A method and system for evaluating the effectiveness of electronic countermeasure tactics

Abstract

The present invention discloses an electronic countermeasure tactical effectiveness evaluation method and system. The present invention proposes an electronic countermeasure tactical effectiveness evaluation method and system, which implements multiple radar monitoring and electronic countermeasure strikes on target drones by configuring a drone countermeasure system. The system collects radar and image positioning data, calculates positioning accuracy; records flight characteristics before and after the countermeasure interference node, performs deviation evaluation to generate countermeasure interference indicators; obtains system power consumption values, combines positioning accuracy and countermeasure interference indicators, fits the performance change curve, and then screens out the optimal electronic countermeasure solution. Through the present invention, it is possible to effectively implement countermeasure optimization evaluation and improve the equipment control capability during the countermeasure process, thereby achieving improvements in countermeasure solutions and test solutions.

Description

Technical Field

[0001] The present invention relates to the field of electronic countermeasures, and more specifically, to an electronic countermeasures tactical effectiveness evaluation method and system. Background Art

[0002] Electronic countermeasures (ECM) is an indispensable part of modern military operations. By interfering with and destroying the enemy's communications, radar, navigation and other electronic equipment, it can effectively reduce the enemy's combat capability and cover its own combat intentions and actions. Especially in the field of drones, the quality of the countermeasure system is of great significance. However, the effectiveness evaluation of electronic countermeasure tactics is a complex and difficult process involving multiple technical factors and uncertainties. Most of the existing countermeasure test evaluation methods focus on a single aspect of evaluation, lack of systematicity and comprehensiveness, and lack of fast and efficient test analysis solutions. It is difficult to screen out the optimized countermeasure solution, and it is difficult to reasonably optimize and evaluate different countermeasure solutions, resulting in the difficulty of improving the system's electronic countermeasure capability. Summary of the invention

[0003] The present invention overcomes the defects of the prior art and proposes an electronic countermeasure tactical effectiveness evaluation method and system.

[0004] A first aspect of the present invention provides an electronic countermeasure tactical effectiveness evaluation method, comprising:

[0005] Configure the drone countermeasure system to conduct radar monitoring and electronic countermeasure strikes on at least one target drone based on the preset electronic countermeasure solution;

[0006] In a confrontation test, radar monitoring and signal interference confrontation are carried out on the target drone that enters the preset range. The radar positioning data of the target drone is collected through radar confrontation equipment, and image positioning data is collected through imaging equipment.

[0007] Divide multiple time nodes T based on the adversarial test time;

[0008] Set a target drone to extract location information, map the location in the adversarial map model through image positioning data and radar positioning data, and obtain the first location and the second location respectively. Calculate the deviation between the first location and the second location at different time nodes T, and obtain the positioning accuracy S through the deviation.

[0009] Perform electronic interference on the target UAV through the communication countermeasure equipment, and record the corresponding countermeasure interference node T1. For a preset time period before T1, extract flight information of at least one target UAV through radar positioning data, and form a first flight feature set. For a preset time period after T1, extract flight information of at least one target UAV through radar positioning data to obtain a second flight feature set.

[0010] Perform feature vectorization on the first flight feature set and perform data prediction based on a logistic regression model to form a first prediction feature set, perform deviation evaluation on the first prediction feature set and the second flight feature set, and generate an anti-interference index V;

[0011] Obtain the power consumption value P corresponding to the UAV confrontation system at different time nodes T, use T as the independent variable, and P, S, and V as dependent variables to fit the performance curve and obtain the performance change curve;

[0012] Through the effectiveness evaluation curve, the optimal countermeasure plan is selected from the preset electronic countermeasure plans.

[0013] In this solution, the configured drone countermeasure system performs radar monitoring and electronic countermeasure strikes on at least one target drone based on a preset electronic countermeasure solution, specifically:

[0014] Configure the drone countermeasure system within the preset map range and set the preset electronic countermeasure plan based on the drone equipment parameters;

[0015] The preset electronic countermeasure solution includes one or more configuration solutions.

[0016] In this scheme, in a confrontation test, radar monitoring and signal interference confrontation are carried out on the target drone that enters the preset range. The radar positioning data of the target drone is collected through the radar confrontation equipment, and the image positioning data is collected through the imaging equipment. Specifically:

[0017] In an adversarial test, set the adversarial test time;

[0018] During the test, the radar countermeasure equipment was used to monitor the presence of target drones, and radar monitoring and signal interference countermeasures were performed on target drones that entered the preset range. The radar countermeasure equipment was used to collect radar positioning data of the target drones.

[0019] When there is a target drone, the imaging device is started to identify the target drone and locate the image, and the image positioning data is obtained.

[0020] In this solution, among the multiple time nodes T divided based on the adversarial test time, the time intervals corresponding to every two adjacent time nodes T are consistent.

