Radio fuse anti-information interference effectiveness evaluation method
By constructing a multi-level fuzzy comprehensive evaluation index system and optimization model, the inaccuracy problem of evaluating the anti-information interference effectiveness of radio fuses was solved, and efficient evaluation in complex electromagnetic environments was achieved, improving the efficiency and accuracy of fuse anti-interference capability evaluation.
Patent Information
- Application Number
- CN202510943846.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-09
- Publication Date
- 2025-10-17
AI Technical Summary
Existing methods for evaluating the effectiveness of radio fuses against information-based interference lack standardized performance evaluation indicators, making it impossible to objectively and fairly assess the fuse's ability to resist active information-based interference in complex electromagnetic environments, resulting in inaccurate and incomplete evaluation results.
A complete multi-level fuzzy comprehensive evaluation index system for radio fuze anti-interference effectiveness is constructed. The principal component analysis and entropy weight method are combined to optimize the index system. A multi-level fuzzy comprehensive radio fuze anti-information interference effectiveness evaluation model is established. The final quantitative evaluation results are determined by the fuzzy comprehensive evaluation method.
It enables the evaluation of the anti-information interference effectiveness of radio fuses in complex electromagnetic environments, improving the evaluation efficiency and accuracy, and providing strong support for the battlefield adaptability and use of fuses.
Smart Images
Figure CN120805697A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of information countermeasure technology, in particular to a radio fuze anti-information jamming effectiveness evaluation method. BACKGROUND
[0002] The interference environment faced by the radio fuze is very complex. No matter in the current or future battlefield, the most main and fatal danger faced by the radio fuze is the information jamming generated by the jammer. With the development of the information jamming technology of the radio fuze towards portability, intelligence and diversification, the anti-jamming effectiveness gradually becomes one of the most important effectiveness of the fuze, is the quantification of whether the fuze can play its due damage control ability, and is the key to affect the battlefield survivability of the fuze. The anti-information active jamming effectiveness evaluation of the radio fuze not only helps to find out the adaptability of the radio fuze to the complex electromagnetic environment of the future battlefield, but also provides an important reference for the design, operational use and technical improvement of the radio fuze. Although the existing research on the anti-jamming effectiveness evaluation method of the radio fuze has developed some evaluation criteria and methods of the anti-jamming effectiveness, overall, there is no standard for the construction of the interference environment for the effectiveness evaluation, the evaluation index is less, the test environment is imperfect, and the evaluation is usually only for single or small amount of effectiveness of the radio fuze. There is a lack of comprehensive effectiveness evaluation in complex electromagnetic environment, and it is difficult to judge whether the fuze equipment can reliably and efficiently complete the combat mission task in complex electromagnetic environment, and it is difficult to objectively and fairly evaluate the anti-information active jamming effectiveness of the radio fuze in complex electromagnetic environment.
[0003] Therefore, a radio fuze anti-information jamming effectiveness evaluation method is provided. SUMMARY
[0004] The present application aims to overcome the existing defects and provide a radio fuze anti-information jamming effectiveness evaluation method, which solves the problems of less anti-jamming effectiveness evaluation index of the radio fuze, difficulty in quantifying the index, high redundancy of the index system and low evaluation effectiveness.
[0005] The technical solution to achieve the above-mentioned purpose is: A radio fuze anti-information jamming effectiveness evaluation method, comprising: Step S1, constructing a complete radio fuze anti-jamming multi-level fuzzy comprehensive evaluation index system; Step S2, optimizing the radio fuze anti-jamming multi-level fuzzy comprehensive evaluation index system; Step S3, establishing a multi-level fuzzy comprehensive radio fuze anti-information jamming effectiveness evaluation model, and then determining the final quantitative evaluation result.
[0006] Preferably, in step S1, the complete radio fuze anti-interference multi-level fuzzy comprehensive evaluation performance evaluation index system includes: anti-information interference effectiveness under simulated typical combat scenarios, as well as anti-typical information interference mode effectiveness and fuze inherent performance.
[0007] Preferably, the effectiveness of resisting information interference in simulating typical combat scenarios includes: the success rate of resisting key point defense penetration and the success rate of resisting team cover penetration; in, The success rate of anti-key point defense breakthrough and the success rate of anti-team cover breakthrough both include: the success rate of anti-forwarding interference, the success rate of anti-sweeping interference and the success rate of anti-aiming interference.
[0008] Preferably, the effectiveness of resisting typical information-type interference methods and the inherent performance of the fuze include: the success rate of anti-forwarding interference, the success rate of anti-sweeping interference, the success rate of anti-aiming interference, the probability of being intercepted, the time of being intercepted, the duration of interference, the maximum radiation power spectrum density and the interference power factor.
[0009] Preferably, the success rate of resisting forwarding interference includes: the success rate of resisting interference modulated by no information, the success rate of resisting interference modulated by distance information, the success rate of resisting interference modulated by speed information and the success rate of resisting interference modulated by distance-speed combination information; each interference success rate is divided into three intensities, namely: the success rate of resisting weaker interference intensity, the success rate of resisting medium interference intensity and the success rate of resisting stronger interference intensity.
[0010] Preferably, the success rate of anti-sweep frequency interference includes: the success rate of anti-square wave sweep frequency interference, the success rate of anti-sine wave sweep frequency interference, the success rate of anti-noise sweep frequency interference, the success rate of anti-triangular wave sweep frequency interference and the success rate of anti-sawtooth wave sweep frequency interference; in, The success rate of anti-noise sweep frequency jamming includes the success rate of anti-noise amplitude modulation sweep frequency jamming and the success rate of anti-noise non-amplitude modulation sweep frequency jamming. The success rate of anti-noise AM sweep frequency interference includes: the success rate of anti-noise AM modulation sweep frequency interference and the success rate of anti-noise AM non-modulation sweep frequency interference; The success rate of anti-noise non-AM sweep frequency interference includes: the success rate of anti-noise non-AM modulation sweep frequency interference and the success rate of anti-noise non-AM non-modulation sweep frequency interference; The success rate of resisting noise AM modulation swept frequency interference, the success rate of resisting noise AM non-modulation swept frequency interference, the success rate of resisting noise non-AM modulation swept frequency interference and the success rate of resisting noise non-AM non-modulation swept frequency interference are divided into three intensities, namely: the success rate of resisting weak interference intensity, the success rate of resisting medium interference intensity and the success rate of resisting strong interference intensity.
