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

Abstract

The invention discloses an interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of a multi-mode sensor, and the method comprises the implementation steps: constructing a multi-mode sensor interference strategy database, and generating interference strategy coarse knowledge; constructing a multimode sensor interference strategy matching model, and performing process, slicing and style optimization according to the confrontation situation to obtain a strategy matching result; constructing a multimode sensor interference strategy decision model, and performing parameter optimization based on a strategy matching result to obtain strategy parameters; and establishing a multi-mode sensor interference strategy effectiveness evaluation index system, evaluating the generated strategy, and feeding back the optimal strategy as fine knowledge to the database. According to the method, a multi-dimensional optimization strategy matching, decision-making and evaluation method is constructed aiming at the problem of resisting a multimode sensor, and the generated optimal interference strategy is flexible, diversified and high in applicability; according to the method, the strategy is not generated from a single style dimension any more, the generated strategy information is more comprehensive, and the degree of dependence on the accuracy and completeness of the database is low.

Description

Technical Field

[0001] The invention belongs to the technical field of radar infrared sensors, and in particular relates to a multi-dimensional optimization interference strategy matching, decision-making and evaluation method for a multi-mode sensor. Background Art

[0002] The generation of jamming strategies for radar infrared multi-mode sensors is an important issue in the operation of electronic warfare systems and a prerequisite for the confrontation between multi-mode sensors and jamming devices. Therefore, the establishment of scientific and effective jamming strategy matching, decision-making and evaluation methods is the prerequisite for the generation of jamming strategies. Jamming matching, decision-making and evaluation are methods for making decisions on how to implement jamming strategies for radar infrared multi-mode sensors and evaluating the effectiveness of jamming strategies. Only by fully understanding the enemy situation, our situation and the working process of multi-mode sensors can we reasonably generate effective jamming strategies for multi-mode sensors. At present, there are two main methods for jammer jamming decision-making: 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 the accurate known radar sensor radiation source database, so the accuracy and completeness of the database directly determine the effectiveness of this type of method. The second is an infrared sensor jamming strategy generation method based on an intelligent bionic algorithm. It designs certain evolutionary principles through genetic algorithms, and performs multiple iterative calculations to find the optimal solution of the jamming strategy. The number of iterations and the dimension of the jamming strategy determine the performance of this method.

[0003] Wang Kunpeng et al. published a method for generating interference strategies based on template matching in their paper “Research on Radar Emitter Signal Identification Based on Template Matching” (Ship Electronic Countermeasures, 2014, 37(5): 31-33). The implementation steps are as follows: the first step is to build a radar sensor radiation source database, which stores the signal samples of the radar sensor radiation source sensor and the best interference pattern corresponding to each signal sample; the second step is to compare the detected radar signal with the signal samples in the interference library; the third step is to use the Euclidean distance as the template matching criterion, select the interference pattern corresponding to the signal sample with the highest similarity to the radar signal, and use the interference pattern as the generation interference strategy. The shortcomings of this method are that the method is heavily dependent on the accuracy and completeness of the database. Once the database does not meet the conditions, it will be impossible to match the effective interference strategy; in addition, this method only considers the case of radar single-mode sensors and only considers the one-dimensional parameters of the interference pattern. When multi-mode sensors work in a complex electromagnetic confrontation environment, there is a problem of failure of the interference strategy.

[0004] Zhu Zhuo et al. published a genetic algorithm-based interference decision method for infrared sensors in their paper "Autonomous Interference Decision Method Based on Genetic Algorithm" (Journal of Civil Aviation University of China, 2023, 41(04): 58-64.). The implementation steps of this method are as follows: the first step is to establish an infrared adversarial sample library based on the jammer delivery strategy set and typical adversarial situations to provide data support for the selection of jammer delivery strategies; the second step is to optimize the infrared sensor adversarial sample library in combination with genetic algorithms to obtain the optimal typical situation strategy set; the third step is to obtain the interference strategy for different adversarial situations through linear interpolation, comprehensive adversarial situations and mapping models between adversarial decisions. This method improves the quality of interference strategy generation in complex infrared adversarial situations. However, this method still has shortcomings. The number of iterations and the dimension of the interference strategy seriously restrict the applicability of the method, and it is still only solved by optimizing the one-dimensional parameters of the interference style, which makes it difficult to effectively interfere with multi-mode sensors in complex electromagnetic adversarial environments. Summary of the invention

[0005] The present invention proposes a multi-dimensional optimization interference strategy matching, decision-making and evaluation method for multi-mode sensors, aiming to solve the problems that the existing interference strategy generation method has poor applicability in the complex electromagnetic confrontation environment of multi-mode sensors and the existing methods have great limitations.

