An intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle

Abstract

The present invention discloses an intelligent monitoring system for signal countermeasure of unmanned aerial vehicles, which relates to the technical field of signal countermeasure. The system includes a multi-source acquisition module, a dynamic verification module, a decision analysis module and an intelligent communication module, integrating a radar unit, a spectrum analysis unit, an infrared sensing unit and a sound wave detection unit. Multi-source data fusion is realized through a data association evaluation value, and hierarchical threat response is carried out through a threat evaluation value R to accurately match countermeasure strategies, improving the signal countermeasure effect of unmanned aerial vehicles. When there is a conflict between the radar signal strength normalization value S1 and the infrared feature matching degree S3, a verification value is calculated according to the voiceprint matching degree S4 and the spectrum matching degree S2, or a standby unmanned aerial vehicle signal recognition system is started for synchronous detection to solve the contradiction of multi-source data and reduce the misjudgment rate. The satellite, 5G, and Mesh networks are dynamically switched, and the communication performance of the system is ensured through a priority strategy, reducing the data transmission delay rate.

Description

Technical Field

[0001] The present invention relates to the technical field of signal countermeasures, and particularly to an intelligent monitoring system for signal countermeasures of unmanned aerial vehicles. Background Art

[0002] With the rapid popularization of unmanned aerial vehicle technology in fields such as logistics, aerial photography, and agriculture, the risks of airspace security and privacy leakage caused by it have become increasingly prominent. Especially in sensitive areas such as airports, military bases, and large public event venues, illegally intruding unmanned aerial vehicles may cause major safety accidents. However, there are two major problems with existing countermeasure systems when dealing with high-speed moving targets: response delay and insufficient environmental adaptability, which seriously restrict the defense effectiveness.

[0003] Regarding the response delay problem, relying on a fixed communication protocol to transmit interference instructions, it is easy to cause link delay due to data packet loss or retransmission in a high-noise environment. Actual measurements show that the average response time of the system exceeds 6 seconds, while an unmanned aerial vehicle can fly 200 meters within 5 seconds, resulting in the target being out of the monitoring range and the countermeasure success rate being less than 40%. For example, when an unmanned aerial vehicle adopts adaptive frequency hopping technology, the system needs an additional 2 seconds to re-match the suppression strategy, further exacerbating the response lag.

[0004] The misjudgment problem caused by insufficient environmental adaptability uses static power parameter settings and cannot dynamically adjust the threshold according to environmental noise. In strong electromagnetic interference scenarios (such as thunderstorm weather or dense urban areas), the false trigger rate is as high as 40%, which not only causes waste of resources but also may damage legal communication devices. Summary of the Invention

[0005] In view of the above situation, the present invention significantly reduces the misjudgment risk of a single sensor through multi-source data fusion, hierarchical threat response, and introduces an arbitration mechanism to solve multi-source data contradictions and reduce the misjudgment rate.

[0006] The technical solution it adopts is to include a multi-source acquisition module, a dynamic verification module, a decision analysis module, and an intelligent communication module;

[0007] The multi-source acquisition module integrates a radar unit, a spectrum analysis unit, an infrared sensing unit, and a sound wave detection unit. The radar unit acquires the reflected signal intensity P, the environmental noise intensity Pn, and the actual target speed. The spectrum analysis unit acquires the signal frequency f, the modulation mode M, and the frequency hopping rule H. The infrared sensing unit acquires the temperature signal T and the shape signal C. The sound wave detection unit acquires the sound wave spectrum V;

[0008] The dynamic verification module calculates the data correlation evaluation value S according to the data signals sent by the multi-source acquisition module. S = 0.4×S1 + 0.3×S2 + 0.2×S3 + 0.1×S4, where S1 represents the normalized value of the radar signal intensity, S2 represents the spectrum matching degree, S3 represents the infrared feature matching degree, and S4 represents the voiceprint matching degree;

[0009] The decision analysis module calculates the threat evaluation value R = 0.6×S + 0.4×W, where W = the actual speed of the target / the preset speed threshold. The preset speed threshold is set manually. According to the threat evaluation value R, the target is divided into three levels: high risk, medium risk, and low risk, and the corresponding interference strategies are matched. If R≥0.8, it is determined as a high-risk UAV signal; if 0.5≤R<0.8, it is determined as a medium-risk UAV signal; if R<0.5, it is determined as a low-risk UAV signal;

[0010] The intelligent communication module supports dynamic switching between satellite communication, 5G communication, and Mesh network.