[0011] Preferably, the anti-aiming jamming success rate comprises: an anti-square wave aiming jamming success rate, an anti-sine wave aiming jamming success rate, an anti-noise aiming jamming success rate, an anti-triangular wave aiming jamming success rate and an anti-sawtooth wave aiming jamming success rate; wherein, The anti-noise aiming jamming success rate comprises: an anti-noise amplitude modulation aiming jamming success rate and an anti-noise non-amplitude modulation aiming jamming success rate; The anti-noise amplitude modulation aiming jamming success rate comprises: an anti-noise amplitude modulation modulation aiming jamming success rate and an anti-noise amplitude modulation non-modulation aiming jamming success rate; The anti-noise non-amplitude modulation aiming jamming success rate comprises: an anti-noise non-amplitude modulation modulation aiming jamming success rate and an anti-noise non-amplitude modulation non-modulation aiming jamming success rate; The anti-noise amplitude modulation modulation aiming jamming success rate, the anti-noise amplitude modulation non-modulation aiming jamming success rate, the anti-noise non-amplitude modulation modulation aiming jamming success rate and the anti-noise non-amplitude modulation non-modulation aiming jamming success rate are each divided into three intensities, namely: an anti-weak interference intensity success rate, an anti-medium interference intensity success rate and an anti-strong interference intensity success rate.
[0012] Preferably, in the step S2, the principal component method is used to reduce the data dimension without losing information, and the entropy weight method is used to retain indexes with high sensitivity and independence, to establish a comprehensive index system optimization method combining the principal component analysis and the entropy weight method, comprising: Data standardization The original data matrix is standardized to obtain a standardized data matrix , where n is the number of samples, and m is the number of variables. : ; In the formula, and are the mean and standard deviation of the first index, respectively, is the original data matrix of the first sample and the first index; is the original data matrix of the first sample and the first index; is the original data matrix of the first sample and the first index; is the original data matrix of the first sample and the first index; The covariance matrix of the standardized data matrix is calculated as follows: ; In the formula, is the transpose of the standardized data matrix ; Eigenvalue decomposition is performed on the covariance matrix Perform eigenvalue decomposition: ; Where, is the characteristic matrix, and the corresponding eigenvectors are the coefficients of each principal component. is a diagonal matrix with diagonal elements ; Calculating contribution rate Single principal component : ; Indicates the The proportion of the variance explained by the principal components to the total variance; Grand total : ; represents the proportion of variance jointly explained by the first m principal components; Retain indicators with a cumulative contribution rate greater than 85%-95%.
[0013] Preferably, in step S2, the index system optimized by the principal component analysis method is further optimized using the entropy weight method, including: Data standardization With samples, indicators, the original data matrix is ; Since the dimensions of different indicators may be different, they need to be standardized first: Positive indicators, the bigger the better: ; Negative indicators, the smaller the better: ; Where, ,like , then add a minimum value ; Calculate the weight of each indicator After normalization, the matrix , calculate each indicator In the sample The proportion of: ; Where, ; Calculating information entropy Calculate each indicator Information entropy: ; ; In the formula, is a normalization coefficient, , the entropy value The greater the index data distribution is more uniform, the less the amount of information provided, the entropy value The smaller the index data difference is greater, the more the amount of information provided; Calculate the information entropy redundancy: ; The greater the index is more important; Calculate the weight The weight of each index is: ; In the formula, If the entropy value of a certain index The index data is completely uniform, and can be considered for deletion, and needs to be distinguished between positive and negative indicators during standardization; Finally, the index with a cumulative contribution rate greater than 85%-95% is retained.
[0014] Preferably, in step S3, after determining the index system and the weight coefficients and evaluation matrix of each level index, the bottom-level index data is taken as the input of the evaluation system, and the final evaluation result is obtained through evaluation model calculation; For radio fuze anti-information active jamming effectiveness evaluation, it is divided into two levels of evaluation, namely comprehensive evaluation and bottom-level index evaluation; The bottom-level index of the comprehensive evaluation index system is divided into two categories: One is a single-level evaluation index, and the experimental data obtained is directly used as the bottom-level index of the comprehensive evaluation system; The other is a multi-level bottom-level evaluation index, and after the experimental data obtained is evaluated by a multi-level bottom-level system, the bottom-level index data of the comprehensive evaluation system is obtained; Specifically, it includes: Determine the weight The index weight determination method based on the analytic hierarchy process, a judgment matrix : ; In the formula, is the quantitative value of the importance of the index and the index relative to the upper level index in the same level evaluation index; ; Wherein, The greater the value of , the higher the importance of relative to The matrix is normalized by column: ; The sum is taken by row: ; After normalization, the weight coefficient is obtained: ; The maximum eigenvalue of the matrix is calculated: : ; The eigenvector corresponding to the maximum eigenvalue of the matrix is the weight vector: ; Consistency check According to the AHP (Analytic Hierarchy Process) principle, the difference between and can be used to check consistency, and the index is: ; As increases, the judgment error will increase, so the consistency is considered to be affected by , and the randomness consistency ratio is further used to reflect consistency: ; In the formula, is the average random consistency index, when , it is considered that the judgment matrix has satisfactory consistency, and the eigenvector is the weight of each factor. When , the elements of the judgment matrix are adjusted until satisfactory consistency is achieved. Determination of the judgment matrix: the membership degree of each index can be obtained by using the membership function, so as to determine the judgment matrix. Multi-level fuzzy evaluation: through the analysis and judgment of the performance of the fuze, the evaluation scale is determined, the membership matrix is established, the comprehensive evaluation vector is calculated, and finally the evaluation result is obtained, which includes: Establishment of factor set: ; In the formula, the element is a number of influencing factors corresponding to a number of evaluation indexes in the evaluation index system. Establishment of weight set Different weights are given to each element according to its importance, and the factor weight set is ; ; In the formula, the element is a factor set right The membership degree of Satisfies normalization and non-negativity conditions; Determine the evaluation set: ; In the formula, the element There are several possible judgment results; Calculate the single factor fuzzy comprehensive evaluation matrix Factor Set and evaluation set The fuzzy relationship between them can be evaluated by the matrix: ; Where, ; Fuzzy comprehensive evaluation According to the above discussion, the fuzzy comprehensive evaluation set can be obtained: ; Where, is the fuzzy comprehensive evaluation result, is a fuzzy operator; Single-level fuzzy comprehensive evaluation result processing For the single-level fuzzy comprehensive evaluation results The maximum membership, weighted average or fuzzy distribution method is used to determine the judgment. If the maximum membership method is used to determine the judgment, then: Find the evaluation index After that, put the biggest evaluation The corresponding evaluation set Take as the judgment result; Multi-level evaluation After completing the single-level fuzzy comprehensive evaluation, repeat the above steps to perform the single-level fuzzy comprehensive evaluation of the previous level until the top-level evaluation result is obtained; Set up the first The layer evaluation matrix is , the weight set is , the evaluation results ; The top-level evaluation matrix is , the weight set is , the evaluation results right Normalize, if the evaluation set is , the final quantitative evaluation result is: .
