Small unmanned aerial vehicle investigation system and method based on weak signals

Abstract

The invention belongs to the technical field of signal investigation, and particularly relates to a small unmanned aerial vehicle investigation system and method based on weak signals, and the system comprises a radio monitoring module, an investigation positioning module, a control module, a sound wave monitoring module, a sound wave amplification module, a sound wave equipment information module, a scheme refining module and an effect ratio module. Through the arrangement of the sound wave monitoring module, when weak signals in the investigation range are monitored, not only can the electromagnetic environment when the weak signals appear be monitored, but also the fluctuation environment when the weak signals appear can be monitored, so that the unmanned aerial vehicle can be investigated more accurately, a user can customize a sensitive threshold value, and the detection accuracy is improved. The method adapts to different investigation scenes and requirements, can improve the attention and investigation effect on specific weak signals, can obtain the information and precision of different sound wave monitoring devices, can generate a precision compensation scheme and analyze a matching value, can give full play to the potential of the devices, and can avoid resource waste while improving the monitoring precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal detection, and in particular to a small unmanned aerial vehicle detection system and method based on weak signals. Background Art

[0002] With the rapid development of small drone technology and its increasingly widespread application in various fields, drones have brought many conveniences to people's lives and work, but also brought many potential threats to important areas such as public safety and privacy protection that cannot be ignored. On the one hand, small drones are small in size and flexible in flight. On the other hand, the signals they may send during operation are relatively weak, which makes it inevitable that traditional reconnaissance methods have certain limitations when dealing with the challenges brought by small drones.

[0003] When using a general small UAV reconnaissance system based on weak signals and its method, it is inconvenient to monitor the sound wave environment within the reconnaissance range. The single electromagnetic environment causes the weak signal monitoring to be inaccurate, and when improving the monitoring accuracy of different equipment, it is impossible to produce the best accuracy compensation solution for different equipment. For this reason, we propose a small UAV reconnaissance system based on weak signals and its method. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art and solve the above-mentioned technical problems, the present invention proposes a small UAV reconnaissance system based on weak signals and a method thereof.

[0005] The technical solution adopted by the present invention to solve its technical problem is: a small UAV detection system based on weak signals, including a radio monitoring module, a detection and positioning module and a control module, the radio monitoring module can monitor the electromagnetic environment of the detection range, and transmit the monitored electromagnetic environment to the detection and positioning module, the detection and positioning module can locate the UAV that emits electromagnetic signals according to the monitored electromagnetic environment, and then transmit the located weak signal information to the control module, the control module can control the UAV that emits electromagnetic signals, and also includes an acoustic wave monitoring module, an acoustic wave amplification module, an acoustic wave equipment information module, a detailed solution module and an effectiveness ratio module;

[0006] The sound wave monitoring module monitors the sound waves within the detection range in real time and establishes a three-dimensional sound wave change model to check the sound wave changes in a small range within the detection range. When a small range of sound wave changes occur, the location where the sound wave changes occur will be marked immediately;

[0007] The sound wave amplification module can perform proportional amplification on small-scale sound wave changes, thereby more accurately analyzing the frequency, amplitude and wavelength information of the sound waves, and improving the accuracy of detecting abnormal signals;

[0008] The acoustic wave equipment information module obtains the information of different acoustic wave monitoring equipment and analyzes the monitoring accuracy of different acoustic wave monitoring equipment;

[0009] The refinement scheme module generates a first-level refinement scheme, obtains the monitoring accuracy information of different acoustic wave monitoring equipment, and establishes an accuracy limit unit. The user can enter the required monitoring accuracy information in the accuracy limit unit. At this time, the accuracy limit unit will generate different second-level refinement schemes based on the first-level refinement scheme and the monitoring accuracy information of different acoustic wave monitoring equipment.

[0010] The effectiveness ratio module analyzes the execution costs of the primary fine plan and different secondary fine plans, and calculates the input fine values ​​of the primary fine plan and different secondary fine plans, analyzes the advantages and disadvantages of the primary fine plan and different secondary fine plans, and can set an extraction threshold for the input fine values, and extract and display the input fine values, corresponding equipment information and corresponding secondary fine plan information that exceed the extraction threshold.

[0011] Preferably, the radio monitoring module includes a TDOA passive high-precision positioning unit, an ultra-wide monitoring unit, a noise separation unit, a multi-frequency scanning unit, a seamless coverage unit and a multi-target tracking unit;

[0012] The TDOA passive high-precision positioning unit can use the TDOA algorithm to locate the signal source with high precision;

[0013] The monitoring frequency band of the ultra-wide monitoring unit is 20MHz-6GHz;

[0014] The noise separation unit can separate, identify and locate suspicious signal sources through spectrum big data;

[0015] The multi-frequency scanning unit can perform spectrum scanning on multiple discontinuous frequency bands;

[0016] Seamless coverage units can be distributed networked to achieve seamless coverage of the monitoring area;

[0017] The multi-target tracking unit can locate and track multiple signal sources simultaneously;

[0018] The cooperation of TDOA passive high-precision positioning unit, ultra-wide monitoring unit, noise separation unit, multi-frequency scanning unit, seamless coverage unit and multi-target tracking unit enables users to use the radio monitoring module to achieve unified management of distributed intelligent perception nodes.

