An unmanned aerial vehicle detection countermeasure system
By collecting multi-source data to calculate the environmental confidence coefficient and generate a target fusion trajectory sequence, and combining speed and loitering time to calculate the threat quantification value, countermeasures equipment is screened. This solves the problems of high false alarm rate and insensitive loitering behavior recognition in UAV detection and countermeasures systems, and achieves more accurate threat assessment and countermeasures.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- QINGDAO LEIZHENZI TECH CO LTD
- Filing Date
- 2026-05-11
- Publication Date
- 2026-07-31
AI Technical Summary
Existing drone detection and countermeasure systems have a high false alarm rate and are not sensitive to loitering behavior that poses a potential threat, making it impossible to effectively distinguish the target's movement intentions.
By collecting radar echo data, radio frequency spectrum data, and photoelectric image data, the noise-to-signal ratio variance is calculated to generate an environmental confidence coefficient. A weighted average is then used to calculate the target fusion trajectory sequence. The threat quantification value is calculated by combining the normal approach velocity and the tangential loitering time. Countermeasures are then selected based on the resource consumption potential energy value being less than the threat quantification value.
It reduced the false alarm rate for non-malicious approach to targets, improved the ability to identify loitering behavior with potential reconnaissance intentions, and achieved accuracy and relevance in threat assessment.
Smart Images
Figure CN122496149A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a drone detection and countermeasure system. Background Technology
[0002] With the widespread application of drones, the low-altitude safety risks they bring are becoming increasingly prominent, creating an urgent need for automated and intelligent detection and countermeasure systems.
[0003] In related technologies, threat assessment of intruding drones is often based on whether the straight-line distance between the drone and the protected area exceeds a fixed threshold. This method fails to effectively distinguish the target's movement intentions; for example, it cannot distinguish whether the target is rushing straight towards the protected area at high speed or merely flying at low speed or loitering briefly on the periphery. This can lead to false alarms for non-malicious approaching targets or insufficient warnings for loitering behavior with potential reconnaissance intentions, thus requiring improvement in the accuracy and specificity of threat assessment. Summary of the Invention
[0004] This application provides a drone detection and countermeasure system that solves the problems of high false alarm rate and insensitivity to loitering behavior with potential threats in the prior art. It reduces the false alarm rate of non-malicious approach to targets and improves the ability to identify loitering behavior with potential reconnaissance intentions.
[0005] This application provides a drone detection and countermeasure system, including: a data acquisition module, a sequence generation module, a threat quantification module, an equipment screening module, a difference analysis module, and a data update module;
[0006] The data acquisition module is used to collect radar echo data, radio frequency spectrum data and photoelectric image data, and calculate the noise-to-signal ratio variance of each data source within the current time window, and generate the corresponding environmental reliability coefficient based on the noise-to-signal ratio variance.
[0007] The sequence generation module is used to perform a weighted average calculation of the target coordinates in radar echo data, radio frequency spectrum data and photoelectric image data using the environmental confidence coefficient, and generate a target fusion trajectory sequence.
[0008] The threat quantification module is used to calculate the normal approach velocity and tangential wander time of the target fusion trajectory sequence relative to the preset defense boundary, and to generate a threat quantification value based on the normal approach velocity and tangential wander time.
[0009] The device screening module is used to obtain a list of countermeasure devices, read the energy consumption parameters and cooling time parameters corresponding to each countermeasure device, calculate and generate a resource consumption potential value, screen out countermeasure devices whose resource consumption potential value is less than the threat quantification value, generate a corresponding suppression command and output it.
[0010] The difference analysis module is used to continuously collect the signal strength change data of the target within a preset monitoring time after the output suppression command, calculate the actual signal attenuation slope, calculate the difference between the actual signal attenuation slope and the preset theoretical attenuation slope, and generate the performance residual value.
[0011] The data update module is used to generate a correction factor using the performance residual value, and feed the correction factor back to the calculation step of the environmental reliability coefficient in the next time window to update the environmental reliability coefficient.
[0012] Furthermore, the step of using the environmental confidence coefficient to perform a weighted average calculation of the target coordinates in radar echo data, radio frequency spectrum data, and photoelectric image data to generate a target fusion trajectory sequence includes:
[0013] Extract the first coordinate sequence from the radar echo data, the second coordinate sequence from the radio frequency spectrum data, and the third coordinate sequence from the photoelectric image data;
[0014] Map the first, second, and third coordinate sequences to the Cartesian coordinate system.