[0015] The beneficial effects of the present application are: the present application establishes an anti-interference performance evaluation index system and its corresponding normalization formula, optimizes the index system according to the experimental data of different types of fuzes, improves the anti-interference performance evaluation efficiency of the fuzes, forms a radio fuze anti-information type interference performance evaluation model, realizes the anti-information type interference performance evaluation of the radio fuze in a specific interference environment, and thus provides strong support for finding out the adaptability of the fuze to the complex electromagnetic environment of the battlefield and giving suggestions for the battlefield use of the radio fuze. BRIEF DESCRIPTION OF DRAWINGS
[0016] Figure 1 is a flow chart of a radio fuze anti-information type interference performance evaluation method of the present application; Figure 2 is a schematic diagram of a complete radio fuze anti-interference multi-level fuzzy comprehensive evaluation performance evaluation index system in the present application; Figure 3 is an anti-retransmission type interference success rate index system diagram in the present application; Figure 4 is an anti-sweep frequency type interference success rate index system diagram in the present application; Figure 5 is an anti-sighting type interference success rate index system diagram in the present application; Figure 6 is an anti-territory defense penetration success rate index system diagram in the present application; Figure 7 is an anti-formation protection penetration success rate index system diagram in the present application; Figure 8 is a multi-level fuzzy comprehensive radio fuze anti-information type interference performance evaluation model schematic diagram in the present application; Figure 9 is an optimized performance evaluation index system schematic diagram in a specific embodiment of the present application. DETAILED DESCRIPTION
[0017] The technical solutions of the present application will be described clearly and completely below in combination with the drawings. In the description of the present application, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as limiting the devices or elements indicated to have a particular orientation, to be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying the importance of the opposite.
[0018] The present invention will be further described below with reference to the accompanying drawings.
[0019] like Figure 1 As shown, a method for evaluating the effectiveness of a radio fuze against information-based interference includes: Step S1, constructing a complete performance evaluation index system for multi-level fuzzy comprehensive evaluation of radio fuze anti-interference.
[0020] like Figure 2 As shown in the figure, the complete performance evaluation index system of multi-level fuzzy comprehensive evaluation of radio fuze anti-interference includes: the effectiveness of anti-information interference under simulated typical combat scenarios, as well as the effectiveness of anti-typical information interference methods and the inherent performance of the fuze.
[0021] like Figure 6 、 7 As shown in the figure, the effectiveness of anti-information interference in the simulated typical combat scenario includes: the success rate of anti-defense penetration of key points and the success rate of anti-team cover penetration; in, like Figure 6 、 7 As shown in the figure, the success rate of anti-key point defense breakthrough and the success rate of anti-team cover breakthrough both include: the success rate of anti-forwarding interference, the success rate of anti-sweeping interference and the success rate of anti-aiming interference.
[0022] like Figure 2 As shown in the figure, the effectiveness of resisting typical information-type interference methods and the inherent performance of the fuze include: the success rate of anti-relay interference, the success rate of anti-sweep frequency interference, the success rate of anti-aiming interference, the probability of being intercepted, the time of being intercepted, the duration of interference, the maximum radiation power spectrum density and the interference power factor.
[0023] like Figure 3 As shown in the figure, the success rate of resisting forwarding interference includes: the success rate of resisting no information modulation interference, the success rate of resisting distance information modulation interference, the success rate of resisting speed information modulation interference and the success rate of resisting distance-speed combination information modulation interference; each interference success rate is divided into three intensities, namely: the success rate of resisting weak interference intensity, the success rate of resisting medium interference intensity and the success rate of resisting strong interference intensity.
[0024] like Figure 4 As shown, the success rate of anti-sweep frequency interference includes: the success rate of anti-square wave sweep frequency interference, the success rate of anti-sine wave sweep frequency interference, the success rate of anti-noise sweep frequency interference, the success rate of anti-triangular wave sweep frequency interference and the success rate of anti-sawtooth wave sweep frequency interference; in, The success rate of anti-noise sweep frequency jamming includes the success rate of anti-noise amplitude modulation sweep frequency jamming and the success rate of anti-noise non-amplitude modulation sweep frequency jamming. The anti-noise amplitude modulation sweep frequency jamming success rate includes: an anti-noise amplitude modulation modulation sweep frequency jamming success rate and an anti-noise amplitude modulation non-modulation sweep frequency jamming success rate; The anti-noise non-amplitude modulation sweep frequency jamming success rate includes: an anti-noise non-amplitude modulation modulation sweep frequency jamming success rate and an anti-noise non-amplitude modulation non-modulation sweep frequency jamming success rate; The anti-noise amplitude modulation modulation sweep frequency jamming success rate, the anti-noise amplitude modulation non-modulation sweep frequency jamming success rate, the anti-noise non-amplitude modulation modulation sweep frequency jamming success rate and the anti-noise non-amplitude modulation non-modulation sweep frequency jamming success rate are divided into three intensities, respectively: an anti-weak interference intensity success rate, an anti-medium interference intensity success rate and an anti-strong interference intensity success rate.
[0025] As shown in Figure 5 The anti-aiming jamming success rate includes: an anti-square wave aiming jamming success rate, an anti-sine wave aiming jamming success rate, an anti-noise aiming jamming success rate, an anti-triangular wave aiming jamming success rate and an anti-sawtooth wave aiming jamming success rate; Among them, The anti-noise aiming jamming success rate includes: an anti-noise amplitude modulation aiming jamming success rate and an anti-noise non-amplitude modulation aiming jamming success rate; The anti-noise amplitude modulation aiming jamming success rate includes: an anti-noise amplitude modulation modulation aiming jamming success rate and an anti-noise amplitude modulation non-modulation aiming jamming success rate; The anti-noise non-amplitude modulation aiming jamming success rate includes: an anti-noise non-amplitude modulation modulation aiming jamming success rate and an anti-noise non-amplitude modulation non-modulation aiming jamming success rate; The anti-noise amplitude modulation modulation aiming jamming success rate, the anti-noise amplitude modulation non-modulation aiming jamming success rate, the anti-noise non-amplitude modulation modulation aiming jamming success rate and the anti-noise non-amplitude modulation non-modulation aiming jamming success rate are divided into three intensities, respectively: an anti-weak interference intensity success rate, an anti-medium interference intensity success rate and an anti-strong interference intensity success rate.
[0026] Step S2, the efficiency evaluation index system of the multi-level fuzzy comprehensive evaluation of the anti-jamming of the radio fuze is optimized.
[0027] The existing radio fuze anti-information jamming efficiency evaluation index system follows the basic principles of evaluation criteria and system establishment. The evaluation index selection mostly relies on the personal experience and knowledge of evaluation experts. Although the evaluation index is rich and complete, it is subjective and it is difficult to avoid the redundancy of the index set. Therefore, the optimization direction of the index system is to reduce the redundancy between the indexes.
