Interference strategy matching, decision and evaluation method for multi-dimensional optimization of multi-mode sensor

Abstract

The application discloses a kind of multi-mode sensor multi-dimensional optimization interference strategy matching, decision and evaluation method, and implementation steps include: constructing multi-mode sensor interference strategy database, generates interference strategy rough knowledge;Multi-mode sensor interference strategy matching model is constructed, and according to the process of confrontation situation, slice, pattern optimization is carried out, and strategy matching result is obtained;Multi-mode sensor interference strategy decision model is constructed, and parameter optimization is carried out based on strategy matching result, and strategy parameter is obtained;Establish multi-mode sensor interference strategy effectiveness evaluation index system, evaluate the strategy generated, and the best strategy is fed back to database as fine knowledge.The application is aimed at confrontation multi-mode sensor problem, and constructs multi-dimensional optimization strategy matching, decision and evaluation method, and the best interference strategy generated is flexible and diverse, and has strong applicability;The method no longer generates strategy from single pattern dimension, and the strategy information generated is more comprehensive, and the dependence degree of database accuracy and completeness is low.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of radar infrared sensor, and particularly relates to a jamming strategy matching, decision and evaluation method for multi-dimensional optimization of a multi-mode sensor. BACKGROUND

[0002] Jamming strategy generation of a radar infrared multi-mode sensor is an important problem in electronic warfare system combat, and is a prerequisite for multi-mode sensor and jamming device combat. Therefore, establishing a scientific and effective jamming strategy matching, decision and evaluation method is a prerequisite for jamming strategy generation. Jamming matching, decision and evaluation is a method for deciding how to implement a jamming strategy for a radar infrared multi-mode sensor and evaluating the effectiveness of the jamming strategy. Only by fully mastering the enemy situation, our situation and the action process of the multi-mode sensor can an effective jamming strategy be generated for the multi-mode sensor. At present, there are two main methods for jamming decision of a jammer: the first is a radar sensor jamming strategy generation method based on template matching, which is a simple and intuitive jamming strategy generation method. The key to the jamming strategy generation method based on template matching lies in an accurate known radar sensor emitter database, and therefore the accuracy and completeness of the database directly determine the effectiveness of this method. The second is an infrared sensor jamming strategy generation method based on intelligent bionic algorithm. It designs certain evolution principles through a genetic algorithm to perform multiple iteration calculations to find the optimal solution of the jamming strategy, and the number of iterations and the dimension of the jamming strategy determine the performance of the method.

[0003] A jamming strategy generation method based on template matching is disclosed in the paper "Research on Radar Emitter Signal Recognition Based on Template Matching" (Shipboard Electronics Countermeasures, 2014, 37(5): 31-33.) by Wang Kunpeng et al. The implementation steps are as follows: first, a radar sensor emitter database is constructed, which stores signal samples of radar sensor emitters and the best jamming pattern corresponding to each signal sample; second, the detected radar signal is compared with the signal samples in the jamming library; third, the Euclidean distance is used as the template matching criterion, the jamming pattern corresponding to the signal sample with the highest similarity to the radar signal is selected, and the jamming pattern is used as the generated jamming strategy. The disadvantage of this method is that it relies heavily on the accuracy and completeness of the database, and once the database does not meet the conditions, it will not be able to match an effective jamming strategy. In addition, this method only considers the case of radar single-mode sensors, and only considers one-dimensional parameters of the jamming pattern, which will result in ineffective jamming strategies when facing complex electromagnetic countermeasure environments and multi-mode sensors.

[0004] In their published paper "Autonomous jamming decision method based on genetic algorithm" (Journal of China Civil Aviation University, 2023, 41(04): 58-64.), Zhu Zhuo et al. disclose an infrared sensor jamming decision method based on genetic algorithm. The implementation steps of the method are as follows: first, based on the jamming bomb release strategy set and the typical countermeasure situation, an infrared countermeasure sample library is established to provide data support for the selection of jamming bomb release strategy; second, according to the infrared sensor countermeasure sample library, combined with genetic algorithm, the optimal typical situation strategy set is obtained; third, through the linear interpolation, the mapping model between the comprehensive countermeasure situation and the countermeasure decision is obtained, and the jamming strategy for different countermeasure situations is obtained. This method realizes the generation of jamming strategy in complex infrared countermeasure situation, and improves the quality of jamming strategy generation. However, this method still has some shortcomings, such as the iteration number and the dimension of the jamming strategy seriously restrict the applicability of the method, and the one-dimensional parameter optimization of the jamming pattern is still used for solving, which is difficult to effectively interfere with the multi-mode sensor in the complex electromagnetic countermeasure environment. SUMMARY

