Multi-factor bait flicker interference decision-making method of weighted priority

Abstract

The invention discloses a multi-factor bait flicker interference decision-making method based on weighted priority. The method comprises the following steps: constructing a one-to-one confrontation scene of an opposite-side guidance radar and our radar; three interference parameters influencing bait flicker interference are constructed; defining bait flicker interference parameter weights and constraint ranges of different distances; designing an interference decision objective function; and solving the target function by using a genetic algorithm to obtain an optimal interference parameter, and calculating the miss distance of the radar of the opposite side under different missile-target distances. The target function comprehensively considers the bait number, the bait flicker period and the interference parameters of the bait RCS, the parameter weight is adaptively adjusted according to the distance between the two radars, the miss distance of the opposite radar under different missile target distances is calculated, the interference efficiency of bait flicker interference is improved, and application scenes are more diversified.

Description

Technical Field

[0001] This invention belongs to the field of electronic data digital processing technology, and further relates to a weighted priority multi-factor decoy blinking jamming decision-making method in the field of electronic countermeasures technology. This invention can be used in situations with complex and varied application scenarios and limited decoy resources to comprehensively consider multiple factors affecting the effectiveness of decoy blinking jamming patterns, and provide the optimal combination of jamming parameters for different decoy blinking jamming patterns. Background Technology

[0002] Optimal parameter selection for decoy blinking interference patterns is a key aspect of decoy blinking interference decision-making. This requires understanding the implementation mechanism of decoy blinking interference and identifying the interference parameters that significantly influence the pattern. Therefore, constructing a scientific and comprehensive set of interference parameters is fundamental to parameter selection. The selected parameters should be as relevant as possible to the main influencing factors of decoy blinking interference and be closely aligned with practical applications.

[0003] Xiao Qinding, Liu Xiaodong, and Li Hailin, in their jointly published paper "Analysis of Angle Deception Interference with Airborne Towed Decoys" (Aerospace Electronic Countermeasures, Vol. 27, No. 4, 2021), disclosed a method for using airborne towed decoys to deceive the single-pulse angle tracking system of a surface-to-air missile's terminal guidance radar. This method establishes a three-dimensional computational model of the angle deception interference of airborne towed decoys against the radar. It proposes that the energy center of the missile's aim before distinguishing between the aircraft and the decoy may be located outside the line connecting the aircraft and the decoy. The method also simulates and analyzes the changes in miss distance with angle and voltage amplitude ratio under three interference methods, and proposes corresponding tactical application principles. However, this method still has shortcomings. It only considers the condition of a fixed missile-target distance and does not analyze the decoy interference parameters under different missile-target distances, thus having limitations.

[0004] Xi'an University of Electronic Science and Technology disclosed a cooperative interference method based on an intelligent optimization algorithm in its patent application "Cooperative Interference Method Based on Intelligent Optimization Algorithm" (Application No. 202310272840.2, Publication No. CN 11640611 A). This method constructs an interference decision model including a common interference decision matrix, gain matrix, interference matrix, interference gain matrix, interference bandwidth ratio factor, interference-to-signal ratio, and interference benefit. It establishes the objective function and constraints of the interference decision model based on the interference benefit. An artificial bee colony algorithm is used, with the interference benefit as the fitness function, to optimize the cooperative interference decision matrix. This method addresses complex electromagnetic spectrum environments, such as limited spectrum resources and the sharing of the same frequency band between jammers and illegal users. While ensuring normal communication for the jammer, it rationally allocates limited interference resources to achieve greater interference benefits. However, this method still has shortcomings. It does not consider the impact of missile-target distance on interference benefits in the construction of the interference decision model, and it cannot solve the problem of spatial position changes between opposing sides in the confrontation scenario, thus failing to meet the requirements of scenario changes.