[0028] In view of this requirement, a comprehensive index system optimization method combining principal component analysis and entropy weight method is established by using principal component method to reduce the data dimension without losing information, and by using entropy weight method to retain indexes with high sensitivity and independence.
[0029] In the embodiment, the principal component method (PCA) is used to reduce the data dimension without losing information, and the entropy weight method is used to retain the indicators with high sensitivity and independence, to establish a comprehensive index system optimization method combining principal component analysis and entropy weight method, including: Data standardization The original data matrix dimension, is the number of samples, is the number of variables, and the standardized matrix is obtained after standardization: ; In the formula, and are the mean and standard deviation of the first indicator, is the original data matrix of the first sample and the first indicator; Calculate the covariance matrix of the standardized data matrix : ; In the formula, is the transpose of the standardized data matrix ; Eigenvalue decomposition Eigenvalue decomposition is performed on the covariance matrix : ; In the formula, is the characteristic matrix, and the corresponding eigenvector is the coefficient of each principal component, is a diagonal matrix, and the diagonal elements ; Calculate the contribution rate of a single principal component : ; represents the proportion of the variance explained by the first principal component to the total variance; Cumulative : ; represents the proportion of variance explained by the first principal component to the total variance;
[0030] In the embodiment, the index system optimized by the principal component analysis method is then optimized using the entropy weight method (EWM). The core idea is that the smaller the information entropy of an indicator, the greater its degree of variation, the more information it provides, and the greater its weight should be, including: Data standardization With samples, indicators, the original data matrix is ; Since the dimensions of different indicators may be different, they need to be standardized first: Positive indicators, the bigger the better: ; Negative indicators, the smaller the better: ; Where, ,like , then add a minimum value ; Calculate the weight of each indicator After normalization, the matrix , calculate each indicator In the sample The proportion of: ; Where, ; Calculating information entropy Calculate each indicator Information entropy: ; ; Where, is the normalization coefficient, , entropy The larger the value, the more evenly the data distribution of the indicator is, the less information is provided, and the entropy value is The smaller it is, the greater the difference in the indicator data is, and the more information it provides; Calculate information entropy redundancy: ; The larger it is, the more important the indicator is; Calculating weights The weights of each indicator are: ; Where, , if the entropy value of an indicator The index data is completely uniform and can be considered for deletion. When standardized, the positive / negative indicators need to be distinguished. Finally, the indicators with cumulative contribution rates greater than 85%-95% are retained.
[0031] Step S3: Establish a multi-level fuzzy comprehensive radio fuze anti-information jamming effectiveness evaluation model to determine the final quantitative evaluation results.
[0032] As shown in Figure 8 , after determining the index system, weight coefficients and evaluation matrix of each level of indicators, the bottom-level indicator data is used as the input of the evaluation system, and the final evaluation results are obtained through evaluation model calculation. For radio fuze anti-information active jamming effectiveness evaluation, it is divided into two levels of evaluation, namely comprehensive evaluation and bottom-level indicator evaluation. The bottom-level indicators of the comprehensive evaluation index system are divided into two categories: One is single-level evaluation indicators, and the experimental data obtained is directly used as the bottom-level indicators of the comprehensive evaluation system; The other is multi-level bottom-level evaluation indicators, and the experimental data obtained is evaluated by a multi-level bottom-level system to obtain the bottom-level indicator data of the comprehensive evaluation system. Specifically including: Determine the weight Based on the index weight determination method of the analytic hierarchy process, construct the judgment matrix ; Where, is the quantitative value of the importance of the th indicator in the same level of evaluation indicators relative to the th indicator relative to the upper level indicator; ; Where, The larger the value of , the higher the importance of relative to ;Here, the 1-9 scale quantitative value is used, as shown in Table 1:
[0033] Table 1 Scale Quantitative Value The matrix is normalized by column: ; Sum by row: ; After normalization, the weight coefficient is obtained: ; the maximum eigenvalue of matrix : ; the maximum eigenvalue of matrix corresponding eigenvector , i.e. weight vector; consistency check According to the AHP principle, the difference between and can be used to check consistency, and the index is: ; With the increase of , the judgment error will increase, so the consistency of the judgment considers the influence of , and further uses the randomness consistency ratio to reflect the consistency: ; In the formula, is the average random consistency index, which can be found in Table 2 and Table 3;
[0034] Table 2 Average random consistency index table of order 1-7
[0035] Table 3 Average random consistency index table of order 7-14 When , it is considered that the judgment matrix has satisfactory consistency, and the eigenvector is the weight of each factor. When , the elements of the judgment matrix are adjusted until satisfactory consistency is achieved; Determination of judgment matrix: The judgment matrix is a matrix used to connect the fuzzy relationship between the index set and the evaluation set. The membership degree of each index can be obtained by using the membership function, so as to determine the judgment matrix. Considering the characteristics of radio fuze anti-jamming system and the basic properties of each factor, according to the results of correlation analysis and data processing, the membership function of each factor is determined; Multi-level fuzzy evaluation: fuzzy comprehensive evaluation method is used to evaluate the things influenced by multiple factors by using fuzzy set theory; through the analysis and judgment of the performance of fuze factors, the evaluation scale is determined, the membership degree matrix is established, the comprehensive evaluation vector is calculated, and finally the evaluation result is obtained, which includes: Establishment of factor set: ; In the formula, the element These are several influencing factors, corresponding to several evaluation indicators in the evaluation indicator system; Establishing weight sets For each element Give different weights according to their importance , the factor weight set is ; ; In the formula, the element is a factor set right The membership degree of Satisfies normalization and non-negativity conditions; Determine the evaluation set: ; In the formula, the element There are several possible judgment results; Calculate the single factor fuzzy comprehensive evaluation matrix Factor Set and evaluation set The fuzzy relationship between them can be evaluated by the matrix: ; Where, ; Fuzzy comprehensive evaluation According to the above discussion, the fuzzy comprehensive evaluation set can be obtained: ; Where, is the fuzzy comprehensive evaluation result, is a fuzzy operator; Single-level fuzzy comprehensive evaluation result processing For the single-level fuzzy comprehensive evaluation results The maximum membership, weighted average or fuzzy distribution method is used to determine the judgment. If the maximum membership method is used to determine the judgment, then: Find the evaluation index After that, put the biggest evaluation The corresponding evaluation set Take as the judgment result; Multi-level evaluation After completing the single-level fuzzy comprehensive evaluation, repeat the above steps to perform the single-level fuzzy comprehensive evaluation of the previous level until the top-level evaluation result is obtained; Set up the first The layer evaluation matrix is , the weight set is , the evaluation results ; The top layer evaluation matrix is , the weight set is , the evaluation result is The normalization is performed on , if the evaluation set is , the final quantitative evaluation result is: .
[0036] The application will be further described in detail below in combination with specific embodiments, but the embodiments of the application are not limited thereto.