[0005] The present application provides a multi-dimensional optimization jamming strategy matching, decision and evaluation method for multi-mode sensors, aiming to solve the problems of poor applicability of existing jamming strategy generation methods in complex electromagnetic countermeasure environment of multi-mode sensors, and large limitations of existing methods.

[0006] The technical solution of the present application is as follows: a multi-dimensional optimization jamming strategy matching, decision and evaluation method for multi-mode sensors, four optimization dimensions of process optimization, slice optimization, pattern optimization and parameter optimization are constructed, the four optimization dimension parameters are combined to realize the matching and decision of the best jamming strategy for multi-mode sensors, an effectiveness evaluation index system of jamming strategy for multi-mode sensors is established, different evaluation modes are set according to the indexes in the index system, a chromatography model is adopted to evaluate the jamming effectiveness for multi-mode sensors, and the most effective jamming strategy is output to the multi-mode sensor jamming strategy database; the specific steps include the following:

[0007] Step 1, constructing a multi-mode sensor jamming strategy database to generate coarse knowledge of jamming strategy.

[0008] Step 2, constructing a multi-mode sensor jamming strategy matching model, and performing process optimization, slice optimization and pattern optimization according to the countermeasure situation of multi-mode sensors to obtain the matching result of jamming strategy.

[0009] Step 3, constructing a multi-mode sensor jamming strategy decision model, and performing parameter optimization according to the jamming matching result of multi-mode sensors to obtain the decision parameters of jamming strategy, thereby obtaining the jamming strategy.

[0010] Step 4, establish a multi-mode sensor interference strategy effectiveness evaluation index system, according to the index in the established index system, set up the interference evaluation mode to evaluate the interference effect of the generated interference strategy, and output the best interference strategy as the fine knowledge feedback to the interference strategy database.

[0011] Compared with the prior art, the present application has the following advantages:

[0012] Firstly, the present application generates the best interference strategy through interference strategy matching, decision-making and evaluation for the working of multi-mode sensors in complex electromagnetic countermeasure environment, overcomes the defects of the prior art that only generates simple and intuitive interference strategy for single-mode sensors with small applicability, so that the present application makes the interference strategy generated by the jammer more flexible and diverse under the electronic countermeasure of multi-mode sensors, and more universal.

[0013] Secondly, the present application constructs a multi-dimensional optimization interference strategy matching, decision-making and evaluation method, compared with the prior art based on template matching and based on intelligent simulation, the present application no longer only optimizes the single dimension of interference style as the interference strategy, has more comprehensive interference strategy information, and the interference strategy after interference effect evaluation is fed back to the database, reducing the dependence of the prior art on the accuracy and completeness of the database. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A flow chart of the multi-dimensional optimization interference strategy matching, decision-making and evaluation method for multi-mode sensors of the present application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application, obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0016] The technical solutions of the various embodiments of the present application can be combined with each other, but it must be based on the fact that a person skilled in the art can realize it, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the scope of protection required by the present application.

[0017] The specific embodiments, technical difficulties and points of the present application will be further introduced in conjunction with the design examples.

[0018] In combination with Figure 1The present application constructs four optimization dimensions of process optimization, slice optimization, style optimization and parameter optimization, and combines the four optimization dimension parameters to realize multi-dimensional optimization of the interference strategy of the multi-mode sensor, so as to realize the matching and decision of the optimal interference strategy. According to the optimal interference strategy obtained by matching and decision, an interference strategy effectiveness evaluation index system for the multi-mode sensor is further established, three evaluation modes of influence effect model, guidance information transmission model and internal and external field test data are set according to the indexes in the established index system, and a chromatography model is adopted according to the change of signal parameters before and after the implementation of interference, so as to evaluate the interference effectiveness for the multi-mode sensor, verify the effectiveness of the generated interference strategy, and finally output the most effective interference strategy and feed back to the multi-mode sensor interference strategy database, so that the interference strategy of the multi-mode sensor in the complex electromagnetic countermeasure environment is more effective and flexible, thereby solving the problems of poor adaptability in the complex electromagnetic interference environment and the limitation of the interference strategy obtained by one-dimensional parameter optimization caused by the existing single-mode sensor interference strategy generation method. The specific steps are as follows:

[0019] Step 1, constructing a multi-mode sensor interference strategy database to generate interference strategy rough knowledge.