[0021] In this scheme, a target drone is set to extract location information. Through image positioning data and radar positioning data, the position is mapped in the adversarial map model, and the first position and the second position are obtained respectively. The deviation between the first position and the second position at different time nodes T is calculated. Through the deviation, the positioning accuracy S is obtained, which is specifically:

[0022] According to the UAV confrontation system and the preset confrontation test range, a three-dimensional confrontation map model is constructed;

[0023] A target UAV is set as a research object, and the position information of the target UAV is extracted through image positioning data and radar positioning data, and two types of position information are obtained respectively, and the two types of position information are respectively mapped to the adversarial map model to obtain a first position and a second position respectively;

[0024] A positioning accuracy S is generated based on the deviation between the first position and the second position, and S is in inverse proportion to the deviation.

[0025] In this solution, the communication countermeasure device performs electronic interference on the target UAV and records the corresponding countermeasure interference node T1. For a preset time period before T1, flight information of at least one target UAV is extracted through radar positioning data, and a first flight feature set is formed. For a preset time period after T1, flight information of at least one target UAV is extracted through radar positioning data to obtain a second flight feature set, which is specifically:

[0026] Electronic jamming is achieved by sending jamming signals to the target UAV through communication countermeasure equipment, and the countermeasure jamming node T1 corresponding to the electronic jamming is recorded;

[0027] In the time dimension, mark the N time nodes before T1 to obtain N T2 nodes, and mark the N time nodes after T1 to obtain N T3 nodes;

[0028] Through radar positioning data, the positioning data corresponding to N T2 nodes are analyzed, and flight information of at least one target UAV is extracted. The flight information includes speed, altitude, azimuth, and distance parameters. For the N T2 nodes, corresponding flight feature data are respectively formed, and the flight feature data are sorted based on the time dimension to obtain a first flight feature set;

[0029] Through the radar positioning data, the positioning data corresponding to the N T2 nodes are analyzed and the flight information of at least one target UAV is extracted to form a corresponding second flight feature set.

[0030] In this scheme, the first flight feature set is feature vectorized and data prediction is performed based on the logistic regression model to form a first prediction feature set. The first prediction feature and the second flight feature set are evaluated for deviation, and the anti-interference index V is generated, which is specifically:

[0031] The first flight feature set is feature vectorized and imported into a logistic regression model for data prediction, a prediction time period is set to a preset time period, and a first prediction feature set is obtained;

[0032] The first predicted feature set is used to perform similarity analysis on the data set based on the feature vector form. The similarity calculation is introduced into the Manhattan distance calculation method, and the feature deviation is obtained;

[0033] The anti-interference index V is generated based on the feature deviation, and V is positively proportional to the feature deviation.

[0034] In this scheme, the power consumption value P corresponding to the UAV confrontation system at different time nodes T is obtained, and T is used as the independent variable, and P, S, and V are used as dependent variables to fit the efficiency curve to obtain the efficiency change curve, which is specifically:

[0035] Obtain the device power consumption value P corresponding to the drone confrontation system at different time nodes T;

[0036] With T as the independent variable and P, S, and V as the dependent variables, the efficacy curve is fitted. The efficacy value is calculated based on P, S, and V. The curve is fitted by a linear regression algorithm and the original fitting curve is obtained.

[0037] A linear prediction is performed based on the original fitting curve to generate a corresponding prediction curve, and the original fitting curve and the corresponding prediction curve are integrated into an efficacy change curve.

[0038] In this scheme, the performance evaluation curve is used to select the optimal countermeasure scheme from the preset electronic countermeasure schemes. Specifically, the performance evaluation curve corresponding to each preset configuration scheme in the preset electronic countermeasure scheme is calculated, and multiple data points are randomly selected to perform performance evaluation and comparison, and the optimal countermeasure scheme is selected.

[0039] The second aspect of the present invention further provides an electronic countermeasure tactical effectiveness evaluation system, the system comprising: a memory, a processor, the memory comprising an electronic countermeasure tactical effectiveness evaluation program, the electronic countermeasure tactical effectiveness evaluation program when executed by the processor implements the following steps:

[0040] Configure the drone countermeasure system to conduct radar monitoring and electronic countermeasure strikes on at least one target drone based on the preset electronic countermeasure solution;

[0041] In a confrontation test, radar monitoring and signal interference confrontation are carried out on the target drone that enters the preset range. The radar positioning data of the target drone is collected through radar confrontation equipment, and image positioning data is collected through imaging equipment.

[0042] Divide multiple time nodes T based on the adversarial test time;

[0043] Set a target drone to extract location information, map the location in the adversarial map model through image positioning data and radar positioning data, and obtain the first location and the second location respectively. Calculate the deviation between the first location and the second location at different time nodes T, and obtain the positioning accuracy S through the deviation.

[0044] Perform electronic interference on the target UAV through the communication countermeasure equipment, and record the corresponding countermeasure interference node T1. For a preset time period before T1, extract flight information of at least one target UAV through radar positioning data, and form a first flight feature set. For a preset time period after T1, extract flight information of at least one target UAV through radar positioning data to obtain a second flight feature set.

[0045] Perform feature vectorization on the first flight feature set and perform data prediction based on a logistic regression model to form a first prediction feature set, perform deviation evaluation on the first prediction feature set and the second flight feature set, and generate an anti-interference index V;

[0046] Obtain the power consumption value P corresponding to the UAV confrontation system at different time nodes T, use T as the independent variable, and P, S, and V as dependent variables to fit the performance curve and obtain the performance change curve;

[0047] Through the effectiveness evaluation curve, the optimal countermeasure plan is selected from the preset electronic countermeasure plans.

