An anti-unmanned aerial vehicle method based on electromagnetic countermeasure technology
Patent Information
- Application Number
- CN202610865082.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-16
- Publication Date
- 2026-11-03
AI Technical Summary
In the drone defense environment, the signal acquisition process of electromagnetic countermeasures technology is easily affected by environmental noise, leading to data distortion and deviation, which affects the accuracy of threat level assessment and the precision of countermeasures strategies.
By collecting electromagnetic signals and flight status data from drones, intelligent data preprocessing and noise filtering are performed. Threat features are extracted and matched using machine learning algorithms, electromagnetic countermeasure strategies are dynamically adjusted, and the countermeasure effect is monitored and optimized in real time.
It improves the accuracy of data collection and the precision of countermeasures, enhances the real-time nature and adaptability of the countermeasures process, and improves the accuracy of drone threat identification and the reliability of the countermeasures system.
Smart Images
Figure CN123028475A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of anti-drone technology, specifically to an anti-drone method based on electromagnetic countermeasures technology. Background Technology
[0002] Counter-drone technology is a technical system that uses various methods to detect, identify, and counter drones. It is widely used in military, public safety, and civilian protection fields.
[0003] Currently, due to the complex electromagnetic interference and dynamic flight behavior in the drone defense environment, when implementing anti-drone methods based on electromagnetic countermeasures technology, the collected drone electromagnetic signal data and flight status data are affected by environmental noise. It is impossible to detect in real time whether data distortion and deviation occur during the signal acquisition process. When there are errors in the collected data, the accuracy of threat level assessment will be reduced, and the accuracy of matching countermeasure strategies cannot be guaranteed.
[0004] Therefore, a counter-drone method based on electromagnetic countermeasures technology is proposed to solve the above problems. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides an anti-drone method based on electromagnetic countermeasures technology, which solves the problems mentioned in the background art, such as the inability to detect data distortion and deviation during signal acquisition in real time, and the inability to guarantee the accuracy of countermeasures strategy matching.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a counter-drone method based on electromagnetic countermeasure technology, the method comprising the following steps: S1. Collect electromagnetic signal data and flight status data of the UAV; S2. Based on the UAV electromagnetic signal data and UAV flight status data, perform UAV threat level assessment processing and generate UAV threat level assessment data. S3. Perform electromagnetic countermeasure strategy matching processing based on the UAV threat level assessment data to generate target electromagnetic countermeasure strategy feature data. S4. Based on the target electromagnetic countermeasure strategy feature data, perform electromagnetic countermeasure signal parameter configuration processing to generate electromagnetic countermeasure signal parameter configuration data. S5. Perform electromagnetic countermeasure signal transmission processing according to the electromagnetic countermeasure signal parameter configuration data, and generate electromagnetic countermeasure signal transmission control data. S6. Based on the electromagnetic countermeasure signal transmission control data, perform real-time monitoring and processing of electromagnetic countermeasure effect to generate electromagnetic countermeasure effect monitoring data. S7. Based on the electromagnetic countermeasure effect monitoring data, perform dynamic adjustment processing of the countermeasure strategy to generate dynamic adjustment data of the countermeasure strategy. S8. Based on the countermeasure strategy dynamic adjustment data and the electromagnetic countermeasure effect monitoring data, construct the anti-drone electromagnetic countermeasure summary data, and perform countermeasure effect optimization processing to generate anti-drone electromagnetic countermeasure optimization data.
[0007] Preferably, the specific operations for collecting UAV electromagnetic signal data and UAV flight status data in step S1 include the following steps: S11. Collect electromagnetic signal parameters emitted by the UAV during flight through electromagnetic signal monitoring equipment and generate UAV electromagnetic signal data. The electromagnetic signal monitoring equipment includes a spectrum analyzer and a signal receiver. The UAV electromagnetic signal data represents the combination parameters of electromagnetic signal strength parameters and signal frequency parameters established by the UAV in flight state with time series as a one-dimensional coordinate axis. S12. The position, speed and heading parameters of the UAV during flight are collected by the flight status monitoring equipment, and the UAV flight status data is generated. The flight status monitoring equipment includes radar and optical sensors. The UAV flight status data represents the combination parameters of position, speed and heading parameters established by the UAV in flight status with time series as a one-dimensional coordinate axis. S13. Import the UAV electromagnetic signal data and the UAV flight status data into the anti-UAV monitoring platform, perform data preprocessing and fusion, and generate standardized UAV collection data. The data quality score is calculated using the following formula during data preprocessing: ; in, To score data quality, the reliability of the collected data is assessed. The average signal strength. For environmental noise levels, The data consistency coefficient. and These are the weighting coefficients, and .
