Unmanned aerial vehicle target detection method based on space-time synchronization association mechanism

Abstract

The invention relates to an unmanned aerial vehicle target detection method based on a space-time synchronization association mechanism, and relates to the technical field of wireless detection, and the method comprises the following steps: S1, building a unified time reference system, and ensuring that precise timestamp alignment is carried out on IQ data streams collected by two technologies, and the precision reaches a microsecond level; s2, acquiring unmanned aerial vehicle signal data obtained by protocol reverse analysis and a radio frequency fingerprint identification technology; s3, constructing a three-dimensional incidence matrix containing a time window, a frequency domain position and a space arrival angle, and performing space-time matching on the detection events in the synchronous window; and S4, analyzing a matching result to identify a real unmanned aerial vehicle target. According to the method, the unmanned aerial vehicle signal can be effectively and accurately analyzed, the accuracy and reliability of unmanned aerial vehicle target detection are improved, and the possibility of multi-target false alarm is reduced.

Description

Technical Field

[0001] The present application relates to the field of wireless detection technology, and in particular to a method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism. The present application aims to improve the accuracy of unmanned aerial vehicle target detection and reduce the incidence of multi-target false alarms. Background Art

[0002] In radio spectrum monitoring systems, detection of drone targets usually adopts dual technical paths: protocol reverse analysis and radio frequency fingerprint recognition.

[0003] The protocol reverse analysis technology is based on the in-depth analysis of the drone communication protocol stack. It obtains the drone identification code (UID), telemetry data and other core information by demodulating the physical layer signal, parsing the data link layer frame structure, decoding the application layer protocol, etc. The radio frequency fingerprint recognition technology realizes the classification and recognition of drone signals by analyzing the instantaneous spectrum characteristics of the signal (including center frequency, bandwidth, modulation type), long-term spectrum occupancy mode (such as frequency hopping cycle, TDMA time slot structure) and transmitter hardware characteristics (such as IQ imbalance, phase noise and other radio frequency fingerprints).

[0004] When a single UAV target appears, since the two technologies independently implement signal analysis at the protocol layer and the physical layer respectively, multiple target false alarms may occur due to asynchronous detection mechanisms or lack of feature correlation; therefore, an effective mechanism is urgently needed to solve the problem of multiple target false alarms. Summary of the invention

[0005] The purpose of this application is to provide a UAV target detection method based on a spatiotemporal synchronization association mechanism, which can effectively identify real UAV targets and reduce the probability of multi-target false alarms through precise time synchronization and the construction of a three-dimensional association matrix.

[0006] The present application provides a method for detecting drone targets based on a spatiotemporal synchronization association mechanism, which adopts the following technical solution: comprising the following steps: S1. Establish a unified time reference system to ensure precise time stamp alignment of the IQ data streams collected by the two technologies with microsecond accuracy; S2. Collect drone signal data obtained through protocol reverse analysis and radio frequency fingerprinting technology, including but not limited to the drone’s identification code, telemetry data, signal strength indication, and modulation mode; S3, constructing a three-dimensional correlation matrix including time window, frequency domain position and spatial arrival angle, and performing spatiotemporal matching of detection events within the synchronization window; S4. Analyze the matching results to identify the real UAV target and reduce the probability of multiple target false alarms; The acquisition of the spatial arrival angle is achieved by the following steps: I. Using the signal difference of at least two receiving antennas and using the time difference positioning technology; II. Use the phase difference measurement method to calculate the phase difference of the signals received by the two antennas; III. Combined with the signal propagation model, the incident angle of the signal is derived, and multipath signals are considered to improve accuracy; The matching result analysis step includes: a. Apply machine learning algorithms to classify matching events and identify the identity characteristics of drones; b. Generate alarm information and promptly notify monitoring personnel for follow-up processing. The alarm information includes the type, location, flight status and potential threat level of the drone.

[0007] Preferably, the time reference system includes a high-precision clock source capable of providing a unified time reference for multiple signal acquisition units and a time synchronization protocol for achieving time synchronization between acquisition devices, so as to ensure the time consistency of each signal acquisition device.