[0048] The present invention discloses an electronic countermeasure tactical effectiveness evaluation method and system. The present invention proposes an electronic countermeasure tactical effectiveness evaluation method and system, which implements multiple radar monitoring and electronic countermeasure strikes on target drones by configuring a drone countermeasure system. The system collects radar and image positioning data, calculates positioning accuracy; records flight characteristics before and after the countermeasure interference node, performs deviation evaluation to generate countermeasure interference indicators; obtains system power consumption values, combines positioning accuracy and countermeasure interference indicators, fits the performance change curve, and then screens out the optimal electronic countermeasure solution. Through the present invention, it is possible to effectively implement countermeasure optimization evaluation and improve the equipment control capability during the countermeasure process, thereby achieving improvements in countermeasure solutions and test solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of an electronic countermeasure tactical effectiveness evaluation method of the present invention is shown;

[0050] Figure 2 A block diagram of an electronic countermeasure tactical effectiveness evaluation system of the present invention is shown. DETAILED DESCRIPTION

[0051] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0053] Figure 1 A flow chart of an electronic countermeasure tactical effectiveness evaluation method of the present invention is shown.

[0054] like Figure 1 As shown, the first aspect of the present invention provides an electronic countermeasure tactical effectiveness evaluation method, comprising:

[0055] S102, configuring the UAV countermeasure system to perform radar monitoring and electronic countermeasure strikes on at least one target UAV based on a preset electronic countermeasure solution;

[0056] S104, in a confrontation test, radar monitoring and signal interference confrontation are performed on the target UAV that enters the preset range, radar positioning data of the target UAV is collected through radar confrontation equipment, and image positioning data is collected through imaging equipment;

[0057] S106, dividing a plurality of time nodes T based on the adversarial test time;

[0058] S108, setting a target drone to extract location information, performing location mapping in the adversarial map model through image positioning data and radar positioning data, and obtaining a first location and a second location respectively, calculating the deviation between the first location and the second location at different time nodes T, and obtaining the positioning accuracy S through the deviation;

[0059] S110, electronically jamming the target UAV through the communication countermeasure device, and recording the corresponding countermeasure jamming node T1, for a preset time period before T1, extracting flight information of at least one target UAV through radar positioning data, and forming a first flight feature set, and for a preset time period after T1, extracting flight information of at least one target UAV through radar positioning data, and obtaining a second flight feature set;

[0060] S112, quantizing the first flight feature set and performing data prediction based on a logistic regression model to form a first prediction feature set, performing deviation evaluation between the first prediction feature and the second flight feature set, and generating an anti-interference index V;

[0061] S114, obtaining the power consumption value P corresponding to the UAV confrontation system at different time nodes T, taking T as the independent variable and P, S, and V as the dependent variables to perform efficiency curve fitting, and obtaining an efficiency change curve;

[0062] S116, selecting the best countermeasure solution from the preset electronic countermeasure solutions through the effectiveness evaluation curve.

[0063] It should be noted that each time node can be represented by T, representing nodes with the same interval. In order to identify a node at a specific time, T can be marked, such as T1, T2. The drone countermeasure system includes: imaging equipment, radar countermeasure equipment, and communication countermeasure equipment.

[0064] According to an embodiment of the present invention, the configuration of the drone countermeasure system performs radar monitoring and electronic countermeasure strikes on at least one target drone based on a preset electronic countermeasure solution, specifically:

[0065] Configure the drone countermeasure system within the preset map range and set the preset electronic countermeasure plan based on the drone equipment parameters;

[0066] The preset electronic countermeasure solution includes one or more configuration solutions.

[0067] It should be noted that the drone equipment parameters include drone power, specifications, signal frequency band, endurance, etc. The preset electronic countermeasures solution can be set based on test settings or based on general models of drones to improve the applicability of the solution. The preset electronic countermeasures solution includes the corresponding test configuration and countermeasures tactical solution.

[0068] According to an embodiment of the present invention, in a confrontation test, radar monitoring and signal interference confrontation are performed on a target UAV that enters a preset range, radar positioning data of the target UAV is collected by a radar confrontation device, and image positioning data is collected by an imaging device, specifically:

[0069] In an adversarial test, set the adversarial test time;

[0070] During the test, the radar countermeasure equipment was used to monitor the presence of target drones, and radar monitoring and signal interference countermeasures were performed on target drones that entered the preset range. The radar countermeasure equipment was used to collect radar positioning data of the target drones.

[0071] When there is a target drone, the imaging device is started to identify the target drone and locate the image, and the image positioning data is obtained.

[0072] It should be noted that one confrontation test generally includes monitoring and counterattack of multiple target drones. Radar and image positioning data generally include positioning information of multiple drones.

[0073] According to an embodiment of the present invention, among the multiple time nodes T divided based on the adversarial test time, the time intervals corresponding to every two adjacent time nodes T are consistent.

