Small Unmanned Aerial Vehicle Reconnaissance System Based on Weak Signals and Its Method

Abstract

The present invention belongs to the technical field of signal detection, specifically a small unmanned aerial vehicle detection system and method based on weak signals, including a radio monitoring module, a detection and positioning module, and a control module, and further including a sound wave monitoring module, a sound wave amplification module, a sound wave device information module, a refinement scheme module, and an effectiveness ratio module; through the setting of the sound wave monitoring module, when monitoring weak signals within the detection range, the present invention can not only monitor the electromagnetic environment when weak signals appear but also monitor the fluctuation environment when weak signals appear, so as to detect the unmanned aerial vehicle more accurately. Moreover, users can customize the sensitivity threshold to adapt to different detection scenarios and requirements, which can improve the attention and detection effect on specific weak signals, obtain the information and accuracy of different sound wave monitoring devices, generate an accuracy compensation scheme and analyze the matching value, can give full play to the potential of the device, improve the monitoring accuracy while avoiding resource waste.

Description

Technical Field

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

[0002] With the rapid development of small unmanned aerial vehicle technology and its increasingly widespread application in various fields, while unmanned aerial vehicles bring many conveniences to people's lives and work, they also pose many potential threats that cannot be ignored to important fields such as public safety and privacy protection. On the one hand, due to the small size and flexible flight characteristics of small unmanned aerial vehicles themselves, on the other hand, the signals they may emit during operation are relatively weak, which makes traditional detection means inevitably have certain limitations when dealing with various challenges brought by small unmanned aerial vehicles;

[0003] When a general small unmanned aerial vehicle detection system and method based on weak signals are in use, it is inconvenient to monitor the acoustic wave environment within the detection range. The single electromagnetic environment results in inaccurate weak signal monitoring, and when improving the monitoring accuracy of different devices, it is impossible to produce the best accuracy compensation scheme for different devices. For this reason, we propose a small unmanned aerial vehicle detection system and method based on weak signals. Summary of the Invention

[0004] In order to make up for the deficiencies of the prior art and solve the above technical problems; the present invention proposes a small unmanned aerial vehicle detection system and method based on weak signals.

[0005] The technical solution adopted by the present invention to solve its technical problems is: a small unmanned aerial vehicle 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 within the detection range and transmit the monitored electromagnetic environment to the detection and positioning module. The detection and positioning module can locate the unmanned aerial vehicle emitting the electromagnetic signal according to the monitored electromagnetic environment, and then transmit the located weak signal information to the control module. The control module can control the unmanned aerial vehicle emitting the electromagnetic signal. It also includes an acoustic wave monitoring module, an acoustic wave amplification module, an acoustic wave device information module, a refinement scheme module, and an effectiveness ratio module;

[0006] The acoustic wave monitoring module monitors the acoustic waves within the detection range in real time, establishes a three-dimensional acoustic wave change model, and views the acoustic wave changes in a small area within the detection range. When there are acoustic wave changes in a small area, it will immediately mark the position where the acoustic wave changes occur;

[0007] The acoustic wave amplification module can perform equal ratio amplification processing on the acoustic wave changes in a small area, and then more accurately analyze the frequency, amplitude, and wavelength information of the acoustic waves, improving the accuracy of detecting abnormal signals;

[0008] The acoustic wave device information module acquires information of different acoustic wave monitoring devices and analyzes the monitoring accuracy of different acoustic wave monitoring devices;

[0009] The refinement plan module generates a primary refinement plan, acquires the monitoring accuracy information of different acoustic wave monitoring devices, and establishes an accuracy limit unit. The user enters the required monitoring accuracy information in the accuracy limit unit, and at this time, the accuracy limit unit generates different secondary refinement plans according to the primary refinement plan and the monitoring accuracy information of different acoustic wave monitoring devices;

[0010] The effectiveness ratio module analyzes the execution costs of the primary refinement plan and different secondary refinement plans, calculates the input refinement values of the primary refinement plan and different secondary refinement plans, analyzes the advantages and disadvantages of the primary refinement plan and different secondary refinement plans, and sets an extraction threshold for the input refinement values. It extracts and displays the input refinement values exceeding the extraction threshold, the corresponding device information, and the corresponding secondary refinement plan information.