[0037] I. Optimization of the evaluation index system First, the preliminary evaluation index system has been obtained. According to the experimental data, the preliminary evaluation result is obtained, and the optimization calculation is performed, and the result is shown in the table.
[0038] According to the established radio fuze anti-information jamming evaluation index system, the index experimental data are obtained through the fuze effectiveness experiment. The anti-repeater jamming success rate, the anti-aiming jamming success rate, the anti-sweep frequency jamming success rate, the anti-territory defense penetration success rate and the anti-random cover penetration success rate are five indexes, which need to be quantitatively evaluated and analyzed by using the fuzzy comprehensive evaluation method. The remaining five single index systems are single-layer index systems, and do not need to be evaluated and analyzed in multiple layers, and the single evaluation index value can be directly obtained. Finally, ten single evaluation index data are obtained.
[0039] Since the evaluation indexes are different, it is difficult to directly compare the indexes, and therefore, the normalization is performed before comparison. The ten single evaluation index data are normalized and processed, and are used as the index optimization method data input.
[0040] Through the principal component analysis method and the entropy weight method, the index data are analyzed after normalization, so as to optimize the index system.
[0041]
[0042] Table 4 Information contribution rate of the evaluation index based on the principal component analysis method From table 4, it can be seen that by comparing the sizes of the index dispersion coefficients, it can be obviously seen that the dispersion coefficients of the anti-aiming jamming success rate and the intercepted time are far less than those of other indexes, and it can be considered that the contribution rates of the indexes to the index system evaluation are small, and the deletion of these indexes basically does not affect the final evaluation result. And through the deletion of these indexes, the index system is greatly simplified, and it has great significance for the simplification of the evaluation. According to the related knowledge of the principal component analysis method, the cumulative contribution rate value is generally set at 85%-95%, and the information degree of the indexes can be well protected. Therefore, the indexes with a cumulative contribution rate less than 92% are retained, and the indexes with a higher cumulative contribution degree are deleted.
[0043] When the index optimization method based on principal component analysis is completed, the redundancy of the remaining indicators is further studied by entropy weight method and correlation coefficient method.
[0044]
[0045] Table 5 Evaluation index based on entropy weight method information contribution rate From the statistical results of Table 5, it can be seen that the comprehensive values of the three indicators of interference power factor, anti-escort penetration success rate and interference duration are much larger than those of other indicators, which indicates that the three indicators have strong independence and good redundancy. The contribution rate of the anti-repeater jamming success rate is the smallest, and the cumulative contribution rate of the first six indicators has reached 83.2%, indicating that the first six indicators have included most of the information, so the interference power factor indicator can be deleted from the system. The entropy weight method can objectively evaluate the contribution rate of the indicators, greatly reducing the redundancy of the indicators in the index system.
[0046] The optimized index system is shown in Table 6. Figure 9 Table 6 Optimized index system II. Determination of weights and membership functions 2.1 Determination of weights Take the first-level indicators as an example for analysis. The single indicator of "anti-typical information type jamming mode effectiveness and inherent performance" reflects the anti-jamming effectiveness of radio fuze through its inherent anti-reconnaissance performance, anti-jamming performance and anti-jamming success rate. The single indicator of "anti-information type jamming effectiveness in simulated typical combat scenario" is measured by penetration success rates in two combat scenarios of anti-site defense and escort cover from the perspective of the real combat environment faced by the fuze. Considering the actual damage control ability and the real battlefield survivability of the fuze, it is obvious that the importance of the first-level indicator "anti-typical information type jamming mode effectiveness and inherent performance" is weaker than that of "anti-information type jamming effectiveness in simulated typical combat scenario". The judgment matrix is constructed as follows: ; The following processing is performed on : ; wherein , so the judgment matrix has satisfactory consistency.
[0047] Therefore, the weight matrix of "anti-typical information type jamming mode effectiveness and inherent performance" and "anti-information type jamming effectiveness in simulated typical combat scenario" is: ; The weight matrix of anti-escort penetration success rate and anti-site defense penetration success rate is: ; The weight matrix of the fifth-level indicators of weak, medium, and strong strength interference is: ; The weight matrix of the anti-noise non-amplification non-modulation sweep frequency interference success rate indicator and the anti-noise non-amplification modulation sweep frequency interference success rate indicator is: ; The weight of the anti-noise amplification non-modulation interference capability and the anti-noise amplification modulation interference capability indicator is equal to: and : ; The weight of the anti-noise non-amplification sweep frequency interference capability and the anti-amplification sweep frequency interference capability indicator is: ; The weight of the anti-square wave sweep frequency interference success rate, the anti-sine wave sweep frequency interference success rate, the anti-sawtooth wave sweep frequency interference success rate, the anti-triangle wave sweep frequency interference success rate, and the noise sweep frequency interference success rate is: ; The second-level indicator of anti-repeater interference is the anti-three-level interference strength interference success rate, and the second-level indicator weight is determined in the same way as the weight of the bottom-level indicator of anti-sweep frequency interference, that is: ; The weight matrix of the anti-no information modulation repeater interference success rate, the anti-distance information modulation repeater interference success rate, the anti-speed information modulation repeater interference success rate, and the anti-combination information modulation repeater interference indicator is: .
[0048] 2.2 Determination of evaluation matrix 2.2.1 Determination of membership functionFor the fuze evaluation indicators, the evaluation criteria are set, and three fuzzy sets of evaluation radio fuze interference power factor are established according to the three levels of evaluation: A ~ = "excellent", B ~ = "pass", and C ~ = "fail", wherein "excellent" adopts a large bias trapezoidal distribution, "pass" adopts a middle trapezoidal distribution, and "fail" adopts a large bias trapezoidal distribution, and a membership function is designed. 1) Maximum radiation power spectral density
[0049] The smaller the maximum radiation power spectral density, the stronger the anti-reconnaissance performance of the fuze; ; 2) Anti-interference success rate The higher the radio fuze anti-jamming success rate, the stronger the radio fuze anti-jamming capability is; ; 3) Interference duration The higher the radio fuze anti-jamming success rate, the stronger the radio fuze anti-jamming capability is; ; 2.2.2 Index membership degree The original data obtained by the experiment is brought into the above corresponding index membership function formula to obtain the corresponding membership degree.
[0050] The bottom layer of the comprehensive evaluation index system has 6 indexes, of which 2 indexes are single-layer indexes. The result obtained by bringing the original data into the membership function is the index membership degree, and the membership degree matrix is as follows: Maximum radiation power spectral density: ; Interference duration: ; Multi-level bottom layer evaluation index membership degree The multi-level bottom layer evaluation index membership degree mainly has three calculation methods, which are consistent with the index system weight classification in section 4.1.2. The three systems are different due to the different establishment, but the principles based on them are consistent. The first is the calculation method taking the anti-sweep frequency jamming success rate membership degree as an example. Because the index system architecture, weight coefficient and evaluation matrix are the same as those of the anti-sighting jamming success rate, the membership degree calculation process is the same as that of the anti-sighting jamming success rate. The second is the anti-repeater jamming success rate single index membership degree calculation method, and the third is the anti-territory defense success rate membership degree calculation method. The anti-formation defense success rate is completely consistent with the index system architecture, weight coefficient and evaluation matrix, so the calculation method is the same.