[0074] According to an embodiment of the present invention, a target drone is set to extract position information, and position mapping is performed in the adversarial map model through image positioning data and radar positioning data, and the first position and the second position are obtained respectively, and the deviations corresponding to the first position and the second position at different time nodes T are calculated. The positioning accuracy S is obtained through the deviation, which is specifically:

[0075] According to the UAV confrontation system and the preset confrontation test range, a three-dimensional confrontation map model is constructed;

[0076] A target UAV is set as a research object, and the position information of the target UAV is extracted through image positioning data and radar positioning data, and two types of position information are obtained respectively, and the two types of position information are respectively mapped to the adversarial map model to obtain a first position and a second position respectively;

[0077] A positioning accuracy S is generated based on the deviation between the first position and the second position, and S is in inverse proportion to the deviation.

[0078] It should be noted that the confrontation map model is used to analyze and record the location of the confrontation equipment and the location of the strike target. The preset confrontation test range is generally larger than the preset range, and the preset range is used for drone monitoring and assessment of the feasibility of the strike. In the later performance fitting, if there are multiple target drones, the positioning accuracy S can take the average of the positioning accuracy S of multiple drones as the subsequent fitting analysis data. The greater the deviation, the smaller the positioning accuracy S. Positioning accuracy S is an important value table for evaluating electronic countermeasure capabilities.

[0079] According to an embodiment of the present invention, the target UAV is electronically interfered with by the communication countermeasure device, and the corresponding countermeasure interference node T1 is recorded. For a preset time period before T1, flight information of at least one target UAV is extracted through radar positioning data, and a first flight feature set is formed. For a preset time period after T1, flight information of at least one target UAV is extracted through radar positioning data to obtain a second flight feature set, which is specifically:

[0080] Electronic jamming is achieved by sending jamming signals to the target UAV through communication countermeasure equipment, and the countermeasure jamming node T1 corresponding to the electronic jamming is recorded;

[0081] In the time dimension, mark the N time nodes before T1 to obtain N T2 nodes, and mark the N time nodes after T1 to obtain N T3 nodes;

[0082] Through radar positioning data, the positioning data corresponding to N T2 nodes are analyzed, and flight information of at least one target UAV is extracted. The flight information includes speed, altitude, azimuth, and distance parameters. For the N T2 nodes, corresponding flight feature data are respectively formed, and the flight feature data are sorted based on the time dimension to obtain a first flight feature set;

[0083] Through the radar positioning data, the positioning data corresponding to the N T2 nodes are analyzed and the flight information of at least one target UAV is extracted to form a corresponding second flight feature set.

[0084] It should be noted that the preset time period is the time period corresponding to the accumulation of N time nodes. N time nodes are continuous time nodes. For the N T2 nodes, corresponding flight feature data are formed respectively, and each node corresponds to a flight feature data, which is specifically the data analyzed at a certain time node. The first feature data set includes flight feature data corresponding to N T2 nodes. The analysis process of the second flight feature set is consistent with that of the first flight feature set.

[0085] In the present invention, by analyzing the characteristics of the before and after time periods of the flight characteristic set, it is possible to effectively conduct rapid flight characteristic situation analysis from multiple target UAVs, improve the group confrontation deviation analysis capability, and achieve rapid and accurate analysis and judgment of the confrontation effect. Through the corresponding index values, the performance curve evaluation is subsequently performed. Based on the performance curve, different confrontation schemes can be efficiently analyzed and compared from the overall process of the test process, so as to screen out a better confrontation scheme.

[0086] According to an embodiment of the present invention, the first flight feature set is feature vectorized and data is predicted based on a logistic regression model to form a first prediction feature set, the first prediction feature is evaluated for deviation from the second flight feature set, and an anti-interference index V is generated, specifically:

[0087] The first flight feature set is feature vectorized and imported into a logistic regression model for data prediction, a prediction time period is set to a preset time period, and a first prediction feature set is obtained;

[0088] The first prediction feature set is used to perform similarity analysis on the data set based on the feature vector form. The similarity calculation introduces the Manhattan distance calculation method and obtains the feature deviation.

[0089] The anti-interference index V is generated based on the feature deviation, and V is positively proportional to the feature deviation.

[0090] It should be noted that the logistic regression model can be trained based on the historical flight feature data set. The greater the feature deviation, the higher the overall deviation of the target drone group from the original flight state, and the better the confrontation effect.

[0091] It should be noted that, through the present invention, rapid and efficient evaluation of large-scale UAV electronic countermeasures can be achieved, and it has better test and evaluation applications.

[0092] According to an embodiment of the present invention, the power consumption value P corresponding to the UAV confrontation system at different time nodes T is obtained, T is used as an independent variable, and P, S, and V are used as dependent variables to perform performance curve fitting to obtain a performance change curve, specifically:

[0093] Obtain the device power consumption value P corresponding to the drone confrontation system at different time nodes T;

[0094] With T as the independent variable and P, S, and V as the dependent variables, the efficacy curve is fitted. The efficacy value is calculated based on P, S, and V. The curve is fitted by a linear regression algorithm and the original fitting curve is obtained.

[0095] A linear prediction is performed based on the original fitting curve to generate a corresponding prediction curve, and the original fitting curve and the corresponding prediction curve are integrated into an efficacy change curve.

[0096] It should be noted that P includes the cumulative power consumption of various devices in the drone countermeasure system.