[0019] Preferably, the acoustic wave monitoring module can establish a sensitive limit unit when monitoring the acoustic wave information, and the user can enter the required sensitive threshold in the sensitive limit unit;

[0020] When the sensitivity threshold is a range value, the concentration on the sound wave information within the sensitivity threshold range value will be increased;

[0021] When the sensitivity threshold is a specified value, the sensitive boundary unit will establish options of less than the specified value and greater than the specified value. At this time, the user can choose according to their own needs;

[0022] The use of the sensitivity limit unit can further improve the observation level of weak signals that meet the sensitivity threshold.

[0023] Preferably, the specific division method of the observation level is as follows:

[0024] Level 1: It can detect weak signals and analyze the model of the drone that sent the signal;

[0025] The second level focuses on the performance and impact of weak signals in specific areas;

[0026] At the third level, a comprehensive and in-depth study of weak signals is conducted to analyze the underlying causes, related factors and potential development directions, and to predict future development trends and impacts of weak signals.

[0027] Preferably, the sound wave amplification module can receive the information processed in the sound wave monitoring module. After amplifying the sound wave, the sound wave amplification module will establish a primary analysis unit and a secondary analysis unit, wherein the primary analysis unit can directly extract the frequency, amplitude and wavelength information of the amplified sound wave, determine the model information of the drone that sends the weak signal, and obtain the primary drone model information. The secondary analysis unit can segment the frequency, amplitude and wavelength information of the amplified sound wave, and then randomly extract information from different segments, analyze the model information of the drone according to the randomly extracted information, obtain the secondary drone model information, and comprehensively analyze the drone model according to the primary drone model information and the secondary drone model information;

[0028] When analyzing the monitoring accuracy of different acoustic wave monitoring devices, the acoustic wave equipment information module generates an accuracy compensation scheme. At this time, the scheme information for improving the monitoring accuracy of different monitoring devices is retrieved, and the retrieved scheme information is included as the accuracy compensation scheme. The monitoring accuracy that can be improved by the different accuracy compensation scheme information is analyzed, and the matching values ​​of different accuracy compensation schemes and different devices are determined. The monitoring accuracy that can be theoretically improved by the accuracy compensation scheme is set to T. J , assuming that the monitoring accuracy actually improved by the proposed accuracy compensation scheme on the equipment is S J , let the matching value between the precision compensation scheme and the equipment be P Z , assuming the original monitoring accuracy of the equipment is S Y , assuming that the equipment monitoring accuracy after the proposed accuracy compensation scheme is used is N J ;

[0029] S J =N J -S Y

[0030]

[0031] According to the above formula, the matching value between the accuracy compensation scheme and the equipment can be calculated, so that the best accuracy compensation scheme can be analyzed for the monitoring equipment.

[0032] Preferably, the refinement scheme module can receive information processed by the acoustic wave device information module. When generating the secondary refinement scheme, the refinement scheme module will analyze the degree of fit between the proposed compensation schemes with different precisions, so as to obtain the precision improvement effect when the proposed compensation schemes with different precisions are coordinated. The degree of fit refers to the effect of improving the monitoring precision when the two proposed compensation schemes with different precisions are coordinated. Suppose the monitoring precision actually improved by the two proposed compensation schemes on the equipment is S respectively. J1 and S J2 , assuming that the monitoring accuracy of the equipment improved by the two precision compensation schemes is J H , let the degree of fit between the two precision complementary schemes be Q H ;

[0033]

[0034] According to the above formula, the degree of compatibility of the two precision compensation schemes can be calculated, thereby judging the effect of the two precision compensation schemes when used together.

[0035] Preferably, the effectiveness ratio module can receive information processed by the detailed solution module, and use the input fine numerical analysis to analyze the relationship between capital investment and improved equipment accuracy when improving equipment monitoring accuracy. Suppose the investment capital is T Z , assuming that the corresponding equipment monitoring accuracy of the investment is S T , assuming that the input fine value is T S ;

[0036]

[0037] According to the above formula, the investment fine values ​​of the first-order fine plan and different second-order fine plans can be calculated, so that users can choose the fine plan according to their own situation.