[0006] The technical solution to realize the present invention is: a method for matching, deciding and evaluating interference strategies for multi-mode sensors in multi-dimensional optimization, constructing four optimization dimensions of process optimization, slice optimization, style optimization and parameter optimization, combining the parameters of these four optimization dimensions to achieve matching and decision-making of the best interference strategy for multi-mode sensors, establishing an interference strategy effectiveness evaluation index system for multi-mode sensors, and setting different evaluation modes according to the indicators in the index system, adopting a tomography model, evaluating the interference effectiveness for multi-mode sensors, and outputting the most effective interference strategy to feed back to the multi-mode sensor interference strategy database; the specific steps include the following:

[0007] Step 1: Build a multi-mode sensor jamming strategy database and generate coarse knowledge of jamming strategies.

[0008] Step 2: construct a multi-mode sensor interference strategy matching model, and perform process optimization, slice optimization, and style optimization according to the multi-mode sensor confrontation situation to obtain the matching result of the interference strategy.

[0009] Step 3: construct a multi-mode sensor interference strategy decision model, and perform parameter optimization based on the multi-mode sensor interference matching result to obtain the decision parameters of the interference strategy, thereby obtaining the interference strategy.

[0010] Step 4, establish a multi-mode sensor interference strategy effectiveness evaluation index system, set the interference evaluation mode to evaluate the interference effect of the generated interference strategy according to the indicators in the established index system, and feed back the output optimal interference strategy to the interference strategy database as refined knowledge.

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

[0012] First, the present invention aims at multi-mode sensors working in complex electromagnetic confrontation environments, and generates the best interference strategy through interference strategy matching, decision-making and evaluation, overcoming the defect of the prior art that the interference strategy is simply and intuitively generated only for single-mode sensors and has low applicability. The present invention makes the interference strategy generated by the jammer more flexible, diverse and universal under the electronic confrontation of multi-mode sensors.

[0013] Second, the present invention constructs a multi-dimensional optimization interference strategy matching, decision-making and evaluation method. Compared with the existing template matching-based method and intelligent bionics-based method, the interference strategy is no longer optimized based on a single dimension of interference style. It has more comprehensive interference strategy information, and the interference strategy after the interference effect evaluation will be fed back to the database, reducing the dependence of the existing method on the accuracy and completeness of the database. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] The technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0017] The following will further introduce the specific implementation method, as well as the technical difficulties and inventive points of this invention in combination with this design example.

[0018] Combination Figure 1The present invention constructs four optimization dimensions of process optimization, slice optimization, style optimization and parameter optimization, and combines the parameters of these four optimization dimensions to realize multi-dimensional optimization of the interference strategy of the multi-mode sensor, thereby realizing the matching and decision of the optimal interference strategy. According to the optimal interference strategy obtained by matching and decision-making, a interference strategy effectiveness evaluation index system for multi-mode sensors is further established. According to the indicators in the established index system, three evaluation modes are set, namely, the influence effect model, the guidance information transmission model and the internal and external field test data. According to the changes in the signal parameters before and after the implementation of the interference, the tomography model is adopted to evaluate the interference effectiveness of the multi-mode sensor, verify the effectiveness of the generated interference strategy, and finally output the most effective interference strategy, and feed it back to the multi-mode sensor interference strategy database, so that the interference strategy of the multi-mode sensor in the complex electromagnetic confrontation environment is more effective and flexible, thereby solving the problem that the existing single-mode sensor interference strategy generation method has poor adaptability in the complex electromagnetic interference environment, and the interference strategy obtained by one-dimensional parameter optimization has limitations. The specific steps are as follows:

[0019] Step 1: Build a multi-mode sensor jamming strategy database and generate coarse knowledge of jamming strategies.