[0097] The potency value is calculated as follows:

[0098] ;

[0099] Among them, K1 and K2 are correction coefficients. is the performance value, P is the power consumption value, S is the positioning accuracy, and V is the anti-interference index.

[0100] The corresponding equation of the original fitting curve is expressed as , the independent variable of each data point in the curve is T, and the dependent variable is the efficiency value corresponding to time T .

[0101] According to an embodiment of the present invention, the method of selecting the optimal countermeasure scheme from the preset electronic countermeasure schemes through the performance evaluation curve specifically involves calculating the performance evaluation curve corresponding to each preset configuration scheme in the preset electronic countermeasure scheme, performing performance evaluation and comparison by randomly selecting multiple data points, and selecting the optimal countermeasure scheme.

[0102] According to an embodiment of the present invention, it also includes:

[0103] During one confrontation test, the confrontation effectiveness evaluation is performed based on the positioning accuracy S and the confrontation interference index V, and the corresponding real-time confrontation index is calculated;

[0104] Calculate the real-time confrontation index corresponding to each time node T, and determine whether the real-time confrontation index meets the expected confrontation performance based on the preset value range;

[0105] The time nodes T that do not meet the expected adversarial performance are screened and recorded to obtain multiple target nodes. , mark the remaining time nodes as non-target nodes ;

[0106] Calculate each target node With adjacent target nodes , adjacent non-target nodes The time interval between the two, and the two time interval values ​​are used as parameter data for each target node;

[0107] Based on the time dimension, for each target node The corresponding parameter data is serialized to form target sequence data;

[0108] Import the target sequence data into the LSTM-based prediction model for sequence prediction, generate a prediction sequence, perform data analysis on the prediction sequence, and form prediction node parameter data;

[0109] By predicting node parameter data, countermeasure strategies are planned for each predicted time node, and an optimized test plan is generated.

[0110] It should be noted that, through the above-mentioned embodiments, it is possible to mark the effective time nodes based on the confrontation, and perform interval analysis and LSTM prediction on the target and non-target nodes of the confrontation test to generate corresponding predicted time node sequence data. By predicting the time node sequence data, it is possible to effectively set a test process for a certain confrontation scheme. The test process includes corresponding counter-confrontation time nodes. The test process is applicable to the confrontation time nodes and confrontation intensity of the corresponding confrontation system, effectively performs confrontation optimization evaluation and efficient regulation during the confrontation process, quickly discovers problems existing in the confrontation, and effectively performs rapid equipment tuning and scheme improvement.

[0111] The calculation of each target node With adjacent target nodes , adjacent non-target nodes The time interval between the two, that is, to calculate a selected The closest to the rest , The target sequence data includes multiple ordered data points, each of which corresponds to a target node. The parameter data of the prediction node includes a plurality of prediction time nodes and corresponding prediction parameter data. The configuration scheme of one confrontation test time is one confrontation test.

[0112] Figure 2 A block diagram of an electronic countermeasure tactical effectiveness evaluation system of the present invention is shown.

[0113] The second aspect of the present invention further provides an electronic countermeasure tactical effectiveness evaluation system 2, which includes: a memory 21 and a processor 22, wherein the memory 21 includes an electronic countermeasure tactical effectiveness evaluation program, and when the electronic countermeasure tactical effectiveness evaluation program is executed by the processor 22, the following steps are implemented:

[0114] Configure the drone countermeasure system to conduct radar monitoring and electronic countermeasure strikes on at least one target drone based on the preset electronic countermeasure solution;

[0115] In a confrontation test, radar monitoring and signal interference confrontation are carried out on the target drone that enters the preset range. The radar positioning data of the target drone is collected through radar confrontation equipment, and image positioning data is collected through imaging equipment.

[0116] Divide multiple time nodes T based on the adversarial test time;

[0117] Set a target drone to extract location information, map the location in the adversarial map model through image positioning data and radar positioning data, and obtain the first location and the second location respectively. Calculate the deviation between the first location and the second location at different time nodes T, and obtain the positioning accuracy S through the deviation.

[0118] Perform electronic interference on the target UAV through the communication countermeasure equipment, and record the corresponding countermeasure interference node T1. For a preset time period before T1, extract flight information of at least one target UAV through radar positioning data, and form a first flight feature set. For a preset time period after T1, extract flight information of at least one target UAV through radar positioning data to obtain a second flight feature set.

[0119] Perform feature vectorization on the first flight feature set and perform data prediction based on a logistic regression model to form a first prediction feature set, perform deviation evaluation on the first prediction feature set and the second flight feature set, and generate an anti-interference index V;

[0120] Obtain the power consumption value P corresponding to the UAV confrontation system at different time nodes T, use T as the independent variable, and P, S, and V as dependent variables to fit the performance curve and obtain the performance change curve;

[0121] Through the effectiveness evaluation curve, the optimal countermeasure plan is selected from the preset electronic countermeasure plans.

[0122] It should be noted that each time node can be represented by T, representing nodes with the same interval. In order to identify a node at a specific time, T can be marked, such as T1, T2. The drone countermeasure system includes: imaging equipment, radar countermeasure equipment, and communication countermeasure equipment.