[0008] Preferably, the specific steps in step S2 for performing drone threat level assessment processing based on the drone electromagnetic signal data and drone flight status data, and generating drone threat level assessment data, include the following steps: S21. Obtain the standardized UAV-collected data; S22. Based on the standardized UAV collected data, machine learning algorithms are used to extract UAV threat features and generate UAV threat feature data. The machine learning algorithms include support vector machines and neural networks. The UAV threat feature data includes UAV type identification features, flight intention analysis features, and preliminary threat level score. S23. Based on the UAV threat characteristic data, threat level matching processing is performed in conjunction with the historical threat database to generate UAV threat level assessment data. The historical threat database stores standard threat level parameters corresponding to different UAV types. Threat level matching processing is achieved through similarity calculation.
[0009] Preferably, the specific operation of performing electromagnetic countermeasure strategy matching processing based on the UAV threat level assessment data in step S3 to generate target electromagnetic countermeasure strategy feature data includes the following steps: S31. Establish an electromagnetic countermeasure strategy library, which includes a standard threat level data matrix corresponding to various electromagnetic countermeasure strategy types. The electromagnetic countermeasure strategy types include frequency band interference strategy, power suppression strategy and signal deception strategy. The standard threat level data represents the combination of standard countermeasure parameters set for different threat levels. S32. The UAV threat level assessment data is matched and analyzed with the standard threat level data in the electromagnetic countermeasure strategy library. An optimization search algorithm is used to search for the optimal electromagnetic countermeasure strategy type and generate target electromagnetic countermeasure strategy feature data. The optimization search algorithm includes genetic algorithm and particle swarm algorithm. The matching analysis is based on threat level similarity and countermeasure efficiency weight calculation.
[0010] Preferably, the specific operation of generating electromagnetic countermeasure signal parameter configuration data based on the target electromagnetic countermeasure strategy feature data in step S4 includes the following steps: S41. Obtain the target electromagnetic countermeasure strategy feature data; S42. Based on the target electromagnetic countermeasure strategy feature data, perform fine-grained configuration processing of electromagnetic countermeasure signal parameters, including setting the signal frequency, transmission power and modulation mode, and generate electromagnetic countermeasure signal parameter configuration data. The optimized value for the transmit power configuration is calculated using the following formula: ; in, For the optimized transmission power, This is the threat level assessment value. The relative distance between the drone and the countermeasures equipment. This is the proportionality coefficient. This is the base power offset; S43. Verify the rationality of the electromagnetic countermeasure signal parameter configuration data through the anti-drone monitoring platform to ensure that the parameters meet the requirements of electromagnetic compatibility and countermeasure effect.
[0011] Preferably, the specific operation of performing electromagnetic countermeasure signal transmission processing and generating electromagnetic countermeasure signal transmission control data according to the electromagnetic countermeasure signal parameter configuration data in step S5 includes the following steps: S51. According to the electromagnetic countermeasure signal parameter configuration data, control the electromagnetic countermeasure transmitting device to transmit signals. The electromagnetic countermeasure transmitting device includes a directional antenna and a power amplifier. The signal transmission processing adjusts the transmission parameters based on a real-time feedback mechanism. S52. Monitor environmental interference factors during signal transmission and generate transmission environment monitoring data; S53. Optimize the signal transmission timing based on the aforementioned transmission environment monitoring data to generate electromagnetic countermeasure signal transmission control data.
[0012] Preferably, the specific operation of generating electromagnetic countermeasure effect monitoring data in step S6 based on the electromagnetic countermeasure signal transmission control data includes the following steps: S61. Real-time acquisition of UAV response data after electromagnetic countermeasures through effect monitoring equipment, wherein the effect monitoring equipment includes an electromagnetic signal receiver and a flight trajectory tracker, and the UAV response data includes signal interruption rate, flight path deviation and UAV state change parameters. S62. Based on the UAV response data, perform a quantitative evaluation of the countermeasure effect and generate electromagnetic countermeasure effect monitoring data. The quantitative evaluation adopts an index weighting method, including countermeasure success rate and efficiency score.
[0013] Preferably, the specific operation of dynamically adjusting the countermeasure strategy based on the electromagnetic countermeasure effect monitoring data in step S7 to generate dynamic adjustment data for the countermeasure strategy includes the following steps: S71. Obtain the electromagnetic countermeasure effect monitoring data; S72. An adaptive control algorithm is used to dynamically adjust the countermeasure strategy, including adjusting the countermeasure strategy type and signal parameters, and generating dynamic adjustment data for the countermeasure strategy. The adaptive control algorithm includes fuzzy logic and a PID controller to ensure real-time optimization of the countermeasure process. S73. Feedback the dynamic adjustment data of the countermeasure strategy to step S3 to achieve closed-loop control.