[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 perform high-precision positioning of the signal source using the TDOA algorithm;

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

[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] The seamless coverage unit can perform distributed networking to achieve seamless coverage of the monitoring area;

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

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

[0019] Preferably, when the acoustic wave monitoring module monitors acoustic wave information, it establishes a sensitivity limit unit, and the user enters the required sensitivity threshold in the sensitivity limit unit;

[0020] When the sensitivity threshold is a range value, the degree of concentration on the acoustic wave information within the range value of the sensitivity threshold will increase;

[0021] When the sensitivity threshold is a specified value, the sensitivity boundary unit will establish selections less than and greater than the specified value. At this time, the user makes a selection according to their own needs;

[0022] Using the sensitivity boundary 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] First-level, can monitor weak signals and analyze the model of the UAV that emits the weak signal;

[0025] Second-level, focus on the performance and impact of weak signals in a specific field;

[0026] Third-level, conduct a comprehensive and in-depth study of weak signals, analyze the reasons, associated factors, and potential development directions behind them, and predict the future development trends and impacts of weak signals.

[0027] Preferably, the acoustic wave amplification module can receive the information processed in the acoustic wave monitoring module. After amplifying the acoustic wave, the acoustic 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 acoustic wave, judge the UAV model information that emits the weak signal, and obtain the primary UAV model information. The secondary analysis unit can segment the frequency, amplitude, and wavelength information of the amplified acoustic wave, then randomly extract the information in different segments, analyze the UAV model information according to the randomly extracted information, obtain the secondary UAV model information, and comprehensively analyze the UAV model according to the primary UAV model information and the secondary UAV model information;

[0028] When the acoustic wave device information module analyzes the monitoring accuracy of different acoustic wave monitoring devices, it will generate an accuracy compensation plan. At this time, retrieve the plan information for improving the monitoring accuracy of different monitoring devices, incorporate the retrieved plan information into the accuracy compensation plan, analyze the monitoring accuracy that different accuracy compensation plan information can improve, judge the matching value between different accuracy compensation plans and different devices. Let the monitoring accuracy that the accuracy compensation plan can theoretically improve be T J , let the monitoring accuracy that the accuracy compensation plan actually improves on the device be S J , let the matching value between the accuracy compensation plan and the device be P Z , let the original monitoring accuracy of the device be S Y , let the monitoring accuracy of the device after using the accuracy compensation plan be N J ;

[0029] S J = N J - S Y

[0030]

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

[0032] Preferably, the refinement scheme module can receive the information processed by the acoustic wave device information module. When generating the secondary fine scheme, the refinement scheme module will analyze the compatibility between different precision compensation schemes, so as to obtain the precision improvement effect when different precision compensation schemes cooperate. The compatibility refers to the effect of improving the monitoring precision when two precision compensation schemes cooperate. Let the actual monitoring precision improvements of two precision compensation schemes on the device be S J1 and S J2 , let the monitoring precision improvement of the device when two precision compensation schemes cooperate be J H , and let the compatibility of two precision compensation schemes be Q H ;

[0033]

[0034] According to the above formula, the compatibility of two precision compensation schemes can be calculated, so as to judge the effect when two precision compensation schemes are used in combination.

[0035] Preferably, the effectiveness ratio module can receive the information processed by the refinement scheme module, and use the input fine value to analyze the relationship between the capital investment and the improvement of the device monitoring precision when improving the device monitoring precision. Let the invested capital be T Z , let the corresponding device monitoring precision improvement of the invested capital be S T , and let the input fine value be T S ;

[0036]

[0037] According to the above formula, the input fine values of the primary fine scheme and different secondary fine schemes can be calculated, so as to facilitate the user to select the fine scheme according to their own situation.