[0123] According to an embodiment of the present invention, the configuration of the drone countermeasure system performs radar monitoring and electronic countermeasure strikes on at least one target drone based on a preset electronic countermeasure solution, specifically:

[0124] Configure the drone countermeasure system within the preset map range and set the preset electronic countermeasure plan based on the drone equipment parameters;

[0125] The preset electronic countermeasure solution includes one or more configuration solutions.

[0126] It should be noted that the drone equipment parameters include drone power, specifications, signal frequency band, endurance, etc. The preset electronic countermeasures solution can be set based on test settings or based on general models of drones to improve the applicability of the solution. The preset electronic countermeasures solution includes the corresponding test configuration and countermeasures tactical solution.

[0127] According to an embodiment of the present invention, in a confrontation test, radar monitoring and signal interference confrontation are performed on a target UAV that enters a preset range, radar positioning data of the target UAV is collected by a radar confrontation device, and image positioning data is collected by an imaging device, specifically:

[0128] In an adversarial test, set the adversarial test time;

[0129] During the test, the radar countermeasure equipment was used to monitor the presence of target drones, and radar monitoring and signal interference countermeasures were performed on target drones that entered the preset range. The radar countermeasure equipment was used to collect radar positioning data of the target drones.

[0130] When there is a target drone, the imaging device is started to identify the target drone and locate the image, and the image positioning data is obtained.

[0131] It should be noted that one confrontation test generally includes monitoring and counterattack of multiple target drones. Radar and image positioning data generally include positioning information of multiple drones.

[0132] According to an embodiment of the present invention, among the multiple time nodes T divided based on the adversarial test time, the time intervals corresponding to every two adjacent time nodes T are consistent.

[0133] According to an embodiment of the present invention, a target drone is set to extract position information, and position mapping is performed in the adversarial map model through image positioning data and radar positioning data, and the first position and the second position are obtained respectively, and the deviations corresponding to the first position and the second position at different time nodes T are calculated. The positioning accuracy S is obtained through the deviation, which is specifically:

[0134] According to the UAV confrontation system and the preset confrontation test range, a three-dimensional confrontation map model is constructed;

[0135] A target UAV is set as a research object, and the position information of the target UAV is extracted through image positioning data and radar positioning data, and two types of position information are obtained respectively, and the two types of position information are respectively mapped to the adversarial map model to obtain a first position and a second position respectively;

[0136] A positioning accuracy S is generated based on the deviation between the first position and the second position, and S is in inverse proportion to the deviation.

[0137] It should be noted that the confrontation map model is used to analyze and record the location of the confrontation equipment and the location of the strike target. The preset confrontation test range is generally larger than the preset range, and the preset range is used for drone monitoring and assessment of the feasibility of the strike. In the later performance fitting, if there are multiple target drones, the positioning accuracy S can take the average of the positioning accuracy S of multiple drones as the subsequent fitting analysis data. The greater the deviation, the smaller the positioning accuracy S. Positioning accuracy S is an important value table for evaluating electronic countermeasure capabilities.

[0138] According to an embodiment of the present invention, the target UAV is electronically interfered with by the communication countermeasure device, and the corresponding countermeasure interference node T1 is recorded. For a preset time period before T1, flight information of at least one target UAV is extracted through radar positioning data, and a first flight feature set is formed. For a preset time period after T1, flight information of at least one target UAV is extracted through radar positioning data to obtain a second flight feature set, which is specifically:

[0139] Electronic jamming is achieved by sending jamming signals to the target UAV through communication countermeasure equipment, and the countermeasure jamming node T1 corresponding to the electronic jamming is recorded;

[0140] In the time dimension, mark the N time nodes before T1 to obtain N T2 nodes, and mark the N time nodes after T1 to obtain N T3 nodes;

[0141] Through radar positioning data, the positioning data corresponding to N T2 nodes are analyzed, and flight information of at least one target UAV is extracted. The flight information includes speed, altitude, azimuth, and distance parameters. For the N T2 nodes, corresponding flight feature data are respectively formed, and the flight feature data are sorted based on the time dimension to obtain a first flight feature set;

[0142] Through the radar positioning data, the positioning data corresponding to the N T2 nodes are analyzed and the flight information of at least one target UAV is extracted to form a corresponding second flight feature set.

[0143] It should be noted that the preset time period is the time period corresponding to the accumulation of N time nodes. N time nodes are continuous time nodes. For the N T2 nodes, corresponding flight feature data are formed respectively, and each node corresponds to a flight feature data, which is specifically the data analyzed at a certain time node. The first feature data set includes flight feature data corresponding to N T2 nodes. The analysis process of the second flight feature set is consistent with that of the first flight feature set.

[0144] In the present invention, by analyzing the characteristics of the before and after time periods of the flight characteristic set, it is possible to effectively conduct rapid flight characteristic situation analysis from multiple target UAVs, improve the group confrontation deviation analysis capability, and achieve rapid and accurate analysis and judgment of the confrontation effect. Through the corresponding index values, the performance curve evaluation is subsequently performed. Based on the performance curve, different confrontation schemes can be efficiently analyzed and compared from the overall process of the test process, so as to screen out a better confrontation scheme.