[0015] Using the environmental confidence coefficient as the weight, the first, second, and third coordinate sequences at the same timestamp are weighted and summed to obtain fused coordinate points. The continuous fused coordinate points are then connected to generate the target fused trajectory sequence.
[0016] Furthermore, the second coordinate sequence obtained from the radio frequency spectrum data is obtained as follows:
[0017] The clocks of each monitoring station are synchronized via GPS.
[0018] Each monitoring station records the arrival time of the target radio frequency signal under a unified time reference;
[0019] Using one monitoring station as a reference, calculate the signal arrival time difference between other monitoring stations and the reference station;
[0020] Multiplying each time difference by the speed of light constant yields the path distance difference between the target and the corresponding monitoring station and reference station;
[0021] The known coordinates of each monitoring station and the calculated path distance difference are input into the TDOA positioning algorithm to calculate the target's position coordinates;
[0022] Repeat the above steps for continuously acquired signal segments to output a sequence of target coordinates arranged in chronological order.
[0023] Furthermore, before the steps of extracting the first coordinate sequence from the radar echo data, the second coordinate sequence from the radio frequency spectrum data, and the third coordinate sequence mapped from the photoelectric image data, the method further includes:
[0024] Read the environmental reliability coefficient and determine whether the environmental reliability coefficient is lower than the preset minimum threshold.
[0025] If the environmental reliability coefficient corresponding to a certain data source is lower than the minimum threshold, then all values in the coordinate sequence corresponding to that data source are set to zero, and the data source is removed in the subsequent weighted summation operation.
[0026] Furthermore, the step of calculating the normal approach velocity and tangential loitering time of the target fusion trajectory sequence relative to the preset defense boundary, and calculating and generating a threat quantification value based on the normal approach velocity and tangential loitering time, includes:
[0027] Obtain the set of coordinates of the geometric polygons of the preset defense boundary;
[0028] Calculate the perpendicular distance from the current point in the target fusion trajectory sequence to the nearest line segment in the set of geometric polygon coordinates, and calculate the rate of change of the perpendicular distance over time as the normal approximation velocity;
[0029] The length of time that the statistical target fusion trajectory sequence stays within a preset warning range of the geometric polygon coordinate set is taken as the tangential wandering time;
[0030] By substituting the normal approach velocity and tangential wander time into a preset linear weighted formula, the threat quantification value is calculated.
[0031] Furthermore, the steps of obtaining the countermeasures device list, reading the energy consumption parameters and cooling time parameters corresponding to each countermeasures device, and calculating and generating the resource consumption potential energy value include:
[0032] Iterate through the list of countermeasures devices and extract the remaining available time and current operating temperature of each countermeasures device.
[0033] The energy consumption parameters, cooling time parameters, remaining available time, and current operating temperature are normalized.
[0034] The normalized values are summed to obtain the resource consumption potential value corresponding to each countermeasure device, and a mapping table between threats and resources is established.
[0035] Furthermore, the step of selecting countermeasures devices with resource consumption potential values less than threat quantification values, generating corresponding suppression commands, and outputting them includes:
[0036] In the mapping table, find all candidate devices whose resource consumption potential value is less than the threat quantification value;
[0037] Among the candidate devices, the device with the highest resource consumption potential value is selected as the target execution device;
[0038] Construct a control data packet containing the target execution device identifier and the target fused trajectory sequence prediction points, and output the control data packet as a suppression command.
[0039] Furthermore, the step of continuously collecting signal strength change data of the target within a preset monitoring time after outputting the suppression command and calculating the actual signal attenuation slope includes:
[0040] The target's radio frequency signal amplitude is recorded at a preset sampling frequency, with the time point of the output suppression command as the starting zero point.
[0041] Construct a two-dimensional array of time amplitudes and use the least squares method to linearly fit the two-dimensional array;
[0042] Extract the slope value of the fitted straight line, and use the absolute value of the slope value as the actual signal attenuation slope.