[0011] Furthermore, when there is a conflict between the normalized value of the radar signal intensity S1 and the infrared feature matching degree S3, the decision analysis module starts the following arbitration mechanism:

[0012] When S1 is greater than or equal to 0.7 and S3 is less than 0.4, the verification value S5 is calculated according to the voiceprint matching degree S4 and the spectrum matching degree S2. S5 = 0.6×S4 + 0.4×S2. If S5 is greater than or equal to 0.5, it is determined that the threat evaluation value R of the target is valid; otherwise, it is marked as a misjudgment;

[0013] When S1 is less than 0.3 and S3 is greater than or equal to 0.6, the standby UAV signal recognition system is started for synchronous detection. If the detection results are inconsistent, it is marked as a misjudgment; otherwise, it is determined that the threat evaluation value R of the target is valid;

[0014] In other states of the normalized value of the radar signal intensity S1 and the infrared feature matching degree, the decision analysis module works normally and does not start the arbitration mechanism.

[0015] Furthermore, it also includes a dynamic adjustment module. The dynamic adjustment module marks the misjudged data and the data for which the threat evaluation value R of the target is determined to be valid, and stores the misjudged data and the valid data in the database in real time. The weight coefficient of the dynamic calibration verification value is continuously optimized through an iterative algorithm according to the misjudged data and the valid data in the database.

[0016] Further, the dynamic verification module calculates the radar signal strength normalization value S1 and the spectrum matching degree S2, extracts the minimum signal strength P1 and the radar saturation signal strength P2 that the corresponding radar in the database can detect, filters out the background noise, and the reflected signal strength PC after filtering out the background noise is PC = P - Pn, where the environmental noise strength Pn is obtained by real-time acquisition of the radar unit when there is no target. Then, the radar signal strength normalization value S1 is calculated, S1 = (PC - P1) ÷ (P2 - P1). When S1 is greater than 1, S1 = 1; when S1 is less than 0, S1 = 0; when S1 is greater than or equal to 0 and less than or equal to 1, S1 = S1;

[0017] When P2 - P1 is 0, the dynamic verification module is paused and the task of calculating the corresponding radar signal strength normalization value S1 is cleared. Manually readjust the minimum signal strength P1 and the radar saturation signal strength P2 that the corresponding radar in the database can detect, and restart the task after adjustment.

[0018] Extract the signal frequency table, modulation mode table, and frequency hopping law coefficient table corresponding to the UAV in the database, and calculate the spectrum matching degree S2, S2 = f1×0.6 + M1×0.3 + H1×0.2, where all signal frequencies f fall within the signal frequency table, f1 takes 1; some fall within the signal frequency table, f1 takes 0.6; not falling within the signal frequency table, f1 takes 0. The modulation mode M falls within the modulation mode table, M1 takes 1; otherwise, M1 takes 0. According to the frequency hopping law H, match the corresponding coefficient H1 in the frequency hopping law coefficient table, and H1 is greater than or equal to 0 and less than or equal to 1.

[0019] Further, the dynamic verification module calculates the infrared feature matching degree S3 and the voiceprint matching degree S4, extracts the engine temperature table and shape parameter table corresponding to the UAV in the database, and calculates the infrared feature matching degree S3, S3 = T1×0.6 + C1×0.4. Among them, when the temperature signal T falls within the engine temperature table, T1 takes 1; when some fall within the engine temperature table, T1 takes 0.4; when not falling within the engine temperature table, T1 takes 0. The shape signal C matches the corresponding parameter in the shape parameter table, and the ratio C1 of the same matching parameters is obtained, and C1 is greater than or equal to 0 and less than or equal to 1;

[0020] Extract the frequency V1 with the highest energy and the harmonic V2 in the acoustic wave spectrum V. At the same time, extract the corresponding fundamental frequency matching degree coefficient table and harmonic distribution similarity coefficient table in the database, and calculate the voiceprint matching degree S4, S4 = V11×0.6 + V21×0.4, where the corresponding fundamental frequency matching degree coefficient V11 is matched according to the frequency V1, and the corresponding harmonic distribution similarity coefficient V21 is matched according to the harmonic V2, and V11 and V21 are greater than or equal to 0 and less than or equal to 1.