[0005] The Marketing Service Center of State Grid Jiangsu Electric Power Co., Ltd. disclosed a bandwidth resource allocation method for a power load management system in its patent application "A Bandwidth Resource Allocation Method and System for a Novel Power Load Management System" (Application No. 202311655829.0, Publication No. CN117640397 A). This method includes calculating data interaction parameters between the master station and various load control terminals in the simulation architecture of the novel power load management system. These parameters include communication delay, bit error rate, and communication energy consumption. A bandwidth resource allocation optimization function is constructed based on these parameters. The dynamic inertia weight of the binary particle swarm optimization algorithm is dynamically adjusted according to the relationship between the fitness of each particle and the average fitness value of the entire particle swarm, resulting in an optimized binary particle swarm optimization algorithm. The bandwidth resource allocation optimization function is solved based on this algorithm, and bandwidth resources are allocated to multiple load control terminals according to the solution results. While this method improves bandwidth resource utilization, it still has shortcomings. It only considers bandwidth resource allocation as a single parameter, resulting in an insufficient number of parameters and a limited impact on the interference effect of decoy flickering interference, failing to provide effective interference parameter selection results. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of the prior art by providing a weighted priority multi-factor decoy blinking interference decision-making method, which aims to solve the problems that the prior art cannot consider different target distances, has an insufficient number of parameters, and cannot cope with complex scenarios.

[0007] The technical approach to achieving the objective of this invention is as follows: This invention establishes a "one-to-one" confrontation scenario between the enemy radar and our own radar. It considers decoy flashing interference parameters such as the number of decoys, the decoy flashing period, and the decoy RCS. Based on the mechanism of the decoy flashing interference pattern, it designs a corresponding weighted multi-factor objective function, thereby solving the problem of insufficient parameter considerations in existing tactical decision-making database technologies. After obtaining information about the detected enemy radar, this invention comprehensively considers decoy flashing interference parameters such as the number of decoys, the decoy flashing period, and the decoy RCS. Based on the mechanism of the decoy flashing interference pattern, it designs a corresponding weighted multi-factor objective function. The parameter weights in the objective function are adaptively adjusted according to the distance between the enemy radar and our own radar, combined with the influencing factors of decoy flashing interference, considering different target-missile distances, and thus calculating the parameter combination corresponding to our decoy flashing interference. Finally, it calculates the miss distance of the enemy radar under different interference parameter combinations, solving the problem that existing decision-making database technologies do not consider target-missile distances and cannot cope with complex scenarios.

[0008] To achieve the above objectives, the specific implementation steps of the present invention include the following:

[0009] Step 1: Construct a one-to-one confrontation scenario between the enemy's guidance radar and our radar; our radar uses decoys for cover and interferes with the enemy's guidance radar by using the signals generated by the flashing of the decoys.

[0010] Step 2: Construct three parameters to measure the impact of bait quantity, bait flashing period, and bait RCS on bait flashing interference.

[0011] Step 3: Define the weights and constraint ranges of the decoy blinking interference parameters corresponding to different distances;

[0012] Step 4: Design an interference decision objective function for interference parameters such as the number of decoys, the flashing period of the decoys, and the RCS of the decoys;

[0013] Step 5: Use a genetic algorithm to solve the objective function and obtain the optimal number of decoys, decoy flashing period, and decoy RCS value as interference parameters.

[0014] Step 6: Calculate the miss distance of the enemy radar at different missile-target distances.

[0015] Furthermore, the number of decoys is mainly selected based on the distance between the opposing sides. When the distance is far, more decoys are needed to create saturation interference, while when the distance is close, fewer decoys are needed, otherwise they will be easily identified by the opponent. The minimum number of decoys is set to launch only one decoy, and the maximum number is set to a maximum of 8 decoys that our side can launch.

[0016] Furthermore, the flashing period of the decoy is mainly selected based on the distance between the two parties. The farther the distance, the lower the sampling frequency of the opponent's radar signal; the closer the distance, the higher the sampling frequency of the opponent's radar signal. This is to ensure that the flashing period is close to the sampling period of the opponent's radar signal. Otherwise, it will be easily identified by the opponent. The minimum setting for the number of decoys is 0.3s, and the maximum setting is 5.0s.

[0017] Furthermore, the decoy RCS is mainly selected based on our own RCS. The decoy RCS value ranges from 1 to 2 times our own RCS to ensure that the decoy RCS matches our target RCS and forms an effective deception. The minimum number of decoys is set to 1 times our own RCS and the maximum number is set to 2 times our own RCS.