[0043] Furthermore, the step of generating a correction factor using the performance residual value and feeding the correction factor back into the calculation of the environmental reliability coefficient for the next time window, and updating the environmental reliability coefficient, includes:
[0044] Determine whether the performance residual value is greater than the preset error tolerance threshold;
[0045] If the error tolerance threshold is exceeded, the environmental confidence coefficient corresponding to the data source with the highest weight in the current time window will be reduced by a preset step size ratio.
[0046] The adjusted coefficients are used as initial values and assigned to the environmental reliability coefficient calculation process for the next time window to complete the closed-loop correction.
[0047] Furthermore, prior to the steps of acquiring radar echo data, radio frequency spectrum data, and photoelectric image data, the following steps are also included:
[0048] Initialize the system log database and create an index table in the system log database with timestamp as the primary key;
[0049] The generated environmental confidence coefficient, threat quantification value, resource consumption potential value, and performance residual value are written into the index table in real time for historical data backtracking and auditing.
[0050] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:
[0051] This application provides a UAV detection and countermeasure system that collects radar, spectrum, and photoelectric data and calculates their noise-to-signal-ratio variance to generate an environmental confidence coefficient. Using this coefficient as a weight, a weighted average of the coordinates of multiple target sources is calculated to generate a target fusion trajectory sequence. The normal approach velocity and tangential loitering time of this sequence relative to the defense boundary are calculated to generate a threat quantification value. The system also obtains countermeasure equipment parameters to calculate resource consumption potential energy values and selects equipment with potential energy values less than the threat quantification value for countermeasures.
[0052] In this process, by generating an environmental reliability coefficient based on the noise-to-signal-ratio variance of each data source and performing a weighted average on the coordinates, the impact of data sources with low instantaneous reliability on the fusion results is reduced, and the stability of the target fusion trajectory sequence is improved.
[0053] Furthermore, by calculating the normal approach velocity and tangential loitering time of the target fusion trajectory sequence to generate threat quantification values, the threat assessment not only considers distance, but also the speed at which the target approaches the defense boundary and the duration of its stay near the boundary, making the judgment of threat behavior more comprehensive.
[0054] Furthermore, by calculating the potential energy value of resource consumption, which includes parameters such as energy consumption, cooling time, remaining available time, and operating temperature, and comparing it with the threat quantification value to screen countermeasures, the system achieves matching and scheduling of countermeasures actions with the current threat level and the real-time status of the equipment. Attached Figure Description
[0055] Figure 1 This is a schematic diagram of a drone detection and countermeasure system provided in an embodiment of this application. Detailed Implementation
[0056] This application provides a drone detection and countermeasure system that solves the problems of high false alarm rate and insensitivity to loitering behavior with potential threats in the prior art. By combining the speed at which the target approaches the defense boundary with the duration of its stay near the boundary for threat assessment, the system reduces the false alarm rate for non-malicious approaching targets and improves the ability to identify loitering behavior with potential reconnaissance intentions.
[0057] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.
[0058] like Figure 1 As shown in the figure, this application provides a drone detection and countermeasure system, including: a data acquisition module, a sequence generation module, a threat quantification module, a device screening module, a difference analysis module, and a data update module.
[0059] The data acquisition module is used to collect radar echo data, radio frequency spectrum data and photoelectric image data, and calculate the noise-to-signal ratio variance of each data source within the current time window, and generate the corresponding environmental reliability coefficient based on the noise-to-signal ratio variance.
[0060] In this embodiment, radar echo data is collected by radar detection equipment, radio frequency spectrum data is collected by spectrum monitoring equipment, and photoelectric image data is collected by photoelectric turret.
[0061] Calculate the radar echo data within the current time window. The first noise-to-signal ratio variance Radio frequency spectrum data in the current time window The second noise-to-signal ratio variance Photoelectric image data in the current time window The third noise-to-signal ratio variance ;
[0062] The formula for calculating variance is: ,in The number of sampling points. For the first Signal-to-noise ratio (SNR) The mean noise-to-signal ratio is denoted as .
[0063] The noise-to-signal ratio (NSR) is defined as the noise power. With signal power The ratio, i.e. The noise-to-signal-ratio variance is the variance of a series of calculated NSR values within that time window. For example:
[0064] Radar Noise-to-Signal Ratio (RSR): The signal power of the target's range gate is extracted from the radar echo data as... Extract the average power of adjacent targetless distance gates as ,calculate .