[0021] Further, when the decision analysis module determines a high-risk UAV signal, corresponding to a military-grade UAV or a high-speed intrusion target, it triggers laser blinding and fixed-point deception. For laser blinding, a high-energy pulsed laser is used to irradiate the optoelectronic sensor of the UAV. The wavelength of the high-energy pulsed laser is 1064 nm, and the emission power of the high-energy pulsed laser is dynamically adjusted. The laser emission power U = U1×(1 + 0.05×d÷100), where d is the target distance received by the radar unit in meters, and U1 is the reference emission power corresponding to the high-energy pulsed laser in watts. For fixed-point deception, false navigation signals are emitted to make the UAV fly or land according to a preset path.

[0022] When it is determined as a medium-risk UAV signal, corresponding to an industrial-grade UAV or a medium-speed target, the corresponding suppression frequency band parameters and acoustic pulse parameters in the database are extracted according to the signal frequency f and modulation mode M, and frequency band suppression and acoustic interference are performed on the target based on the suppression frequency band parameters and acoustic pulse parameters.

[0023] When it is determined as a low-risk UAV signal, corresponding to a consumer-grade UAV or a low-speed straying target, basic signal interference is performed. A 2.4 GHz or 5.8 GHz broadband noise signal is emitted with a power of 10 watts, and the modulation mode is continuous wave interference with a duty cycle of 100% to block the communication link between the UAV and the remote controller and trigger the target UAV to return out of control.

[0024] Further, when the intelligent communication module receives a high-risk UAV signal, it enables parallel transmission of the satellite communication and 5G communication dual links, with the priority being satellite > 5G communication > Mesh network.

[0025] When it is a medium-risk UAV signal, 5G communication is enabled as the main link, and the Mesh network caches and backs up data.

[0026] When it is a low-risk or normal UAV signal, data is transmitted through a single Mesh network link.

[0027] Due to the adoption of the above technical solutions, the present invention has the following advantages compared with the prior art;

[0028] 1. It integrates a radar unit, a spectrum analysis unit, an infrared sensing unit, and an acoustic detection unit, and realizes multi-source data fusion through a data association evaluation value, significantly reducing the misjudgment risk of a single sensor. At the same time, background noise is dynamically filtered to ensure data reliability in a complex electromagnetic environment.

[0029] 2. By means of the threat assessment value R, hierarchical threat response is carried out, and countermeasure strategies are accurately matched, which improves the signal countermeasure effect of the UAV. When the radar signal strength normalization value S1 conflicts with the infrared feature matching degree S3, a verification value is calculated according to the voiceprint matching degree S4 and the spectrum matching degree S2, or the standby UAV signal recognition system is started for synchronous detection to solve the contradiction of multi-source data, reduce the misjudgment rate, and realize dynamic switching among satellite, 5G, and Mesh networks. The communication performance of the system is ensured through the priority strategy, and the data transmission delay rate is reduced. Brief Description of the Drawings

[0030] Figure 1 It is a flowchart of an intelligent monitoring system for signal countermeasures of a UAV according to the present invention. Detailed Embodiment

[0031] Regarding the foregoing and other technical contents, features, and effects of the present invention, they will be clearly presented in the following detailed description of the embodiments in conjunction with the attached Figure 1 drawings. The structural contents mentioned in the following embodiments are all referenced to the drawings of the specification.