[0008] Preferably, the implementation scheme of the time synchronization protocol includes a network time protocol, a precision time protocol, a time synchronization algorithm, a redundant synchronization mechanism and synchronization status monitoring.

[0009] Preferably, the network time protocol includes: Deploy NTP server: Deploy a high-precision NTP server in the monitoring system as a time reference source to provide unified time information; Timestamp synchronization: Each signal acquisition device periodically requests a timestamp from the NTP server through the NTP client to ensure the time consistency of each device with microsecond accuracy; The precision time protocol includes: PTP network architecture: Establish a PTP network between devices and use the IEEE 1588 standard to achieve high-precision time synchronization; Master-slave clock configuration: select one device as the master clock and the rest as slave clocks. The master clock broadcasts time information regularly, and the slave clocks receive the time and correct it to maintain microsecond synchronization. The time synchronization algorithm includes: Time deviation calculation: For each timestamp request, the request response time is calculated and the deviation is adjusted to ensure synchronization accuracy; Delay compensation: Considering network delay, delay compensation is performed on synchronization information to further improve the accuracy and reliability of time synchronization; The redundant synchronization mechanism includes: Multi-source time synchronization: supports synchronization from multiple time sources, such as GPS time signals, atomic clocks, etc., to improve the robustness of the system; Fault detection and switching: Real-time monitoring of time synchronization status, automatic switching to the backup time source when the main time source fails, ensuring continuous and stable operation of the system; The synchronization status monitoring includes: Status report: Generate time synchronization status reports regularly to monitor the synchronization accuracy and status of each device to ensure that the system runs under optimal conditions; Alarm mechanism: When it is detected that the time synchronization deviation exceeds the set range, an alarm is triggered immediately to remind maintenance personnel to check and adjust.

[0010] Preferably, the IQ data stream collection step includes real-time monitoring of drone signals and preliminary processing of the data to extract effective feature information.

[0011] Preferably, the construction of the three-dimensional correlation matrix further includes: The signal intensity within the time window is normalized to ensure comparability between different signal sources; Perform cluster analysis in the frequency domain to identify signal features with similar frequencies and enhance the accuracy of signal recognition; Multiple measurements of the spatial angle of arrival are taken and averaged to improve accuracy.

[0012] Preferably, the setting of the time window is dynamically adjusted according to actual applications to adapt to signal characteristics in different environments, and the dynamic adjustment includes: Automatically adjust the width of the time window according to the ambient noise level to optimize signal detection; The time window setting is automatically optimized according to the signal characteristics of different types of drones to improve detection accuracy.

[0013] Preferably, the steps of the dynamic adjustment algorithm of the time window are as follows: Input parameters: Ambient noise level (N): the background noise level measured by the receiver in decibels (dB); Signal strength (S): The received strength of the drone signal, in decibel milliwatts (dBm); Signal Stability Indicator (SSI): A stability indicator calculated by comparing historical signal strength. The smaller the value, the smaller the signal fluctuation. Initial time window (T_initial): the initial time window set by the system, in milliseconds (ms); Dynamic adjustment algorithm steps: 1. Monitor environmental noise and signal strength Continuously monitor the current ambient noise level (N) and drone signal strength (S); 2. Calculate the signal-to-noise ratio (SNR) Calculate the signal-to-noise ratio using the formula: ; 3. Set the dynamic adjustment threshold Set the signal-to-noise ratio threshold (SNR_threshold), such as 10 dB. When the SNR is lower than this threshold, it indicates that the background noise is strong and may affect signal detection; 4. Adjust the time window a) When SNR ≥ SNR_threshold: Increase the time window to improve the probability of signal capture. The adjustment formula is: ; where k is the adjustment coefficient, which can be set to 0.1; b) When SNR < SNR_threshold: Decrease the time window to reduce the influence of background noise on signal analysis. The adjustment formula is: ; 5. Introduce the signal stability indicator (SSI) Further adjust the time window according to the value of the signal stability indicator (SSI): If SSI < 5 dB (indicating that the signal is very stable), increase the time window; If SSI > 15 dB (indicating that the signal fluctuates greatly), decrease the time window; Perform the final adjustment in combination with the current time window: ; where m is the stability adjustment coefficient, which can be set to 0.05; 6. Output the adjusted time window Update the adjusted time window (T_final) to the system for subsequent signal acquisition and processing.