[0065] Spectral noise-to-signal ratio: The power at the peak of the target signal in radio frequency spectrum data is used as... The average power of the noise floor on both sides of the signal is extracted as... ,calculate .
[0066] Image signal-to-noise ratio: From photoelectric image data, the target region is selected and its average pixel gray value is calculated as the signal-to-noise ratio. Select the background area surrounding the target and calculate its pixel grayscale variance as... ,calculate .
[0067] The environmental reliability coefficients are generated based on the variance of each noise-to-signal-ratio (NRFR). The environmental reliability coefficient corresponding to the radar echo data is denoted as the radar environmental reliability coefficient. The environmental confidence coefficient corresponding to the radio frequency spectrum data is denoted as the spectrum environmental confidence coefficient. The environmental reliability coefficient corresponding to the photoelectric image data is denoted as the photoelectric environmental reliability coefficient. ;
[0068] Environmental reliability coefficient Sampling-to-noise ratio The mapping relationship is as follows:
[0069] ;
[0070] in, The environmental reliability coefficient is represented, with a value range of (0,1]; e is the natural constant. The preset reliability decay factor, ,For example It is used to adjust the sensitivity of variance to the influence of reliability.
[0071] Furthermore, prior to the steps of acquiring radar echo data, radio frequency spectrum data, and photoelectric image data, the following steps are also included:
[0072] Initialize the system log database and create an index table in the system log database with timestamp as the primary key;
[0073] The generated environmental confidence coefficient, threat quantification value, resource consumption potential value, and performance residual value are written into the index table in real time for historical data backtracking and auditing.
[0074] In this embodiment, the system log database is initialized, and an index table with millisecond-level timestamps as the primary key is created in the system log database;
[0075] The generated environmental reliability coefficient Threat Quantification Value The potential energy value of resource consumption of each countermeasure device and performance residual value Real-time association with the current timestamp and writing to the index table;
[0076] The logs are used for historical data backtracking, system performance auditing, and subsequent optimization and adjustment of algorithm parameters, including weights. threshold wait.
[0077] The sequence generation module is used to perform a weighted average calculation of the target coordinates in radar echo data, radio frequency spectrum data and photoelectric image data using the environmental confidence coefficient, and generate a target fusion trajectory sequence.
[0078] It needs to be explained that the environmental reliability coefficient is used. A weighted average is calculated using the target coordinates resolved from radar echo data, target coordinates resolved from radio frequency spectrum data, and target coordinates mapped from electro-optical image data to generate a target fusion trajectory sequence. .
[0079] Furthermore, the step of using the environmental confidence coefficient to perform a weighted average calculation of the target coordinates in radar echo data, radio frequency spectrum data, and photoelectric image data to generate a target fusion trajectory sequence includes:
[0080] Extract the first coordinate sequence from the radar echo data, the second coordinate sequence from the radio frequency spectrum data, and the third coordinate sequence from the photoelectric image data;
[0081] Map the first, second, and third coordinate sequences to the Cartesian coordinate system.
[0082] Using the environmental confidence coefficient as the weight, the first, second, and third coordinate sequences at the same timestamp are weighted and summed to obtain fused coordinate points. The continuous fused coordinate points are then connected to generate the target fused trajectory sequence.
[0083] In this embodiment, the first coordinate sequence of the target is parsed and extracted from the radar echo data. ,in Let j be the j-th timestamp; the second coordinate sequence of the target is extracted from the radio frequency spectrum data using the time difference of arrival (TDOA) positioning method. The third coordinate sequence of the target is obtained from photoelectric image data through image recognition and spatial coordinate mapping. ;
[0084] The first, second, and third coordinate sequences are uniformly mapped to the Northeast Sky (ENU) Cartesian coordinate system with the center of the defense area as the origin.
[0085] For each unified timestamp The coordinates below, with the corresponding environmental confidence coefficient Using the weights as values, perform a weighted summation operation to obtain the merged coordinate points. The calculation formula is:
[0086] ;
[0087] Connect the fused coordinate points under consecutive timestamps in chronological order to generate the target fused trajectory sequence. .