[0032] On the basis of the existing technology, through multi-source data fusion, the misjudgment risk of a single sensor is significantly reduced. Through threat assessment value for hierarchical threat response and accurate matching of countermeasure strategies, and by introducing an arbitration mechanism to solve the contradiction of multi-source data, it specifically includes a multi-source acquisition module, a dynamic verification module, a decision analysis module, and an intelligent communication module;

[0033] The multi-source acquisition module integrates a radar unit, a spectrum analysis unit, an infrared sensing unit, and a sound wave detection unit. The radar unit acquires the reflected signal strength P, the ambient noise strength Pn, and the actual speed of the target. The spectrum analysis unit acquires the signal frequency f, the modulation mode M, and the frequency hopping rule H. The infrared sensing unit acquires the temperature signal T and the shape signal C. The sound wave detection unit acquires the sound wave spectrum V;

[0034] The dynamic verification module calculates the data correlation assessment value S according to the data signals sent by the multi-source acquisition module. S = 0.4×S1 + 0.3×S2 + 0.2×S3 + 0.1×S4, where S1 represents the radar signal strength normalization value, S2 represents the spectrum matching degree, S3 represents the infrared feature matching degree, and S4 represents the voiceprint matching degree;

[0035] To further illustrate the effectiveness of the weight values of the data correlation assessment value S, a weight combination comparison test table is used for display;

[0036] Table 1 Weight Combination Comparison Test Table (Sample size: 500 UAV intrusion events):

[0037] Weight combination (radar: spectrum: infrared: acoustic wave) Average false judgment rate Response time (seconds) 0.4:0.3:0.2:0.1 4.2% 1.3 0.5:0.2:0.2:0.1 5.8% 1.5 0.3:0.4:0.2:0.1 6.1% 1.6

[0038] Conclusion: The combination of 0.4:0.3:0.2:0.1 performs optimally in both misjudgment rate and response time.

[0039] The decision analysis module calculates the threat assessment value R = 0.6×S + 0.4×W, where W = actual speed of the target / preset speed threshold, and the preset speed threshold is set artificially. According to the threat assessment value R, the target is divided into three levels: high risk, medium risk, and low risk, and corresponding interference strategies are matched. If R≥0.8, it is determined as a high-risk drone signal; if 0.5≤R<0.8, it is determined as a medium-risk drone signal; if R<0.5, it is determined as a low-risk drone signal.

[0040] The intelligent communication module supports dynamic switching between satellite communication, 5G communication, and Mesh network.

[0041] Furthermore, when the radar signal strength normalization value S1 conflicts with the infrared feature matching degree S3, the decision analysis module activates the following arbitration mechanism:

[0042] When S1 is greater than or equal to 0.7 and S3 is less than 0.4, calculate the verification value S5 according to the voiceprint matching degree S4 and the spectrum matching degree S2, S5 = 0.6×S4 + 0.4×S2. If S5 is greater than or equal to 0.5, it is determined that the target threat assessment value R is valid, otherwise it is marked as a misjudgment.

[0043] When S1 is less than 0.3 and S3 is greater than or equal to 0.6, start the synchronous detection of the backup drone signal recognition system. If the detection results are inconsistent, it is marked as a misjudgment, otherwise, it is determined that the target threat assessment value R is valid.

[0044] In other states of the radar signal strength normalization value S1 and the infrared feature matching degree, the decision analysis module works normally and does not activate the arbitration mechanism.

[0045] It also includes a dynamic adjustment module. The dynamic adjustment module marks misjudgment data and data for which the target threat assessment value R is determined to be valid, and stores the misjudgment data and valid data in the database in real time. According to the misjudgment data and valid data in the database, the weight coefficient of the dynamic calibration verification value is continuously optimized through an iterative algorithm.

[0046] Furthermore, the dynamic verification module calculates the radar signal strength normalization value S1 and the spectrum matching degree S2, extracts the minimum signal strength P1 that the radar can detect and the radar saturation signal strength P2 corresponding to them in the database, filters out the background noise, and the reflected signal strength PC after filtering out the background noise = P - Pn, where the environmental noise strength Pn is obtained by real-time acquisition of the radar unit without a target. Then calculate the radar signal strength normalization value S1, S1 = (PC - P1) ÷ (P2 - P1). When S1 is greater than 1, S1 = 1; when S1 is less than 0, S1 = 0; when S1 is greater than or equal to 0 and less than or equal to 1, S1 = S1.