[0038] Preferably, the detection and positioning module includes a spectrum detection device and a control unit, which uses the spectrum detection device to sense and identify suspicious drone communication signals within the detection range in real time, and uses the time difference of drone signals reaching different sites to achieve continuous locking and tracking of devices that send suspicious signals. The detection and positioning module also includes a device model unit and a black and white list unit;

[0039] The equipment model unit records the information of common drone models on the market, and can quickly identify the drone model when a drone signal is detected;

[0040] The black and white list units can effectively distinguish between cooperative and non-cooperative drones;

[0041] The control module can transmit anti-interference signals to the anti-interference equipment. The anti-interference equipment can effectively cut off the communication command and navigation link signals of the UAV, thereby driving away the UAV.

[0042] In addition, the present invention also provides a small UAV detection method based on weak signals, comprising the following steps:

[0043] Step 1: Monitor the electromagnetic environment within the detection range, including the cooperation of TDOA passive high-precision positioning unit, ultra-wideband monitoring unit, sound-noise separation unit, multi-frequency scanning unit, seamless coverage unit and multi-target tracking unit to achieve high-precision positioning of the signal source, ultra-wideband monitoring, signal-noise separation, multi-frequency scanning, seamless coverage and multi-target tracking;

[0044] Step 2: Monitor the sound waves within the detection range in real time, establish a three-dimensional sound wave change model, and mark the locations where small-scale sound wave changes occur. Users can enter the sensitivity threshold in the sensitive limit unit to improve the observation level of weak signals that meet the threshold;

[0045] Step 3: Perform geometric amplification processing on the sound wave changes in a small range, and comprehensively analyze the drone model through the primary analysis unit and the secondary analysis unit;

[0046] Step 4: Obtain information and monitoring accuracy of different acoustic wave monitoring devices, generate accuracy compensation schemes, analyze the matching values ​​of different accuracy compensation schemes with different devices, and obtain the monitoring accuracy that can actually be improved when different accuracy compensation schemes are used on different devices;

[0047] Step 5: Generate a primary fine plan, generate different secondary fine plans according to the monitoring accuracy information in the accuracy limit unit entered by the user, analyze the fit between the proposed compensation plans with different accuracy, and judge the effect of the coordinated use of the proposed compensation plans with different accuracy;

[0048] Step 6: Analyze the execution costs of the first-order and different second-order fine plans, calculate the input fine values, analyze the advantages and disadvantages of the plans, and extract and display relevant information that exceeds the extraction threshold;

[0049] Step 7: Locate the drone that sends out electromagnetic signals based on the monitored electromagnetic environment and sound wave information. The equipment model unit it contains can quickly identify the drone model, and the black and white list unit can distinguish between cooperative and non-cooperative drones. Then, control the drone that sends out electromagnetic signals, and transmit interference countermeasure signals through interference countermeasure equipment to cut off the drone's communication and navigation links to drive it away.

[0050] The beneficial effects of the present invention are as follows:

[0051] 1. The small UAV detection system and method based on weak signals described in the present invention, through the setting of the acoustic wave monitoring module, can monitor not only the electromagnetic environment when weak signals appear, but also the fluctuation environment when weak signals appear, so as to detect UAVs more accurately, and users can customize sensitive thresholds to adapt to different detection scenarios and needs, which can improve the attention and detection effect of specific weak signals, obtain the information and accuracy of different acoustic wave monitoring equipment, generate precision compensation schemes and analyze matching values, so as to give full play to the potential of the equipment, improve the monitoring accuracy while avoiding waste of resources, and continuously optimize the system configuration by analyzing the fit and coordination effect between different precision compensation schemes.

[0052] 2. The small UAV detection system and method based on weak signals described in the present invention can comprehensively and accurately monitor and locate various radio signals through the radio monitoring module. The spectrum detection device in the detection and positioning module can sense and identify suspicious UAV communication signals in real time, and continuously lock and track them by using the time difference between the signals arriving at different sites. The equipment model unit can quickly identify the UAV model, and the black and white list unit can distinguish between cooperative and non-cooperative UAVs, which makes the detection and identification of UAVs faster and more accurate. The control module can effectively prevent the potential threat of UAVs and ensure the safety of specific areas by transmitting interference counter signals and cutting off the UAV communication and navigation links.

[0053] 3. The small UAV detection system and method based on weak signals described in the present invention can enable users to customize sensitive thresholds through the acoustic wave monitoring module, making acoustic wave monitoring more targeted. Through clear observation level division, weak signals can be deeply studied from different levels. It can not only monitor and identify UAV models, but also analyze their potential impacts and development trends, provide a more comprehensive basis for decision-making, calculate the matching values ​​of different schemes and equipment, help to select the best scheme for monitoring equipment, improve the monitoring accuracy and performance of the equipment, and calculate the detailed input values ​​so that users can clearly understand the relationship between capital investment and accuracy improvement, so as to select the most appropriate detailed scheme according to their own situation and realize the rational allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0056] Figure 2 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1:

[0059] With the rapid development of small drone technology and its increasingly widespread application in various fields, drones have brought many conveniences to people's lives and work, but also brought many potential threats to important areas such as public safety and privacy protection that cannot be ignored. On the one hand, small drones are small in size and flexible in flight. On the other hand, the signals they may send during operation are relatively weak, which makes it inevitable that traditional reconnaissance methods have certain limitations when dealing with the challenges brought by small drones.