[0088] Specifically, the method for obtaining the second coordinate sequence from the radio frequency spectrum data is as follows:
[0089] The clocks of each monitoring station are synchronized via GPS.
[0090] Each monitoring station records the arrival time of the target radio frequency signal under a unified time reference;
[0091] Using one monitoring station as a reference, calculate the signal arrival time difference between other monitoring stations and the reference station;
[0092] Multiplying each time difference by the speed of light constant yields the path distance difference between the target and the corresponding monitoring station and reference station;
[0093] The known coordinates of each monitoring station and the calculated path distance difference are input into the TDOA positioning algorithm to calculate the target's position coordinates;
[0094] Repeat the above steps for continuously acquired signal segments to output a sequence of target coordinates arranged in chronological order.
[0095] Specifically, before the steps of extracting the first coordinate sequence from the radar echo data, the second coordinate sequence from the radio frequency spectrum data, and the third coordinate sequence mapped from the photoelectric image data, the method further includes:
[0096] Read the environmental reliability coefficient and determine whether the environmental reliability coefficient is lower than the preset minimum threshold.
[0097] If the environmental reliability coefficient corresponding to a certain data source is lower than the minimum threshold, then all values in the coordinate sequence corresponding to that data source are set to zero, and the data source is removed in the subsequent weighted summation operation.
[0098] In this embodiment, after elimination, the denominator in the weighted fusion formula is adjusted to the sum of the environmental reliability coefficients of the remaining valid data sources.
[0099] The threat quantification module is used to calculate the normal approach velocity and tangential wander time of the target fusion trajectory sequence relative to the preset defense boundary, and to generate a threat quantification value based on the normal approach velocity and tangential wander time.
[0100] It should be explained that, based on the target fusion trajectory sequence Calculate its normal approximation velocity relative to the preset defense boundary. Tangential hesitant time ;
[0101] And based on the normal approximation velocity Tangential hesitant time Through threat quantification function Calculate and generate threat quantification values ,Right now .
[0102] Furthermore, the step of calculating the normal approach velocity and tangential loitering time of the target fusion trajectory sequence relative to the preset defense boundary, and calculating and generating a threat quantification value based on the normal approach velocity and tangential loitering time, includes:
[0103] Obtain the set of coordinates of the geometric polygons of the preset defense boundary;
[0104] Calculate the perpendicular distance from the current point in the target fusion trajectory sequence to the nearest line segment in the set of geometric polygon coordinates, and calculate the rate of change of the perpendicular distance over time as the normal approximation velocity;
[0105] The length of time that the statistical target fusion trajectory sequence stays within a preset warning range of the geometric polygon coordinate set is taken as the tangential wandering time;
[0106] By substituting the normal approach velocity and tangential wander time into a preset linear weighted formula, the threat quantification value is calculated.
[0107] In this embodiment, the set of coordinates of the geometric polygons of the preset defense boundary is obtained. ,in Typically, these are fixed values or set according to the terrain.
[0108] For the target fusion trajectory sequence Current point in Calculate its distance to the defense boundary All line segments (from adjacent points) and The minimum vertical distance among the components is taken. ;calculate In recent The rate of change over a time period is taken as the absolute value of the normal approximation velocity. ,Right now ;
[0109] Preset a warning distance around the defense boundary. In the statistical target fusion trajectory sequence, all those that satisfy... and Less than a certain hovering speed threshold The total duration of the trajectory points is taken as the tangential wandering time. ;
[0110] Approximation speed of the normal direction Tangential hesitant time Substituting the values into the preset linear weighting formula, the threat quantification value is calculated. The formula is: ,in and The preset weighting coefficients and ,For example , and For example, a preset normalization constant. m / s, s.
[0111] The device screening module is used to obtain a list of countermeasure devices, read the energy consumption parameters and cooling time parameters corresponding to each countermeasure device, calculate and generate a resource consumption potential energy value, screen out countermeasure devices whose resource consumption potential energy value is less than the threat quantification value, generate corresponding suppression commands and output them.
[0112] Furthermore, the steps of obtaining the countermeasures device list, reading the energy consumption parameters and cooling time parameters corresponding to each countermeasures device, and calculating and generating the resource consumption potential energy value include:
[0113] Iterate through the list of countermeasures devices and extract the remaining available time and current operating temperature of each countermeasures device.