[0051] The first membership degree calculation method takes the anti-sweep frequency jamming success rate as an example to calculate the membership degree of four-level indexes. The membership degree calculation methods of anti-five kinds of waveforms are the same, taking the anti-noise sweep frequency jamming success rate as an example to calculate the membership degrees of two-level, three-level and four-level.
[0052] 1) Anti-sweep frequency four-level index membership degree calculation method For the four-level index anti-noise non-amplitude non-modulation sweep frequency jamming, according to the anti-jamming success rate values measured under three kinds of interference intensity, the anti-weak interference intensity membership degree is , the anti-medium interference intensity membership degree is , and the strong intensity interference membership degree is . The membership degree matrix is .
[0053] Similarly, the membership degrees of other four-level indexes are as follows: The membership matrix of the anti-noise non-amplification modulation sweep frequency jamming is .
[0054] The membership matrix of the anti-noise non-amplification modulation sweep frequency jamming is .
[0055] 2) Anti-sweep frequency three-level index membership degree calculation method For three-level index membership degree, the anti-noise non-amplification non-modulation sweep frequency jamming success rate index membership degree , the anti-noise non-amplification modulation sweep frequency jamming success rate index membership degree , the anti-noise amplification non-modulation sweep frequency jamming success rate index membership degree , the anti-noise amplification modulation sweep frequency jamming success rate index membership degree .
[0056] The membership degree calculation formula is respectively: ; ; ; ; Among them, is the weight matrix of the five-level indexes weak intensity jamming, medium intensity jamming, and strong intensity jamming; 3) Anti-sweep frequency two-level index membership degree calculation method For two-level index membership degree, the anti-noise non-amplification sweep frequency jamming success rate index membership and the anti-noise amplification sweep frequency jamming success rate index membership .
[0057] The membership degree calculation formula is respectively: ; ; Among them, is the weight matrix of the membership and the membership corresponding indexes, is the weight matrix of the membership and the membership corresponding indexes.
[0058] 4) Anti-sweep frequency one-level index membership degree calculation method For one-level index membership degree, the anti-noise sweep frequency jamming success rate The membership degree calculation formula is as follows: ; For membership degree and membership degree Weight matrix of corresponding indicators; The same calculation method can get the anti-sine wave sweep frequency jamming success rate membership degree , anti-square wave sweep frequency jamming success rate membership degree , anti-triangular wave sweep frequency jamming success rate membership degree , anti-sawtooth wave sweep frequency jamming success rate membership degree .
[0059] Therefore, the anti-sweep frequency jamming success rate membership degree is: ; For membership degree , , , , Weight matrix of corresponding indicators.
[0060] The second membership function calculation method is the anti-repeater jamming success rate, which is used to calculate the membership degree of the secondary indicators.
[0061] 1) Anti-repeater secondary indicator membership degree calculation For the primary indicator anti-no information modulation jamming success rate, the secondary indicator is the anti-jamming success rate value measured under three intensity interferences, the anti-weak interference intensity membership degree is , the anti-medium interference intensity membership degree is , and the anti-strong intensity interference membership degree is . The membership degree matrix is .
[0062] Similarly, the membership degrees of other secondary indicators are as follows: The anti-distance information modulation jamming success rate membership degree matrix is .
[0063] The anti-speed information modulation jamming success rate membership degree matrix is .
[0064] The anti-distance and speed combination information modulation jamming success rate membership degree matrix is .
[0065] 2) Anti-repeater primary indicator membership degree calculation For the primary indicator membership degree, the anti-no information modulation jamming success rate membership degree is , the anti-distance information modulation jamming success rate membership degree is , anti-speed information modulation interference success rate membership , anti-distance and speed combination information modulation interference success rate membership .
[0066] The membership calculation formula is respectively: ; ; ; ; Among them, , , The weight matrix of the corresponding index; In summary, the anti-repeater interference success rate membership is: ; Among them, The membership , , , The weight of the corresponding index.
[0067] The third membership calculation method takes the anti-territory defense success rate as an example to calculate the membership of the first-level index. The bottom-level indexes are the anti-aiming interference territory defense success rate, the anti-sweep frequency interference territory defense success rate, and the anti-repeater interference territory defense success rate.
[0068] For the index anti-territory defense success rate membership calculation, according to the measured anti-interference success rate values under anti-repeater interference, anti-sweep frequency interference and aiming interference, the anti-territory defense aiming interference success rate membership is , the anti-territory defense sweep frequency interference success rate membership is , and the anti-territory defense repeater interference success rate membership is The membership matrix is .
[0069] ; Similarly, the membership of the anti-escort defense success rate is as follows: For the index anti-escort success rate, according to the measured anti-interference success rate values under anti-repeater interference, anti-sweep frequency interference and aiming interference, the anti-escort aiming interference success rate membership is , the anti-escort sweep frequency interference success rate membership is , and the anti-escort repeater interference success rate membership is The membership matrix is .
[0070] The membership calculation formulas are: ; Therefore, the four indicator memberships in the multi-level bottom-level evaluation indicator system are: Anti-forwarding interference success rate membership: ; Anti-sweep frequency interference success rate membership: ; Degree of success rate of breakthrough in key defense: ; Degree of success rate of anti-team cover breakthrough: ; The evaluation matrix is composed of the membership degrees of each indicator.
[0071] 3 Comprehensive evaluation of fuze anti-interference effectiveness 3.1 Evaluation of the underlying indicators of the comprehensive indicator system 1) Evaluation of the effectiveness and inherent performance of typical information interference resistance methods From the above analysis, we can see that the maximum radiation power spectrum density, interference duration, anti-forwarding interference success rate membership, and anti-sweep frequency interference success rate membership are respectively 、 、 、 .
[0072] The evaluation matrix composed of the above membership degrees is : ; Given that the weight of the interference power factor, interference duration, success rate of anti-forwarding interference, and success rate of anti-sweep interference is A2, the effectiveness and inherent performance evaluation results of the anti-typical information interference method are as follows: ; The fuzzy operator is weighted average type, and we can get: ; 2) Evaluation of the effectiveness of countering information jamming in simulated typical combat scenarios From the above analysis, we can see that the membership degrees of the two indicators of the success rate of the defense breakthrough against key points and the success rate of the defense breakthrough against the team cover are 、 .