[0020] Firstly, for radar active jammers, radar passive decoy jammers and infrared decoy jammers, a jammer strategy model is constructed, which includes information on multiple dimensions, such as jammer timing confirmation, jammer equipment selection, jammer time control, jammer style selection, and jammer parameter tolerance. It can be used to describe the jammer strategy related content for single-mode sensors.

[0021] Next, based on the multi-mode sensor jamming strategy model, a jamming strategy database is constructed to generate rough knowledge of jamming strategies. The jamming strategy database includes a jamming strategy matching table, a decoy jamming pattern parameter table, an active jamming pattern parameter table, and a jamming equipment information table.

[0022] The jamming strategy matching table includes information such as jamming opportunity, jamming equipment, jamming time control, and jamming style. The jamming opportunity is used to describe information such as relative distance and sensor working status; the jamming equipment is used to describe the jamming equipment information used; the jamming time control is used to describe the duration or range of the jamming; and the jamming style includes typical radar active jamming style, radar passive jamming style, and infrared jamming style.

[0023] The decoy jammer pattern parameter table includes decoy jammer patterns and decoy jammer pattern parameters, wherein the decoy jammer patterns correspond to radar passive jammer patterns and infrared jammer patterns, and the decoy jammer pattern parameters are used to describe parameter information such as the emission direction and emission distance of the decoy jammers.

[0024] 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 pattern contents in the interference strategy matching table. The radar active interference pattern parameters include frequency, bandwidth, power, polarization mode and other information.

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

[0026] Step 2: construct a multi-mode sensor interference strategy matching model, and perform process optimization, slice optimization, and style optimization according to the multi-mode sensor confrontation situation to obtain the matching result of the interference strategy.

[0027] First, the objective function of the multi-mode sensor interference strategy matching is established as: f(v1, v2, v3)

[0028] Among them, v1 is the optimization factor for the multi-mode sensor electronic countermeasure process, which is obtained based on the distance between the radar sensor and the target when both the radar and infrared sensors are turned on. v2 is the optimization factor for the multi-mode sensor countermeasure situation based on the static slice. m x (x=1,2,…,N2) is the slices into which the received radar signal is divided. The working state information in each radar signal slice is different. N2 is the number of slices. v3 is the interference pattern optimization factor during multi-mode sensor confrontation. It is matched according to the multi-mode sensor interference strategy database. q y (y=1,2,....,N3) is the corresponding interference pattern in the database, and N3 is the number of interference patterns. The working status information of the multi-mode sensor includes the working status information of the radar and infrared sensor in the multi-mode sensor, wherein the working status of the radar sensor includes the working stage information such as search, tracking, and shutdown; the working status of the infrared sensor includes the working stage information such as search, tracking, and shutdown.

[0029] Using the relative distance between the multi-mode sensor and the target and the working status information of the multi-mode sensor as input, the multi-mode sensor interference strategy database is queried and matched, and the radar interference strategy information and the infrared sensor interference strategy information are output respectively; the strategy information output by the preliminary matching includes interference equipment, working status, and interference style, thereby realizing process optimization, slice optimization, and style optimization.

[0030] In the embodiment of the present invention, when the relative distance is 5 km, the radar in the multi-mode sensor is in the tracking stage, and the infrared sensor is in the tracking stage, the preliminary decision outputs the following information:

[0031] Information on radar jamming strategies:

[0032] Relative distance: 5km belongs to the [0~10km] field; radar working status: tracking; based on the distance field and working status matching with the database, select the corresponding jamming equipment: active jamming equipment; then based on the jamming equipment A, select its jamming style: distance-drag jamming;

[0033] Information on the interference strategy for infrared sensors:

[0034] Relative distance: 5km belongs to the [3.0~10km] field, sensor working status: tracking, interference equipment: shipborne infrared equipment, interference style: point source infrared decoy bomb.

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

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

[0037] Step 3: construct a multi-mode sensor interference strategy decision model, and perform parameter optimization based on the multi-mode sensor interference matching results, sort possible interference pattern parameter items and output the optimal interference pattern parameter combination, so as to obtain the interference 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 established as

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

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

[0041] The embodiment of the present invention is described by using broadband blocking interference. The interference parameter item related to broadband blocking interference obtained by querying the interference strategy database is the interference bandwidth Δf J , interference power P J , interference time T3,pmJ Polarization mode pm J Then, these interference parameter items are divided, where the interference bandwidth Δf J , equivalent radiated power P J , the interference timing T3 belongs to the optimization parameter; and the polarization mode pm J To restrict the optimization parameters.