[0014] Preferably, the machine learning algorithm includes, but is not limited to, support vector machines, decision trees, and neural networks to improve the classification accuracy and efficiency of matching events.

[0015] Preferably, the training process of the machine learning algorithm adopts an incremental learning method to adapt to new input data and environmental changes.

[0016] Preferably, the generation method of the alarm information includes real-time data sending, SMS notification, and email alert to ensure that the monitoring personnel can obtain information in a timely manner.

[0017] In summary, the present application includes at least one of the following beneficial technical effects: 1. Adopting the time-space synchronization association mechanism, establishing a unified time reference system, aligning the IQ data streams collected by the two technologies with precise time stamps, and then constructing a three-dimensional association matrix: time window, frequency domain position and spatial arrival angle, and performing time-space matching of detection events within the synchronization window, it can effectively and accurately analyze drone signals, improve the accuracy and reliability of drone target detection, reduce the possibility of multi-target false alarms, and provide more accurate technical support for drone monitoring; 2. Using a three-dimensional correlation matrix for time-space matching, combined with protocol reverse analysis and radio frequency fingerprint recognition technology, can effectively reduce multi-target false alarms caused by asynchronous detection or lack of feature correlation; 3. By real-time monitoring of environmental noise levels and signal strength, the time window settings are dynamically adjusted, so that the system can adapt to different environmental conditions, improving the robustness and flexibility of signal detection; 4. Ability to generate alarm information based on matching results and notify monitoring personnel in a timely manner through various means, such as SMS and email, to ensure timely response and handling of drone activities; 5. The application of machine learning algorithms and their incremental learning capabilities enable the system to quickly adapt to new input data and environmental changes, further improving the efficiency and accuracy of drone target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the drone target detection method of this application; Figure 2 It is a flowchart of the step of obtaining the spatial angle of arrival in step S3 of the present application; Figure 3 It is a flowchart of the analysis steps of the matching results in step S4 of the present application. DETAILED DESCRIPTION

[0019] The following is combined with Figure 1 -Attached Figure 3 , further details of this application are given.

[0020] The present application provides a method for detecting drone targets based on a spatiotemporal synchronous association mechanism, comprising the following steps: S1. Establish a unified time reference system to ensure precise time stamp alignment of the IQ data streams collected by the two technologies with microsecond accuracy; S2. Collect drone signal data obtained through protocol reverse analysis and radio frequency fingerprinting technology, including but not limited to the drone’s identification code, telemetry data, signal strength indication, and modulation mode; S3, constructing a three-dimensional correlation matrix including time window, frequency domain position and spatial arrival angle, and performing spatiotemporal matching of detection events within the synchronization window; S4. Analyze the matching results to identify the real UAV target and reduce the probability of multiple target false alarms; The spatial arrival angle is obtained by the following steps: I. Using the signal difference of at least two receiving antennas and using the time difference positioning technology; II. Use the phase difference measurement method to calculate the phase difference of the signals received by the two antennas; III. Combined with the signal propagation model, the incident angle of the signal is derived, and multipath signals are considered to improve accuracy; The analysis steps of the matching results include: a. Apply machine learning algorithms to classify matching events and identify the identity characteristics of drones; b. Generate alarm information and promptly notify monitoring personnel for follow-up processing. The alarm information includes the type, location, flight status and potential threat level of the drone.

[0021] Among them, the time reference system includes a high-precision clock source that can provide a unified time reference for multiple signal acquisition units and a time synchronization protocol for achieving time synchronization between acquisition devices to ensure the time consistency of each signal acquisition device.

