performing uniqueness analysis on the 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;

[0015] 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.

[0016] As a further solution of the present invention, based on the airspace signal monitoring information, a set of independent signal sources is extracted, the time difference between multiple monitoring terminals is corrected, the reception 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:

[0017] S201: Extracting a set of independent signal sources based on the airspace signal monitoring information, calling the reception time data recorded in each monitoring terminal and the corresponding local time record, comparing the time records between each terminal, extracting and correcting the time difference, and obtaining a time calibration reference value;

[0018] 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 reception records between multiple monitoring nodes to obtain a signal spatial cross-distribution value;

[0019] S203: Extracting the spatial center of gravity of the signal concentration area based on the signal spatial cross-distribution value, identifying multiple signal source locations, and identifying the altitude information of the signal source based on the spatial coordinate information of the signal source, detecting the drone, and obtaining the drone identification record.

[0020] As a further solution of the present invention, based on 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 sequence of fitting trajectory points with equal time intervals is constructed. The steps of obtaining the drone trajectory reconstruction result are specifically as follows:

[0021] S301: Based on 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;

[0022] 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;

[0023] S303: According to the speed direction trend value, a fitting trajectory point sequence with equal time intervals is constructed to reconstruct the moving trajectory sequence of the target UAV signal source, including position information at multiple time points, and obtain a UAV trajectory reconstruction result.

[0024] As a further solution of the present invention, based on the drone trajectory reconstruction result, by comparing it 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 steps of obtaining airspace supervision records are specifically as follows:

[0025] S401: Extracting trajectory sequences of multiple reported flight paths based on the drone trajectory reconstruction result, calculating the trajectory similarity between the drone trajectory and each reported trajectory, identifying the drone based on the drone information corresponding to the reported path, detecting abnormal drones, and obtaining trajectory similarity recognition results;

[0026] S402: Based on the trajectory similarity recognition results and in combination with the deviations of the flight trajectory at multiple spatial positions, the spatial distance value between each reconstructed trajectory point and the corresponding declared path point is extracted to obtain a trajectory offset sequence value;

[0027] 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.

[0028] As a further solution of the present invention, the specific formula for calculating the trajectory similarity between the drone trajectory and each reported trajectory is:

[0029] ;

[0030] Calculate the similarity index value of the trajectory space point set;

[0031] in, is the trajectory space point set similarity index value 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, and 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 k-th point in trajectory p, is the Y-axis coordinate value of the k-th point in trajectory p, is the Z-axis coordinate value of the k-th point in trajectory p, is the X-axis coordinate value of the k-th point in trajectory q, is the Y-axis coordinate value of the k-th point in trajectory q, is the Z-axis coordinate value of the k-th point in trajectory q, is the spatial distance between reference segments corresponding to the k-th point in trajectory q, indicating the ideal path segment length of the declared trajectory near the k-th point. is the directional weight coefficient of the kth point in the trajectory.

[0032] As a further embodiment of the present invention, the method further comprises:

[0033] S5: Extracting 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. Recalling the broadcast time sequence of multiple drone signal sources, comparing them with the broadcast time of multiple non-drone signal sources, extracting the time overlap interval, identifying the remote control signal source corresponding to each drone and extracting its location information, thereby obtaining the control signal source identification result.

[0034] The control signal source association result includes the remote control terminal signal source identification number, the remote control signal spatial position, and the remote control source synchronization control tag.

[0035] As a further solution of the present invention, based on the airspace signal monitoring information, drone identification records, and airspace supervision records, the spatial coordinates and broadcast time sequence of the identified signal source are extracted, the broadcast time sequence of multiple drone signal sources is called, and the time overlapping interval is extracted by comparing the broadcast time with the broadcast time of multiple non-drone signal sources. The remote control source corresponding to each drone is identified and the location information is extracted. The steps of obtaining the control signal source identification result are specifically as follows:

[0036] S501: Extracting the spatial coordinates and broadcast time sequence of each identified signal source based on the airspace signal monitoring information, drone identification records, and airspace supervision records to obtain signal source spatiotemporal data;

[0037] S502: Based on the signal source spatiotemporal data, compare the broadcast time series of multiple identified drone signal sources with the broadcast time series of multiple non-drone signal sources, extract the time overlapping intervals, and obtain time overlapping interval data;

[0038] The specific formula for extracting the time overlap interval is:

[0039] ;

[0040] Calculate the time intersection offset index value;

[0041] in, represents the time intersection offset index value between drone source i and non-drone source j, represents the starting point of the broadcast time of drone source i, represents the end point of the broadcast time of drone source i, represents the starting time of the broadcast 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 period of drone source i, represents the center 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 center time point of the broadcast time period;

[0042] S503: Identify the remote control signal source corresponding to each UAV based on the time overlapping interval data, extract the position information of the target remote control signal source, and obtain the control signal source identification result.

[0043] On the other hand, a UAV flight trajectory detection system is provided, which is applied to a UAV flight trajectory detection method, and the system includes:

[0044] The signal monitoring module extracts the identification field, frequency band field, and channel structure field from the target airspace broadcast signal, performs attribute analysis on 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.

[0045] 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 drones through spatial cross-reconstruction, and generates drone identification records;

[0046] 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 the drone trajectory reconstruction result;

[0047] The anomaly detection module, based on the drone trajectory reconstruction results, compares them with the trajectory sequences of multiple declared flight paths, calculates trajectory similarity, identifies the drone identity and detects abnormal drones. It combines the deviations of the flight trajectory at multiple spatial locations, calculates the trend of trajectory deviation, detects abnormal flight paths, and generates airspace supervision records.