[0014] Preferably, the specific steps in step S8 to construct summary data for anti-drone electromagnetic countermeasures based on the countermeasure strategy dynamic adjustment data and the electromagnetic countermeasure effect monitoring data, and to perform countermeasure effect optimization processing to generate optimized anti-drone electromagnetic countermeasures data include the following steps: S81. Combine the dynamic adjustment data of the countermeasure strategy and the monitoring data of the electromagnetic countermeasure effect to construct a summary data of electromagnetic countermeasure against unmanned aerial vehicles. S82. Based on the summarized anti-drone electromagnetic countermeasure data, perform big data analysis and processing to generate optimized anti-drone electromagnetic countermeasure data. The big data analysis and processing includes pattern recognition and trend prediction to optimize future countermeasure strategies. S83. Output the optimized electromagnetic countermeasure data for anti-drone systems through the anti-drone monitoring platform to achieve system self-learning and strategy updates.
[0015] Preferably, the method further includes step S9, performing system self-check and log recording processing to generate countermeasure system maintenance data, specifically including: periodically checking the status of electromagnetic countermeasure equipment, recording countermeasure operation logs, and ensuring the reliability and traceability of the method.
[0016] Compared with existing technologies, this invention provides a counter-drone method based on electromagnetic countermeasures technology, which has the following advantages: 1. In this invention, when collecting electromagnetic signal data and flight status data of UAVs, an integrated intelligent data preprocessing and noise filtering mechanism is used to detect and correct distortions and deviations in the signal acquisition process in real time, ensuring the accuracy and reliability of data acquisition. This reduces the impact of environmental noise on threat level assessment, ensures the accuracy of electromagnetic countermeasure strategy matching, and further improves the accuracy of UAV threat identification.
[0017] 2. In this invention, when matching electromagnetic countermeasure strategies and configuring electromagnetic countermeasure signal parameters, a real-time verification mechanism and an adaptive adjustment algorithm are introduced to dynamically evaluate the adaptability of the countermeasure strategy to the current UAV threat characteristics. When the parameter configuration is abnormal, optimization and correction are performed immediately, so that the system can avoid improper configuration of electromagnetic countermeasure signal parameters, enhance the real-time performance and adaptability of the countermeasure process, and improve the overall countermeasure efficiency.
[0018] 3. In this invention, when monitoring and dynamically adjusting the countermeasure effect, a comprehensive and dynamic evaluation of the UAV response behavior is achieved through multi-dimensional data fusion and real-time feedback loop. This ensures that the countermeasure strategy adjustment is based on complete and accurate information, thereby reducing decision-making errors caused by incomplete monitoring and improving the reliability of the anti-UAV system and the stability of the countermeasure effect. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating the steps of an anti-drone method based on electromagnetic countermeasures technology according to the present invention. Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Please see Figure 1 The specific implementation of an anti-drone method based on electromagnetic countermeasure technology is as follows, and the method includes the following steps: S1. Collect electromagnetic signal data and flight status data of the UAV; S2. Based on UAV electromagnetic signal data and UAV flight status data, perform UAV threat level assessment and generate UAV threat level assessment data. S3. Based on the UAV threat level assessment data, perform electromagnetic countermeasure strategy matching processing to generate target electromagnetic countermeasure strategy characteristic data. S4. Based on the target electromagnetic countermeasure strategy feature data, perform electromagnetic countermeasure signal parameter configuration processing to generate electromagnetic countermeasure signal parameter configuration data. S5. Perform electromagnetic countermeasure signal transmission processing according to the electromagnetic countermeasure signal parameter configuration data, and generate electromagnetic countermeasure signal transmission control data. S6. Based on the electromagnetic countermeasure signal transmission control data, perform real-time monitoring and processing of electromagnetic countermeasure effect to generate electromagnetic countermeasure effect monitoring data. S7. Based on electromagnetic countermeasure effect monitoring data, dynamically adjust the countermeasure strategy to generate countermeasure strategy dynamic adjustment data. S8. Construct a summary of anti-drone electromagnetic countermeasures data based on the dynamic adjustment data of countermeasures strategy and the monitoring data of electromagnetic countermeasures effect, and perform countermeasures effect optimization processing to generate optimized anti-drone electromagnetic countermeasures data.