[0047] Extract the signal frequency table, modulation mode table, and frequency hopping rule coefficient table corresponding to the UAV in the database, and calculate the spectrum matching degree S2. S2 = f1×0.6 + M1×0.3 + H1×0.2, where all signal frequencies f fall within the signal frequency table, f1 takes 1; some fall within the signal frequency table, f1 takes 0.6; those not falling within the signal frequency table, f1 takes 0. For the modulation mode M falling within the modulation mode table, M1 takes 1; otherwise, M1 takes 0. According to the frequency hopping rule H, match the corresponding coefficient H1 in the frequency hopping rule coefficient table, and H1 is greater than or equal to 0 and less than or equal to 1.

[0048] Further, the dynamic verification module calculates the infrared feature matching degree S3 and the voiceprint matching degree S4. Extract the engine temperature table and shape parameter table corresponding to the UAV in the database, and calculate the infrared feature matching degree S3. S3 = T1×0.6 + C1×0.4. Among them, for the temperature signal T falling within the engine temperature table, T1 takes 1; for those partially falling within the engine temperature table, T1 takes 0.4; for those not falling within the engine temperature table, T1 takes 0. For the shape signal C and the shape parameter table, the corresponding parameters are matched to obtain the matching parameter same ratio C1, and C1 is greater than or equal to 0 and less than or equal to 1.

[0049] Extract the frequency V1 with the highest energy and the harmonic V2 in the acoustic wave spectrum V. At the same time, extract the corresponding fundamental frequency matching degree coefficient table and harmonic distribution similarity coefficient table in the database, and calculate the voiceprint matching degree S4. S4 = V11×0.6 + V21×0.4, where according to the frequency V1, match the corresponding fundamental frequency matching degree coefficient V11, and according to the harmonic V2, match the corresponding harmonic distribution similarity coefficient V21. V11 and V21 are greater than or equal to 0 and less than or equal to 1.

[0050] Further, when the decision analysis module determines a high-risk UAV signal, corresponding to a military-grade UAV or a high-speed intrusion target, it triggers laser blinding and fixed-point deception. Laser blinding uses a high-energy pulsed laser to irradiate the optoelectronic sensor of the UAV. The wavelength of the high-energy pulsed laser is 1064nm, and the emission power of the high-energy pulsed laser is dynamically adjusted. The laser emission power U = U1×(1 + 0.05×d÷100), where d is the target distance received by the radar unit in meters, U1 is the reference emission power corresponding to the high-energy pulsed laser in watts, and fixed-point deception emits false navigation signals to make the UAV fly or land according to the preset path.

[0051] When it is determined as a medium-risk UAV signal, corresponding to an industrial-grade UAV or a medium-speed target, according to the signal frequency f and the modulation mode M, extract the corresponding suppression frequency band parameters and acoustic wave pulse parameters in the database, and perform frequency band suppression and acoustic wave interference on the target based on the suppression frequency band parameters and acoustic wave pulse parameters.

[0052] When it is determined that the signal is from a low-risk UAV, for consumer UAVs or low-speed UAVs that have strayed into the target area, basic signal interference is implemented. A broadband noise signal of 2.4 GHz or 5.8 GHz is emitted, with a power of 10 watts, a modulation method of continuous wave interference, and a duty cycle of 100%. The communication link between the UAV and the remote controller is blocked, triggering the target UAV to return out of control.

[0053] To further verify the signal countermeasure effect of the UAV, it is shown in the performance index table as follows;

[0054] Table 2 Performance Index Table:

[0055] Interference mode Response time Success rate Friendly fire rate Laser blinding and fixed-point decoy ≤1.2 seconds 98% 0.1% Band suppression and acoustic interference ≤1.5 seconds 90% 1.5% Basic signal interference ≤0.8 seconds 85% 5%

[0056] Through the hierarchical interference strategy and dynamic optimization mechanism, the system can accurately match the threat level, while ensuring efficient countermeasures, minimizing the risk of misoperation.

[0057] When the intelligent communication module receives a high-risk UAV signal, it enables parallel transmission of dual links of satellite communication and 5G communication, with the priority being satellite > 5G communication > Mesh network;

[0058] When it is a medium-risk UAV signal, 5G communication is enabled as the main link, and the Mesh network caches and backs up data;

[0059] When it is a low-risk or normal UAV signal, the Mesh network transmits data through a single link.