[0022] Among them, the implementation scheme of the time synchronization protocol includes the network time protocol, the precision time protocol, the time synchronization algorithm, the redundant synchronization mechanism and the synchronization status monitoring.

[0023] Among them, the network time protocol includes: Deploy NTP server: Deploy a high-precision NTP server in the monitoring system as a time reference source to provide unified time information; Timestamp synchronization: Each signal acquisition device periodically requests a timestamp from the NTP server through the NTP client to ensure the time consistency of each device with microsecond accuracy; The Precision Time Protocol includes: PTP network architecture: Establish a PTP network between devices and use the IEEE 1588 standard to achieve high-precision time synchronization; Master-slave clock configuration: select one device as the master clock and the rest as slave clocks. The master clock broadcasts time information regularly, and the slave clocks receive the time and correct it to maintain microsecond synchronization. The time synchronization algorithms include: Time deviation calculation: For each timestamp request, the request response time is calculated and the deviation is adjusted to ensure synchronization accuracy; Delay compensation: Considering network delay, delay compensation is performed on synchronization information to further improve the accuracy and reliability of time synchronization; The redundant synchronization mechanisms include: Multi-source time synchronization: supports synchronization from multiple time sources, such as GPS time signals, atomic clocks, etc., to improve the robustness of the system; Fault detection and switching: Real-time monitoring of time synchronization status, automatic switching to the backup time source when the main time source fails, ensuring continuous and stable operation of the system; Synchronous status monitoring includes: Status report: Generate time synchronization status reports regularly to monitor the synchronization accuracy and status of each device to ensure that the system runs under optimal conditions; Alarm mechanism: When it is detected that the time synchronization deviation exceeds the set range, an alarm is triggered immediately to remind maintenance personnel to check and adjust.

[0024] Among them, the collection steps of IQ data stream include real-time monitoring of drone signals and preliminary processing of data to extract effective feature information.

[0025] The construction of the three-dimensional correlation matrix further includes: The signal intensity within the time window is normalized to ensure comparability between different signal sources; Perform cluster analysis in the frequency domain to identify signal features with similar frequencies and enhance the accuracy of signal recognition; Multiple measurements of the spatial angle of arrival are taken and averaged to improve accuracy.

[0026] The time window setting is dynamically adjusted according to the actual application to adapt to the signal characteristics in different environments. The dynamic adjustment includes: Automatically adjust the width of the time window according to the ambient noise level to optimize signal detection; The time window setting is automatically optimized according to the signal characteristics of different types of drones to improve detection accuracy.

[0027] The steps of the dynamic adjustment algorithm of the time window are as follows: Input parameters: Ambient noise level (N): the background noise level measured by the receiver in decibels (dB); Signal strength (S): The received strength of the drone signal, in decibel milliwatts (dBm); Signal Stability Indicator (SSI): A stability indicator calculated by comparing historical signal strength. The smaller the value, the smaller the signal fluctuation. Initial time window (T_initial): the initial time window set by the system, in milliseconds (ms); Dynamic adjustment algorithm steps: 1. Monitor environmental noise and signal strength Continuously monitor the current ambient noise level (N) and drone signal strength (S); 2. Calculate the signal-to-noise ratio (SNR) Calculate the SNR using the formula: ; 3. Set the dynamic adjustment threshold Set the SNR threshold (SNR_threshold), such as 10 dB. When the SNR is lower than this threshold, it indicates that the background noise is strong and may affect signal detection; 4. Adjust the time window a) When SNR ≥ SNR_threshold: Increase the time window to improve the probability of signal capture. The adjustment formula is: ; where k is the adjustment coefficient, which can be set to 0.1; b) When SNR < SNR_threshold: Decrease the time window to reduce the impact of background noise on signal analysis. The adjustment formula is: ; 5. Introduce the signal stability indicator (SSI) Further adjust the time window according to the value of the signal stability indicator (SSI): If SSI < 5 dB (indicating that the signal is very stable), increase the time window; If SSI > 15 dB (indicating that the signal fluctuates greatly), decrease the time window; Make the final adjustment in combination with the current time window: ; where m is the stability adjustment coefficient, which can be set to 0.05; 6. Output the adjusted time window Update the adjusted time window (T_final) to the system for subsequent signal acquisition and processing.