[0048] 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. By comparing the broadcast time with multiple non-drone signal sources, the time overlapping interval is extracted, the remote control source corresponding to each drone is identified and the location information is extracted to generate the control source identification result.

[0049] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0050] By real-time monitoring of airspace signals and dynamic detection of 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 technology of multiple monitoring terminals, the positioning accuracy of drone positions is optimized. The trajectory reconstruction method is adopted, and abnormal flight paths are detected by comparing the declared paths, which enhances the real-time monitoring capability of abnormal behavior. By identifying the signal source of the control end and extracting the location information, the accurate identification of the operator's identity is ensured, improving the airspace safety management and the prevention of illegal flights. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0053] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0056] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0057] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 The present invention provides a technical solution, a method for detecting the flight trajectory of an unmanned aerial vehicle, comprising the following steps:

[0060] S1: Acquire the target airspace broadcast signal, extract the identification field, frequency band field, and channel structure field of each broadcast signal, construct a signal source attribute group sequence, identify the number of independent signal sources in the airspace, detect new signals in the airspace in real time, and obtain airspace signal monitoring information;

[0061] Identification field: refers to a unique device identifier that complies with the RemoteID standard, such as FAAUASID, EU Class C drone unique serial number, and GB / T41300-2022;

[0062] Frequency band field: refers to the physical transmission frequency range used by broadcast signals, commonly 2.4GHz or 5.8GHz, which belongs to the international standard radio spectrum classification;

[0063] Channel structure field: refers to the protocol format structure tag used by the signal, which can be based on the Wi-Fi NAN frame structure, BLE broadcast structure or custom remote control protocol header;

[0064] S2: Based on airspace signal monitoring information, extract a set of independent signal sources, call each monitoring terminal to record the reception time data and local time of the signal source, correct the time difference between multiple monitoring terminals, build a unified time reference sequence, extract the spatial distribution of signal receiving nodes under synchronized time, perform spatial cross-reconstruction of the reception records between multiple monitoring nodes, extract the spatial center of gravity of the signal concentration area, and detect drones based on the spatial coordinate information of the signal source to obtain drone identification records;

[0065] Time difference correction: refers to the unified clock offset processing of the local reception timestamps recorded by multiple terminals. Common methods are GPS clock reference or network synchronization.

[0066] Spatial center of gravity: refers to the geometric mean center of multiple monitoring coordinates in the spatial distribution of the signal, used to estimate the location of the target emission source;

[0067] Drone identification record: refers to a structured identification information set established for a target with stable broadcast trajectory and spatial movement characteristics;

[0068] S3: Based on 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, the speed change direction between the coordinate points is trend extracted, and a fitting trajectory point sequence with equal time intervals is constructed to obtain the drone trajectory reconstruction result;

[0069] Time interval value: the timestamp difference between adjacent broadcast data;

[0070] Coordinate difference: the difference in longitude and latitude between consecutive location points;

[0071] Speed ​​change direction: The speed vector sequence calculated by the coordinate difference and time interval reflects the direction of the flight path;

[0072] S4: Based on the drone trajectory reconstruction results, the trajectory sequences are compared with multiple declared flight paths to calculate trajectory similarity, identify the drone identity and detect abnormal drones. Combined with the deviation of the flight trajectory at multiple spatial locations, the trend of trajectory deviation is calculated, abnormal flight paths are detected, and airspace supervision records are obtained.

[0073] Trajectory similarity: Based on the principle of Euclidean distance sequence of point sets, the shape overlap between any two trajectories is calculated;

[0074] Track deviation change trend: This is the continuous change sequence of the offset distance between the actual track and the reported track at different spatial points over time or path length;

[0075] Airspace supervision record: an event status dataset generated by the system based on the deviation behavior and identity mapping results;

[0076] S5: Based on airspace signal monitoring information, drone identification records, and airspace supervision records, extract the spatial coordinates and broadcast time series of the identified signal source. Call the broadcast time series of multiple drone signal sources and compare them with the broadcast time of multiple non-drone signal sources to extract the time overlapping intervals. Identify the remote control signal source corresponding to each drone and extract its location information to obtain the control signal source identification result.

[0077] Broadcast time series: refers to the timestamp sequence of continuous broadcast records of a specific source by the monitoring terminal;

[0078] Time overlap interval: refers to the intersection period of two sets of broadcast events in the time dimension, which represents the possibility of synchronous behavior;

[0079] Control source identification results: Based on the correspondence between time overlap and spatial relative position, the logical control ownership between the UAV and its remote control terminal is established.

[0080] Airspace signal monitoring information includes the number of independent signal sources, the sequence of signal source attribute groups, and the signal time distribution characteristics. UAV identification records include the UAV spatial coordinate point set, the broadcast time sequence, and the signal concentration area identification. UAV trajectory reconstruction results include the trajectory speed trend sequence, the direction change trajectory, the time synchronization trajectory point set, and the trajectory segment connection structure. Airspace supervision records include the UAV identity recognition results, the abnormal flight identification label, and the trajectory deviation cumulative index. The control source association results include the remote control source identification number, the remote control signal spatial position, and the remote control source synchronization control label.