[0114] The energy consumption parameters, cooling time parameters, remaining available time, and current operating temperature are normalized.
[0115] The normalized values are summed to obtain the resource consumption potential value corresponding to each countermeasure device, and a mapping table between threats and resources is established.
[0116] In this embodiment, the countermeasures device list is traversed, and each countermeasures device is extracted. Remaining available time With current operating temperature ;
[0117] Energy consumption parameters Cooling time parameters Remaining available time and current operating temperature Perform normalization to map it to the [0,1] interval:
[0118] Normalized energy consumption ;
[0119] Normalized Cooldown ;
[0120] Normalized Remaining Available Time ;
[0121] Normalized operating temperature ;
[0122] in These are the maximum and minimum boundary values preset based on historical data or specifications of all countermeasure equipment;
[0123] The normalized values are then processed according to preset weights. Perform a weighted summation to obtain the values for each countermeasure device. Corresponding resource consumption potential value ;
[0124] ;
[0125] in, These are preset weights corresponding to energy consumption, cooling time, remaining duration, and temperature, respectively. The preset weights satisfy... .
[0126] Establish a mapping table between threats and resources, recording each countermeasure device in the table. Resource consumption potential value and the status of its equipment.
[0127] Specifically, the step of selecting countermeasures devices with resource consumption potential values less than threat quantification values, generating corresponding suppression commands, and outputting them includes:
[0128] In the mapping table, find all candidate devices whose resource consumption potential value is less than the threat quantification value;
[0129] Among the candidate devices, the device with the highest resource consumption potential value is selected as the target execution device;
[0130] Construct a control data packet containing the target execution device identifier and the target fused trajectory sequence prediction points, and output the control data packet as a suppression command.
[0131] In this embodiment, in the mapping table, all conditions that satisfy the following are searched. Furthermore, countermeasures devices that are in a ready state are considered as a set of candidate devices;
[0132] Select the resource consumption potential value from the candidate device set. The largest device is selected as the target execution device, with the aim of prioritizing the scheduling of devices with sufficient current resources and reserving devices with low potential value to deal with higher threats.
[0133] Construct prediction points that include the target execution device identifier and the target fusion trajectory sequence. The control data packet contains prediction points extrapolated from the trajectory sequence; the control data packet is output as a suppression command to the target execution device.
[0134] The difference analysis module is used to continuously collect signal strength change data of the target within a preset monitoring time after the output suppression command, calculate the actual signal attenuation slope, calculate the difference between the actual signal attenuation slope and the preset theoretical attenuation slope, and generate an efficiency residual value.
[0135] Furthermore, the step of continuously collecting signal strength change data of the target within a preset monitoring time after outputting the suppression command and calculating the actual signal attenuation slope includes:
[0136] The target's radio frequency signal amplitude is recorded at a preset sampling frequency, with the time point of the output suppression command as the starting zero point.
[0137] Construct a two-dimensional array of time amplitudes and use the least squares method to linearly fit the two-dimensional array;
[0138] Extract the slope value of the fitted straight line, and use the absolute value of the slope value as the actual signal attenuation slope.
[0139] In this embodiment, the time point at which the suppression command is output is taken as the starting zero point. At a preset sampling frequency Record the target's radio frequency signal amplitude The duration is Second;
[0140] Construct a two-dimensional array of time amplitudes M represents the total number of sampling points during the monitoring period, where ;
[0141] The best-fit line is found by performing a linear fit on a two-dimensional array using the least squares method. ,in, The intercept; The slope of the fitted line is calculated as follows:
[0142] ;
[0143] Will The absolute value of the value is used as the actual signal attenuation slope. This value is usually negative, and the absolute value represents the attenuation rate.
[0144] The data update module is used to generate a correction factor using the performance residual value, and feed the correction factor back to the calculation step of the environmental reliability coefficient in the next time window to update the environmental reliability coefficient.
[0145] Furthermore, the step of generating a correction factor using the performance residual value and feeding the correction factor back into the calculation of the environmental reliability coefficient for the next time window, and updating the environmental reliability coefficient, includes:
[0146] Determine whether the performance residual value is greater than the preset error tolerance threshold;
[0147] If the error tolerance threshold is exceeded, the environmental confidence coefficient corresponding to the data source with the highest weight in the current time window will be reduced by a preset step size ratio.