[0028] Among them, machine learning algorithms include but are not limited to support vector machines, decision trees, and neural networks to improve the classification accuracy and efficiency of matching events; Support vector machines take the extracted signal features, such as signal strength, center frequency, and modulation method, as inputs. SVM can effectively distinguish different types of drones. In the training stage, the model learns the features of sample data, finds the optimal separation surface, and optimizes the classification accuracy; Decision trees classify based on the extracted features such as signal strength and frequency pattern, generating simple and easy-to-understand classification rules, which are suitable for quick decision-making in real-time monitoring and can provide classification results quickly; Neural networks using deep learning models can handle more complex pattern recognition tasks, especially in the extraction and classification of signal features. They can automatically extract signal features through a multi-layer network structure for more efficient classification.

[0029] Among them, the training process of the machine learning algorithm adopts an incremental learning method, which allows the model to be updated when new data is received without having to be retrained from scratch to adapt to new input data and environmental changes. When the system detects new drone types or signal characteristics, incremental learning can enable the model to adapt quickly, reducing training time and computing resources.

[0030] The implementation steps of incremental learning are as follows: Data collection and preprocessing Real-time data collection: The system continuously monitors drone signals and collects new data in real time, including signal strength, frequency characteristics, modulation mode and other information; Data annotation: The new data collected needs to be annotated so that it can be used for incremental learning. The annotation process can be performed by expert review or automated tools; Feature extraction: Extract features from newly collected signal data and generate feature vectors for subsequent model updates; Model update mechanism Incremental training: After new data is labeled and feature extracted, it is directly added to the existing training data set. An appropriate machine learning algorithm, such as SVM, decision tree, or neural network, is selected for incremental training. At this point, the model will be updated based on the new data without having to start training from scratch. Training parameter adjustment: During the incremental learning process, adjust the model's training parameters, such as learning rate and regularization parameters, to avoid overfitting and maintain the model's generalization ability; Model validation: After incremental training, the updated model is validated using the retained test set to evaluate the performance changes of the model and ensure the accuracy and stability of the model on the new dataset; Dynamic update strategy Adaptive update frequency: Dynamically adjust the frequency of model updates based on the frequency of drone activities and the speed at which new data appears. For example, when drone activities are frequent, the update frequency is increased; when drone activities are less frequent, the update frequency is reduced. Error feedback mechanism: The classification results of the monitoring system are compared with the actual situation. If it is found that the model produces incorrect classification in some cases, these error instances can be used for targeted incremental learning to further optimize the model; Performance monitoring and optimization Real-time monitoring: During system operation, the model's performance indicators, such as accuracy, recall, and F1-score, are continuously monitored to ensure the model's adaptability in different environments. Regular evaluation: Regularly conduct comprehensive model evaluation and retrain with new data to ensure that the model can adapt to long-term environmental changes and the evolution of drone signal characteristics; User feedback integration: Collect feedback from monitoring personnel and use this feedback information to adjust and optimize the model to improve the actual application effect of the system.

[0031] Among them, the alarm information generation methods include real-time data transmission, SMS notification and email alerts to ensure that monitoring personnel obtain information in a timely manner.

[0032] The embodiments of the present application are as follows: Example 1: Drone Target Detection in an Urban Environment Background: In urban environments, drone flight activities are becoming increasingly frequent, which may pose a threat to public safety and privacy. This embodiment demonstrates how to use the method of the present application to perform drone target detection in urban areas.