[0081] S101: Acquire the target airspace broadcast signal, extract the identification field, frequency band field, and channel structure field of each data in the broadcast signal, and construct a signal source attribute group sequence;

[0082] To acquire broadcast signals in the target airspace, the listening terminal must continuously receive broadcast data within the designated communication frequency band and convert the raw signal data into a structured data stream. When parsing each broadcast data item, the identification field, frequency band field, and channel structure field are sequentially extracted. The identification field is typically represented by a MAC address, the frequency band field indicates the operating frequency, such as 2.4 GHz or 5.8 GHz, and the channel structure field characterizes the channel bandwidth and its frequency hopping characteristics. After extracting these fields, a sequence of attribute groups is constructed, combining these three fields. Each attribute group serves as the basis for subsequent signal source identification. In practice, if a listening terminal receives 500 broadcast frames, field extraction and combination yields 500 attribute groups. The system verifies the uniqueness of the attribute groups by first converting each group into a string format and then comparing them for exact identity. Duplicates indicate the same source. The number of unique attribute groups obtained after deduplication can be used to determine the distribution of source types. Furthermore, the channel structure field is compared with historical signal congestion records for the current frequency band to determine if there is any frequency band overlap between sources. The broadcast time intervals corresponding to each attribute group are calculated to determine whether adjacent broadcasts are continuous, using a set broadcast interval value as the criterion. If the proportion of adjacent time intervals less than the standard value for a given source is too high, it is marked as a high-frequency broadcast source. During this process, the time distribution sequence of each field is plotted, combining the broadcast frame number and time tag. The stability is assessed by whether there is regular recurrence. The signal source attribute group sequence constructed in this way will be used for subsequent independent source identification and spatial signal monitoring.

[0083] S102: performing uniqueness analysis on the 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 the number value of independent signal sources;

[0084] Based on the constructed sequence of signal source attribute groups, the unique identifier field value in each group is extracted and uniformly converted into a standard string format to form a set of identifier values. The system then repeatedly compares each value in this set to determine which combinations are unique. This comparison adheres to three criteria: the identifier field must be identical, the differences between frequency band fields must not exceed the set bandwidth tolerance, and the channel structure field must maintain the same structure. After removing duplicates, the number of remaining combinations is the number of identified independent signal sources. In a real-world scenario, if a total of 800 broadcast data sets are processed and 520 unique combinations are found after the above determination, the number of valid independent signal sources in that airspace is 520. The system also performs frequency band distribution statistics for these unique signal sources, calculating the number of signal sources in each frequency band and comparing it to the capacity limit for each band. For example, if the capacity limit for the 2.4 GHz band is set to 300 and the actual count is 340, the system should issue a band capacity limit warning. In addition, to prevent duplicate sources from repeatedly entering the judgment process, an efficient data structure is used to cache the confirmed unique combinations to support rapid filtering of redundant data in subsequent source identification.

[0085] S103: Detect the number of new signals in the airspace in real time based on the number of independent signal sources, and obtain airspace signal monitoring information;

[0086] Based on the independent source count calculated in the previous phase, the system retrieves and compares the independent source count stored in the previous round of data. The difference between the current identified count and the previous count is calculated to determine the trend in the number of newly identified sources. If this difference is positive, it indicates the presence of a new source in the current airspace. The system sets a time monitoring window. If an increase in the number of sources is detected for multiple consecutive periods within this window, it is considered a stable trend. Newly identified sources are then separated from the attribute group and the difference between their first appearance time stamp and the current system time is analyzed. If this difference is less than the set time threshold, the source is considered a genuine new source. Furthermore, the broadcast frequency of newly identified sources is monitored and the difference between this frequency and the global average broadcast frequency is calculated to determine whether their broadcast behavior is stable. In practice, if 15 newly identified sources are found, and the difference between the broadcast frequency and the average is within the set range, these 12 sources are classified as frequency-stable sources. Next, the system needs to count the distribution center values ​​of the frequency band where this part of the signal source is located, and perform linear fitting on the changes in the center value on the time axis to construct a trend sequence of the current airspace frequency band usage. This trend information will serve as the basis for subsequent airspace density analysis and ultimately obtain airspace signal monitoring information.

[0087] Based on the airspace signal monitoring information, the independent signal source set is extracted, the time difference between multiple monitoring terminals is corrected, the reception records between multiple monitoring nodes are spatially cross-reconstructed, the signal source location is identified and the drone is detected. The specific steps for obtaining the drone identification record are as follows:

[0088] S201: Extracting a set of independent signal sources based on airspace signal monitoring information, calling the reception time data recorded in each monitoring terminal and the corresponding local time record, comparing the time records between each terminal, extracting and correcting the time difference, and obtaining a time calibration reference value;

[0089] To extract a set of independent signal sources based on airspace signal monitoring information, it is necessary to summarize the signal identification fields in the monitoring information and construct a set, which corresponds to the broadcast source with a unique identifier in the data frame recorded by the monitoring terminal. In the monitoring network, multiple monitoring terminals respectively record the broadcast time of the same signal source received and its local system time. In order to obtain the accurate broadcast reception moment, it is necessary to extract its receiving time tag and local time tag from the data record of each monitoring terminal, pair the two one by one, perform difference calculation on the paired data and record the time difference sequence. The time difference represents the synchronization error between the terminal local clock and the unified standard clock. The system calculates the mean error between different terminals based on the time difference sequence, and uses the mean as the initial calibration value to calibrate the original receiving time of each terminal. For example, if the monitoring terminal A records the receiving time as 10:00:05.120 and the local time is 10:00:05.000, the time difference is 0.120 seconds. Similarly, the time difference of terminal B is -0.080 seconds. 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 timestamp of each receiving record is regenerated. During the correction process, the system sets the time difference adjustment threshold to 0.5 seconds. If the average difference between a terminal and other terminals exceeds this threshold, it is marked as a time abnormal terminal and its data record is removed to ensure the uniformity and continuity of the time tags of subsequent processing. The standardized corrected time set formed on this basis is the time calibration reference value.