[0020] Firstly, interference strategy models containing interference time confirmation, interference equipment selection, interference time control, interference style selection, interference parameter tolerance and other multi-dimensional information are constructed for radar active interference, radar passive decoy interference and infrared decoy interference, which can be used to describe the related content of single-mode sensor interference strategy.

[0021] Then, based on the multi-mode sensor interference strategy model, an interference strategy database is constructed to generate interference strategy rough knowledge. The interference strategy database includes interference strategy matching table, decoy interference style parameter table, active interference style parameter table and interference equipment information table.

[0022] Among them, the interference strategy matching table contains interference time, interference equipment, interference time control, interference style and other information. The interference time is used to describe the relative distance, sensor working state and other information; the interference equipment is used to describe the interference equipment information; the interference time control is used to describe the interference duration or range; and the interference style contains typical radar active interference style, radar passive interference style and infrared interference style.

[0023] The decoy interference style parameter table contains decoy interference style and decoy interference style parameter, wherein the decoy interference style corresponds to radar passive interference style and infrared interference style, and the decoy interference style parameter is used to describe the emission direction, emission distance and other parameter information of the decoy interference.

[0024] The active jamming pattern parameter table comprises radar active jamming patterns and radar active jamming pattern parameters, and the radar active jamming pattern corresponds to the jamming pattern content in the jamming strategy matching table, and the radar active jamming pattern parameter comprises frequency, bandwidth, power, polarization mode and the like.

[0025] The jamming equipment information table comprises jamming equipment and jamming equipment capability parameters, and is mainly used for describing capability parameters (such as frequency range, maximum power, infrared radiation intensity) of the jamming equipment.

[0026] Step 2, a multi-mode sensor jamming strategy matching model is constructed, and process optimization, slice optimization and pattern optimization are performed according to a multi-mode sensor countermeasure situation, so that a matching result of the jamming strategy is obtained.

[0027] Firstly, a target function of multi-mode sensor jamming strategy matching is established as follows: f (v1, v2, v3)

[0028] Wherein, v1 is a multi-mode sensor electronic countermeasure process optimization factor, which is obtained according to the sensor-to-target distance when the radar sensor is turned on and the radar infrared sensor is turned on. v2 is a multi-mode sensor countermeasure situation static slice optimization factor, m x (x = 1, 2,..., N2) is a received radar signal slice, the working state information in each radar signal slice is different, and N2 is the number of slices. v3 is a multi-mode sensor countermeasure jamming pattern optimization factor, which is matched according to a multi-mode sensor jamming strategy database, q y (y = 1, 2,..., N3) is a corresponding jamming pattern in the database, and N3 is the number of jamming patterns. Wherein, the working state information of the multi-mode sensor comprises the working state information of the radar and the infrared sensor in the multi-mode sensor, wherein the working state of the radar sensor comprises search, tracking, shutdown and the like working phase information; the working state of the infrared sensor comprises search, tracking, shutdown and the like working phase information.

[0029] The relative distance between the multi-mode sensor and the target and the working state information of the multi-mode sensor are used as inputs to query and match the multi-mode sensor jamming strategy database, and radar jamming strategy information and infrared sensor jamming strategy information are respectively output; the preliminary matching output strategy information comprises jamming equipment, working state and jamming pattern, and process optimization, slice optimization and pattern optimization are further realized.