[0060] The network dynamic switching effect is shown through the dynamic switching test results;

[0061] Table 3 Dynamic Switching Test Table:

[0062] Scenario Average handover delay (ms) Data transmission continuity rate 5G → satellite (urban area occlusion) 43 99.98% Mesh → 5G (node failure) 38 99.95%

[0063] When the present invention is specifically used, on the basis of the existing technology, the multi-source acquisition module integrates a radar unit, a spectrum analysis unit, an infrared sensing unit, and an acoustic wave detection unit. The radar unit acquires the reflected signal intensity P, the ambient noise intensity Pn, and the actual speed of the target. The spectrum analysis unit acquires the signal frequency f, the modulation method M, and the frequency hopping rule H. The infrared sensing unit acquires the temperature signal T and the shape signal C. The acoustic wave detection unit acquires the acoustic wave spectrum V;

[0064] The dynamic verification module calculates the data correlation evaluation value S according to the data signals sent by the multi-source acquisition module. S = 0.4×S1 + 0.3×S2 + 0.2×S3 + 0.1×S4, where S1 represents the radar signal intensity normalization value, S2 represents the spectrum matching degree, S3 represents the infrared feature matching degree, and S4 represents the voiceprint matching degree;

[0065] The decision analysis module calculates the threat assessment value R = 0.6×S + 0.4×W, where W = the actual speed of the target / the preset speed threshold, and the preset speed threshold is set manually. According to the threat assessment value R, the target is divided into three levels: high risk, medium risk, and low risk, and corresponding interference strategies are matched. If R≥0.8, it is determined as a high-risk drone signal; if 0.5≤R<0.8, it is determined as a medium-risk drone signal; if R<0.5, it is determined as a low-risk drone signal;

[0066] The intelligent communication module supports dynamic switching between satellite communication, 5G communication, and Mesh network.

[0067] When the radar signal strength normalization value S1 conflicts with the infrared feature matching degree S3, the decision analysis module starts the following arbitration mechanism:

[0068] When S1 is greater than or equal to 0.7 and S3 is less than 0.4, calculate the verification value S5 according to the voiceprint matching degree S4 and the spectrum matching degree S2, S5 = 0.6×S4 + 0.4×S2. If S5 is greater than or equal to 0.5, it is determined that the target threat assessment value R is valid, otherwise it is marked as a misjudgment;

[0069] When S1 is less than 0.3 and S3 is greater than or equal to 0.6, start the synchronous detection of the standby drone signal recognition system. If the detection results are inconsistent, it is marked as a misjudgment, otherwise, it is determined that the target threat assessment value R is valid;

[0070] In other states of the radar signal strength normalization value S1 and the infrared feature matching degree, the decision analysis module works normally and does not start the arbitration mechanism.

[0071] It also includes a dynamic adjustment module. The dynamic adjustment module marks misjudged data and data for which the target threat assessment value R is determined to be valid, and stores the misjudged data and valid data in the database in real time. According to the misjudged data and valid data in the database, the weight coefficient of the dynamic calibration verification value is continuously optimized through an iterative algorithm.

[0072] When the decision analysis module determines a high-risk drone signal, corresponding to a military-grade drone or a high-speed intrusion target, it triggers laser blinding and fixed-point deception. Laser blinding uses high-energy pulsed laser to irradiate the optoelectronic sensor of the drone. The wavelength of the high-energy pulsed laser is 1064nm, and the emission power of the high-energy pulsed laser is dynamically adjusted. The laser emission power U = U1×(1 + 0.05×d÷100), where d is the target distance received by the radar unit, in meters, and U1 is the reference emission power corresponding to the high-energy pulsed laser, in watts. Fixed-point deception emits false navigation signals to make the drone fly or land according to the preset path;

[0073] When it is determined as a medium-risk UAV signal, corresponding to industrial UAVs or medium-speed targets, extract the corresponding suppression frequency band parameters and acoustic pulse parameters in the database according to the signal frequency f and modulation mode M, and implement frequency band suppression and acoustic interference on the target based on the suppression frequency band parameters and acoustic pulse parameters;

[0074] When it is determined as a low-risk UAV signal, corresponding to consumer UAVs or low-speed straying targets, implement basic signal interference, transmit a 2.4 GHz or 5.8 GHz broadband noise signal with a power of 10 watts, modulation mode continuous wave interference, duty cycle 100%, block the communication link between the UAV and the remote controller, and trigger the target UAV to return out of control. Through multi-source data fusion, the misjudgment risk of a single sensor is significantly reduced, threat response is graded, and an arbitration mechanism is introduced to solve multi-source data contradictions and reduce the misjudgment rate.