[0022] The specific operations for collecting UAV electromagnetic signal data and UAV flight status data in step S1 include the following steps: S11. Collect electromagnetic signal parameters emitted by the UAV during flight through electromagnetic signal monitoring equipment and generate UAV electromagnetic signal data. The electromagnetic signal monitoring equipment includes a spectrum analyzer and a signal receiver. The UAV electromagnetic signal data represents the combination parameters of electromagnetic signal strength parameters and signal frequency parameters established by the UAV in flight state with time series as a one-dimensional coordinate axis. S12. Collect the position, speed and heading parameters of the UAV during flight through flight status monitoring equipment, and generate UAV flight status data. The flight status monitoring equipment includes radar and optical sensors. The UAV flight status data represents the combination parameters of position, speed and heading parameters established by the UAV in flight status with time series as a one-dimensional coordinate axis. S13. Import the drone electromagnetic signal data and drone flight status data into the anti-drone monitoring platform, perform data preprocessing and fusion, and generate standardized drone data collection data. The data quality score is calculated using the following formula during data preprocessing: ; in, To score data quality, the reliability of the collected data is assessed. The average signal strength. For environmental noise levels, The data consistency coefficient. and These are the weighting coefficients, and The training process uses least squares optimization. The specific operation includes: measuring the average signal strength in real time using a spectrum analyzer. The unit is decibels, and the noise level is obtained through an environmental noise monitoring module. The unit is decibels, and the coefficient is calculated using a data consistency algorithm. Based on the variance assessment of multiple data collections, the variance calculation formula is as follows: ; in, For a single data collection, The mean, Let be the number of data collections, and be the variance. If the value is ≤0.1, the data is considered consistent. Values ,ensure The value range is [0, 1], and the weighting coefficients are... and The settings were trained using historical data, and .
[0023] The specific steps in step S2, which involve processing drone threat level data based on drone electromagnetic signal data and drone flight status data to generate drone threat level assessment data, include the following: S21. Acquire standardized drone-collected data; S22. Based on standardized UAV-collected data, machine learning algorithms are used to extract UAV threat features, generating UAV threat feature data. The machine learning algorithms include support vector machines and neural networks. Specifically, the training data comes from a historical UAV flight database, containing 1000 sets of sample data, each set including electromagnetic signal strength, location coordinates, and threat labels. The support vector machine uses a radial basis function kernel, with a penalty parameter... Kernel function parameters The neural network is a three-layer feedforward network. The number of nodes in the input layer corresponds to the feature dimension, the number of nodes in the hidden layer is 20, and the number of nodes in the output layer is 3. The activation function is ReLU, the training cycle is 100 times, the learning rate is 0.01, and the feature extraction operation includes standardizing the original data and then reducing the dimensionality to 10 features through principal component analysis. Drone threat profile data includes drone type identification features, flight intent analysis features, and a preliminary threat level score; S23. Based on the UAV threat characteristic data and combined with the historical threat database, threat level matching processing is performed to generate UAV threat level assessment data. The historical threat database stores standard threat level parameters corresponding to different UAV types. Threat level matching processing is achieved through similarity calculation. The threat level assessment uses the following formula to calculate the threat index: ; The weighting coefficients were determined using the analytic hierarchy process (AHP): a judgment matrix was constructed to compare the importance of flight threat, intent analysis, and proximity rate, with a scale of 1-9. The eigenvectors were then calculated and normalized. , , ,and ; in, As a threat index, For flight threat level, For intention analysis scoring, For proximity rate, , , These are the weighting coefficients, and .
[0024] Step S3, which involves matching electromagnetic countermeasure strategies based on UAV threat level assessment data to generate target electromagnetic countermeasure strategy characteristic data, includes the following steps: S31. Establish an electromagnetic countermeasure strategy library. The electromagnetic countermeasure strategy library includes a standard threat level data matrix corresponding to various electromagnetic countermeasure strategy types. The electromagnetic countermeasure strategy types include frequency band jamming strategy, power suppression strategy and signal deception strategy. The standard threat level data represents the combination of standard countermeasure parameters set for different threat levels. S32. Match and analyze the UAV threat level assessment data with the standard threat level data in the electromagnetic countermeasure strategy library. Use an optimization search algorithm to find the optimal electromagnetic countermeasure strategy type and generate target electromagnetic countermeasure strategy feature data. The optimization search algorithm includes genetic algorithm and particle swarm optimization algorithm. Specific parameter settings: genetic algorithm population size is 50, crossover rate is 0.8, mutation rate is 0.01, maximum number of iterations is 100; particle swarm optimization algorithm particle number is 30, inertia weight is 0.7, individual learning factor and group learning factor are both 2.0. The search process includes initializing the population, calculating fitness, selection, crossover and mutation operations. The fitness function is: ; in, To match similarity, For matching time; Matching analysis is calculated based on threat level similarity and countermeasure efficiency weights; The strategy matching efficiency is evaluated using the following formula: ; in, For strategy matching efficiency, To match similarity, To match the time, This refers to resource availability.