[0038] Preferably, the detection and positioning module includes a spectrum detection device and a control unit, uses the spectrum detection device to sense and identify the suspicious UAV communication signals in the detection range in real time, and uses the time difference of the UAV signals reaching different stations to realize the continuous locking and tracking of the device emitting the suspicious signals. The detection and positioning module also includes a device model unit and a black and white list unit;

[0039] The device model unit records the information of common UAV models in the market and can quickly identify the UAV model when detecting the UAV signal;

[0040] The black and white list unit can effectively distinguish cooperative UAVs and non - cooperative UAVs;

[0041] The control module can emit interference counter - measure signals to the interference counter - measure device. The anti - interference device can effectively cut off the communication command and navigation link signals of the UAV, realizing the expulsion of the UAV.

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

[0043] Step 1, monitor the electromagnetic environment within the detection range, including through the cooperation of the TDOA passive high - precision positioning unit, ultra - wide 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 - wide - band monitoring, signal - noise separation, multi - frequency scanning, seamless coverage and multi - target tracking;

[0044] Step 2, conduct real - time monitoring of the sound waves within the detection range, establish a three - dimensional sound wave change model, mark the positions where small - range sound wave changes occur, and the user enters a sensitive threshold in the sensitive limit unit to improve the observation level of weak signals that meet the threshold;

[0045] Step 3, perform equal - ratio amplification processing on the small - range sound wave changes, and comprehensively analyze the UAV model through the primary analysis unit and the secondary analysis unit;

[0046] Step 4, obtain the information and monitoring accuracy of different sound wave monitoring devices, generate an accuracy compensation plan, analyze the matching values of different accuracy compensation plans with different devices, and obtain the actual monitoring accuracy that can be improved when different accuracy compensation plans 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, and analyze the compatibility between different accuracy compensation plans to judge the effect of using different accuracy compensation plans in combination;

[0048] Step 6, analyze the execution costs of the primary fine plan and different secondary fine plans, calculate the input fine value, analyze the advantages and disadvantages of the plans, and extract and display the relevant information that exceeds the extraction threshold;

[0049] Step 7: Locate the UAV that emits electromagnetic signals based on the monitored electromagnetic environment and acoustic wave information. The device model unit it contains can quickly identify the UAV model, and the black and white list unit can distinguish between cooperative and non-cooperative UAVs. Then, control the UAV that emits electromagnetic signals, and transmit interference countermeasure signals through the interference countermeasure device to cut off the UAV communication and navigation links to achieve driving away.

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

[0051] 1. For the small UAV detection system and method based on weak signals of the present invention, through the setting of the acoustic wave monitoring module, when monitoring weak signals within the detection range, it can not only monitor the electromagnetic environment when weak signals appear but also monitor the fluctuation environment when weak signals appear, so as to detect UAVs more accurately. And users can customize the sensitivity threshold to adapt to different detection scenarios and requirements, which can improve the attention and detection effect on specific weak signals, obtain the information and accuracy of different acoustic wave monitoring devices, generate an accuracy compensation plan and analyze the matching value, which can give full play to the potential of the device, improve the monitoring accuracy while avoiding resource waste. By analyzing the fit and cooperation effect between different accuracy compensation plans, the system configuration can be continuously optimized.

[0052] 2. For the small UAV detection system and method based on weak signals of the present invention, through the radio monitoring module, it can comprehensively and accurately monitor and locate various radio signals. The spectrum detection device in the detection and positioning module can sense and identify suspicious UAV communication signals in real time, continuously lock and track using the time difference of the signal arriving at different stations. The device 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 countermeasure signals to cut off the UAV communication and navigation links to achieve driving away.

[0053] 3. For the small UAV detection system and method based on weak signals of the present invention, through the acoustic wave monitoring module, users can customize the sensitivity threshold, making the acoustic wave monitoring more targeted. Through the clear division of observation levels, weak signals can be studied in depth from different levels. It can not only monitor and identify the UAV model but also analyze its potential impact and development trend, providing a more comprehensive basis for decision-making. Calculating the matching value between different plans and the device helps to select the best plan for the monitoring device, improve the monitoring accuracy and performance of the device. By calculating the input fine values, users can clearly understand the relationship between capital investment and accuracy improvement, which is convenient to select the most suitable fine plan according to their own situation and achieve the reasonable allocation of resources. Description of the Drawings

[0054] The present invention will be further described below with reference to 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. Specific embodiments

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiment 1:

[0059] With the rapid development of small unmanned aerial vehicle (UAV) technology and its increasingly widespread application in various fields, while UAVs bring many conveniences to people's lives and work, they also pose many potential threats that cannot be ignored to important fields such as public safety and privacy protection. On the one hand, due to the small size and flexible flight characteristics of small UAVs themselves, on the other hand, the signals they may emit during operation are relatively weak, which makes traditional detection means inevitably have certain limitations when dealing with the various challenges brought by small UAVs;

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

[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 shown, it includes 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 emitting the electromagnetic signal 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 emitting the electromagnetic signal. It also includes an acoustic wave monitoring module, an acoustic wave amplification module, an acoustic wave device information module, a refinement scheme module, and an effectiveness ratio module;

[0062] The acoustic wave monitoring module monitors the acoustic waves within the detection range in real time, establishes a three-dimensional acoustic wave change model to view the acoustic wave changes in a small area within the detection range, and marks the location where the acoustic wave changes occur when there are small-area acoustic wave changes;

[0063] The acoustic wave amplification module can perform equi-ratio amplification processing on small-area acoustic wave changes, and then more accurately analyze the frequency, amplitude, and wavelength information of the acoustic waves, improving the accuracy of detecting abnormal signals;

[0064] The acoustic wave device information module obtains the information of different acoustic wave monitoring devices and analyzes the monitoring accuracy of different acoustic wave monitoring devices at the same time;

[0065] The refinement plan module generates a primary refinement plan, obtains the monitoring accuracy information of different acoustic wave monitoring devices, and establishes a precision boundary unit. The user enters the required monitoring accuracy information in the precision boundary unit. At this time, the precision boundary unit will generate different secondary refinement plans according to the primary refinement plan and the monitoring accuracy information of different acoustic wave monitoring devices;

[0066] The effectiveness ratio module analyzes the execution costs of the primary refinement plan and different secondary refinement plans, calculates the input refinement values of the primary refinement plan and different secondary refinement plans, analyzes the advantages and disadvantages of the primary refinement plan and different secondary refinement plans, and sets an extraction threshold for the input refinement values. Extract and display the input refinement values, the corresponding device information, and the corresponding secondary refinement plan information that exceed the extraction threshold;

[0067] The specific work process: Monitor the acoustic waves within the detection range in real time, establish a three-dimensional acoustic wave change model, mark the location where small-area acoustic wave changes occur. The user enters a sensitivity threshold in the sensitivity boundary unit to improve the observation level of weak signals that meet the threshold. Perform equi-ratio amplification processing on small-area acoustic wave changes, comprehensively analyze the UAV model through the primary analysis unit and the secondary analysis unit, obtain the information and monitoring accuracy of different acoustic wave monitoring devices, and generate a precision compensation plan. Analyze the matching values of different precision compensation plans with different devices, obtain the actual monitoring accuracy that can be improved when different precision compensation plans are used on different devices, generate a primary refinement plan, generate different secondary refinement plans according to the monitoring accuracy information entered by the user in the precision boundary unit, analyze the compatibility between different precision compensation plans, judge the effect of using different precision compensation plans in combination, analyze the execution costs of the primary refinement plan and different secondary refinement plans, calculate the input refinement values, analyze the advantages and disadvantages of the plans, and extract and display the 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 can not only monitor the electromagnetic environment when the weak signal appears but also the fluctuation environment when the weak signal appears, thus enabling more accurate detection of the drone. Moreover, the user can customize the sensitivity threshold to adapt to different detection scenarios and requirements, which can improve the attention and detection effect on specific weak signals, obtain the information and accuracy of different acoustic wave monitoring devices, generate an accuracy compensation plan and analyze the matching value, fully leveraging the potential of the device, improving the monitoring accuracy while avoiding resource waste. By analyzing the compatibility and cooperation effect between different accuracy compensation plans, the system configuration can be continuously optimized.