[0090] S202: extracting the spatial distribution of signal receiving nodes at the synchronization time based on the time calibration reference value, and performing spatial cross-reconstruction on the reception records between multiple monitoring nodes to obtain a signal spatial cross-distribution value;

[0091] 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 base, 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. 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 the 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.

[0092] S203: Extracting the spatial center of the signal concentration area based on the signal spatial cross-distribution value, identifying multiple signal source locations, and identifying the altitude information of the signal source based on the spatial coordinate information of the signal source, detecting the drone, and obtaining the drone identification record;

[0093] According to the spatial cross-distribution value of the signal, the spatial center of gravity of the signal concentration area is extracted. The system needs to perform three-dimensional weighted calculation on the coordinates of all receiving nodes constituting the spatial cross-distribution area, and extract the arithmetic mean in the X and Y directions as the plane center of gravity coordinates. The value in the Z-axis direction needs to be calibrated in combination with the surface altitude of the corresponding coordinates in the terrain database. The digital elevation model DEM is used to obtain the surface elevation of the plane center of gravity, and then the broadcast signal strength received by the monitoring terminal is spatially attenuated. The relative height of the signal source emission point is inferred based on the signal path loss model. If the signal emission strength is set to 20dBm, the actual receiving strength of the monitoring terminal is -60dBm. Bm, based on the free space path loss formula, estimates the propagation distance to be 100 meters. 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. The signal source altitude is estimated as the surface elevation plus the propagation height, and its three-dimensional coordinates are ultimately determined. The system uses these coordinates to determine that the signal is within the low-altitude activity range. Combined with its broadcast frequency band field and broadcast frequency value, it further determines 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 location is between 30 and 150 meters above the ground, it is recorded as a drone signal source, and a corresponding identifier is generated, ultimately obtaining a drone identification record.

[0094] Based on the drone identification records, 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 sequence of fitting trajectory points with equal time intervals is constructed. The specific steps for obtaining the drone trajectory reconstruction result are as follows:

[0095] S301: Based on the drone identification record, the spatial position sequence of the drone signal source coordinates is called, and the time interval value and coordinate difference between each adjacent position point are extracted to obtain the position change parameter value;

[0096] According to the drone identification record, the system extracts the corresponding signal source coordinate information set, constructs a spatial position sequence in chronological order, extracts the corresponding time tags for each two adjacent position points and calculates the time interval, calculates the absolute value of the difference between the two coordinate values ​​in the three axes and calculates the straight-line distance between the coordinates, and divides the distance by the time interval to obtain the position change rate value, which is used as the basic position change parameter. The difference in the three directions is then vector-synthesized to obtain the spatial direction change unit vector to form the change direction parameter. The system performs this calculation process on all coordinate points in the sequence to construct the time and space change sequence of the entire path. In this example, if the time tags of the two adjacent points are 12:00:01.500 and 12:00:02.000, and the spatial coordinates are (10.0, 15.0, If the time interval is 0.5 seconds and the differences in the three axes are 2.0, 1.5, and 1.0, respectively, and the path length is the square root of 7.25, which is approximately 2.69 meters, then the position change rate of this segment is 2.69 divided by 0.5, which is 5.38 meters per second. The difference sequence is used as data input to construct a complete change parameter sequence. When multiple data with inconsistent time intervals exist in the same trajectory, the system segments the time interval values ​​according to a fixed time window. The maximum rate, minimum rate, and average rate are extracted from each segment and then compared with the system-set movement rate range of 0.5 to 30 meters per second. If the rate value is lower than 0.5 or higher than 30, the corresponding segment is marked as a speed anomaly segment. Further analysis is conducted to determine whether there are sudden changes in the data segment. Abnormal fluctuation segments are marked and eliminated by speed difference exceeding the limit, forming a stable position change parameter value.

[0097] 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;

[0098] According to the acquired position change parameter value, the direction vector difference is extracted for each group of continuous coordinate points in the sequence, and the vector angle calculation is used as the judgment basis. The system sets the direction change sensitivity threshold to 15 degrees, that is, when the angle between two consecutive speed direction vectors exceeds this value, it is considered that the direction has changed. The calculation method is to perform dot product operation on adjacent speed vectors and use the ratio of the product of the two moduli to perform arc cosine processing to obtain the angle value. For example, if the first speed vector is (2,2,0) and the second speed vector is (3,0,0), the dot product of the two is 6, the moduli are 2.83 and 3.0 respectively, the cosine value of the angle is 0.707, and the arc cosine value is 45 degrees. , is greater than the set threshold, the 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 in the numbered segment, and then judges the angle increment of all average direction vectors. If the direction changes in three consecutive numbered segments always show similar direction offsets, a trend vector sequence is constructed. The trend vector is composed of the difference vector between the average directions of each segment, which is used to reflect the continuous change trend of the trajectory. The system requires that the direction is consistent when the trend vector angle is less than 10 degrees. If the angle is between 10 and 30 degrees, it is considered a weak trend. If the angle exceeds 30 degrees, it is considered a mutation or turning segment, and finally the speed direction trend value is generated.