[0018] Furthermore, the definition of the weights and constraints of the decoy flashing interference parameters corresponding to different distances refers to the fact that the weights of the decoy flashing interference parameters are mainly selected based on the distance between the two parties. When the distance between the two parties is greater than 50km, it is defined as long distance, at which time the weight of the number of decoys is relatively large, and the weights of the number of decoys, the flashing period of the decoys, and the RCS of the decoys are 0.8, 0.1, and 0.1, respectively. When the distance between the two parties is greater than 10~50km, it is defined as medium distance, at which time the weights of the number of decoys and the flashing period of the decoys are relatively large, and the weights of the number of decoys and the flashing period of the decoys are relatively large. The weights of the period and the decoy RCS are 0.5, 0.3, and 0.2, respectively. When the distance between the two sides is less than 10km, it is defined as close range. At this time, the weights of the number of decoys and the decoy RCS are relatively large. The weights of the number of decoys, the decoy flashing period, and the decoy RCS are 0.5, 0.2, and 0.3, respectively. The constraint range of the decoy flashing interference parameters refers to the value range of each parameter. The constraint range of the number of decoys is 1 to 8, the constraint range of the decoy flashing period is 0.1 to 5s, and the constraint range of the decoy RCS is 1 to 2 times that of our own RCS.

[0019] The interference decision objective function for the interference parameters of the number of decoys, the flashing period of the decoys, and the RCS of the decoys is designed as follows:

[0020] ;

[0021] in, The weighting coefficient represents the number of baits. The weighting coefficients representing the flashing period of the decoy. The weighting coefficient represents the weighting factor of the decoy's RCS. The sub-score represents the number of baits. The sub-score represents the decoy flashing period. This indicates the sub-score for the decoy's RCS.

[0022] The scores for the quantity items are obtained by the following formula:

[0023] ;

[0024] in, This indicates a modulo operation. This represents the optimal number of decoys to be determined. This indicates the number of baits randomly selected within the range of possible bait quantities. This indicates the maximum possible number of baits. This represents the minimum number of baits.

[0025] The component score for the flicker period is obtained by the following formula:

[0026] ;

[0027] in, This represents the optimal decoy flashing period to be solved. This indicates a randomly selected decoy flashing period within the range of decoy flashing period values. This indicates the maximum value of the decoy's flashing period. This represents the minimum value of the decoy's flashing period.

[0028] The sub-scores of RCS are obtained by the following formula:

[0029] ;

[0030] in, This represents the optimal decoy RCS to be solved. This represents the RCS value of a decoy randomly selected within the range of decoy RCS values. This indicates the maximum RCS value of the decoy. This represents the minimum RCS value of the decoy.

[0031] Furthermore, the steps for solving the objective function using a genetic algorithm are as follows:

[0032] The first step is to generate an initial population based on the current distance between the two opposing sides within the parameter constraints, with the initial population size set to 200.

[0033] The second step is to use a genetic algorithm to iteratively solve the objective function value, with a maximum number of iterations set to 100.

[0034] The third step is to use a tournament method to calculate the objective function value of each of the five randomly selected individuals. The higher the objective function value, the greater the probability that the individual will be selected.

[0035] The fourth step involves swapping the parameters of the individual with the maximum objective function value by using the number of decoys, the decoy flashing period, and the decoy RCS parameters. This generates a new individual.

[0036] The fifth step is to randomly change some parameters of the offspring chromosomes after exchanging parameters, and introduce a small number of new individuals to avoid the population getting trapped in a local optimum.

[0037] The sixth step is to merge the selected individuals with the offspring individuals after the exchange and parameter changes, calculate their respective objective function values, sort them in descending order of objective function values, and retain the top 200 individuals to form a new generation population.

[0038] Step 7: Determine if the maximum number of iterations has been reached. If yes, proceed to step 8; otherwise, proceed to step 3.