[0025] Step S4 involves configuring electromagnetic countermeasure signal parameters based on the target electromagnetic countermeasure strategy feature data. The specific steps for generating electromagnetic countermeasure signal parameter configuration data include the following: S41. Obtain the target electromagnetic countermeasure strategy characteristic data; S42. Based on the target electromagnetic countermeasure strategy feature data, perform fine-grained configuration processing of electromagnetic countermeasure signal parameters, including setting the signal frequency, transmission power and modulation mode, and generate electromagnetic countermeasure signal parameter configuration data. The optimized value for the transmit power configuration is calculated using the following formula: ; in, For the optimized transmission power, This is the threat level assessment value. The relative distance between the drone and the countermeasures equipment. This is the proportionality coefficient. This is the base power offset; S43. Verify the rationality of the electromagnetic countermeasure signal parameter configuration data through the anti-drone monitoring platform to ensure that the parameters meet the requirements of electromagnetic compatibility and countermeasure effect.
[0026] The specific operations in step S5, which involve configuring electromagnetic countermeasure signal parameters, performing electromagnetic countermeasure signal transmission processing, and generating electromagnetic countermeasure signal transmission control data, include the following steps: S51. Based on the electromagnetic countermeasure signal parameter configuration data, control the electromagnetic countermeasure transmitting equipment to transmit signals. The electromagnetic countermeasure transmitting equipment includes a directional antenna and a power amplifier. The signal transmission processing adjusts the transmission parameters based on a real-time feedback mechanism. S52. Monitor environmental interference factors during signal transmission and generate transmission environment monitoring data; S53. Optimize signal transmission timing by combining transmission environment monitoring data to generate electromagnetic countermeasure signal transmission control data; The signal transmission timing optimization is calculated using the following formula: ; in, For timing adjustment, As the reference time interval, To maximize interference tolerance, This represents the current interference intensity.
[0027] Step S6 involves real-time monitoring and processing of electromagnetic countermeasures effect based on electromagnetic countermeasures signal transmission control data to generate electromagnetic countermeasures effect monitoring data. The specific steps include: S61. Real-time acquisition of UAV response data after electromagnetic countermeasures through effect monitoring equipment. The effect monitoring equipment includes an electromagnetic signal receiver and a flight trajectory tracker. The UAV response data includes signal interruption rate, flight path deviation and UAV status change parameters. S62. Quantitatively evaluate the countermeasure effect based on UAV response data, generate electromagnetic countermeasure effect monitoring data, and adopt the index weighting method for quantitative evaluation, including countermeasure success rate and efficiency score. The countermeasure effectiveness score is calculated using the following formula: ; in, The effectiveness of the countermeasures is scored. For signal interruption rate, This is the path deviation rate. The coefficient of state change. , , These are the weighting coefficients, and .
[0028] Step S7 involves dynamically adjusting the countermeasure strategy based on electromagnetic countermeasure effect monitoring data. The specific steps for generating dynamic adjustment data for the countermeasure strategy include the following: S71. Obtain monitoring data on the electromagnetic countermeasure effect; S72. An adaptive control algorithm is used for dynamic adjustment of the countermeasure strategy, including adjusting the countermeasure strategy type and signal parameters, and generating dynamic adjustment data for the countermeasure strategy. The adaptive control algorithm includes fuzzy logic and a PID controller to ensure real-time optimization of the countermeasure process. Specifically, the fuzzy logic rule base contains 10 rules, the membership function uses a triangular function, and the PID controller uses a proportional gain. Integral coefficient Differential coefficients The coefficients were determined using the Ziegler-Nichols tuning method, and the effect deviation was... Calculated by comparing the difference between real-time monitoring data and the target value; The dynamic adjustment parameters are calculated using the following formula: ; in, To adjust the amount of parameter change, For the effect deviation, , , These are the proportional, integral, and differential coefficients, respectively. S73. Feedback the dynamic adjustment data of the countermeasure strategy to step S3 to achieve closed-loop control.
[0029] Step S8 involves constructing a summary of anti-drone electromagnetic countermeasures data based on countermeasure strategy dynamic adjustment data and electromagnetic countermeasures effect monitoring data, and then performing countermeasures effect optimization processing to generate optimized anti-drone electromagnetic countermeasures data. The specific operations include the following steps: S81. Combine the dynamic adjustment data of countermeasure strategies and the monitoring data of electromagnetic countermeasure effects to construct a summary data of electromagnetic countermeasures against unmanned aerial vehicles. S82. Based on the aggregated data of anti-drone electromagnetic countermeasures, perform big data analysis and processing to generate optimized anti-drone electromagnetic countermeasures data. Big data analysis and processing includes pattern recognition and trend prediction to optimize future countermeasure strategies. Pattern recognition employs... Mean clustering algorithm, number of clusters Based on Euclidean distance, trend prediction uses the ARIMA time series model, and the parameters are determined by the automatic order determination method. The optimization effect is predicted using the following formula: ; in, To optimize the predicted performance, For historical accuracy, For trend matching degree, For learning rate, , , These are the weighting coefficients, and ; S83. Output anti-drone electromagnetic countermeasure optimization data through the anti-drone monitoring platform to achieve system self-learning and strategy update.