[0060] The general small UAV detection system based on weak signals and its method are inconvenient to monitor the acoustic environment within the detection range when in use. The single electromagnetic environment leads to inaccurate weak signal monitoring, and when improving the monitoring accuracy of different equipment, it is impossible to produce the best accuracy compensation solution for different equipment. Therefore, we propose a small UAV detection system based on weak signals and its method;

[0061] Therefore, in order to effectively solve the above problems, the present application proposes a small UAV detection system and method based on weak signals, as shown in the accompanying drawings of the specification. Figure 1-2 As shown, it includes a radio monitoring module, a reconnaissance and positioning module and a control module. The radio monitoring module can monitor the electromagnetic environment of the reconnaissance range and transmit the monitored electromagnetic environment to the reconnaissance and positioning module. The reconnaissance and positioning module can locate the UAV that emits electromagnetic signals according to the monitored electromagnetic environment, and then transmit the located weak signal information to the control module. The control module can control the UAV that emits electromagnetic signals. It also includes an acoustic wave monitoring module, an acoustic wave amplification module, an acoustic wave equipment information module, a detailed plan module and an effectiveness ratio module.

[0062] The sound wave monitoring module monitors the sound waves within the detection range in real time and establishes a three-dimensional sound wave change model to check the sound wave changes in a small range within the detection range. When a small range of sound wave changes occur, the location where the sound wave changes occur will be marked immediately;

[0063] The sound wave amplification module can perform proportional amplification on small-scale sound wave changes, thereby more accurately analyzing the frequency, amplitude and wavelength information of the sound waves, and improving the accuracy of detecting abnormal signals;

[0064] The acoustic wave equipment information module obtains the information of different acoustic wave monitoring equipment and analyzes the monitoring accuracy of different acoustic wave monitoring equipment;

[0065] The refinement scheme module generates a first-level refinement scheme, obtains the monitoring accuracy information of different acoustic wave monitoring equipment, and establishes an accuracy limit unit. The user can enter the required monitoring accuracy information in the accuracy limit unit. At this time, the accuracy limit unit will generate different second-level refinement schemes based on the first-level refinement scheme and the monitoring accuracy information of different acoustic wave monitoring equipment.

[0066] The effectiveness ratio module analyzes the execution costs of the primary fine plan and different secondary fine plans, calculates the input fine values ​​of the primary fine plan and different secondary fine plans, analyzes the advantages and disadvantages of the primary fine plan and different secondary fine plans, and can set an extraction threshold for the input fine values, extract and display the input fine values ​​that exceed the extraction threshold, relative equipment information, and relative secondary fine plan information;

[0067] Specific workflow: Real-time monitoring of sound waves within the detection range, establishment of a three-dimensional sound wave change model, marking the location where small-scale sound wave changes occur, users can enter a sensitive threshold in the sensitive limit unit to improve the observation level of weak signals that meet the threshold, and perform geometric amplification on small-scale sound wave changes. The drone model is comprehensively analyzed through the primary analysis unit and the secondary analysis unit to obtain information and monitoring accuracy of different sound wave monitoring equipment, and generate precision compensation plans, analyze the matching values ​​of different precision compensation plans and different equipment, obtain the monitoring accuracy that can be actually improved when different precision compensation plans are used on different equipment, generate a primary fine plan, generate different secondary fine plans based on the monitoring accuracy information in the precision limit unit entered by the user, and analyze the fit between different precision compensation plans, judge the effect of the coordinated use of different precision compensation plans, analyze the execution cost of the primary fine plan and different secondary fine plans, calculate the input fine value, analyze the advantages and disadvantages of the plan, and extract and display relevant information that exceeds the extraction threshold;

[0068] Furthermore, through the setting of the acoustic wave monitoring module, when monitoring weak signals within the detection range, it is possible to monitor not only the electromagnetic environment when weak signals appear, but also the fluctuation environment when weak signals appear, so as to detect drones more accurately. In addition, users can customize sensitive thresholds to adapt to different detection scenarios and needs, improve attention to specific weak signals and detection effects, obtain information and accuracy of different acoustic wave monitoring equipment, generate precision compensation plans and analyze matching values, so as to give full play to the potential of the equipment, improve monitoring accuracy while avoiding waste of resources, and continuously optimize the system configuration by analyzing the fit and coordination effects between different precision compensation plans.