[0148] The adjusted coefficients are used as initial values and assigned to the environmental reliability coefficient calculation process for the next time window to complete the closed-loop correction.
[0149] In this embodiment, the performance residual value is the absolute value of the difference between the actual attenuation rate and the theoretical attenuation rate, used to reflect the deviation between the actual effect and the expected effect of this countermeasure. The system pre-stores preset theoretical attenuation slopes derived through experimental calibration or theoretical derivation in different scenarios, used to represent the signal attenuation rate under ideal countermeasure effects.
[0150] Determine the performance residual value Is it greater than the preset error tolerance threshold? ;
[0151] like If the countermeasures in the current round are significantly different from the expected results, it is likely that the environmental reliability of a certain data source is not accurately estimated.
[0152] According to the preset step size ratio For example, 0.1 reduces the environmental reliability coefficient of the data source with the highest weight in the weighted fusion within the current time window; the weight ratio is the ratio of the environmental reliability coefficient of this data source to the sum of the environmental reliability coefficients of the three data sources.
[0153] If the radar has the highest weighting, then update the radar environment confidence coefficient. As a correction factor;
[0154] The adjusted environmental reliability coefficients of the radar data source, along with the environmental reliability coefficients of the current spectrum and optoelectronic data sources, are used as the initial prior values for calculating the environmental reliability coefficients in the next time window, or directly participate in the weighted average of the next round, thus completing the performance-based closed-loop correction.
[0155] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.
[0156] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.
[0157] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0158] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.
[0159] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0160] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. An unmanned aerial vehicle detection countermeasure system, comprising: include: Data acquisition module, sequence generation module, threat quantification module, device screening module, difference analysis module, and data update module; The data acquisition module is used to collect radar echo data, radio frequency spectrum data and photoelectric image data, and calculate the noise-to-signal ratio variance of each data source within the current time window, and generate the corresponding environmental reliability coefficient based on the noise-to-signal ratio variance. The sequence generation module is used to perform a weighted average calculation of the target coordinates in radar echo data, radio frequency spectrum data and photoelectric image data using the environmental confidence coefficient, and generate a target fusion trajectory sequence. The threat quantification module is used to calculate the normal approach velocity and tangential wander time of the target fusion trajectory sequence relative to the preset defense boundary, and to generate a threat quantification value based on the normal approach velocity and tangential wander time. The device screening module is used to obtain a list of countermeasure devices, read the energy consumption parameters and cooling time parameters corresponding to each countermeasure device, calculate and generate a resource consumption potential value, screen out countermeasure devices whose resource consumption potential value is less than the threat quantification value, generate a corresponding suppression command and output it. The difference analysis module is used to continuously collect the signal strength change data of the target within a preset monitoring time after the output suppression command, calculate the actual signal attenuation slope, calculate the difference between the actual signal attenuation slope and the preset theoretical attenuation slope, and generate the performance residual value. The data update module is used to generate a correction factor using the performance residual value, and feed the correction factor back to the calculation step of the environmental reliability coefficient in the next time window to update the environmental reliability coefficient.
2. The drone detection countermeasure system of claim 1, wherein, The step of generating a target fusion trajectory sequence by weighted averaging of target coordinates in radar echo data, radio frequency spectrum data, and photoelectric image data using environmental confidence coefficients includes: Extract the first coordinate sequence from the radar echo data, the second coordinate sequence from the radio frequency spectrum data, and the third coordinate sequence from the photoelectric image data; Map the first, second, and third coordinate sequences to the Cartesian coordinate system. Using the environmental confidence coefficient as the weight, the first, second, and third coordinate sequences at the same timestamp are weighted and summed to obtain fused coordinate points. The continuous fused coordinate points are then connected to generate the target fused trajectory sequence.
3. The drone detection countermeasure system of claim 2, wherein, The second coordinate sequence obtained from the parsed radio frequency spectrum data is obtained as follows: The clocks at each monitoring station are synchronized via GPS. Each monitoring station records the arrival time of the target radio frequency signal under a unified time reference; Using one monitoring station as a reference, calculate the signal arrival time difference between other monitoring stations and the reference station; Multiplying each time difference by the speed of light constant yields the path distance difference between the target and the corresponding monitoring station and reference station; The known coordinates of each monitoring station and the calculated path distance difference are input into the TDOA positioning algorithm to calculate the target's position coordinates; Repeat the above steps for continuously acquired signal segments to output a sequence of target coordinates arranged in chronological order.