[0030] The embodiment of the application outputs the following information when the relative distance is 5km, the radar in the multi-mode sensor is in the tracking stage, and the infrared sensor is in the tracking stage:

[0031] The jamming strategy information for the radar:

[0032] Relative distance: 5km belongs to [0~10km] field; Radar working state: tracking; Based on the distance field and the working state matching with the database, the corresponding jamming equipment is selected: active jamming equipment; Based on the jamming equipment A, the jamming mode possessed by the jamming equipment A is selected: distance dragging jamming;

[0033] The jamming strategy information for the infrared sensor is:

[0034] Relative distance: 5km belongs to [3.0~10km] field, sensor working state: tracking, jamming equipment: shipborne infrared equipment, jamming mode: point source infrared decoy bomb.

[0035] Based on the above jamming strategy, the matching result of the jamming strategy is obtained:

[0036] f(v1, v2, v3) = {[active jamming equipment], [tracking], [distance dragging jamming]; [shipborne infrared equipment], [tracking], [point source infrared decoy bomb]}

[0037] Step 3, a multi-mode sensor jamming strategy decision model is constructed, and parameter optimization is performed according to the multi-mode sensor jamming matching result, the possible jamming mode parameter items are sorted and the optimal jamming mode parameter combination is output, so as to obtain the jamming strategy.

[0038] Based on the jamming strategy matching result f(v1, v2, v3) obtained in step 2, the objective function of the multi-mode sensor jamming strategy decision is

[0039] f2(f(v1, v2, v3), v4) (1)

[0040] Wherein, v4 is a factor for optimizing the multiple parameters of the jamming mode corresponding to the multi-mode sensor jamming strategy under the jamming strategy matching result f(v1, v2, v3). When the jamming strategy output by the multi-mode sensor jamming strategy matching in step 2 contains an active jamming mode, the active jamming mode is taken as the input, the jamming strategy database is queried to obtain the jamming mode parameter items under the corresponding jamming mode. After obtaining the jamming mode parameter items, the jamming parameters are divided into membership optimization parameters and limit optimization parameters according to the characteristics of the jamming parameters. For the membership optimization parameters, the membership function is constructed to perform parameter optimization and output the optimized jamming parameter value; for the limit optimization parameters, the objective discrete category value is directly selected to output the jamming parameter value. Finally, based on the obtained jamming parameter value, the best parameter combination of the jamming mode is obtained.

[0041] The embodiment of the present application illustrates the wideband blocking jamming, and the jamming parameter items related to the wideband blocking jamming are obtained by querying the jamming strategy database, which are jamming bandwidth Δf J , jamming power P J , and jamming opportunity T3, pmJ polarization mode pm J Then, the interference parameter items are divided, wherein the interference bandwidth Δf J , the equivalent radiation power P J , and the interference time T3 belong to the membership optimization parameters; and the polarization mode pm J is a limited optimization parameter.

[0042] The following is the optimization process of the membership optimization parameters:

[0043] First, the membership function of the above membership optimization parameters is constructed:

[0044] ① Membership function of interference bandwidth

[0045] The membership function μ4 of the interference bandwidth of the multi-mode sensor is constructed as follows:

[0046]

[0047] In the formula, the interference signal bandwidth Δf J = f J2 -f J1 , wherein f J2 is the maximum value of the interference frequency of the jammer, and f J1 is the minimum value of the interference frequency of the jammer; the multi-mode sensor transmission signal bandwidth Δf T = f T2 -f T1 , wherein f T2 is the maximum value of the working frequency of the radar part of the multi-mode sensor, and f T1 is the minimum value of the working frequency of the radar part of the multi-mode sensor.

[0048] ② Membership function of interference power

[0049] The membership function μ2 of the interference power of the multi-mode sensor is constructed as follows:

[0050]

[0051] In the formula, P J represents the interference power received by the radar sensor or the infrared sensor, P Ts is the target signal power received by the radar sensor or the infrared sensor, and K T represents the minimum signal-to-noise ratio necessary for the radar sensor or the infrared sensor to work normally.

[0052] ③ Membership function of interference time

[0053] The threat time of the multi-mode sensor is the time period [T1, T2], and the time for implementing effective jamming is the time period [T3, T4]. At different times in the threat time period [T1, T2], the threat degree of the multi-mode sensor is also different, and the closer to T2, the greater the threat degree of the multi-mode sensor. Based on this, the membership function μ3 of the jamming time of the multi-mode sensor is constructed as follows. First, the entire radar threat time period [T1, T2] is divided into three segments Segment number l = 1, 2, 3, where T1 0 = T1, T1 3 = T2 and Then, the threat degree of each segment

[0054] The membership function μ3 of the jamming time is as follows

[0055]

[0056] In the formula, T3 is the start time of jamming, that is, the jamming time.