[0075] The above is a further detailed description made in combination with specific implementation manners, and it cannot be determined that the specific implementation of the present invention is only limited thereto; for those skilled in the art of the present invention and related technical fields, the expansions, operation methods, and data replacements made on the premise of the technical solution idea of the present invention should all fall within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle, characterized in that, It includes a multi-source acquisition module, a dynamic verification module, a decision analysis module, and an intelligent communication module. The multi-source acquisition module integrates a radar unit, a spectrum analysis unit, an infrared sensing unit, and an acoustic wave detection unit. The radar unit acquires the reflected signal intensity P, the ambient noise intensity Pn, and the actual target speed. The spectrum analysis unit acquires the signal frequency f, the modulation mode M, and the frequency hopping pattern H. The infrared sensing unit acquires the temperature signal T and the shape signal C. The acoustic wave detection unit acquires the acoustic wave spectrum V. The dynamic verification module calculates the data association evaluation value S according to the data signals sent by the multi-source acquisition module. S = 0.4×S1 + 0.3×S2 + 0.2×S3 + 0.1×S4, where S1 represents the radar signal intensity normalization value, S2 represents the spectrum matching degree, S3 represents the infrared feature matching degree, and S4 represents the voiceprint matching degree. The decision analysis module calculates the threat assessment value R = 0.6×S + 0.4×W, where W = actual target speed / preset speed threshold, and the preset speed threshold is set manually. According to the threat assessment value R, the target is divided into three levels: high risk, medium risk, and low risk, and corresponding interference strategies are matched. When R≥0.8, it is determined as a high-risk drone signal; when 0.5≤R<0.8, it is determined as a medium-risk drone signal; when R<0.5, it is determined as a low-risk drone signal. The intelligent communication module supports dynamic switching between satellite communication, 5G communication, and Mesh network. The dynamic verification module calculates the radar signal intensity normalization value S1 and the spectrum matching degree S2, extracts the minimum signal intensity P1 that the corresponding radar in the database can detect and the radar saturation signal intensity P2, filters out the background noise, and the reflected signal intensity PC after filtering out the background noise = P - Pn, where the ambient noise intensity Pn is obtained by real-time acquisition of the radar unit when there is no target. Then, the radar signal intensity normalization value S1 is calculated, S1 = (PC - P1) ÷ (P2 - P1). When S1 is greater than 1, S1 = 1; when S1 is less than 0, S1 = 0; when S1 is greater than or equal to 0 and less than or equal to 1, S1 = S1. Extract the signal frequency table, modulation mode table, and frequency hopping pattern coefficient table corresponding to the drone in the database, and calculate the spectrum matching degree S2, S2 = f1×0.6 + M1×0.3 + H1×0.2, where all the signal frequencies f fall within the signal frequency table, f1 takes 1; some fall within the signal frequency table, f1 takes 0.6; those not falling within the signal frequency table, f1 takes 0. If the modulation mode M falls within the modulation mode table, M1 takes 1, otherwise M1 takes 0. According to the frequency hopping pattern H, the corresponding coefficient H1 in the frequency hopping pattern coefficient table is matched, and H1 is greater than or equal to 0 and less than or equal to 1.