[0039] Step 8: The chromosome with the highest objective function value in the population at the end of the iteration is taken as the optimal solution, and the optimal number of decoys, the decoy flashing period, and the decoy RCS value are obtained.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] First, when designing the multi-factor objective function for decoy scintillation jamming, this invention comprehensively considers the jamming parameters of the number of decoys, the decoy scintillation period, and the decoy RCS. Furthermore, the parameter weights in the objective function can be adaptively adjusted according to the distance between the enemy radar and our radar, overcoming the problem of insufficient parameter consideration in existing decision-making database technologies. This improves the jamming efficiency of decoy scintillation jamming in the "one-to-one" confrontation scenario between the enemy radar and our radar established by this invention, and also makes the application scenarios more diverse.

[0042] Second, when obtaining the interference parameters under decoy scintillation interference, this invention calculates the miss distance of the enemy radar at different missile-target distances, overcoming the problem that existing traditional tactical decision-making database methods do not consider missile-target distance and cannot cope with complex scenarios. This invention demonstrates the superiority of genetic algorithm optimization in selecting interference parameters. Attached Figure Description

[0043] Figure 1 This is a flowchart of the method of the present invention;

[0044] Figure 2 This is a comparison chart of the miss distance of the enemy radar using different methods in the simulation experiment of this invention. Detailed Implementation

[0045] The specific implementation steps of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0046] Reference Figure 1 The specific implementation steps of the embodiments of the present invention will be described in further detail below:

[0047] Step 1: Construct an adversarial scenario.

[0048] The enemy's guidance radar is paired with our radar in a one-to-one manner. Our radar can use decoys for cover and interfere with the enemy's guidance radar by flashing the decoys' signals, thereby reducing the enemy's radar's detection performance against our radar.

[0049] Step 2: Define the weights and constraint ranges of the decoy blinking interference parameters corresponding to different distances.

[0050] use The weights representing the number of baits The weights representing the flashing period of the decoy. The RCS of the decoy is represented by the values ​​of the different parameters given based on the distance between the enemy radar and our radar, as shown in Table 1.

[0051] Table 1 Weighting Table of Decoy Scintillation Interference Parameters

[0052]

[0053] The following is an analysis of the mechanism behind the specific weight settings:

[0054] Long distance (weight: =0.5, =0.2, =0.3).

[0055] Number of decoys: This is the most important factor. The primary objective is to create a dense, indistinguishable "cloud" or "center of mass." A sufficient number of decoys is fundamental to creating this confusion. Insufficient numbers allow radar to easily identify individual targets or small clusters.

[0056] Decoy RCS: Second most important. The decoy's RCS needs to be large enough (usually close to or slightly larger than the target) to ensure a significant echo on the radar display and participate in forming an effective "center of mass". However, precise matching has a relatively low priority because details are difficult to distinguish at long distances.

[0057] Decoy scintillation period: Relatively the least important. At this range, the radar processing cycle is relatively long, with primary focus on energy accumulation and coarse target localization. Rapid scintillation may be less effective than the "centroid" interference formed by multiple stable echoes. Slow or non-cooperative scintillation may even help the radar distinguish targets. Cooperative scintillation (such as creating a moving false centroid) requires higher weighting, but is difficult to implement, hence the average weighting remains low.

[0058] Mid-range (weight: =0.3, =0.5, =0.2).

[0059] Decoy scintillation period: This is the most important. At this stage, radar / seeker resolution improves, allowing for the differentiation of individual targets. Dynamic scintillation jamming becomes a core tactic. Precise control of the decoy's on / off timing (scintillation period and pattern) is crucial for deceiving the radar's range gate, velocity gate (via Doppler scintillation), and angle gate (via spatial alternating scintillation). The period needs to match the radar processing cycle and the target's maneuverability; too fast or too slow, and it may fail.

[0060] Number of decoys: Second most important. Quantity remains fundamental, ensuring enough "participants" for effective flashing coordination. However, sheer quantity is insufficient to fool more precise sensors; dynamic characteristics are needed in conjunction.

[0061] Decoy RCS: Relatively the least important, but its importance is increasing. The importance of RCS matching is beginning to rise because radar can better compare echo intensities. Significantly different RCS will make decoys easier to identify and eliminate when flashing "bright" or "dark" phases. However, dynamic flashing itself remains the dominant deception technique.