[0073] The evaluation matrix composed of the above membership degrees is for: ; The weight of the anti-site defense penetration success rate and the anti-team cover penetration success rate is A3, and the simulation of the anti-information jamming effectiveness evaluation result in the typical combat scene is: ; Among them, the fuzzy operator selects the weighted average type, and the following can be obtained: .
[0074] 3.2 First-level evaluation of comprehensive effectiveness evaluation system The top-level index of the comprehensive effectiveness evaluation system is the anti-typical information jamming mode effectiveness and inherent performance, and the anti-information jamming effectiveness in the simulation of typical combat scene. According to the above analysis, the evaluation matrix composed of the evaluation results of the anti-typical information jamming mode effectiveness and inherent performance, and the anti-information jamming effectiveness in the simulation of typical combat scene is: ; The weight of the anti-typical information jamming mode effectiveness and inherent performance, and the anti-information jamming effectiveness in the simulation of typical combat scene is , and the top-level evaluation result of the comprehensive effectiveness evaluation system is: ; Among them, the fuzzy operator selects the weighted average type, and the following can be obtained: ; 3.3 Evaluation result of comprehensive effectiveness evaluation system According to the three levels of evaluation, three fuzzy sets are established to evaluate the comprehensive effectiveness of radio fuze against information active jamming: =“excellent”, =“pass”, =“fail”, and the evaluation level is .
[0075] The quantitative result of radio fuze anti-information effectiveness evaluation is: ;
[0076] Table 6 As shown in Table 6, taking the evaluation process of the #1 fuze numbered RF1 as an example, the fuzzy comprehensive method is used as the evaluation method, and the evaluation process is as follows: According to the above analysis, the maximum radiation power spectral density, the interference time, the anti-repeater jamming success rate membership degree, and the anti-sweep frequency jamming success rate membership degree are respectively , , , The quantitative evaluation is carried out taking the RF1 with the number 1 as an example.
[0077] The evaluation matrix with the above membership degrees as rows is as follows: ; The weights of the interference power factor, the interference duration, the anti-repeater jamming success rate and the anti-sweep jamming success rate are known as A1, A2, A3 and A4 respectively. The membership degree results of the fuze anti-reconnaissance and anti-jamming efficiency evaluation are as follows: ; From the above analysis, the membership degrees of the anti-urban defense penetration success rate and the anti-escort cover penetration success rate are A1 and A2 respectively. , The evaluation matrix with the membership degrees as rows is as follows: .
[0078] The weights of the anti-urban defense penetration success rate and the anti-escort cover penetration success rate are known as A3. ; The top-level indexes of the comprehensive efficiency evaluation system are the fuze anti-reconnaissance and anti-jamming efficiency and the simulated fuze combat scene experiment efficiency. ; The weights of the fuze anti-reconnaissance and anti-jamming efficiency and the simulated fuze combat scene experiment efficiency are known as A1 and A2 respectively. The top-level evaluation results of the comprehensive efficiency evaluation system are as follows: ; The quantitative results of the radio fuze anti-information efficiency evaluation are as follows: .
[0079] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit the same; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for evaluating the effectiveness of a radio fuze against information-based interference, characterized in that: include: Step S1, constructing a complete performance evaluation index system for multi-level fuzzy comprehensive evaluation of radio fuze anti-interference; Step S2, optimizing the effectiveness evaluation index system of the multi-level fuzzy comprehensive evaluation of radio fuze anti-interference; Step S3, establishing a multi-level fuzzy comprehensive radio fuze anti-information interference effectiveness evaluation model, and then determining the final quantitative evaluation result.
2. The method for evaluating the effectiveness of radio fuze against information interference according to claim 1, wherein: In step S1, the complete radio fuze anti-interference multi-level fuzzy comprehensive evaluation performance evaluation index system includes: anti-information interference effectiveness under simulated typical combat scenarios, as well as anti-typical information interference mode effectiveness and fuze inherent performance.
3. The method for evaluating the effectiveness of radio fuze against information interference according to claim 2, wherein: The effectiveness of anti-information interference in simulated typical combat scenarios includes: the success rate of penetration against key point defense and the success rate of penetration against team cover; in, The success rate of anti-key point defense breakthrough and the success rate of anti-team cover breakthrough both include: the success rate of anti-forwarding interference, the success rate of anti-sweeping interference and the success rate of anti-aiming interference.
4. The method for evaluating the effectiveness of radio fuze against information interference according to claim 2, wherein: The effectiveness of resisting typical information-based interference methods and the inherent performance of the fuze include: the success rate of anti-relay interference, the success rate of anti-sweep frequency interference, the success rate of anti-aiming interference, the probability of being intercepted, the time of being intercepted, the duration of interference, the maximum radiation power spectrum density and the interference power factor.
5. A method for evaluating the effectiveness of radio fuze against information interference according to claim 4, characterized in that: The success rate of resisting forwarding interference includes: the success rate of resisting interference modulated by no information, the success rate of resisting interference modulated by distance information, the success rate of resisting interference modulated by speed information and the success rate of resisting interference modulated by distance and speed combination information; each interference success rate is divided into three intensities, namely: the success rate of resisting weak interference intensity, the success rate of resisting medium interference intensity and the success rate of resisting strong interference intensity.
6. A method for evaluating the effectiveness of radio fuze against information interference according to claim 4, characterized in that: The success rate of anti-sweep frequency interference includes: the success rate of anti-square wave sweep frequency interference, the success rate of anti-sine wave sweep frequency interference, the success rate of anti-noise sweep frequency interference, the success rate of anti-triangular wave sweep frequency interference and the success rate of anti-sawtooth wave sweep frequency interference; in, The success rate of anti-noise sweep frequency jamming includes the success rate of anti-noise amplitude modulation sweep frequency jamming and the success rate of anti-noise non-amplitude modulation sweep frequency jamming. The success rate of anti-noise AM sweep frequency interference includes: the success rate of anti-noise AM modulation sweep frequency interference and the success rate of anti-noise AM non-modulation sweep frequency interference; The success rate of anti-noise non-AM sweep frequency interference includes: the success rate of anti-noise non-AM modulation sweep frequency interference and the success rate of anti-noise non-AM non-modulation sweep frequency interference; The success rate of resisting noise AM modulation swept frequency interference, the success rate of resisting noise AM non-modulation swept frequency interference, the success rate of resisting noise non-AM modulation swept frequency interference and the success rate of resisting noise non-AM non-modulation swept frequency interference are divided into three intensities, namely: the success rate of resisting weak interference intensity, the success rate of resisting medium interference intensity and the success rate of resisting strong interference intensity.