2. The intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle according to claim 1, wherein When there is a conflict between the radar signal intensity normalization value S1 and the infrared feature matching degree S3, the decision analysis module starts the following arbitration mechanism: When S1 is greater than or equal to 0.7 and S3 is less than 0.4, calculate the verification value S5 according to the voiceprint matching degree S4 and the spectrum matching degree S2, S5 = 0.6×S4 + 0.4×S2. When S5 is greater than or equal to 0.5, it is determined that the target threat assessment value R is valid, otherwise it is marked as a misjudgment. When S1 is less than 0.3 and S3 is greater than or equal to 0.6, start the synchronous detection of the standby UAV signal recognition system. If the detection results are inconsistent, mark it as a misjudgment. Otherwise, determine that the target threat assessment value R is valid; When the radar signal strength normalization value S1 is in other states of the infrared feature matching degree, the decision analysis module works normally and does not start the arbitration mechanism.

3. The intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle according to claim 2, characterized in that, It also includes a dynamic adjustment module. The dynamic adjustment module marks the misjudgment data and the data with the determined target threat assessment value R being valid, and stores the misjudgment data and valid data in the database in real time. According to the misjudgment data and valid data in the database, the weight coefficient of the dynamic calibration verification value is continuously optimized through an iterative algorithm.

4. The intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle according to claim 1, characterized in that, The dynamic verification module calculates the infrared feature matching degree S3 and the voiceprint matching degree S4, extracts the engine temperature table and shape parameter table of the corresponding UAV in the database, and calculates the infrared feature matching degree S3. S3 = T1×0.6 + C1×0.

4. Among them, when the temperature signal T falls within the engine temperature table, T1 takes 1; when it partially falls within the engine temperature table, T1 takes 0.4; when it does not fall within the engine temperature table, T1 takes 0. The shape signal C matches the corresponding parameters in the shape parameter table, and the proportion C1 of the same matching parameters obtained is greater than or equal to 0 and less than or equal to 1; Extract the frequency V1 with the highest energy and the harmonic V2 in the acoustic wave spectrum V. At the same time, extract the corresponding fundamental frequency matching degree coefficient table and harmonic distribution similarity coefficient table in the database, and calculate the voiceprint matching degree S4. S4 = V11×0.6 + V21×0.

4. Among them, according to the frequency V1, the corresponding fundamental frequency matching degree coefficient V11 is matched, and according to the harmonic V2, the corresponding harmonic distribution similarity coefficient V21 is matched. V11 and V21 are greater than or equal to 0 and less than or equal to 1.

5. The intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle according to any one of claims 1-4, characterized in that When the decision analysis module determines a high-risk UAV signal, corresponding to a military-grade UAV or a high-speed intrusion target, it triggers laser blinding and fixed-point deception. For laser blinding, a high-energy pulsed laser is used to irradiate the UAV's optoelectronic sensor. The wavelength of the high-energy pulsed laser is 1064nm, and the emission power of the high-energy pulsed laser is dynamically adjusted. The laser emission power U = U1×(1 + 0.05×d÷100), where d is the target distance received by the radar unit, in meters, and U1 is the reference emission power corresponding to the high-energy pulsed laser, in watts. For fixed-point deception, false navigation signals are emitted to make the UAV fly or land according to the preset path; When it is determined as a medium-risk UAV signal, corresponding to an industrial-grade UAV or a medium-speed target, according to the signal frequency f and modulation method M, the corresponding suppression frequency band parameters and acoustic wave pulse parameters in the database are extracted, and frequency band suppression and acoustic wave interference are performed on the target according to the suppression frequency band parameters and acoustic wave pulse parameters; When it is determined as a low-risk UAV signal, corresponding to a consumer-grade UAV or a low-speed straying target, basic signal interference is performed. A 2.4GHz or 5.8GHz broadband noise signal is emitted, with a power of 10 watts and a modulation method of continuous wave interference and a duty cycle of 100%. The communication link between the UAV and the remote control is blocked, triggering the target UAV to return out of control.

6. The intelligent monitoring system for signal countermeasure of an unmanned aerial vehicle according to claim 5, wherein When the intelligent communication module receives a signal from a high-risk drone, it enables parallel transmission of dual links of satellite communication and 5G communication, with the priority being satellite > 5G communication > Mesh network; When receiving a signal from a medium-risk drone, it enables 5G communication as the main link and uses the Mesh network to cache and backup data; When receiving a signal from a low-risk or normal drone, it uses a single link of the Mesh network to transmit data.

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