[0030] The method also includes step S9, which involves performing system self-checks and log recording to generate countermeasure system maintenance data. Specifically, this includes periodically checking the status of electromagnetic countermeasure equipment, recording countermeasure operation logs, and ensuring the reliability and traceability of the method.
[0031] The operational steps of an anti-drone method based on electromagnetic countermeasures technology are as follows: Step 1: Collect UAV electromagnetic signal data and UAV flight status data Electromagnetic signal monitoring equipment collects electromagnetic signal parameters emitted by the UAV in real time during flight, generating UAV electromagnetic signal data. This data represents a combination of electromagnetic signal strength and frequency parameters established with a time series as a one-dimensional coordinate axis. Simultaneously, flight status monitoring equipment collects the UAV's position, speed, and heading parameters during flight, generating UAV flight status data. This data represents a combination of position, speed, and heading parameters established with a time series as a one-dimensional coordinate axis. The collected data is then imported into the anti-UAV monitoring platform for data preprocessing and fusion to generate standardized UAV data, providing reliable input for subsequent threat assessment.
[0032] Step 2: Perform drone threat level assessment and generate threat level assessment data. After acquiring standardized drone data, machine learning algorithms are used to extract drone threat features and generate drone threat feature data. This data includes drone type identification features, flight intention analysis features, and a preliminary threat level score. Subsequently, based on the drone threat feature data and combined with a historical threat database, threat level matching is performed, and drone threat level assessment data is generated through similarity calculation.
[0033] Step 3: Perform electromagnetic countermeasure strategy matching processing and generate target electromagnetic countermeasure strategy feature data. An electromagnetic countermeasure strategy library was established, which includes a standard threat level data matrix corresponding to various electromagnetic countermeasure strategy types. Subsequently, the UAV threat level assessment data was matched and analyzed with the standard threat level data in the strategy library. An optimized search algorithm was used to search for the optimal electromagnetic countermeasure strategy type and generate target electromagnetic countermeasure strategy feature data. The matching analysis was based on threat level similarity and countermeasure efficiency weight calculation to ensure the efficiency and adaptability of strategy selection.
[0034] Step 4: Perform electromagnetic countermeasure signal parameter configuration processing and generate electromagnetic countermeasure signal parameter configuration data. After acquiring the target electromagnetic countermeasure strategy characteristic data, the electromagnetic countermeasure signal parameters are finely configured, including setting the signal frequency, transmission power and modulation method, to generate electromagnetic countermeasure signal parameter configuration data. During the parameter configuration process, the rationality of the parameters is verified through the anti-drone monitoring platform to ensure that the parameters meet the requirements of electromagnetic compatibility and countermeasure effect.
[0035] Step 5: Perform electromagnetic countermeasure signal transmission processing and generate electromagnetic countermeasure signal transmission control data. Based on the electromagnetic countermeasure signal parameter configuration data, the electromagnetic countermeasure transmitting equipment is controlled to transmit signals. The signal transmission processing adjusts the transmission parameters based on a real-time feedback mechanism. At the same time, environmental interference factors during the signal transmission process are monitored to generate transmission environment monitoring data. The signal transmission timing is optimized by combining the transmission environment monitoring data to generate electromagnetic countermeasure signal transmission control data.
[0036] Step Six: Perform real-time monitoring and processing of electromagnetic countermeasures effect and generate electromagnetic countermeasures effect monitoring data. Real-time response data of the UAV after electromagnetic countermeasures is collected by the effect monitoring equipment. This data includes signal interruption rate, flight path deviation and UAV state change parameters. The countermeasures effect is quantitatively evaluated based on the UAV response data, and electromagnetic countermeasures effect monitoring data is generated by the index weighting method.
[0037] Step 7: Perform dynamic adjustment of countermeasure strategies and generate dynamic adjustment data for countermeasure strategies. After acquiring electromagnetic countermeasure effect monitoring data, an adaptive control algorithm is used to dynamically adjust the countermeasure strategy, including adjusting the countermeasure strategy type or signal parameters and generating dynamic adjustment data for the countermeasure strategy. During the adjustment process, the dynamic adjustment data of the countermeasure strategy is fed back to step three to achieve closed-loop control.