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

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

[0044] ① Membership function of interference bandwidth

[0045] The membership function μ4 of the interference bandwidth to the multimode sensor is constructed as follows:

[0046]

[0047] Where, the interference signal bandwidth Δf J =f J2 -f J1 , where f J2 is the maximum value of the jammer’s interference frequency, f J1 is the minimum value of the jammer’s interference frequency; the multimode sensor transmit signal bandwidth Δf T =f T2 -f T1 , where f T2 is the maximum operating frequency of the radar part of the multimode sensor, f T1 The minimum operating frequency of the radar portion of the multi-mode sensor.

[0048] ② Membership function of interference power

[0049] The membership function μ2 of the interference power to the multimode sensor is constructed as follows:

[0050]

[0051] Where P J It represents the interference power received by the radar sensor or infrared sensor, P Ts K is the target signal power received by the radar sensor or infrared sensor, T Indicates the minimum signal-to-noise ratio required for a radar sensor or infrared sensor to receive data properly.

[0052] ③ Membership function of interference timing

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

[0054] The membership function μ3 of the interference opportunity is as follows

[0055]

[0056] Where T3 is the starting time of interference, that is, the timing of interference.

[0057] Based on the calculation of the membership function of the interference parameter term above, the ambiguity matrix μ of the jammer to the multi-mode sensor can be obtained as follows:

[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 are set according to prior knowledge i , (i=1,2,3,4), w1=0.25, w2=0.25, w3=0.25, w4=0.25, and finally the interference parameter combination μ is calculated by the following formula j :

[0060]

[0061] Based on the above formula, for each parameter term μ i The value range of traversal is obtained to obtain the interference benefit μ j The largest parameter combination and output the result of the membership optimization parameter

[0062] The following is the calculation process of the restricted optimization parameter. Since the restricted optimization parameter is an objective discrete category value, the corresponding category value is directly selected and output as the interference parameter value. J , whose discrete category values ​​are {HH polarization, VV polarization, HV polarization and VH polarization}, so that pm can be selected J It is VV polarization.

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

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

[0065]

[0066] Step 4, establish a multi-mode sensor interference strategy effectiveness evaluation index system, set the interference evaluation mode to evaluate the interference effect of the generated interference strategy according to the indicators in the established index system, and feed back the output optimal interference strategy to the interference strategy database as refined knowledge.

[0067] The present invention establishes a multi-mode sensor interference effect evaluation model from three aspects: an impact effect model, an information transfer model and test data. An evaluation index system based on the impact effect is constructed, and the index system is constructed by selecting quantities related to the real-time working parameters and working status of the multi-mode sensor. The specific index system includes: pulse amplitude change, pulse width change, beam pointing offset, frequency domain change and other index contents. An index system based on the information transfer model is constructed to evaluate the effectiveness of the interference strategy from the information dimension. 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 to evaluate the effectiveness of the interference strategy under the test environment. 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 indicators in the established indicator system, the corresponding interference evaluation mode is set to evaluate the interference effect of the generated interference strategy, and the output optimal interference strategy is fed back to the interference strategy database as fine knowledge. The example of the present invention selects one of the above evaluation indicator systems, and takes the evaluation indicator system of test data as an example to introduce the interference strategy evaluation steps as follows.

[0069] S4-1. Select the evaluation index based on the test data as the evaluation index system, and 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] Among them, a pq It represents the comparison result of the importance of the p-th element relative to the q-th element in the same layer. n represents the number of indicators in the evaluation indicator system of this layer. The relevant value is quantified from 1 to 9. The reference value has the following meaning.

[0072]

[0073] S4-2. Calculate single ranking weight:

[0074] All judgment matrices are normalized by column, then summed by row, and then normalized to obtain the weight coefficient of each indicator. Next, the obtained judgment matrix is ​​subjected to consistency test to eliminate the influence of inconsistent importance. First, the consistency value CI is calculated:

[0075]

[0076] Among them, λ max is the maximum eigenvalue of the judgment matrix A. For the consistency value CI, when CI=0, the judgment matrix fully meets the consistency requirements, and the larger its value, the worse the consistency effect. Next,

[0077] The average random consistency value RI is introduced, as shown in the following table.