[0099] S303: Construct a fitting trajectory point sequence with equal time intervals based on the speed direction trend value, reconstruct the moving trajectory sequence of the target UAV signal source, including the position information of multiple time points, and obtain the UAV trajectory reconstruction result;

[0100] Based on the speed and direction trend values, the system constructs a sequence of trajectory fitting points with equal time intervals. First, the time tag range is extracted to determine the total time span, and then the time interval is divided into equal intervals according to the set time resolution. The time resolution can be set to 0.5 seconds or 1 second. After determining the time tag of each interpolation point, the direction vector of the trend segment and the coordinates of the previous trajectory point are superimposed and translated to generate a new point. The superposition value is calculated by multiplying the unit direction vector by the product of the local velocity average and the time interval. In this example, if the trend segment direction vector is (0.6, 0.8, 0), the average speed is 4 meters per second, and the time interval is 1 second, the position increment is (2.4, 3.2, 0). This increment is added to the coordinates of the previous point to generate a new trajectory point, which is then superimposed to form a complete trajectory point sequence. All fitting points constitute the final trajectory fitting sequence. The system requires comparing the offset between the fitting point and the actual coordinate point. If the offset exceeds the set fitting deviation threshold of 5 meters, the fitting point is recorded as a deviation point and corrected. Finally, a set of reconstructed path data containing time tags and spatial coordinates is output to obtain the drone trajectory reconstruction result.

[0101] Based on the drone trajectory reconstruction results, by comparing them 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 deviations of the flight trajectory at multiple spatial positions, the trend of trajectory deviation is calculated and abnormal flight paths are detected. The specific steps for obtaining airspace supervision records are as follows:

[0102] S401: Based on the drone trajectory reconstruction results, the trajectory sequences of multiple reported flight paths are extracted, the trajectory similarity between the drone trajectory and each reported trajectory is calculated, the drone identity is identified based on the drone information corresponding to the reported path, and abnormal drones are detected to obtain trajectory similarity recognition results;

[0103] The specific formula for calculating the trajectory similarity between the drone trajectory and each reported trajectory is:

[0104] ;

[0105] Calculate the similarity index value of the trajectory space point set;

[0106] in, is the trajectory space point set similarity index value 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, and 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 k-th point in trajectory p, is the Y-axis coordinate value of the k-th point in trajectory p, is the Z-axis coordinate value of the k-th point in trajectory p, is the X-axis coordinate value of the k-th point in trajectory q, is the Y-axis coordinate value of the k-th point in trajectory q, is the Z-axis coordinate value of the k-th 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.

[0107] formula:

[0108] ;

[0109] Detailed explanation of the formula and the process of formula calculation and derivation:

[0110] The formula is used to calculate the similarity between the drone's trajectory and the declared trajectory at the spatial point set level. The Euclidean distance differences between the aligned 3D points at multiple moments are accumulated point by point and normalized using the local segment reference distance. The calculated results are used to determine the degree of structural fit between the trajectories. The formula is based on the spatial distance difference between the reconstructed trajectory points and the declared path points after pairing on the time axis. The formula calculates the absolute error between the 3D position deviation of each pair of corresponding points and their expected reference distance. Each point is assigned a different weight based on its criticality in the trajectory structure. The weighted sum of all point errors is then normalized to construct a similarity index that measures the consistency of 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 structural match. If the relative error concentration shows persistent offset or imbalance in the matching of key segments of points, the index value approaches 0 or even becomes negative, indicating abnormal flight characteristics such as overall offset, local turning misalignment, or key point misalignment between the actual trajectory and the declared path.

[0111] Parameter meaning and setting value:

[0112] The reconstructed trajectory number is UAV_07, which comes from the analysis and trajectory reconstruction of UAV telemetry broadcast data;

[0113] The reported trajectory number is REG_09, which corresponds to the pre-registered flight path on the regulatory platform;

[0114] is the index value of the kth point in the trajectory, and the trajectory point alignment number m is set to 5;

[0115] is the three-dimensional coordinate value of the k-th point of the UAV trajectory, which is solved by three-dimensional positioning. The unit is meters and is set to (120.0, 50.0, 35.0), (122.5, 51.0, 36.0), (125.0, 52.0, 36.5), (127.0, 53.5, 36.7), and (129.0, 55.0, 37.0);

[0116] To report the three-dimensional coordinates of the k-th point of the trajectory, the unit is meters, set to (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);

[0117] is the distance between adjacent points of the k-th point in the declared trajectory,

[0118] ;

[0119] The weight value of the trajectory point is set according to the direction change rate. The direction change angle is calculated by the arc cosine of the angle formed by three adjacent points. If the angle exceeds 15 degrees, the weight is set to 1.5, 10 to 15 degrees is set to 1.2, 5 to 10 degrees is set to 1.0, and below 5 degrees is set to 0.8. The current five-point weight values ​​are set to 1.0, 1.2, 1.5, 1.2, and 1.0 respectively;

[0120] Substitute the parameters into the formula for calculation:

[0121] The difference between the spatial distance of each point and the reference distance is calculated by multiplying the weight term as follows:

[0122] Point 1:

[0123] ;

[0124] ;

[0125] The error term is:

[0126] ;

[0127] Similarly, we get: Point 2: The spatial difference is 0.3317, the reference distance is 2.5295, and the error term is 2.1978×1.2=2.6374; Point 3: The spatial difference is 0.2236, the reference distance is 2.5090, and the error term is 2.2854×1.5=3.4281; Point 4: The spatial difference is 0.2449, the reference distance is 2.4832, and the error term is 2.2383×1.2=2.6859; Point 5: The spatial difference is 0.3606, the reference distance is 2.5170, and the error term is 2.1564×1.0=2.1564;

[0128] The weighted sum of errors is: 2.0082+2.6374+3.4281+2.6859+2.1564=12.9160;

[0129] The sum of the reference distances is: 2.5467+2.5295+2.5090+2.4832+2.5170=12.5854;

[0130] ;

[0131] The result of 0.0263 indicates that the spatial structure of the drone trajectory and the declared trajectory has a large cumulative offset and cannot be matched, indicating that the trajectories are not synchronized or consistent. This value can be used to screen out suspected camouflaged or undeclared flight individuals and output an indicator for judging trajectory similarity recognition results.