[0057] Based on the calculation of the membership function of the jamming parameter term, the fuzziness matrix μ of the jammer to the multi-mode sensor is obtained as

[0058] μ = [μ1 μ2 μ3 μ4] T

[0059] Next, since 0 ≤ μ i ≤ 1, different membership function labels i = 1, 2, 3, 4, different weight factors w i (i = 1, 2, 3, 4) are set according to prior knowledge, w1 = 0.25, w2 = 0.25, w3 = 0.25, w4 = 0.25, and finally the jamming parameter combination μ is calculated by the following formula j :

[0060]

[0061] Based on the above formula, the value range of each parameter term μ i is traversed to obtain the jamming benefit μ j of the maximum parameter combination and output the result of the membership optimization parameter

[0062] The calculation process of the limiting optimization parameter is as follows. Since the limiting optimization parameter is an objective discrete category value, the corresponding belonging category value is directly selected as the jamming parameter value. For example, the polarization mode pm J has discrete category values {HH polarization, VV polarization, HV polarization and VH polarization}, so pm J is selected as VV polarization.

[0063] Based on the final result of the optimization of the above membership optimization parameters and the restriction optimization parameters, the output broadband blocking optimal interference strategy value is:

[0064] Finally, through the above multi-dimensional optimization of the interference strategy matching and decision-making, the generated interference strategy is as follows:

[0065]

[0066] Step 4, establish a multi-mode sensor interference strategy effectiveness evaluation index system, according to the indexes in the established index system, set up an interference evaluation mode to evaluate the interference effect of the generated interference strategy, and output the best interference strategy as the fine knowledge feedback to the interference strategy database.

[0067] The present application establishes a multi-mode sensor interference effect evaluation mode from three aspects of influence effect model, information transmission model and test data. An evaluation index system based on influence effect is constructed, in which the quantities related to real-time working parameters and working state of the multi-mode sensor are selected for construction, and the specific index system includes: pulse amplitude variation, pulse width variation, beam pointing offset, frequency domain variation and other index contents. An index system based on information transmission model is constructed, which evaluates the effectiveness of the interference strategy from the information dimension, and the specific index system includes: search target number, detection probability, false alarm probability, target recognition accuracy and other index contents. An index system based on test data is constructed, which evaluates the effectiveness of the interference strategy in the test environment, and the index system includes three parts: active radar interference effectiveness index, infrared sensor interference effectiveness index and target platform track interference effectiveness index.

[0068] According to the indexes in the established index system, the corresponding interference evaluation mode is set up to evaluate the interference effect of the generated interference strategy, and the output best interference strategy is fed back to the interference strategy database as fine knowledge. The present application selects one of the above evaluation index systems as an example, and the evaluation steps of the interference strategy are introduced as follows with the test data evaluation index system as an example.

[0069] S4-1, select the evaluation index system based on test data as the evaluation index system, construct a judgment matrix for comparing and evaluating the relative importance of different factors, which is defined as follows:

[0070] A=(a pq ) n×n (7)

[0071] Wherein, a pq represents the importance comparison result of the pth element relative to the qth element in the same layer, n represents the number of indexes in the evaluation index system in the layer, the correlation value is quantized as 1-9, and the reference value meaning is shown in Table 1.

[0072] Table 1 Quantitative table of correlation value

[0073]

[0074] S4-2, calculate single ordering weight:

[0075] The obtained all judgment matrix is normalized by column, and then summed by row, and then normalized to obtain the weight coefficient of each index. Next, the obtained judgment matrix is subjected to consistency test to eliminate the influence of inconsistent importance. First, calculate the consistency value CI:

[0076]

[0077] Where λ max is the maximum eigenvalue of the judgment matrix A. For the consistency value CI, CI = 0 means that the judgment matrix completely satisfies the consistency requirement, and the larger the value, the worse the consistency effect. Next,

[0078] The average random consistency value RI is introduced, as shown in Table 2 below.