[0069] Embodiment 2:

[0070] Based on Embodiment 1, as shown in the accompanying drawings of the specification Figure 1-2 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 perform high-precision positioning on the signal source;

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

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

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

[0075] The seamless coverage unit can perform distributed networking to achieve seamless coverage of the monitoring area;

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

[0077] Through the mutual cooperation of the TDOA passive high-precision positioning unit, the ultra-wide monitoring unit, the noise separation unit, the multi-frequency scanning unit, the seamless coverage unit, and the multi-target tracking unit, the user can use the radio monitoring module to achieve unified management of distributed intelligent sensing nodes. The detection and positioning module includes a spectral detection device and a control unit, which can use the spectral detection device to continuously sense and identify suspicious drone communication signals within the detection range, and use the time difference of the drone signal reaching different stations to achieve continuous locking and tracking of the device emitting the suspicious signal. The detection and positioning module also includes a device model unit and a black and white list unit;

[0078] The device model unit records the information of common drone models on the market and can quickly identify the model of the drone when detecting the drone signal;

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

[0080] The control module can transmit interference counter - measure signals to the interference counter - measure equipment. The counter - measure interference equipment can effectively cut off the communication command and navigation link signals of the drone, realizing the expulsion of the drone;

[0081] Specific working process: Monitor the electromagnetic environment within the detection range, including through the cooperation of the TDOA passive high - precision positioning unit, ultra - wide 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. Locate the drone emitting electromagnetic signals according to the monitored electromagnetic environment and acoustic wave information. The device model unit it contains can quickly identify the drone model, and the black and white list unit can distinguish cooperative and non - cooperative drones. Then, control the drone emitting electromagnetic signals, transmit interference counter - measure signals through the interference counter - measure equipment, and cut off the communication and navigation links of the drone to achieve expulsion;

[0082] Furthermore, through the radio monitoring module, various radio signals can be comprehensively and accurately monitored and located. The spectrum detection equipment in the detection and positioning module can sense and identify suspicious drone communication signals in real - time, continuously lock and track using the time difference of the signal arriving at different stations. The device model unit can quickly identify the drone model, and the black and white list unit can distinguish cooperative and non - cooperative drones. This makes the detection and identification of drones faster and more accurate. The control module cuts off the communication and navigation links of the drone by transmitting interference counter - measure signals to achieve expulsion, which can effectively prevent the potential threats of drones and ensure the safety of specific areas.

[0083] Embodiment Three:

[0084] Based on Embodiment Two, as shown in the accompanying drawings of the specification Figure 1-2 When the acoustic wave monitoring module monitors acoustic wave information, a sensitive boundary unit is established, and the user enters the required sensitive threshold value in the sensitive boundary unit;

[0085] When the sensitive threshold is a range value, the degree of focus on the acoustic wave information within the range value of the sensitive threshold will increase;

[0086] When the sensitive threshold is a specified value, the sensitive boundary unit will establish selections less than and greater than the specified value, and then the user makes selections according to their own needs;

[0087] Using the sensitive boundary unit can further improve the observation level of weak signals that meet the sensitive threshold. The specific classification method of the observation level is as follows:

[0088] The first level can detect weak signals and analyze the models of the drones that emit such weak signals;

[0089] The second level focuses on the performance and impacts of weak signals within a specific field;

[0090] The third level conducts a comprehensive and in-depth study of weak signals, analyzes the reasons, associated factors, and potential development directions behind them, and predicts the future development trends and impacts of weak signals. The acoustic wave amplification module can receive the information processed in the acoustic wave monitoring module. After amplifying the acoustic waves, the acoustic 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 acoustic waves, determine the model information of the drones that emit weak signals, and obtain the primary drone model information. The secondary analysis unit can segment the frequency, amplitude, and wavelength information of the amplified acoustic waves, then randomly extract the information in different segments, analyze the model information of the drones based on the randomly extracted information, and obtain the secondary drone model information. The comprehensive analysis of the drone models is carried out based on the primary drone model information and the secondary drone model information;