[0132] S402: Based on the trajectory similarity recognition results and the deviations of the flight trajectory at multiple spatial positions, the spatial distance value between each reconstructed trajectory point and the corresponding declared path point is extracted to obtain a trajectory offset sequence value;

[0133] According to the trajectory similarity recognition results, the system needs to call the successfully recognized trajectory and its corresponding declared path trajectory point sequence, and 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. The spatial distance formula is used to extract the Euclidean distance of the three-dimensional coordinates and store it in the trajectory offset record. Assume that the reconstructed point at a certain time point is (15.0, 20.0, 5.0), and the declared point is (14.0, 19.5, 5.0). The three-axis differences are 1.0, 0.5, and 0 respectively, and the sum of the squares is 1.25. The offset distance after square root is about 1.12 meters. This value is used as the offset value at that time point. After processing all corresponding points in the trajectory in sequence, the system will An offset sequence with a length equal to the number of trajectory points is formed. Each element in the sequence represents the spatial difference between the reconstructed trajectory point and the declared path at that moment. After the sequence is generated, the system calls the time label 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 as 0 to 1 meter, 1 to 3 meters, 3 to 5 meters and above 5 meters. If the offset value of a certain trajectory segment is in the range of 3 to 5 meters for more than 30% of the total time, the segment is marked as a moderate offset segment. If the offset value of more than 5 meters occurs for more than 10 seconds, it is marked as a severe offset segment. The offset sequence and offset level information are combined to form the final trajectory offset sequence value.

[0134] S403: Based on the trajectory offset sequence value, by analyzing the trend of the offset value changing with the trajectory time sequence, identifying abnormal flight paths and obtaining airspace supervision records;

[0135] According to the trajectory offset sequence value, the system uses the time label as the horizontal axis and the offset value as the vertical axis to construct an offset trend curve. The slope analysis is performed on the curve. The slope is calculated by extracting the offset increment change between three adjacent time points. The offset trend judgment standard is set as follows: if the slope is positive and the offset increases for three consecutive time periods, it is judged as a continuous offset trend. If the slope is negative and the absolute value increases, it is considered a fast convergence offset. If the slope fluctuates around zero and the offset value varies between 1 meter and 3 meters, it is an intermittent disturbance trajectory. In the specific example, if the offset sequence is 1.1, 1.5, 2.2, 2.9, and 3.6, the adjacent increments are 0.4, 0.7, 0.7, 0.7, forming a stable upward trend, which is determined to be an upward segment of trajectory deviation. The system labels the entire trajectory trend segment and marks the continuous segments of the same trend type as a trend interval. The average slope and duration within the trend interval are counted. If the duration exceeds 30 seconds and the average slope exceeds 0.1, the trend segment is determined to be an abnormal path segment. At the same time, the geographical location information is combined to determine the airspace location where the deviation occurs. If it occurs within 30 meters of the restricted airspace or no-fly zone boundary, a risk level label is added. All abnormal path segments are structured and recorded and stored in the supervision database to finally obtain the airspace supervision record.

[0136] Based on airspace signal monitoring information, drone identification records, and airspace supervision records, the spatial coordinates and broadcast time series of the identified signal source are extracted. The broadcast time series of multiple drone signal sources are called and compared with the broadcast time of multiple non-drone signal sources to extract the time overlapping interval. The remote control signal source corresponding to each drone is identified and its location information is extracted. The specific steps for obtaining the control signal source identification result are as follows:

[0137] S501: Extract the spatial coordinates and broadcast time series of each identified signal source based on airspace signal monitoring information, drone identification records, and airspace supervision records to obtain signal source spatiotemporal data;

[0138] Based on airspace signal monitoring information, drone identification records, and airspace supervision records, the system calls the unique identification field of the identified source from each data set, extracts the source number and its corresponding spatial coordinate data and broadcast time series records, and establishes a mapping index. The spatial coordinates are the average position of the monitoring terminal receiving the source signal 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 source number and count their start time, end time, and number of consecutive frames to form a time series segment. In this example, a source is from 10:00:01 to 10:00: 20 broadcast frames are sent within the 06 time period. The system regards this period as its continuous broadcast time period and marks its spatial coordinates as the average position (120.5, 85.2, 30.0) calculated when the monitoring nodes A, B, and C receive the frames together. The position information is then bound to the time period label and stored. The system constructs a source data table for all sources. The fields include source number, coordinate point, broadcast start and end time, number of frames, frequency band field, and identification field. The system requires the time label accuracy to reach the millisecond level. If the inter-frame interval is greater than 1 second, it is considered to be interrupted and recorded in segments, ultimately forming the source spatiotemporal data for traceability analysis.

[0139] S502: Based on the signal source spatiotemporal data, the broadcast time series of multiple identified drone signal sources are compared with the broadcast time series of multiple non-drone signal sources, and the time overlapping intervals are extracted to obtain the time overlapping interval data.

[0140] The specific formula for extracting time overlapping intervals is:

[0141] ;

[0142] Calculate the time intersection offset index value;

[0143] in, represents the time intersection offset index value between drone source i and non-drone source j, represents the starting point of the broadcast time of drone source i, represents the end point of the broadcast time of drone source i, represents the starting time of the broadcast 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 period of drone source i, represents the center 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 center time point of the broadcast time period.