[0069] Embodiment 2:

[0070] Based on the first embodiment, as shown in the accompanying drawings of the specification Figure 1-2 As shown, the radio monitoring module includes a TDOA passive high-precision positioning unit, an ultra-wide monitoring unit, a noise separation unit, a multi-frequency scanning unit, a seamless coverage unit, and a multi-target tracking unit;

[0071] The TDOA passive high-precision positioning unit can use the TDOA algorithm to locate the signal source with high precision;

[0072] The monitoring frequency band of the ultra-wide monitoring unit is 20MHz-6GHz;

[0073] The noise separation unit can separate, identify and locate suspicious signal sources through spectrum big data;

[0074] The multi-frequency scanning unit can perform spectrum scanning on multiple discontinuous frequency bands;

[0075] Seamless coverage units can be distributed networked to achieve seamless coverage of the monitoring area;

[0076] The multi-target tracking unit can locate and track multiple signal sources simultaneously;

[0077] According to the cooperation of TDOA passive high-precision positioning unit, ultra-wide monitoring unit, noise separation unit, multi-frequency scanning unit, seamless coverage unit and multi-target tracking unit, users can use the radio monitoring module to achieve unified management of distributed intelligent perception nodes. The detection and positioning module includes spectrum detection equipment and control unit. The spectrum detection equipment is used to perceive and identify suspicious drone communication signals within the detection range in real time, and the time difference of drone signals reaching different sites is used to achieve continuous locking and tracking of devices that send suspicious signals. The detection and positioning module also includes equipment model unit and black and white list unit.

[0078] The equipment model unit records the information of common drone models on the market, and can quickly identify the drone model when a drone signal is detected;

[0079] The black and white list units can effectively distinguish between cooperative and non-cooperative drones;

[0080] The control module can send anti-jamming signals to the anti-jamming equipment, and the anti-jamming equipment can effectively cut off the communication command and navigation link signals of the drone, and drive the drone away;

[0081] Specific work flow: Monitor the electromagnetic environment within the reconnaissance range, including the cooperation of TDOA passive high-precision positioning unit, ultra-wideband monitoring unit, sound-noise separation unit, multi-frequency scanning unit, seamless coverage unit and multi-target tracking unit to achieve high-precision positioning of the signal source, ultra-wideband monitoring, signal-noise separation, multi-frequency scanning, seamless coverage and multi-target tracking. The drone that emits electromagnetic signals is located according to the monitored electromagnetic environment and sound wave information. The equipment model unit it contains can quickly identify the drone model, and the black and white list unit can distinguish between cooperative and non-cooperative drones. Then, the drone that emits electromagnetic signals is controlled, and the interference countermeasure equipment transmits interference countermeasure signals to cut off the drone's communication and navigation links to achieve expulsion.

[0082] Furthermore, the radio monitoring module can comprehensively and accurately monitor and locate various radio signals. The spectrum detection device in the reconnaissance and positioning module can perceive and identify suspicious drone communication signals in real time, and use the time difference between the signals arriving at different sites to continuously lock and track them. The equipment model unit can quickly identify the drone model, and the black and white list unit can distinguish between cooperative and non-cooperative drones. This makes the detection and identification of drones faster and more accurate. The control module can effectively prevent the potential threat of drones and ensure the safety of specific areas by emitting interference counter signals and cutting off the drone communication and navigation links to drive them away.

[0083] Embodiment three:

[0084] Based on the second embodiment, as shown in the accompanying drawings of the specification Figure 1-2 As shown, the acoustic wave monitoring module can establish a sensitive limit unit when monitoring acoustic wave information, and the user can enter the required sensitive threshold in the sensitive limit unit;

[0085] When the sensitivity threshold is a range value, the concentration on the sound wave information within the sensitivity threshold range value will be increased;

[0086] When the sensitivity threshold is a specified value, the sensitive boundary unit will establish options of less than the specified value and greater than the specified value. At this time, the user can choose according to their own needs;

[0087] The use of sensitive limit units can further improve the observation level of weak signals that meet the sensitive threshold. The specific division of the observation level is as follows:

[0088] Level 1: It can detect weak signals and analyze the model of the drone that sent the signal;

[0089] The second level focuses on the performance and impact of weak signals in specific areas;

[0090] Level 3: Conduct a comprehensive and in-depth study of weak signals, analyze the reasons, related factors and potential development directions behind them, and predict the future development trends and impacts of weak signals. The sound wave amplification module can receive the information processed in the sound wave monitoring module. After amplifying the sound wave, the sound wave amplification module will establish a primary analysis unit and a secondary analysis unit. The primary analysis unit can directly extract the frequency, amplitude and wavelength information of the amplified sound wave, determine the model information of the drone that sends the weak signal, and obtain the primary drone model information. The secondary analysis unit can segment the frequency, amplitude and wavelength information of the amplified sound wave, and then randomly extract the information in different segments, analyze the model information of the drone based on the randomly extracted information, obtain the secondary drone model information, and comprehensively analyze the drone model based on the primary drone model information and the secondary drone model information.