[0038] Step 8: Construct a summary data set for electromagnetic countermeasures against drones and optimize the countermeasures effectiveness. By combining dynamic adjustment data of countermeasure strategies and monitoring data of electromagnetic countermeasure effects, a summary data of anti-drone electromagnetic countermeasures is constructed. Based on this summary data, big data analysis and processing are performed to generate optimized anti-drone electromagnetic countermeasures data. Finally, the optimized data is output through the anti-drone monitoring platform to realize system self-learning and strategy updates.
[0039] Step Nine: Perform system self-check and log recording. After step eight, regularly check the status of the electromagnetic countermeasures equipment, record the countermeasures operation log, and generate countermeasures system maintenance data to ensure the reliability and traceability of the method.
[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A counter-drone method based on electromagnetic countermeasure technology, characterized in that, The method includes the following steps: S1. Collect electromagnetic signal data and flight status data of the UAV; S2. Based on the UAV electromagnetic signal data and UAV flight status data, perform UAV threat level assessment processing and generate UAV threat level assessment data. S3. Perform electromagnetic countermeasure strategy matching processing based on the UAV threat level assessment data to generate target electromagnetic countermeasure strategy feature data. S4. Based on the target electromagnetic countermeasure strategy feature data, perform electromagnetic countermeasure signal parameter configuration processing to generate electromagnetic countermeasure signal parameter configuration data. S5. Perform electromagnetic countermeasure signal transmission processing according to the electromagnetic countermeasure signal parameter configuration data, and generate electromagnetic countermeasure signal transmission control data. S6. Based on the electromagnetic countermeasure signal transmission control data, perform real-time monitoring and processing of electromagnetic countermeasure effect to generate electromagnetic countermeasure effect monitoring data. S7. Based on the electromagnetic countermeasure effect monitoring data, perform dynamic adjustment processing of the countermeasure strategy to generate dynamic adjustment data of the countermeasure strategy. S8. Based on the countermeasure strategy dynamic adjustment data and the electromagnetic countermeasure effect monitoring data, construct the anti-drone electromagnetic countermeasure summary data, and perform countermeasure effect optimization processing to generate anti-drone electromagnetic countermeasure optimization data.
2. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific operations for collecting UAV electromagnetic signal data and UAV flight status data in step S1 include the following steps: S11. Collect electromagnetic signal parameters emitted by the UAV during flight through electromagnetic signal monitoring equipment and generate UAV electromagnetic signal data. The electromagnetic signal monitoring equipment includes a spectrum analyzer and a signal receiver. The UAV electromagnetic signal data represents the combination parameters of electromagnetic signal strength parameters and signal frequency parameters established by the UAV in flight state with time series as a one-dimensional coordinate axis. S12. The position, speed and heading parameters of the UAV during flight are collected by the flight status monitoring equipment, and the UAV flight status data is generated. The flight status monitoring equipment includes radar and optical sensors. The UAV flight status data represents the combination parameters of position, speed and heading parameters established by the UAV in flight status with time series as a one-dimensional coordinate axis. S13. Import the UAV electromagnetic signal data and the UAV flight status data into the anti-UAV monitoring platform for data preprocessing and fusion to generate standardized UAV data collection data. The data quality score is calculated using the following formula during data preprocessing: ； in, To score data quality, the reliability of the collected data is assessed. The average signal strength. For environmental noise levels, The data consistency coefficient. and These are the weighting coefficients, and .
3. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific steps in step S2, which involve performing a drone threat level assessment based on the drone's electromagnetic signal data and flight status data, and generating drone threat level assessment data, include the following: S21. Obtain the standardized UAV-collected data; S22. Based on the standardized UAV collected data, machine learning algorithms are used to extract UAV threat features and generate UAV threat feature data. The machine learning algorithms include support vector machines and neural networks. The UAV threat feature data includes UAV type identification features, flight intention analysis features, and preliminary threat level score. S23. Based on the UAV threat characteristic data, threat level matching processing is performed in conjunction with the historical threat database to generate UAV threat level assessment data. The historical threat database stores standard threat level parameters corresponding to different UAV types. Threat level matching processing is achieved through similarity calculation.
4. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific steps in step S3, which involve performing electromagnetic countermeasure strategy matching based on the UAV threat level assessment data to generate target electromagnetic countermeasure strategy feature data, include the following steps: S31. Establish an electromagnetic countermeasure strategy library, which includes a standard threat level data matrix corresponding to various electromagnetic countermeasure strategy types. The electromagnetic countermeasure strategy types include frequency band interference strategy, power suppression strategy and signal deception strategy. The standard threat level data represents the combination of standard countermeasure parameters set for different threat levels. S32. The UAV threat level assessment data is matched and analyzed with the standard threat level data in the electromagnetic countermeasure strategy library. An optimization search algorithm is used to search for the optimal electromagnetic countermeasure strategy type and generate target electromagnetic countermeasure strategy feature data. The optimization search algorithm includes genetic algorithm and particle swarm algorithm. The matching analysis is based on threat level similarity and countermeasure efficiency weight calculation.
5. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific steps in step S4, which involve configuring electromagnetic countermeasure signal parameters based on the target electromagnetic countermeasure strategy feature data, to generate electromagnetic countermeasure signal parameter configuration data, include the following steps: S41. Obtain the target electromagnetic countermeasure strategy feature data; S42. Based on the target electromagnetic countermeasure strategy feature data, perform fine-grained configuration processing of electromagnetic countermeasure signal parameters, including setting the signal frequency, transmission power and modulation mode, and generate electromagnetic countermeasure signal parameter configuration data. The optimized value for the transmit power configuration is calculated using the following formula: ； in, For the optimized transmission power, This is the threat level assessment value. The relative distance between the drone and the countermeasures equipment. This is the proportionality coefficient. This is the base power offset; S43. Verify the rationality of the electromagnetic countermeasure signal parameter configuration data through the anti-drone monitoring platform to ensure that the parameters meet the requirements of electromagnetic compatibility and countermeasure effect.
6. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific operations in step S5, which involve performing electromagnetic countermeasure signal transmission processing based on the electromagnetic countermeasure signal parameter configuration data and generating electromagnetic countermeasure signal transmission control data, include the following steps: S51. According to the electromagnetic countermeasure signal parameter configuration data, control the electromagnetic countermeasure transmitting device to transmit signals. The electromagnetic countermeasure transmitting device includes a directional antenna and a power amplifier. The signal transmission processing adjusts the transmission parameters based on a real-time feedback mechanism. S52. Monitor environmental interference factors during signal transmission and generate transmission environment monitoring data; S53. Optimize the signal transmission timing based on the aforementioned transmission environment monitoring data to generate electromagnetic countermeasure signal transmission control data.
7. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific steps in step S6, which involve real-time monitoring and processing of the electromagnetic countermeasure effect based on the electromagnetic countermeasure signal transmission control data to generate electromagnetic countermeasure effect monitoring data, include the following steps: S61. Real-time acquisition of UAV response data after electromagnetic countermeasures through effect monitoring equipment, wherein the effect monitoring equipment includes an electromagnetic signal receiver and a flight trajectory tracker, and the UAV response data includes signal interruption rate, flight path deviation and UAV state change parameters. S62. Based on the UAV response data, perform a quantitative evaluation of the countermeasure effect and generate electromagnetic countermeasure effect monitoring data. The quantitative evaluation adopts an index weighting method, including countermeasure success rate and efficiency score.
8. The anti-drone method based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific steps in step S7, which involve dynamically adjusting the countermeasure strategy based on the electromagnetic countermeasure effect monitoring data to generate dynamic adjustment data for the countermeasure strategy, include the following: S71. Obtain the electromagnetic countermeasure effect monitoring data; S72. An adaptive control algorithm is used to dynamically adjust the countermeasure strategy, including adjusting the countermeasure strategy type and signal parameters, and generating dynamic adjustment data for the countermeasure strategy. The adaptive control algorithm includes fuzzy logic and a PID controller to ensure real-time optimization of the countermeasure process. S73. Feedback the dynamic adjustment data of the countermeasure strategy to step S3 to achieve closed-loop control.
9. A method for countering unmanned aerial vehicles based on electromagnetic countermeasure technology according to claim 1, characterized in that, The specific steps in step S8 to construct summary data for anti-drone electromagnetic countermeasures based on the countermeasure strategy dynamic adjustment data and the electromagnetic countermeasure effect monitoring data, and to perform countermeasure effect optimization processing to generate optimized anti-drone electromagnetic countermeasures data include the following steps: S81. Combine the dynamic adjustment data of the countermeasure strategy and the monitoring data of the electromagnetic countermeasure effect to construct a summary data of electromagnetic countermeasure against unmanned aerial vehicles. S82. Based on the summarized anti-drone electromagnetic countermeasure data, perform big data analysis and processing to generate optimized anti-drone electromagnetic countermeasure data. The big data analysis and processing includes pattern recognition and trend prediction to optimize future countermeasure strategies. S83. Output the optimized electromagnetic countermeasure data for anti-drone systems through the anti-drone monitoring platform to achieve system self-learning and strategy updates.
10. A method for countering unmanned aerial vehicles based on electromagnetic countermeasure technology according to claim 1, characterized in that, The method also includes step S9, which involves performing system self-checks and log recording to generate countermeasure system maintenance data. Specifically, this includes periodically checking the status of the electromagnetic countermeasure equipment, recording countermeasure operation logs, and ensuring the reliability and traceability of the method.