[0091] When the acoustic wave device information module analyzes the monitoring accuracy of different acoustic wave monitoring devices, it will generate an accuracy compensation plan. At this time, it retrieves the plan information for improving the monitoring accuracy of different monitoring devices and incorporates the retrieved plan information into the accuracy compensation plan. It analyzes the monitoring accuracy that different accuracy compensation plan information can improve, and judges the matching value between different accuracy compensation plans and different devices. Let the monitoring accuracy that the accuracy compensation plan can theoretically improve be T J , let the actual monitoring accuracy improved by the accuracy compensation plan on the device be S J , let the matching value between the accuracy compensation plan and the device be P Z , let the original monitoring accuracy of the device be S Y , let the monitoring accuracy of the device after using the accuracy compensation plan be 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 device can be calculated, so that the best precision compensation scheme for the monitoring device can be analyzed. The refinement scheme module can receive the information processed by the acoustic wave device information module. When generating the secondary fine scheme, the refinement scheme module will analyze the compatibility between different precision compensation schemes, so as to obtain the precision improvement effect when different precision compensation schemes cooperate. The compatibility refers to the effect of improving the monitoring precision when two precision compensation schemes cooperate. Let the actual monitoring precision improvements of two precision compensation schemes on the device be S J1 and S J2 , let the monitoring precision improvement of the device when two precision compensation schemes cooperate be J H , and let the compatibility of two precision compensation schemes be Q H ;

[0095]

[0096] According to the above formula, the compatibility of two precision compensation schemes can be calculated, so as to judge the effect when two precision compensation schemes are used in combination. The effectiveness ratio module can receive the information processed by the refinement scheme module, and use the input fine value analysis to analyze the relationship between the capital investment and the improvement of the device precision when improving the device monitoring precision. Let the investment capital be T Z , let the corresponding monitoring precision improvement of the device for this investment capital be S T , and let the input fine value be T S ;

[0097]

[0098] According to the above formula, the input fine values of the primary fine scheme and different secondary fine schemes can be calculated, so as to facilitate the user to select the fine scheme according to their own situation.

[0099] Specific work process: When monitoring acoustic wave information, the user sets the focus of attention by entering the sensitive threshold in the sensitive boundary unit, directly extracts information such as frequency, amplitude and wavelength through the primary analysis unit to judge the UAV model and obtain the primary UAV model information. The secondary analysis unit then processes these information in segments and randomly extracts and analyzes them to obtain the secondary UAV model information, and comprehensively obtains the final UAV model. When analyzing the monitoring precision of different monitoring devices, a precision compensation scheme is generated, relevant scheme information is retrieved, the monitoring precision improvement that different schemes can achieve and the matching value with the device are calculated, so as to obtain the best precision compensation scheme for different devices. When generating the secondary fine scheme, the compatibility of different precision compensation schemes is analyzed, the cooperation effect is judged, and the input fine value is calculated to analyze the relationship between the capital investment and the precision improvement when improving the device monitoring precision;

[0100] Furthermore, the acoustic wave monitoring module enables users to customize the sensitivity threshold, making the acoustic wave monitoring more targeted. Through the clear classification of observation levels, weak signals can be studied in depth from different aspects. It can not only monitor and identify the drone models, but also analyze their potential impacts and development trends, providing a more comprehensive basis for decision-making. Calculating the matching values between different solutions and the equipment helps to select the best solution for the monitoring equipment, improving the monitoring accuracy and performance of the equipment. By calculating the precise input values, users can clearly understand the relationship between capital investment and accuracy improvement, facilitating the selection of the most suitable precise solution according to their own situations and achieving the rational allocation of resources.

[0101] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended 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 information about different acoustic wave monitoring equipment, analyzes the monitoring accuracy of different acoustic wave monitoring equipment, and generates accuracy compensation plans; 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 enters 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. When generating the second-level refinement scheme, the refinement scheme module will analyze the degree of fit between the proposed compensation schemes with different accuracy. The degree of fit is used to judge the effect of the coordinated use of the proposed compensation schemes with different factors. 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 sets an extraction threshold for the input fine values. The input fine values, corresponding equipment information, and corresponding secondary fine plan information that exceed the extraction threshold are extracted and displayed; 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 ; 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.

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 establishes a sensitive limit unit when monitoring acoustic wave information, and the user enters 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 sends them. 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 of 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 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.

8. 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 7, 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: Real-time monitoring of the sound waves within the detection range, establishing a three-dimensional sound wave change model, marking the location where the small-scale sound wave changes occur, and the user enters 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 refined scheme and different second-order refined schemes, calculate the input refined values, analyze the pros and cons of the schemes, 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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