[0091] When analyzing the monitoring accuracy of different acoustic wave monitoring equipment, the acoustic wave equipment information module will generate an accuracy compensation plan. At this time, the plan information for improving the monitoring accuracy of different monitoring equipment will be retrieved, and the retrieved plan information will be included in the accuracy compensation plan. The monitoring accuracy that can be improved by the information of different accuracy compensation plans will be analyzed, and the matching values ​​of different accuracy compensation plans and different equipment will be determined. Suppose the monitoring accuracy that can be theoretically improved by the accuracy compensation plan is T. J , assuming that the monitoring accuracy actually improved by the proposed accuracy compensation scheme on the equipment is S J , let the matching value between the precision compensation scheme and the equipment be P Z , assuming the original monitoring accuracy of the equipment is S Y , assuming that the equipment monitoring accuracy after the proposed accuracy compensation scheme is used is N J ;

[0092] S J =N J -S Y

[0093]

[0094] According to the above formula, the matching value between the precision compensation scheme and the equipment can be calculated, so that the best precision compensation scheme can be analyzed for the monitoring equipment. The refinement scheme module can receive the information processed by the acoustic wave equipment information module. When generating the secondary refinement scheme, the refinement scheme module will analyze the degree of fit between different precision compensation schemes, so as to obtain the precision improvement effect when different precision compensation schemes are coordinated. The degree of fit refers to the effect of improving the monitoring accuracy when two precision compensation schemes are coordinated. Suppose the monitoring accuracy actually improved by the two precision compensation schemes on the equipment is S respectively. J1 and S J2 , assuming that the monitoring accuracy of the equipment improved by the two precision compensation schemes is J H , let the degree of fit between the two precision complementary schemes be Q H ;

[0095]

[0096] According to the above formula, the degree of fit of the two precision compensation schemes can be calculated, so as to judge the effect of the two precision compensation schemes when used together. The effectiveness ratio module can receive the information processed by the detailed scheme module, and use the investment fine numerical analysis to analyze the relationship between capital investment and equipment accuracy improvement when improving equipment monitoring accuracy. Suppose the investment capital is T Z , assuming that the corresponding equipment monitoring accuracy of the investment is S T , assuming that the input precision value is T S ;

[0097]

[0098] According to the above formula, the investment fine values ​​of the first-order fine plan and different second-order fine plans can be calculated, so that users can choose the fine plan according to their own situation.

[0099] Specific workflow: When monitoring sound wave information, the user sets the focus by typing the sensitive threshold in the sensitive limit unit, and directly extracts information such as frequency, amplitude and wavelength through the primary analysis unit to determine the drone model, and obtains the primary drone model information. The secondary analysis unit processes this information in segments and randomly extracts and analyzes it to obtain the secondary drone model information, and comprehensively obtains the final drone model. When analyzing the monitoring accuracy of different monitoring equipment, a precision compensation plan is generated, and relevant plan information is retrieved. The monitoring accuracy that can be improved by different plans and the matching value with the equipment are calculated, so as to obtain the best precision compensation plan for different equipment. When generating the secondary fine plan, the fit of different precision compensation plans is analyzed, the coordination effect is judged, and the investment fine value is calculated to analyze the relationship between capital investment and precision improvement when improving the monitoring accuracy of the equipment;

[0100] Furthermore, the acoustic wave monitoring module allows users to customize sensitive thresholds, making acoustic wave monitoring more targeted. Through clear observation level division, weak signals can be studied in depth from different levels. Not only can the drone models be monitored and identified, but also their potential impacts and development trends can be analyzed, providing a more comprehensive basis for decision-making. Calculating the matching values ​​of different solutions and equipment helps to select the best solution for monitoring equipment and improve the monitoring accuracy and performance of the equipment. By calculating the detailed values ​​of investment, users can clearly understand the relationship between capital investment and accuracy improvement, so that they can choose the most appropriate detailed solution according to their own situation and achieve reasonable allocation of resources.