[0145] According to an embodiment of the present invention, the first flight feature set is feature vectorized and data is predicted based on a logistic regression model to form a first prediction feature set, the first prediction feature is evaluated for deviation from the second flight feature set, and an anti-interference index V is generated, specifically:

[0146] The first flight feature set is feature vectorized and imported into a logistic regression model for data prediction, a prediction time period is set to a preset time period, and a first prediction feature set is obtained;

[0147] The first predicted feature set is used to perform similarity analysis on the data set based on the feature vector form. The similarity calculation is introduced into the Manhattan distance calculation method, and the feature deviation is obtained;

[0148] The anti-interference index V is generated based on the feature deviation, and V is positively proportional to the feature deviation.

[0149] It should be noted that the logistic regression model can be trained based on the historical flight feature data set. The greater the feature deviation, the higher the overall deviation of the target drone group from the original flight state, and the better the confrontation effect.

[0150] It should be noted that, through the present invention, rapid and efficient evaluation of large-scale UAV electronic countermeasures can be achieved, and it has better test and evaluation applications.

[0151] According to an embodiment of the present invention, the power consumption value P corresponding to the UAV confrontation system at different time nodes T is obtained, T is used as an independent variable, and P, S, and V are used as dependent variables to perform performance curve fitting to obtain a performance change curve, specifically:

[0152] Obtain the device power consumption value P corresponding to the drone confrontation system at different time nodes T;

[0153] With T as the independent variable and P, S, and V as the dependent variables, the efficacy curve is fitted. The efficacy value is calculated based on P, S, and V. The curve is fitted by a linear regression algorithm and the original fitting curve is obtained.

[0154] A linear prediction is performed based on the original fitting curve to generate a corresponding prediction curve, and the original fitting curve and the corresponding prediction curve are integrated into an efficacy change curve.

[0155] It should be noted that P includes the cumulative power consumption of various devices in the drone countermeasure system.

[0156] The potency value is calculated as follows:

[0157] ;

[0158] Among them, K1 and K2 are correction coefficients. is the performance value, P is the power consumption value, S is the positioning accuracy, and V is the anti-interference index.

[0159] The corresponding equation of the original fitting curve is expressed as , the independent variable of each data point in the curve is T, and the dependent variable is the efficiency value corresponding to time T .

[0160] According to an embodiment of the present invention, the method of selecting the optimal countermeasure scheme from the preset electronic countermeasure schemes through the performance evaluation curve specifically involves calculating the performance evaluation curve corresponding to each preset configuration scheme in the preset electronic countermeasure scheme, performing performance evaluation and comparison by randomly selecting multiple data points, and selecting the optimal countermeasure scheme.

[0161] The present invention discloses an electronic countermeasure tactical effectiveness evaluation method and system. The present invention proposes an electronic countermeasure tactical effectiveness evaluation method and system, which implements multiple radar monitoring and electronic countermeasure strikes on target drones by configuring a drone countermeasure system. The system collects radar and image positioning data, calculates positioning accuracy; records flight characteristics before and after the countermeasure interference node, performs deviation evaluation to generate countermeasure interference indicators; obtains system power consumption values, combines positioning accuracy and countermeasure interference indicators, fits the performance change curve, and then screens out the optimal electronic countermeasure solution. Through the present invention, it is possible to effectively implement countermeasure optimization evaluation and improve the equipment control capability during the countermeasure process, thereby achieving improvements in countermeasure solutions and test solutions.

[0162] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0163] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0164] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0165] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0166] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0167] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating the effectiveness of electronic countermeasure tactics, characterized in that: include: Configure the drone countermeasure system to conduct radar monitoring and electronic countermeasure strikes on at least one target drone based on the preset electronic countermeasure solution; In a confrontation test, radar monitoring and signal interference confrontation are carried out on the target drone that enters the preset range. The radar positioning data of the target drone is collected through radar confrontation equipment, and image positioning data is collected through imaging equipment. Divide multiple time nodes T based on the adversarial test time; Set a target drone to extract location information, map the location in the adversarial map model through image positioning data and radar positioning data, and obtain the first location and the second location respectively. Calculate the deviation between the first location and the second location at different time nodes T, and obtain the positioning accuracy S through the deviation. Perform electronic interference on the target UAV through the communication countermeasure equipment, and record the corresponding countermeasure interference node T1. For a preset time period before T1, extract flight information of at least one target UAV through radar positioning data, and form a first flight feature set. For a preset time period after T1, extract flight information of at least one target UAV through radar positioning data to obtain a second flight feature set. Perform feature vectorization on the first flight feature set and perform data prediction based on a logistic regression model to form a first prediction feature set, perform deviation evaluation on the first prediction feature set and the second flight feature set, and generate an anti-interference index V; Obtain the power consumption value P corresponding to the UAV confrontation system at different time nodes T, use T as the independent variable, and P, S, and V as dependent variables to fit the performance curve and obtain the performance change curve; Through the effectiveness evaluation curve, the optimal countermeasure solution is selected from the preset electronic countermeasure solutions; Among them, a target drone is set to extract position information, and the position mapping is performed in the adversarial map model through image positioning data and radar positioning data, and the first position and the second position are obtained respectively, and the deviation corresponding to the first position and the second position at different time nodes T is calculated. The positioning accuracy S is obtained through the deviation, which is specifically: According to the UAV confrontation system and the preset confrontation test range, a three-dimensional confrontation map model is constructed; A target UAV is set as a research object, and the position information of the target UAV is extracted through image positioning data and radar positioning data, and two types of position information are obtained respectively, and the two types of position information are respectively mapped to the adversarial map model to obtain a first position and a second position respectively; Generate a positioning accuracy S based on the deviation between the first position and the second position, where S is inversely proportional to the deviation; The first flight feature set is feature vectorized and data is predicted based on a logistic regression model to form a first prediction feature set, the first prediction feature is evaluated for deviation from the second flight feature set, and an anti-interference index V is generated, specifically: The first flight feature set is feature vectorized and imported into a logistic regression model for data prediction, a prediction time period is set to a preset time period, and a first prediction feature set is obtained; The first prediction feature set is used to perform similarity analysis on the data set based on the feature vector form. The similarity calculation introduces the Manhattan distance calculation method and obtains the feature deviation. Generate an anti-interference index V based on the feature deviation, where V is proportional to the feature deviation; The power consumption value P corresponding to the UAV confrontation system at different time nodes T is obtained, T is used as the independent variable, and P, S, and V are used as dependent variables to perform performance curve fitting to obtain the performance change curve, which is specifically: Obtain the device power consumption value P corresponding to the drone confrontation system at different time nodes T; With T as the independent variable and P, S, and V as the dependent variables, the efficacy curve is fitted. The efficacy value is calculated based on P, S, and V. The curve is fitted by a linear regression algorithm and the original fitting curve is obtained. A linear prediction is performed based on the original fitting curve to generate a corresponding prediction curve, and the original fitting curve and the corresponding prediction curve are integrated into an efficacy change curve.