4. The drone detection countermeasure system of claim 2, wherein, Before the steps of extracting the first coordinate sequence from the radar echo data, the second coordinate sequence from the radio frequency spectrum data, and the third coordinate sequence mapped from the photoelectric image data, the method further includes: Read the environmental reliability coefficient and determine whether the environmental reliability coefficient is lower than the preset minimum threshold. If the environmental reliability coefficient corresponding to a certain data source is lower than the minimum threshold, then all values in the coordinate sequence corresponding to that data source are set to zero, and the data source is removed in the subsequent weighted summation operation.
5. The drone detection countermeasure system of claim 1, wherein, The steps of calculating the normal approach velocity and tangential loitering time of the target fusion trajectory sequence relative to the preset defense boundary, and generating a threat quantification value based on the normal approach velocity and tangential loitering time, include: Obtain the set of coordinates of the geometric polygons of the preset defense boundary; Calculate the perpendicular distance from the current point in the target fusion trajectory sequence to the nearest line segment in the set of geometric polygon coordinates, and calculate the rate of change of the perpendicular distance over time as the normal approximation velocity; The length of time that the statistical target fusion trajectory sequence stays within a preset warning range of the geometric polygon coordinate set is taken as the tangential wandering time; By substituting the normal approach velocity and tangential wander time into a preset linear weighted formula, the threat quantification value is calculated.
6. The drone detection countermeasure system of claim 1, wherein, The steps of obtaining the countermeasures equipment list, reading the energy consumption parameters and cooling time parameters corresponding to each countermeasures equipment, and calculating and generating the resource consumption potential energy value include: Iterate through the list of countermeasures devices and extract the remaining available time and current operating temperature of each countermeasures device. The energy consumption parameters, cooling time parameters, remaining available time, and current operating temperature are normalized. The normalized values are summed to obtain the resource consumption potential value corresponding to each countermeasure device, and a mapping table between threats and resources is established.
7. The drone detection countermeasure system of claim 6, wherein, The step of selecting countermeasures devices with resource consumption potential values less than threat quantification values, generating corresponding suppression commands, and outputting them includes: In the mapping table, find all candidate devices whose resource consumption potential value is less than the threat quantification value; Among the candidate devices, the device with the highest resource consumption potential value is selected as the target execution device; Construct a control data packet containing the target execution device identifier and the target fused trajectory sequence prediction points, and output the control data packet as a suppression command.
8. The drone detection countermeasure system of claim 1, wherein, The step of continuously collecting signal strength change data of the target within a preset monitoring time after the output suppression command and calculating the actual signal attenuation slope includes: The target's radio frequency signal amplitude is recorded at a preset sampling frequency, with the time point of the output suppression command as the starting zero point. Construct a two-dimensional array of time amplitudes and use the least squares method to linearly fit the two-dimensional array; Extract the slope value of the fitted straight line, and use the absolute value of the slope value as the actual signal attenuation slope.
9. The drone detection countermeasure system of claim 1, wherein, The step of generating a correction factor using the performance residual value and feeding the correction factor back to the calculation of the environmental reliability coefficient in the next time window, and the step of updating the environmental reliability coefficient, includes: Determine whether the performance residual value is greater than the preset error tolerance threshold; If the error tolerance threshold is exceeded, the environmental confidence coefficient corresponding to the data source with the highest weight in the current time window will be reduced by a preset step size ratio. The adjusted coefficients are used as initial values and assigned to the environmental reliability coefficient calculation process for the next time window to complete the closed-loop correction.
10. The drone detection countermeasure system of claim 1, wherein, Before the steps of acquiring radar echo data, radio frequency spectrum data, and photoelectric image data, the following steps are also included: Initialize the system log database and create an index table in the system log database with timestamp as the primary key; The generated environmental confidence coefficient, threat quantification value, resource consumption potential value, and performance residual value are written into the index table in real time for historical data backtracking and auditing.