[0033] step: 1. Establish a time reference system: Deploy a high-precision NTP server and ensure that all signal acquisition devices, such as receiving antennas and signal processing units, regularly request timestamps through the NTP client to maintain microsecond-level time synchronization; 2. Signal data acquisition: Using protocol reverse analysis and radio frequency fingerprinting technology to collect signal data from drones flying in the air, including identification codes, telemetry data, and signal strength indicators; 3. Construct a three-dimensional correlation matrix: The time window is set to 100ms, and a three-dimensional correlation matrix including the time window, frequency domain position and spatial arrival angle is constructed; the signal is differentiated using at least two receiving antennas to obtain the spatial arrival angle of the drone; 4. Matching result analysis: Apply machine learning algorithms and use support vector machines to classify signal data and identify the identity characteristics of drones; Generates alarm information including drone type, location, flight status and potential threat level, and notifies monitoring personnel via SMS and email; 5. Dynamically adjust the time window: Dynamically adjust the ambient noise level based on real-time monitoring of urban traffic noise. If the noise level is high, shorten the time window to 50ms to optimize signal detection. Results: Through the above steps, the flight trajectories of multiple drones were successfully identified, and alarm information was issued in time to ensure the safety of the urban area.

[0034] Example 2: Drone detection in farmland monitoring Background: In the agricultural field, drones are often used for crop monitoring and spraying. This embodiment demonstrates how to use the method of the present application to effectively monitor drones in a farmland environment.

[0035] step: 1. Establish a time reference system: Configure a Precision Time Protocol (PTP) network, set one device as the master clock, broadcast time information regularly, and ensure synchronization of all signal acquisition devices; 2. Signal data acquisition: Through the drone’s telemetry system, real-time drone signal data, including identification code and signal strength, is collected; 3. Construct a three-dimensional correlation matrix: The time window is set to 150ms, and a three-dimensional correlation matrix is ​​constructed, including the time window, frequency domain position, and spatial arrival angle. The signal phase difference is calculated using the phase difference measurement method to derive the signal's incident angle. 4. Matching result analysis: Use decision tree algorithms to classify the identity features of drones, generate alarm information, and notify farmland managers in a timely manner; 5. Dynamically adjust the time window: Automatically adjust the time window according to weather changes and environmental noise, such as wind and bird calls. If the wind speed is high, shorten the time window to 75ms to ensure accurate signal capture. Results: The drones flying over farmland were successfully identified, enabling real-time monitoring and management, and improving agricultural production efficiency.

[0036] Example 3: Drone monitoring at public events Background: In large-scale public events, such as music festivals and sports events, monitoring the flight of drones is crucial. This embodiment shows how to apply the method of this application to detect drone targets in public events; step: 1. Establish a time reference system: Deploy multiple high-precision clock sources at the event site to ensure that all signal acquisition units can achieve microsecond-level time synchronization; 2. Signal data acquisition: Using protocol reverse analysis and radio frequency fingerprint recognition technology, drone signal data is collected in real time, and identification codes, telemetry data, and signal strength are recorded; 3. Construct a three-dimensional correlation matrix: The time window is set to 200ms, and a three-dimensional correlation matrix is ​​constructed, including the time window, frequency domain position, and spatial arrival angle. The spatial arrival angle of the UAV is obtained by using multiple receiving antennas to perform signal differentiation. 4. Matching result analysis: Apply neural network algorithms to classify the identity features of drones and generate alarm information, including the type of drone, flight status, and potential threat level; 5. Dynamically adjust the time window: Dynamically adjust the time window based on the ambient noise at the event site (such as audience noise). When the noise level is high, shorten the time window to 100ms to optimize signal detection. Results: By implementing the above steps, multiple drones were successfully discovered and identified during public events, and alarms were issued in a timely manner, ensuring the safety of the event.