2. The electronic countermeasure tactical effectiveness evaluation method according to claim 1 is characterized in that: The configuration of the drone countermeasure system is to perform radar monitoring and electronic countermeasure strikes on at least one target drone based on a preset electronic countermeasure solution, specifically: Configure the drone countermeasure system within the preset map range and set the preset electronic countermeasure plan based on the drone equipment parameters; The preset electronic countermeasure solution includes one or more configuration solutions.

3. The electronic countermeasure tactical effectiveness evaluation method according to claim 1 is characterized in that: In the confrontation test, radar monitoring and signal interference confrontation are performed on the target UAV that enters the preset range, radar positioning data of the target UAV is collected by radar confrontation equipment, and image positioning data is collected by imaging equipment, specifically: In an adversarial test, set the adversarial test time; During the test, the radar countermeasure equipment was used to monitor the presence of target drones, and radar monitoring and signal interference countermeasures were performed on target drones that entered the preset range. The radar countermeasure equipment was used to collect radar positioning data of the target drones. When there is a target drone, the imaging device is started to identify the target drone and locate the image, and the image positioning data is obtained.

4. The electronic countermeasure tactical effectiveness evaluation method according to claim 1 is characterized in that: Among the multiple time nodes T divided based on the adversarial test time, the time intervals corresponding to every two adjacent time nodes T are consistent.

5. The electronic countermeasure tactical effectiveness evaluation method according to claim 1 is characterized in that: The communication countermeasure device performs electronic interference on the target UAV and records the corresponding countermeasure interference node T1. For a preset time period before T1, flight information of at least one target UAV is extracted through radar positioning data, and a first flight feature set is formed. For a preset time period after T1, flight information of at least one target UAV is extracted through radar positioning data to obtain a second flight feature set, which is specifically: Electronic jamming is achieved by sending jamming signals to the target UAV through communication countermeasure equipment, and the countermeasure jamming node T1 corresponding to the electronic jamming is recorded; In the time dimension, mark the N time nodes before T1 to obtain N T2 nodes, and mark the N time nodes after T1 to obtain N T3 nodes; Through radar positioning data, the positioning data corresponding to N T2 nodes are analyzed, and flight information of at least one target UAV is extracted. The flight information includes speed, altitude, azimuth, and distance parameters. For the N T2 nodes, corresponding flight feature data are respectively formed, and the flight feature data are sorted based on the time dimension to obtain a first flight feature set; Through the radar positioning data, the positioning data corresponding to the N T2 nodes are analyzed and the flight information of at least one target UAV is extracted to form a corresponding second flight feature set.

6. The electronic countermeasure tactical effectiveness evaluation method according to claim 1 is characterized in that: The method of selecting the optimal countermeasure scheme from the preset electronic countermeasure schemes through the performance evaluation curve specifically involves calculating the performance evaluation curve corresponding to each preset configuration scheme in the preset electronic countermeasure scheme, performing performance evaluation and comparison by randomly selecting multiple data points, and selecting the optimal countermeasure scheme.

7. An electronic countermeasure tactical effectiveness evaluation system, characterized in that: The system comprises: a memory and a processor, wherein the memory comprises an electronic countermeasure tactical effectiveness evaluation program, and when the electronic countermeasure tactical effectiveness evaluation program is executed by the processor, the steps of an electronic countermeasure tactical effectiveness evaluation method as described in any one of claims 1 to 6 are implemented.

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