[0078] Table average random consistency index

[0079] 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

[0080] Based on the above consistency values ​​and the average random consistency value, calculate the consistency ratio CR:

[0081]

[0082] When CR is less than 0.1, the judgment matrix is ​​considered to be reasonably set and meets the consistency. When CR is greater than or equal to 0.1, the judgment matrix is ​​considered to be inconsistent and needs to be modified appropriately. When the order of the judgment matrix is ​​1 or 2, it completely meets the consistency, and CR is 0.

[0083] S4-3. Calculate the total ranking weight

[0084] After calculating the single ranking weights of each layer of indicators, it is necessary to obtain the total ranking weight of the bottom layer indicator relative to the comprehensive evaluation result of the interference effect. The single ranking weights of each layer of indicators are synthesized from top to bottom.

[0085] Assume that we have found that there are num k-1 The composite weight vector of the indicators relative to the comprehensive interference effect num on the kth layer k The single weight vector of the k-1 layer j-th indicator as the criterion is set as The weights of the indicators not controlled by j are set to zero. Indicates num on layer k kThe composite weight of each indicator on the k-1 layer, then the composite weight vector W of the k-layer indicators on the comprehensive evaluation result of the interference effect is (k) for:

[0086]

[0087] Recursion: W (k) =P (k) P (k-1) …W (2) .

[0088] S4-4. Calculate the comprehensive interference effectiveness evaluation results

[0089] After obtaining the weights of the relative comprehensive interference effects of each underlying indicator, the evaluation result c is calculated by the following formula:

[0090]

[0091] Among them, b k is the k-th index value, and K represents the total number of index layers. Based on the evaluation results, the best interference strategy is fed back to the interference strategy database as refined knowledge.

Claims

1. A method for matching, deciding and evaluating interference strategies for multi-mode sensors in multi-dimensional optimization, characterized in that: Four optimization dimensions, namely process optimization, slice optimization, style optimization and parameter optimization, are constructed. The parameters of these four optimization dimensions are combined to achieve the matching and decision-making of the best interference strategy for multi-mode sensors. An interference strategy effectiveness evaluation index system for multi-mode sensors is established. Different evaluation modes are set according to the indicators in the index system. A tomography model is adopted to evaluate the interference effectiveness for multi-mode sensors. The most effective interference strategy is output and fed back to the multi-mode sensor interference strategy database. The specific steps include the following: Step 1, construct a multi-mode sensor jamming strategy database and generate coarse knowledge of jamming strategies; Step 2: construct a multi-mode sensor interference strategy matching model, and perform process optimization, slice optimization, and style optimization according to the multi-mode sensor confrontation situation to obtain the matching result of the interference strategy; Step 3, construct a multi-mode sensor interference strategy decision model, and perform parameter optimization based on the multi-mode sensor interference matching result to obtain the decision parameters of the interference strategy, thereby obtaining the interference strategy; Step 4, establish a multi-mode sensor interference strategy effectiveness evaluation index system, set the interference evaluation mode to evaluate the interference effect of the generated interference strategy according to the indicators in the established index system, and feed back the output optimal interference strategy to the interference strategy database as refined knowledge.

2. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 1 is characterized in that: The multi-mode sensor interference strategy database described in step 1 refers to building an interference strategy database based on the sorting of the multi-mode sensor interference strategy model. The interference strategy database includes an interference strategy matching table, a decoy interference style parameter table, an active interference style parameter table, and an interference equipment information table.

3. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 2 is characterized in that: The interference strategy matching table includes interference timing, interference equipment, interference time control, and interference style; the interference timing is used to describe the relative distance and sensor working status; 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 style includes typical radar active interference style, radar passive interference style, and infrared interference style.

4. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 2 is characterized in that: The decoy jammer pattern parameter table includes decoy jammer patterns and decoy jammer pattern parameters, wherein the decoy jammer patterns correspond to radar passive jammer patterns and infrared jammer patterns, and the decoy jammer pattern parameters are used to describe the emission direction and emission distance of the decoy jammer patterns.

5. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 2 is characterized in that: 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 pattern contents in the interference strategy matching table. The radar active interference pattern parameters include frequency, bandwidth, power, and polarization.

6. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 2 is characterized in that: The jamming equipment information table includes jamming equipment and jamming equipment capability parameters.

7. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 1 is characterized in that: The multi-mode sensor interference strategy matching model is constructed in step 2, and the process optimization, slice optimization, and style optimization are performed according to the multi-mode sensor confrontation situation to obtain the matching result of the interference strategy. The objective function of the multi-mode sensor interference strategy matching is established as follows: f(v1, v2, v3) Among them, v1 is the optimization factor for the multi-mode sensor electronic countermeasure process, which is obtained according to the distance between the radar sensor and the target when both the radar sensor and the radar infrared sensor are turned on; v2 is the optimization factor for the multi-mode sensor countermeasure situation according to the static slice. m x (x=1,2,…,N2) is the slices into which the received radar signal is divided. The working state information in each radar signal slice is different. N2 is the number of slices. v3 is the interference pattern optimization factor during multi-mode sensor confrontation. It is matched according to the multi-mode sensor interference strategy database. q y (y=1,2,....,N3) is the corresponding interference pattern in the database, and N3 is the number of interference patterns; the working status information of the multi-mode sensor includes the working status information of the radar and the infrared sensor in the multi-mode sensor, wherein the working status of the radar sensor includes searching, tracking, and shutting down; the working status of the infrared sensor includes searching, tracking, and shutting down; using the relative distance and the working status information of the multi-mode sensor as input, the multi-mode sensor interference strategy database is queried and matched, and the radar interference strategy information and the infrared sensor interference strategy information are output respectively; the strategy information output by the preliminary matching includes interference equipment, working status, and interference pattern, thereby realizing process optimization, slice optimization, and pattern optimization.

8. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 1 is characterized in that: In step 3, a multi-mode sensor interference strategy decision model is constructed, and parameter optimization is performed based on the multi-mode sensor interference matching result. The objective function of the multi-mode sensor interference strategy decision is f2(f(v1, v2, v3), v4) Among them, v4 is a factor for optimizing multiple parameters of the interference pattern corresponding to the multi-mode sensor interference strategy under f(v1, v2, v3); The specific optimization process is as follows: when the interference strategy output by the multi-mode sensor interference strategy matching contains an active interference pattern, the active interference pattern is used as input to query the interference strategy database to obtain the interference pattern parameter items under the corresponding interference pattern; after obtaining the interference pattern parameter items, these interference pattern parameter items are divided into membership optimization parameters and restriction optimization parameters according to the characteristics of the interference parameters; for membership optimization parameters, a membership function is constructed to perform parameter optimization and output the optimized interference parameter values; for restriction optimization parameters, the output interference parameter values ​​are directly selected based on their objective discrete category values; finally, based on the obtained interference parameter values, the optimal parameter combination of the interference pattern is obtained.

9. The interference strategy matching, decision-making and evaluation method for multi-dimensional optimization of multi-mode sensors according to claim 1 is characterized in that: The step 4 describes the establishment of a multi-mode sensor jamming strategy effectiveness evaluation index system, that is, the establishment of a multi-mode sensor jamming effect evaluation model from three aspects: the impact effect model, the information transfer model and the test data; the construction of an evaluation index system based on the impact effect, which is constructed by selecting quantities related to the real-time working parameters and working status of the multi-mode sensor, and the specific index system includes: pulse amplitude change, pulse width change, beam pointing offset, frequency domain change; the construction of an index system based on the information transfer model, the evaluation of the effectiveness of the jamming strategy from the information dimension, the specific index system includes: the number of search targets, the detection probability, the false alarm probability, the target recognition accuracy; the construction of an index system based on test data, the evaluation of the effectiveness of the jamming strategy in the test environment, the index system includes three parts: the effectiveness index of active radar jamming, the effectiveness index of infrared sensor jamming and the effectiveness index of target platform track jamming; The interference strategy evaluation adopts a tomography model according to the indicators in the established indicator system, sets an interference evaluation mode, evaluates the interference effect of the generated interference strategy, and feeds back the outputted optimal interference strategy as refined knowledge to the interference strategy database.

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