[0079] Table 2 Average random consistency index

[0080] Matrix order 1 2 3 4 5 6 7 RI 0 0 0.52 0.89 1.12 1.26 1.36 Matrix order 8 9 10 11 12 13 14 RI 1.41 1.46 1.49 1.52 1.54 1.56 1.58

[0081] Based on the above consistency value and average random consistency value, calculate the consistency ratio CR:

[0082]

[0083] When CR < 0.1, it is considered that the judgment matrix is reasonable and satisfies the consistency, and when CR ≥ 0.1, it is considered that the judgment matrix does not satisfy the consistency and needs to be appropriately modified. When the order of the judgment matrix is 1 or 2, it is completely consistent, and at this time CR = 0.

[0084] S4-3, calculate total ordering weight

[0085] After calculating the single ordering weight of each layer index, the total ordering weight of the relative interference effect comprehensive evaluation result of the bottom layer index needs to be obtained. Next, the single ordering weight of each layer index is synthesized from top to bottom.

[0086] Suppose that the synthetic weight vector of num k-1 indices on the k-1 layer relative to the comprehensive interference effect has been obtained The single weight vector of num k indices on the k layer to the jth index on the k-1 layer as a criterion is set as Where the index weight not dominated by j is set to zero. Let denotes the synthetic weight of the k layer num k indicators on each indicator on the k-1 layer, then the synthetic weight vector W (k) of the k layer indicator on the interference effect synthetic evaluation result is:

[0087]

[0088] The recursion is obtained: W (k) = P (k) P (k-1) ...W (2) .

[0089] S4-4, calculate the comprehensive interference performance evaluation result

[0090] After obtaining the weight of each bottom layer indicator relative to the comprehensive interference effect, the evaluation result c is calculated by the following formula:

[0091]

[0092] Where, b k is the k layer indicator value, and K represents the total number of indicators. Based on the evaluation result, the best interference strategy is fed back to the interference strategy database as the refined knowledge.

Claims

1. A multi-dimensional optimization interference strategy matching, decision-making and evaluation method for a multi-mode sensor, characterized in that, The four optimization dimensions of construction process optimization, slice optimization, pattern optimization and parameter optimization are combined to realize the matching and decision of the best interference strategy of the multi-mode sensor. An effectiveness evaluation index system of the interference strategy for the multi-mode sensor is established. According to the indexes in the index system, different evaluation modes are set, a chromatography model is adopted, and the effectiveness of the interference for the multi-mode sensor is evaluated. The most effective interference strategy is fed back to the multi-mode sensor interference strategy database. The specific steps include the following: Step 1, constructing a multi-mode sensor interference strategy database to generate coarse knowledge of the interference strategy; Step 2, constructing a multi-mode sensor interference strategy matching model, and performing process optimization, slice optimization and pattern optimization according to the countermeasure situation of the multi-mode sensor to obtain the matching result of the interference strategy; Step 3, constructing a multi-mode sensor interference strategy decision model, and performing parameter optimization according to the interference matching result of the multi-mode sensor to obtain the decision parameters of the interference strategy, thereby obtaining the interference strategy; Step 4, establishing an effectiveness evaluation index system of the multi-mode sensor interference strategy, setting the interference evaluation mode according to the indexes in the established index system, evaluating the interference effect of the generated interference strategy, and feeding back the output best interference strategy as fine knowledge to the interference strategy database.

2. The method of claim 1, wherein, The multi-mode sensor interference strategy database in step 1 refers to constructing an interference strategy database based on the analysis of the multi-mode sensor interference strategy model. The interference strategy database includes an interference strategy matching table, a decoy interference pattern parameter table, an active interference pattern parameter table, and an interference equipment information table.

3. The method of claim 2, wherein, The interference strategy matching table includes interference opportunity, interference equipment, interference time control, and interference pattern. The interference opportunity is used to describe the relative distance and sensor working state. The interference equipment is used to describe the interference equipment information used. The interference time control is used to describe the duration or range of the interference. The interference pattern includes typical radar active interference patterns, radar passive interference patterns, and infrared interference patterns.

4. The method of claim 2, wherein, The decoy interference pattern parameter table includes decoy interference patterns and decoy interference pattern parameters. The decoy interference patterns correspond to the radar passive interference patterns and the infrared interference patterns, and the decoy interference pattern parameters are used to describe the launch direction and launch distance of the decoy interference.