[0062] Close range (weight: =0.1, =0.3, =0.6).

[0063] Decoy RCS: Absolutely paramount. At terminal high resolution, radar can clearly distinguish the details of the target and decoy (shape, micro-motion characteristics). At this point, a precise match between the decoy's RCS and the real target's (including fluctuation characteristics) is crucial for survival. Any detectable difference in RCS will lead to the decoy being identified and filtered out in a very short time. The decoy must "look" almost exactly the same as our radar (energy level).

[0064] Decoy blink cycle: Second most important. Rapid, complex blink patterns remain a last resort jamming method, attempting to disrupt the tracking loop or angle measurement for a very short time. However, its effectiveness is highly dependent on precise RCS matching. If the RCS does not match, even the best blink pattern will be easily detected. Its weight is lower than RCS because matching is fundamental.

[0065] Quantity of decoys: Least important. Deploying a large number of new decoys within a very short reaction time is usually impractical. More important are the quality (RCS matching) and dynamic characteristics (flickering) of the decoys already deployed. A large quantity of decoys, but with mismatched RCS or ineffective flickering patterns, may actually provide more comparative information to the radar. The "quality" of existing decoys far outweighs their "quantity."

[0066] Below is a table of parameter constraints for decoy blinking interference:

[0067] Table 2. Constraint Range of Decoy Scintillation Interference Parameters

[0068]

[0069] Step 3: Design the interference decision objective function for the interference parameters of the number of decoys, the flashing period of the decoys, and the RCS of the decoys as follows:

[0070] ;

[0071] in, The weighting coefficient represents the number of baits. The weighting coefficients representing the flashing period of the decoy. The weighting coefficient represents the weighting factor of the decoy's RCS. The sub-score represents the number of baits. The sub-score represents the decoy flashing period. This indicates the sub-score for the decoy's RCS.

[0072] The scores for the quantity items are obtained by the following formula:

[0073] ;

[0074] in, This indicates a modulo operation. This represents the optimal number of decoys to be determined. This indicates the number of baits randomly selected within the range of possible bait quantities. This indicates the maximum possible number of baits. This represents the minimum number of baits.

[0075] The component score for the flicker period is obtained by the following formula:

[0076] ;

[0077] in, This represents the optimal decoy flashing period to be solved. This indicates a randomly selected decoy flashing period within the range of decoy flashing period values. This indicates the maximum value of the decoy's flashing period. This represents the minimum value of the decoy's flashing period.

[0078] The sub-scores of RCS are obtained by the following formula:

[0079] ;

[0080] in, This represents the optimal decoy RCS to be solved. This represents the RCS value of a decoy randomly selected within the range of decoy RCS values. This indicates the maximum RCS value of the decoy. This represents the minimum RCS value of the decoy.

[0081] Step 4: Use a genetic algorithm to solve the objective function for perturbation decision-making.

[0082] First, the distance between the enemy radar and our radar is input, which determines the parameter values ​​in the objective function. The particle dimension is determined based on the number of unknowns in the objective function, and the number of unknowns is consistent with the particle dimension.

[0083] Next, input the RCS parameters of the enemy radar, which are information that needs to be known in the target function.

[0084] The specific solution process of the genetic algorithm is as follows:

[0085] The first step is to generate an initial population based on the current distance between the two opposing sides within the parameter constraints, with the initial population size set to 200.

[0086] The second step is to use a genetic algorithm to iteratively solve the objective function value, with a maximum number of iterations set to 100.

[0087] The third step is to use a tournament method to calculate the objective function value of each of the five randomly selected individuals. The higher the objective function value, the greater the probability that the individual will be selected.

[0088] The fourth step involves swapping the parameters of the individual with the maximum objective function value by using the number of decoys, the decoy flashing period, and the decoy RCS parameters. This generates a new individual.

[0089] The fifth step is to randomly change some parameters of the offspring chromosomes after exchanging parameters, and introduce a small number of new individuals to avoid the population getting trapped in a local optimum.