[0037] The embodiments of this specific implementation are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. The same components are represented by the same figure marks. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronous association mechanism, characterized in that: The following steps are involved: S1. Establish a unified time reference system to ensure precise time stamp alignment of the IQ data streams collected by the two technologies with microsecond accuracy; S2. Collect drone signal data obtained through protocol reverse analysis and radio frequency fingerprinting technology, including but not limited to the drone’s identification code, telemetry data, signal strength indication, and modulation mode; S3, constructing a three-dimensional correlation matrix including time window, frequency domain position and spatial arrival angle, and performing spatiotemporal matching of detection events within the synchronization window; S4. Analyze the matching results to identify the real UAV target and reduce the probability of multiple target false alarms; The acquisition of the spatial arrival angle is achieved by the following steps: I. Using the signal difference of at least two receiving antennas and using the time difference positioning technology; II. Use the phase difference measurement method to calculate the phase difference of the signals received by the two antennas; III. Combined with the signal propagation model, the incident angle of the signal is derived, and multipath signals are considered to improve accuracy; The matching result analysis step includes: a. Apply machine learning algorithms to classify matching events and identify the identity characteristics of drones; b. Generate alarm information and promptly notify monitoring personnel for follow-up processing. The alarm information includes the type, location, flight status and potential threat level of the drone.

2. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The time reference system includes a high-precision clock source capable of providing a unified time reference for multiple signal acquisition units and a time synchronization protocol for achieving time synchronization between acquisition devices, so as to ensure the time consistency of each signal acquisition device.

3. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 2, characterized in that: The implementation scheme of the time synchronization protocol includes a network time protocol, a precision time protocol, a time synchronization algorithm, a redundant synchronization mechanism and synchronization status monitoring.

4. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 3 is characterized in that: The network time protocol includes: Deploy NTP server: Deploy a high-precision NTP server in the monitoring system as a time reference source to provide unified time information; Timestamp synchronization: Each signal acquisition device periodically requests a timestamp from the NTP server through the NTP client to ensure the time consistency of each device with microsecond accuracy; The precision time protocol includes: PTP network architecture: Establish a PTP network between devices and use the IEEE 1588 standard to achieve high-precision time synchronization; Master-slave clock configuration: select one device as the master clock and the rest as slave clocks. The master clock broadcasts time information regularly, and the slave clocks receive the time and correct it to maintain microsecond synchronization. The time synchronization algorithm includes: Time deviation calculation: For each timestamp request, the request response time is calculated and the deviation is adjusted to ensure synchronization accuracy; Delay compensation: Considering network delay, delay compensation is performed on synchronization information to further improve the accuracy and reliability of time synchronization; The redundant synchronization mechanism includes: Multi-source time synchronization: supports synchronization from multiple time sources to improve the robustness of the system; Fault detection and switching: Real-time monitoring of time synchronization status, automatic switching to the backup time source when the main time source fails, ensuring continuous and stable operation of the system; The synchronization status monitoring includes: Status report: Generate time synchronization status reports regularly to monitor the synchronization accuracy and status of each device to ensure that the system runs under optimal conditions; Alarm mechanism: When it is detected that the time synchronization deviation exceeds the set range, an alarm is triggered immediately to remind maintenance personnel to check and adjust.

5. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The IQ data stream collection step includes real-time monitoring of drone signals and preliminary processing of the data to extract effective feature information.

6. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The construction of the three-dimensional incidence matrix further includes: The signal intensity within the time window is normalized to ensure comparability between different signal sources; Perform cluster analysis in the frequency domain to identify signal features with similar frequencies and enhance the accuracy of signal recognition; Multiple measurements of the spatial angle of arrival are taken and averaged to improve accuracy.

7. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The setting of the time window is dynamically adjusted according to actual applications to adapt to signal characteristics in different environments. The dynamic adjustment includes: Automatically adjust the width of the time window according to the ambient noise level to optimize signal detection; The time window setting is automatically optimized according to the signal characteristics of different types of drones to improve detection accuracy.

8. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The machine learning algorithms include but are not limited to support vector machines, decision trees, and neural networks to improve the classification accuracy and efficiency of matching events.

9. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The training process of the machine learning algorithm adopts an incremental learning method to adapt to new input data and environmental changes.

10. The method for detecting unmanned aerial vehicle targets based on a spatiotemporal synchronization association mechanism according to claim 1, characterized in that: The alarm information is generated in a manner including real-time data transmission, SMS notification and email alert, ensuring that monitoring personnel obtain information in a timely manner.

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