[0101] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A small UAV detection system based on weak signals, comprising a radio monitoring module, a detection and positioning module and a control module. The radio monitoring module can monitor the electromagnetic environment within the detection range and transmit the monitored electromagnetic environment to the detection and positioning module. The detection and positioning module can locate the UAV that emits electromagnetic signals according to the monitored electromagnetic environment, and then transmit the located weak signal information to the control module. The control module can control the UAV that emits electromagnetic signals, and is characterized in that: It also includes an acoustic wave monitoring module, an acoustic wave amplification module, an acoustic wave equipment information module, a detailed plan module, and an effectiveness ratio module; The sound wave monitoring module monitors the sound waves within the detection range in real time and establishes a three-dimensional sound wave change model to check the sound wave changes in a small range within the detection range. When a small range of sound wave changes occur, the location where the sound wave changes occur will be marked immediately; The sound wave amplification module can perform proportional amplification on small-scale sound wave changes, thereby more accurately analyzing the frequency, amplitude and wavelength information of the sound waves, and improving the accuracy of detecting abnormal signals; The acoustic wave equipment information module obtains the information of different acoustic wave monitoring equipment and analyzes the monitoring accuracy of different acoustic wave monitoring equipment; The refinement scheme module generates a first-level refinement scheme, obtains the monitoring accuracy information of different acoustic wave monitoring equipment, and establishes an accuracy limit unit. The user can enter the required monitoring accuracy information in the accuracy limit unit. At this time, the accuracy limit unit will generate different second-level refinement schemes based on the first-level refinement scheme and the monitoring accuracy information of different acoustic wave monitoring equipment. The effectiveness ratio module analyzes the execution costs of the primary fine plan and different secondary fine plans, and calculates the input fine values ​​of the primary fine plan and different secondary fine plans, analyzes the advantages and disadvantages of the primary fine plan and different secondary fine plans, and can set an extraction threshold for the input fine values, and extract and display the input fine values, corresponding equipment information and corresponding secondary fine plan information that exceed the extraction threshold.

2. The small UAV detection system based on weak signals according to claim 1 is characterized in that: The radio monitoring module includes a TDOA passive high-precision positioning unit, an ultra-wide monitoring unit, a noise separation unit, a multi-frequency scanning unit, a seamless coverage unit and a multi-target tracking unit; The TDOA passive high-precision positioning unit can use the TDOA algorithm to locate the signal source with high precision; The monitoring frequency band of the ultra-wide monitoring unit is 20MHz-6GHz; The noise separation unit can separate, identify and locate suspicious signal sources through spectrum big data; The multi-frequency scanning unit can perform spectrum scanning on multiple discontinuous frequency bands; Seamless coverage units can be distributed networked to achieve seamless coverage of the monitoring area; The multi-target tracking unit can locate and track multiple signal sources simultaneously; The cooperation of TDOA passive high-precision positioning unit, ultra-wide monitoring unit, noise separation unit, multi-frequency scanning unit, seamless coverage unit and multi-target tracking unit enables users to use the radio monitoring module to achieve unified management of distributed intelligent perception nodes.

3. The small UAV detection system based on weak signals according to claim 1 is characterized in that: The acoustic wave monitoring module can establish a sensitive limit unit when monitoring acoustic wave information, and the user can enter the required sensitive threshold in the sensitive limit unit; When the sensitivity threshold is a range value, the concentration on the sound wave information within the sensitivity threshold range value will be increased; When the sensitivity threshold is a specified value, the sensitive boundary unit will establish options of less than the specified value and greater than the specified value. At this time, the user can choose according to their own needs; The use of the sensitivity limit unit can further improve the observation level of weak signals that meet the sensitivity threshold.

4. The small UAV detection system based on weak signals according to claim 3 is characterized in that: The specific classification of the observation levels is as follows: Level 1: It can detect weak signals and analyze the model of the drone that sent the signal; The second level focuses on the performance and impact of weak signals in specific areas; At the third level, a comprehensive and in-depth study of weak signals is conducted to analyze the underlying causes, related factors and potential development directions, and to predict future development trends and impacts of weak signals.

5. The small UAV detection system based on weak signals according to claim 3 is characterized by: The sound wave amplification module can receive the information processed in the sound wave monitoring module. After amplifying the sound wave, the sound wave amplification module will establish a primary analysis unit and a secondary analysis unit, wherein the primary analysis unit can directly extract the frequency, amplitude and wavelength information of the amplified sound wave, judge the model information of the drone that sends the weak signal, and obtain the primary drone model information. The secondary analysis unit can segment the frequency, amplitude and wavelength information of the amplified sound wave, and then randomly extract the information in different segments, analyze the model information of the drone according to the randomly extracted information, obtain the secondary drone model information, and comprehensively analyze the drone model according to the primary drone model information and the secondary drone model information; When analyzing the monitoring accuracy of different acoustic wave monitoring devices, the acoustic wave equipment information module generates an accuracy compensation scheme. At this time, the scheme information for improving the monitoring accuracy of different monitoring devices is retrieved, and the retrieved scheme information is included as the accuracy compensation scheme. The monitoring accuracy that can be improved by the different accuracy compensation scheme information is analyzed, and the matching values ​​of different accuracy compensation schemes and different devices are determined. The monitoring accuracy that can be theoretically improved by the accuracy compensation scheme is set to T. J , assuming that the monitoring accuracy actually improved by the proposed accuracy compensation scheme on the equipment is S J , let the matching value between the precision compensation scheme and the equipment be P Z , assuming the original monitoring accuracy of the equipment is S Y , assuming that the equipment monitoring accuracy after the proposed accuracy compensation scheme is used is N J ; S J =N J -S Y According to the above formula, the matching value between the accuracy compensation scheme and the equipment can be calculated, so that the best accuracy compensation scheme can be analyzed for the monitoring equipment.