[0144] formula:

[0145] ;

[0146] Detailed explanation of the formula and the process of formula calculation and derivation:

[0147] The formula is used to calculate the degree of intersection offset between the broadcast time series of drone and non-drone sources on the time axis. The result is used to measure whether the two types of sources have overlapping or synchronized behavioral characteristics, providing a preliminary temporal logic correlation index for remote control signal recognition. Based on the temporal coincidence judgment logic, the sum of the broadcast durations of drone and non-drone sources is averaged to reflect the typical broadcast cycle lengths of the two types of sources. The offset distance of the center points of their respective time periods is then subtracted from this average length 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. Conversely, if the center offset is large or the broadcast duration is extremely short, the index value tends to decrease, indicating a lack of temporal linkage. By combining the duration and offset difference, a quantitative determination method for dynamic synchronization trends is constructed.

[0148] Parameter meaning and setting value:

[0149] : The start time of the i-th broadcast of the drone source is set to 10:00:05.200;

[0150] : 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 sequential numbering of the 20 broadcast frames, and the interval between each frame is 0.5 seconds;

[0151] : The start time of the jth broadcast of the non-UAV source is set to 10:00:08.000;

[0152] : 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;

[0153] : The center time point of the drone broadcast period,

[0154] ;

[0155] : The central time point of the non-UAV source broadcast period,

[0156] ;

[0157] Substitute the parameters into the formula for calculation:

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] ;

[0163] The result of 7.200 seconds indicates that the broadcast time length of the two signal sources is relatively close, but the difference in the center of gravity of the time axis is close to 3 seconds, and there is a significant gap in the center overlap. This result is used to determine whether there is synchronization or remote control end linkage behavior. It will be used as a priority ranking factor in subsequent spatial distance judgment. The larger the formula calculation result, the greater the degree of overlap between the two signal sources' broadcasts, and the higher the ranking position.

[0164] S503: Identify the remote control signal source corresponding to each UAV based on the time overlapping interval data, extract the location information of the target remote control signal source, and obtain the control signal source identification result;

[0165] Based on the time overlap interval data, the system extracts the numbers of all non-UAV signal sources that continuously overlap with a specific UAV signal source. The system then queries the spatial coordinate fields in the signal source spatiotemporal data table as their broadcast source locations. The system then calculates the spatial distance between these locations and the coordinates of the starting point of the UAV trajectory. If the distance is less than the system-set control radius threshold of 100 meters and the broadcast overlap time covers at least 80% of the trajectory starting time period, the non-UAV signal source is identified as the corresponding remote control source. In this example, if a non-UAV signal source is numbered R123 and its broadcast time overlaps with UAV U456 for 15 seconds, accounting for 90% of U456's total broadcast time, and the spatial distance is 85 meters, R123 is identified as U456's remote control source. The system records the corresponding number relationships and matching parameters for all matching relationships, generates a remote control association record, and finally extracts the spatial location field and source number field of the signal source, writes them into the control information identification table, and obtains the control source identification result.

[0166] See also Figure 2 A UAV flight trajectory detection system is provided. The UAV flight trajectory detection system is used to execute the above-mentioned UAV flight trajectory detection method. The system includes:

[0167] The signal monitoring module extracts the identification field, frequency band field, and channel structure field from the target airspace broadcast signal, performs attribute analysis on 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.

[0168] The signal source positioning module extracts a set of independent signal sources based on airspace signal monitoring information, corrects the time difference between multiple monitoring terminals, and identifies the signal source location and detects drones through spatial cross-reconstruction, generating drone identification records.

[0169] 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 the drone trajectory reconstruction result;

[0170] The anomaly detection module, based on the drone trajectory reconstruction results, compares them with the trajectory sequences of multiple declared flight paths, calculates trajectory similarity, identifies drone identities, and detects abnormal drones. Combining the deviations of flight trajectories at multiple spatial locations, it calculates the trend of trajectory deviations, detects abnormal flight paths, and generates airspace supervision records.

[0171] The control source identification module extracts the spatial coordinates and broadcast time series of the identified signal source based on airspace signal monitoring information, drone identification records and airspace supervision records. By comparing the broadcast time with multiple non-drone signal sources, it extracts the time overlapping interval, identifies the remote control signal source corresponding to each drone, extracts the location information, and generates the control source identification result.

[0172] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. 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 comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0173] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0174] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0175] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0176] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0177] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0178] In the 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 merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0179] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0180] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0181] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the 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 enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection 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's 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, performing spatial cross-reconstruction of reception records between multiple monitoring nodes, identifying the signal source location and detecting drones, and obtaining drone identification records; Based on the airspace signal monitoring information, a set of independent signal sources is extracted, the time difference between multiple monitoring terminals is corrected, the reception records between multiple monitoring nodes are spatially cross-reconstructed, the signal source position is identified, and the drone is detected. The specific steps of obtaining the drone identification record are as follows: S201: Based on the airspace signal monitoring information, the signal identification fields in the monitoring information are aggregated and a set is constructed, a set of independent signal sources is extracted, the reception time data recorded in each monitoring terminal and the corresponding local time record are called, the time records between each terminal are compared, the time difference is extracted and corrected, and a time calibration reference value is obtained; S202: Based on the time calibration reference value, the location information of each listening terminal is matched with its corrected received broadcast record under a unified time base, and a set of spatial receiving points is constructed. The spatial distribution of signal receiving nodes under the synchronized time is extracted, and the receiving records between multiple listening nodes are spatially cross-reconstructed to obtain a signal spatial cross-distribution value. S203: Based on the signal spatial cross-distribution value, a three-dimensional weighted calculation is performed on the coordinates of all receiving nodes constituting the spatial cross-region, the spatial center of gravity of the signal concentration region is extracted, the positions of multiple signal sources are identified, and based on the spatial coordinate information of the signal sources, the altitude information of the signal sources is identified and drones are detected to obtain drone identification records; S3: Based on 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 to obtain the drone trajectory reconstruction result; S4: Based on the reconstructed drone trajectory, the trajectory is compared with the trajectory sequences of multiple declared flight paths to calculate the trajectory similarity, identify the drone identity and detect abnormal drones. Combined with the deviation of the flight trajectory at multiple spatial positions, the trend of trajectory deviation is calculated and abnormal flight paths are detected to obtain airspace supervision records. S5: Based on the airspace signal monitoring information, drone identification records and airspace supervision records, the spatial coordinates and broadcast time series of the identified signal source are extracted, the broadcast time series of multiple drone signal sources are called, and the time overlapping intervals are extracted by comparing them with the broadcast time of multiple non-drone signal sources. The remote control signal source corresponding to each drone is identified and the location information is extracted to obtain the control signal source identification result.