5. The method of claim 2, wherein, The active interference pattern parameter table includes radar active interference patterns and radar active interference pattern parameters. The radar active interference patterns correspond to the interference patterns in the interference strategy matching table. The radar active interference pattern parameters include frequency, bandwidth, power, and polarization mode.

6. The method of claim 2, wherein, The interference equipment information table includes interference equipment and interference equipment capability parameters.

7. The method of claim 1, wherein, In step 2, the multi-mode sensor interference strategy matching model is constructed, and process optimization, slice optimization and pattern optimization are performed according to the countermeasure situation of the multi-mode sensor to obtain the matching result of the interference strategy. The objective function of the multi-mode sensor interference strategy matching is as follows: f(v1, v2, v3) Wherein, v1 is the multi-mode sensor electronic countermeasure process optimization factor, according to the radar sensor and radar infrared sensor are started sensor and target distance obtained; v2 is the multi-mode sensor countermeasure situation according to static slice optimization factor, m x The received radar signal is divided into slices, x = 1, 2, …, N2, the working state information is different in each radar signal slice, N2 is the number of slices; v3 is the multi-mode sensor countermeasure interference pattern optimization factor, which is matched according to the multi-mode sensor interference strategy database, q y The corresponding interference pattern in the database, y = 1, 2, …, N3, N3 is the number of interference patterns; the working state information of multi-mode sensor includes the working state information of radar and infrared sensor in multi-mode sensor, wherein the working state of radar sensor includes search, tracking, shutdown; the working state of infrared sensor includes search, tracking, shutdown; the relative distance and the working state information of multi-mode sensor are used as input to query and match the multi-mode sensor interference strategy database, and the radar interference strategy information and the infrared sensor interference strategy information are output respectively; the strategy information output by preliminary matching includes interference equipment, working state and interference pattern, and process optimization, slice optimization and pattern optimization are realized.

8. The method of claim 1, wherein, The multi-mode sensor jamming strategy decision model is constructed in step 3, and parameter optimization is performed according to the multi-mode sensor jamming matching result, and the objective function of the multi-mode sensor jamming strategy decision is f2(f(v1, v2, v3), v4) Wherein, v4 is a factor for optimizing the multiple parameters of the jamming pattern corresponding to the multi-mode sensor jamming strategy under f(v1, v2, v3); The specific optimization process is as follows: when the jamming strategy matched and output by the multi-mode sensor jamming strategy contains an active jamming pattern, the active jamming pattern is taken as input, and the jamming pattern parameter item under the corresponding jamming pattern is obtained by querying the jamming strategy database; after obtaining the jamming pattern parameter item, the jamming pattern parameter items are divided into membership optimization parameters and limit optimization parameters according to the characteristics of the jamming parameters; for the membership optimization parameters, a membership function is constructed to perform parameter optimization and output the optimized jamming parameter value; for the limit optimization parameters, the objective discrete category value is directly selected to output the jamming parameter value; finally, the optimal parameter combination of the jamming pattern is obtained based on the obtained jamming parameter value.

9. The method of claim 1, wherein, In step 4, the multi-mode sensor jamming strategy effectiveness evaluation index system is established, that is, the multi-mode sensor jamming effect evaluation mode is established from three aspects of influence effect model, information transmission model and test data; the evaluation index system based on influence effect is constructed, the index system is constructed by selecting quantities related to real-time working parameters and working states of the multi-mode sensor, and the specific index system includes: pulse amplitude change quantity, pulse width change quantity, beam pointing offset quantity, frequency domain change quantity; the index system based on the information transmission model is constructed, and the effectiveness of the jamming strategy is evaluated from the information dimension, and the specific index system includes: search target number, detection probability, false alarm probability, target recognition accuracy; the index system based on test data is constructed, and the effectiveness of the jamming strategy in the test environment is evaluated, and the index system includes three parts: active radar jamming effectiveness index, infrared sensor jamming effectiveness index and target platform track jamming effectiveness index; The jamming strategy evaluation adopts a chromatography model according to the indexes in the established index system, sets a jamming evaluation mode, evaluates the jamming effect of the generated jamming strategy, and outputs the optimal jamming strategy as fine knowledge to the jamming strategy database.

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