7. A method for evaluating the effectiveness of radio fuze against information interference according to claim 4, characterized in that: The success rate of anti-aiming interference includes: the success rate of anti-square wave aiming interference, the success rate of anti-sine wave aiming interference, the success rate of anti-noise aiming interference, the success rate of anti-triangular wave aiming interference and the success rate of anti-sawtooth wave aiming interference; in, The success rate of anti-noise aiming jamming includes the success rate of anti-noise AM aiming jamming and the success rate of anti-noise non-AM aiming jamming. The success rate of anti-noise AM aiming jamming includes: the success rate of anti-noise AM modulated aiming jamming and the success rate of anti-noise AM non-modulation aiming jamming; The success rate of anti-noise non-AM aiming jamming includes: the success rate of anti-noise non-AM modulation aiming jamming and the success rate of anti-noise non-AM non-modulation aiming jamming; The success rate of anti-noise AM modulation aimed jamming, the success rate of anti-noise AM non-modulation aimed jamming, the success rate of anti-noise non-AM modulation aimed jamming and the success rate of anti-noise non-AM non-modulation aimed jamming are divided into three intensities, namely: the success rate of anti-weaker interference intensity, the success rate of anti-medium interference intensity and the success rate of anti-strong interference intensity.
8. The method for evaluating the effectiveness of radio fuze against information interference according to claim 1, wherein: In step S2, the principal component method is used to reduce the data dimension without losing information, and the entropy weight method is used to retain the indicators with high sensitivity and independence. A comprehensive indicator system optimization method combining principal component analysis and entropy weight method is established, including: Data standardization For the original data matrix dimension, is the number of samples, is the number of variables, and the matrix is obtained after standardization : ； Where, and Respectively The mean and standard deviation of the indicator, For the Samples, The original data matrix of the indicators; Calculate the covariance matrix Normalized data matrix The covariance matrix of : ； Where, is the standardized data matrix The transpose of Eigenvalue decomposition Covariance matrix Perform eigenvalue decomposition: ； Where, is the characteristic matrix, and the corresponding eigenvectors are the coefficients of each principal component. is a diagonal matrix with diagonal elements ; Calculating contribution rate Single principal component : ； Indicates the The proportion of the variance explained by the principal components to the total variance; Grand total : ； represents the proportion of variance jointly explained by the first m principal components; Retain indicators with a cumulative contribution rate greater than 85%-95%.
9. A method for evaluating the effectiveness of radio fuze against information interference according to claim 8, characterized in that: In step S2, the index system optimized by the principal component analysis method is then optimized using the entropy weight method, including: Data standardization With samples, indicators, the original data matrix is ; Since the dimensions of different indicators may be different, they need to be standardized first: Positive indicators, the bigger the better: ； Negative indicators, the smaller the better: ； Where, ,like , then add a minimum value ; Calculate the weight of each indicator After normalization, the matrix , calculate each indicator In the sample The proportion of: ； Where, ; Calculating information entropy Calculate each indicator Information entropy: ； ； Where, is the normalization coefficient, , entropy The larger the value, the more evenly the data distribution of the indicator is, the less information is provided, and the entropy value is The smaller it is, the greater the difference in the indicator data is, and the more information it provides; Calculate information entropy redundancy: ； The larger it is, the more important the indicator is; Calculating weights The weights of each indicator are: ； Where, , if the entropy value of an indicator ,The data of this indicator is completely uniform and can be considered for deletion. ,Positive / negative indicators need to be distinguished during standardization; Finally, indicators with a cumulative contribution rate greater than 85%-95% are retained.
10. A method for evaluating the effectiveness of radio fuze against information interference according to claim 9, characterized in that: In step S3, after determining the index system and the weight coefficients and evaluation matrix of the indicators at each level, the underlying indicator data is used as the input of the evaluation system and the final evaluation results are obtained through calculations of the evaluation model; The effectiveness evaluation of radio fuze against information-based active jamming is divided into two levels: comprehensive evaluation and bottom-level indicator evaluation. The underlying indicators of the comprehensive evaluation indicator system are divided into two categories: One type is single-level evaluation indicators, and the experimental data obtained are directly used as the underlying indicators of the comprehensive evaluation system; The other type is multi-level bottom-level evaluation indicators. After conducting multi-level bottom-level system evaluation on the experimental data, the bottom-level indicator data of the comprehensive evaluation system is obtained. Specifically include: Determine weights Based on the indicator weight determination method of hierarchical analysis method, the judgment matrix is constructed : ； Where, For the first evaluation indicator in the same level Indicators and The quantitative value of the importance of the indicator relative to the indicator of the previous level; ； in, The larger the value of relatively The higher the importance; The matrix Normalize by columns: ； Sum by row: ； After normalization, the weight coefficient is obtained: ； Find the matrix The maximum eigenvalue of : ； matrix The maximum eigenvalue of The corresponding eigenvector That is the weight vector; Consistency Check According to the AHP principle, we can use and The difference is used to test the consistency, the index for: ； along with As the value of The influence of randomness consistency ratio is further used To reflect consistency: ； Where, is the average random consistency index, when When , the judgment matrix is considered to have satisfactory consistency, and the eigenvector is the weight of each factor. When , readjust the elements of the judgment matrix until there is satisfactory consistency; Determination of the evaluation matrix: The membership function can be used to obtain the membership degree of each indicator, thereby determining the evaluation matrix; Multi-level fuzzy evaluation: By analyzing and judging various factors of fuze performance, determining the evaluation scale, establishing the membership matrix, calculating the comprehensive evaluation vector, and finally obtaining the evaluation results, specifically including: Build factor set: ； In the formula, the element These are several influencing factors, corresponding to several evaluation indicators in the evaluation indicator system; Establishing weight sets For each element Give different weights according to their importance , the factor weight set is ； ； In the formula, the element is a factor set right The membership degree of Satisfies normalization and non-negativity conditions; Determine the evaluation set: ； In the formula, the element There are several possible judgment results; Calculate the single factor fuzzy comprehensive evaluation matrix Factor Set and evaluation set The fuzzy relationship between them can be evaluated by the matrix: ； Where, ; Fuzzy comprehensive evaluation According to the above discussion, the fuzzy comprehensive evaluation set can be obtained: ； Where, is the fuzzy comprehensive evaluation result, is a fuzzy operator; Single-level fuzzy comprehensive evaluation result processing For the single-level fuzzy comprehensive evaluation results The maximum membership, weighted average or fuzzy distribution method is used to determine the judgment. If the maximum membership method is used to determine the judgment, then: Find the evaluation index After that, put the biggest evaluation The corresponding evaluation set Take as the judgment result; Multi-level evaluation After completing the single-level fuzzy comprehensive evaluation, repeat the above steps to perform the single-level fuzzy comprehensive evaluation of the previous level until the top-level evaluation result is obtained; Set up the first The layer evaluation matrix is , the weight set is , the evaluation results ; The top-level evaluation matrix is , the weight set is , the evaluation results right Normalize, if the evaluation set is , the final quantitative evaluation result is: 。