[0090] The sixth step is to merge the selected individuals with the offspring individuals after the exchange and parameter changes, calculate their respective objective function values, sort them in descending order of objective function values, and retain the top 200 individuals to form a new generation population.

[0091] Step 7: Determine if the maximum number of iterations has been reached. If yes, proceed to step 8; otherwise, proceed to step 3.

[0092] Step 8: The chromosome with the highest objective function value in the population at the end of the iteration is taken as the optimal solution, and the optimal number of decoys, the decoy flashing period, and the decoy RCS value are obtained.

[0093] Step 6: Calculate the miss distance of the enemy radar at different missile-target distances.

[0094] The calculation of the miss distance of the enemy radar under different algorithms is based on the distance between the enemy radar's impact point and our radar. The formula for calculating the miss distance is:

[0095]

[0096] in, This indicates the coordinates of the enemy radar's impact point. This indicates the location coordinates of our radar.

[0097] The effectiveness of this invention can be further demonstrated through the following simulation.

[0098] 1. Simulation experimental conditions.

[0099] The hardware platform for the simulation experiment of this invention is: Intel i7 9750H CPU with a main frequency of 2.6GHz and 16GB of memory.

[0100] The software platform for the simulation experiment of this invention is: Windows 10 operating system and Matlab R2024a.

[0101] The input parameters used in the simulation experiment of this invention include the distance between the enemy radar and our radar, the RCS of our target, etc., as shown in Table 3.

[0102] Table 3 Simulation Parameter Table

[0103]

[0104] The simulation conditions are set as shown in Table 3: the initial position of the enemy radar is [0,0,100] km, the initial velocity is 1000 m / s, the initial position of our radar is [100,5,0] km / s, the decoy release position is [102,6,0] km, the decoy release time is the distance between the enemy radar and our radar, the enemy's kill range is 125 m, and the decoy flashing interference is released at 60 km, 20 km, and 5 km respectively. The simulation calculation results are compared with the miss distance of the enemy radar under different algorithms.

[0105] There are three input parameters: ID. 1 represents a distance of 60km (long range) between the enemy radar and our radar; 2 represents a distance of 20km (medium range); and 3 represents a distance of 5km (short range). The RCS of all our targets is 100m. 2 Each input parameter ID corresponds to a row representing a combat scenario between opposing sides.

[0106] 2. Simulation content and result analysis.

[0107] The simulation experiment of this invention uses the present invention and an existing technology (traditional decision base method) to calculate the interference parameter selection results under different missile-target distances, and calculates the miss distance of the target radar for different methods based on the interference parameter selection results.

[0108] In the simulation experiment, the existing decision base method used refers to the interference decision method proposed by Xiao Qinding et al. in "Aerospace Electronic Countermeasures Vol. 27 No. 4 2021", which is referred to as the decision base method.

[0109] The simulation experiment of this invention uses the method of this invention and a prior art to obtain the selection results of interference parameters at long distance, medium distance and short distance, respectively, as shown in Table 4 and Table 5.

[0110] Table 4. Selection of Interference Parameters for Multi-Factor Decoy Scintillation Interference Decision-Making Method

[0111]

[0112] Table 5. Interference Parameter Selection Table Based on Tactical Decision Base

[0113]

[0114] Table 4 shows the interference parameter selection results for decoy flashing interference obtained using the method of this invention, and Table 5 shows the interference parameter selection results for decoy flashing interference obtained using the tactical decision base method. There are three output parameter IDs: 1 represents a distance (projectile-target distance) of 60km (long range), 2 represents a distance of 20km (medium range), and 3 represents a distance of [missing information]km (short range). Each row corresponding to an output parameter ID represents one interference parameter selection result, including three parameters: decoy quantity, decoy flashing period, and decoy RCS.

[0115] The obtained interference parameter selection results are shown in Tables 4 and 5. The miss distance of the opposing radar for each of these tables is then calculated and plotted. Figure 2 .

[0116] The following is combined with Figure 2 The simulation diagrams further illustrate the effects of the present invention.