2. The method for detecting the flight trajectory of a UAV according to claim 1, wherein: 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 a UAV according to claim 1, wherein: The steps for obtaining the target airspace broadcast signal, extracting the signal's identification field, frequency band field, and channel structure field, constructing a signal source attribute group sequence, identifying the number of independent signal sources in the airspace, and detecting new signals are as follows: S101: Acquire the target airspace broadcast signal, extract the identification field, frequency band field, and channel structure field of each data in the broadcast signal, and construct a signal source attribute group sequence; S102: performing uniqueness analysis on the 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 the number value of independent signal sources; 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 a UAV according to claim 3, wherein: Based on 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 specific steps for obtaining the drone trajectory reconstruction result are as follows: S301: Based on 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: According to the speed direction trend value, a fitting trajectory point sequence with equal time intervals is constructed to reconstruct the moving trajectory sequence of the target UAV signal source, including position information at multiple time points, and obtain a UAV trajectory reconstruction result.

5. The method for detecting the flight trajectory of a UAV according to claim 4, characterized in that: Based on the drone trajectory reconstruction results, by comparing them 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 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 based on the drone trajectory reconstruction result, calculating the trajectory similarity between the drone trajectory and each reported trajectory, identifying the drone based on the drone information corresponding to the reported path, detecting abnormal drones, and obtaining trajectory similarity recognition results; S402: Based on the trajectory similarity recognition results and in combination with the deviations of the flight trajectory at multiple spatial positions, the spatial distance value between each reconstructed trajectory point and the corresponding declared path point is extracted 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.

6. The method for detecting the flight trajectory of a UAV according to claim 5, 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 the trajectory space point set; in, is the trajectory space point set similarity index value 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, and 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 k-th point in trajectory p, is the Y-axis coordinate value of the k-th point in trajectory p, is the Z-axis coordinate value of the k-th point in trajectory p, is the X-axis coordinate value of the k-th point in trajectory q, is the Y-axis coordinate value of the k-th point in trajectory q, is the Z-axis coordinate value of the k-th point in trajectory q, is the spatial distance between reference segments corresponding to the k-th point in trajectory q, indicating the ideal path segment length of the declared trajectory near the k-th point. is the directional weight coefficient of the kth point in the trajectory.

7. The method for detecting the flight trajectory of a UAV according to claim 1, wherein: The control signal source identification result includes the remote control terminal signal source identification number, the remote control signal spatial position, and the remote control source synchronization control tag.

8. The method for detecting the flight trajectory of a UAV according to claim 1, wherein: Based on the airspace signal monitoring information, drone identification records, and airspace supervision records, the spatial coordinates and broadcast time series of the identified signal source are extracted. The broadcast time series of multiple drone signal sources are called, and the time overlap intervals are extracted by comparing the broadcast time with the broadcast time of multiple non-drone signal sources. The remote control signal source corresponding to each drone is identified and the location information is extracted. The specific steps for obtaining the control signal source identification result are as follows: S501: Extracting the spatial coordinates and broadcast time sequence of each identified signal source based on the airspace signal monitoring information, drone identification records, and airspace supervision records to obtain signal source spatiotemporal data; S502: Based on the signal source spatiotemporal data, compare the broadcast time series of multiple identified drone signal sources with the broadcast time series of multiple non-drone signal sources, extract the time overlapping intervals, and obtain 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 point of the broadcast time of drone source i, represents the end point of the broadcast time of drone source i, represents the starting time of the broadcast 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 period of drone source i, represents the center 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 center time point of the broadcast time period; S503: Identify the remote control signal source corresponding to each UAV based on the time overlapping interval data, extract the position information of the target remote control signal source, and obtain the control signal source identification result.

9. 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 8, and the system includes: The signal monitoring module extracts the identification field, frequency band field, and channel structure field from the target airspace broadcast signal, performs attribute analysis on 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 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 drones through spatial cross-reconstruction, and generates drone identification records; 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 the drone trajectory reconstruction result; The anomaly detection module, based on the drone trajectory reconstruction results, compares them with the trajectory sequences of multiple declared flight paths, calculates trajectory similarity, identifies the drone identity and detects abnormal drones. It combines the deviations of the flight trajectory at multiple spatial locations, calculates the trend of trajectory deviation, 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. By comparing the broadcast time with multiple non-drone signal sources, the time overlapping interval is extracted, the remote control source corresponding to each drone is identified and the location information is extracted to generate the control source identification result.

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