6. The small UAV detection system based on weak signals according to claim 5 is characterized by: The refinement scheme module can receive information processed by the acoustic device information module. When generating a secondary refinement scheme, the refinement scheme module will analyze the degree of fit between the proposed compensation schemes with different precisions, so as to obtain the precision improvement effect when the proposed compensation schemes with different precisions are coordinated. The degree of fit refers to the effect of improving the monitoring accuracy when the two proposed compensation schemes with different precisions are coordinated. Suppose the monitoring accuracy actually improved by the two proposed compensation schemes on the equipment is S respectively. J1 and S J2 , assuming that the monitoring accuracy of the equipment improved by the two precision compensation schemes is J H , let the degree of fit between the two precision complementary schemes be Q H ; According to the above formula, the degree of compatibility of the two precision compensation schemes can be calculated, thereby judging the effect of the two precision compensation schemes when used together.

7. The small UAV detection system based on weak signals according to claim 6 is characterized by: The effectiveness ratio module can receive information processed by the detailed solution module, and use the input fine numerical analysis to analyze the relationship between capital investment and improved equipment accuracy when improving equipment monitoring accuracy. Suppose the investment capital is T Z , assuming that the corresponding equipment monitoring accuracy of the investment is S T , assuming that the input precision value is T S ; According to the above formula, the investment fine values ​​of the first-order fine plan and different second-order fine plans can be calculated, so that users can choose the fine plan according to their own situation.

8. The small UAV detection system based on weak signals according to claim 7 is characterized by: The detection and positioning module includes a spectrum detection device and a control unit. The spectrum detection device is used to sense and identify suspicious drone communication signals within the detection range in real time, and the time difference between drone signals reaching different sites is used to continuously lock and track the device that sends suspicious signals. The detection and positioning module also includes a device model unit and a black and white list unit. The equipment model unit records the information of common drone models on the market, and can quickly identify the drone model when a drone signal is detected; The black and white list units can effectively distinguish between cooperative and non-cooperative drones; The control module can transmit anti-interference signals to the anti-interference equipment. The anti-interference equipment can effectively cut off the communication command and navigation link signals of the UAV, thereby driving away the UAV.

9. A small UAV detection method based on weak signals applicable to a small UAV detection system based on weak signals according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Monitor the electromagnetic environment within the detection range, including the cooperation of TDOA passive high-precision positioning unit, ultra-wideband monitoring unit, sound-noise separation unit, multi-frequency scanning unit, seamless coverage unit and multi-target tracking unit to achieve high-precision positioning of the signal source, ultra-wideband monitoring, signal-noise separation, multi-frequency scanning, seamless coverage and multi-target tracking; Step 2: Monitor the sound waves within the detection range in real time, establish a three-dimensional sound wave change model, and mark the locations where small-scale sound wave changes occur. Users can enter the sensitivity threshold in the sensitive limit unit to improve the observation level of weak signals that meet the threshold; Step 3: Perform geometric amplification processing on the sound wave changes in a small range, and comprehensively analyze the drone model through the primary analysis unit and the secondary analysis unit; Step 4: Obtain information and monitoring accuracy of different acoustic wave monitoring devices, generate accuracy compensation schemes, analyze the matching values ​​of different accuracy compensation schemes with different devices, and obtain the monitoring accuracy that can actually be improved when different accuracy compensation schemes are used on different devices; Step 5: Generate a primary fine plan, generate different secondary fine plans according to the monitoring accuracy information in the accuracy limit unit entered by the user, analyze the fit between the proposed compensation plans with different accuracy, and judge the effect of the coordinated use of the proposed compensation plans with different accuracy; Step 6: Analyze the execution costs of the first-order and different second-order fine plans, calculate the input fine values, analyze the advantages and disadvantages of the plans, and extract and display relevant information that exceeds the extraction threshold; Step 7: Locate the drone that sends out electromagnetic signals based on the monitored electromagnetic environment and sound wave information. The equipment model unit it contains can quickly identify the drone model, and the black and white list unit can distinguish between cooperative and non-cooperative drones. Then, control the drone that sends out electromagnetic signals, and transmit interference countermeasure signals through interference countermeasure equipment to cut off the drone's communication and navigation links to drive it away.

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