[0117] Figure 2The horizontal axis represents the distance between the opposing forces, encompassing three intervals: 60km, 40km, and 20km (covering long- and medium-range scenarios). The vertical axis represents the miss distance of the enemy radar, ranging from 0 to 250m (a higher miss distance indicates a greater positioning deviation of the enemy radar from our target, resulting in better jamming effect). Gray bars represent traditional tactical decision-making methods, while blue bars represent the method of this invention. The red dashed line marks the "kill range line," located approximately 125m from the vertical axis (representing the effective kill boundary of the enemy weapon; a miss distance exceeding this line indicates that our target has effectively avoided the enemy's kill range).

[0118] from Figure 2 It can be seen that the interference parameters obtained by the method of this invention result in a significantly higher miss rate for the enemy radar than the traditional tactical decision-making database method. It can effectively avoid the enemy's kill range at long, medium, and short distances, increasing the survivability of our radar and improving our security.

[0119] The simulation experiments above show that the method of the present invention comprehensively considers the interference parameters of the number of decoys, the decoy flashing period, and the decoy RCS when designing the multi-factor objective function of decoy flashing interference. Furthermore, the parameter weights in the objective function can be adaptively adjusted according to the distance between the enemy radar and our radar. It also considers the miss distance of the enemy radar under different missile-target distances. This overcomes the problems of insufficient parameter consideration, lack of consideration for missile-target distance, and inability to cope with complex scenarios in existing decision-making database technologies. As a result, the interference efficiency of decoy flashing interference is improved in the "one-to-one" confrontation scenario between enemy radar and our radar established by the present invention, and the application scenarios are more diverse. It also demonstrates the superiority of genetic algorithm optimization in selecting interference parameters.

Claims

1. A weighted priority multi-factor decoy scintillation interference decision-making method, characterized in that, The decision-making method includes the following: Step 1: Construct a one-to-one confrontation scenario between the enemy's guidance radar and our radar; our radar uses decoys for cover and interferes with the enemy's guidance radar by using the signals generated by the flashing of the decoys. Step 2: Construct three parameters to measure the impact of bait quantity, bait flashing period, and bait RCS on bait flashing interference. Step 3: Define the weights and constraint ranges of the decoy blinking interference parameters corresponding to different distances; Step 4: Design an interference decision objective function for interference parameters such as the number of decoys, the flashing period of the decoys, and the RCS of the decoys; Step 5: Use a genetic algorithm to solve the objective function and obtain the optimal number of decoys, decoy flashing period, and decoy RCS value as interference parameters. Step 6: Calculate the miss distance of the enemy radar at different missile-target distances.

2. The multi-factor decoy scintillation interference decision-making method according to claim 1, characterized in that, The number of decoys mentioned in step 2 is mainly selected based on the distance between the two opposing sides. When the distance is far, more decoys are needed to create saturation interference, while when the distance is close, fewer decoys are needed, otherwise they will be easily identified by the opponent. The minimum number of decoys is set to launch only one decoy, and the maximum number is set to a maximum of 8 decoys that our side can launch.

3. The multi-factor decoy scintillation interference decision-making method according to claim 1, characterized in that, The flashing period of the decoy mentioned in step 2 is mainly selected based on the distance between the two sides. The farther the distance, the lower the sampling frequency of the other party's radar signal; the closer the distance, the higher the sampling frequency of the other party's radar signal. This is to ensure that the flashing period is close to the sampling period of the other party's radar signal. Otherwise, it will be easy for the other party to identify it. The minimum setting for the number of decoys is 0.3s, and the maximum setting is 5.0s.

4. The multi-factor decoy scintillation interference decision-making method according to claim 1, characterized in that, The decoy RCS mentioned in step 2 is mainly selected based on our own RCS. The decoy RCS value ranges from 1 to 2 times our own RCS to ensure that the decoy RCS matches our target RCS and forms an effective deception. The minimum number of decoys is set to 1 times our own RCS and the maximum number is set to 2 times our own RCS.

5. The multi-factor decoy scintillation interference decision-making method according to claim 1, characterized in that, The definition of the weights and constraints of the lure flashing interference parameters corresponding to different distances mentioned in step 3 refers to the fact that the weights of the lure flashing interference parameters are mainly selected based on the distance between the two parties. When the distance between the two parties is greater than 50km, it is defined as long distance, at which time the weight of the number of lures is relatively large, and the weights of the number of lures, the flashing period of the lures, and the RCS of the lures are 0.8, 0.1, and 0.1, respectively. When the distance between the two parties is greater than 10~50km, it is defined as medium distance, at which time the weights of the number of lures and the flashing period of the lures are relatively large, and the weights of the number of lures and the flashing period of the lures are relatively large. The weights of the period and the decoy RCS are 0.5, 0.3, and 0.2, respectively. When the distance between the two sides is less than 10km, it is defined as close range. At this time, the weights of the number of decoys and the decoy RCS are relatively large. The weights of the number of decoys, the decoy flashing period, and the decoy RCS are 0.5, 0.2, and 0.3, respectively. The constraint range of the decoy flashing interference parameters refers to the value range of each parameter. The constraint range of the number of decoys is 1 to 8, the constraint range of the decoy flashing period is 0.1 to 5s, and the constraint range of the decoy RCS is 1 to 2 times that of our own RCS.

6. The multi-factor decoy scintillation interference decision-making method according to claim 1, characterized in that, The interference decision objective function described in step 4 for the interference parameters of the number of decoys, the flashing period of the decoys, and the RCS of the decoys is as follows: ； in, The weighting coefficient represents the number of baits. The weighting coefficients representing the flashing period of the decoy. The weighting coefficient represents the weighting factor of the decoy's RCS. The sub-score represents the number of baits. The sub-score represents the decoy flashing period. This indicates the sub-score for the decoy's RCS.

7. The multi-factor decoy scintillation interference decision-making method according to claim 6, characterized in that, The score for the number of bait items is obtained by the following formula: ； in, This indicates a modulo operation. This represents the optimal number of decoys to be determined. This indicates the number of baits randomly selected within the range of possible bait quantities. This indicates the maximum possible number of baits. This represents the minimum number of baits.

8. The multi-factor decoy scintillation interference decision-making method according to claim 7, characterized in that, The sub-scores for the decoy flashing cycle are obtained by the following formula: ； in, This represents the optimal decoy flashing period to be solved. This represents a randomly selected decoy flashing period within the range of decoy flashing period values. This indicates the maximum value of the decoy's flashing period. This represents the minimum value of the decoy's flashing period.

9. The multi-factor decoy scintillation interference decision-making method according to claim 8, characterized in that, The sub-scores of the decoy's RCS are obtained by the following formula: ； in, This represents the optimal decoy RCS to be solved. This represents the RCS value of a decoy randomly selected within the range of decoy RCS values. This indicates the maximum RCS value of the decoy. This represents the minimum RCS value of the decoy.

10. The multi-factor decoy scintillation interference decision-making method according to claim 1, characterized in that, The steps for solving the objective function using a genetic algorithm described in step 5 are as follows: The first step is to generate an initial population based on the current distance between the two opposing sides within the parameter constraints, with the initial population size set to 200. The second step is to use a genetic algorithm to iteratively solve the objective function value, with a maximum number of iterations set to 100. The third step is to use a tournament method to calculate the objective function value of each of the five randomly selected individuals. The higher the objective function value, the greater the probability that the individual will be selected. The fourth step involves swapping the parameters of the individual with the maximum objective function value by using the number of decoys, the decoy flashing period, and the decoy RCS parameters. This generates a new individual. The fifth step is to randomly change some parameters of the offspring chromosomes after exchanging parameters, and introduce a small number of new individuals to avoid the population getting trapped in a local optimum. The sixth step is to merge the selected individuals with the offspring individuals after the exchange and parameter changes, calculate their respective objective function values, sort them in descending order of objective function values, and retain the top 200 individuals to form a new generation population. Step 7: Determine if the maximum number of iterations has been reached. If yes, proceed to step 8; otherwise, proceed to step 3. Step 8: The chromosome with the highest objective function value in the population at the end of the iteration is taken as the optimal solution, and the optimal number of decoys, the decoy flashing period, and the decoy RCS value are obtained.

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