An intermittent sampling and forwarding interference threat evaluation method

Abstract

The application relates to an intermittent sampling and forwarding interference threat evaluation method and relates to the field of radar anti-interference. The application is aimed at solving the problem that the existing interference threat evaluation method has slow recognition speed and low accuracy when applied to intermittent sampling and forwarding interference threat evaluation. The application comprises the following steps: establishing an evaluation parameter index set; fuzzy processing the evaluation parameter index set based on the membership function of the evaluation parameter index to obtain an evaluation parameter index intuitionistic fuzzy subset; establishing a reasoning rule base based on the mapping relationship between the intermittent sampling and forwarding interference and the interference threat grade; obtaining a total intuitionistic fuzzy relationship by using an intuitionistic fuzzy reasoning synthesis algorithm, and obtaining the relationship between the interference threat grade intuitionistic fuzzy subset, the total intuitionistic fuzzy relationship and the evaluation parameter index; and de-fuzzifying the interference threat grade intuitionistic fuzzy subset to obtain the clear quantity of the intermittent sampling and forwarding interference threat. The application is used for evaluating the intermittent sampling and forwarding interference threat.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radar anti-jamming, and particularly relates to a method for evaluating intermittent sampling and retransmission jamming threat. BACKGROUND

[0002] Cognitive electronic warfare gradually occupies a dominant position in modern warfare. With the evolution of new jamming styles and the flexibility and intelligence of jamming parameters, the game between radar and jammer becomes more intense, and the electromagnetic environment faced by the radar becomes more complex. Therefore, as the radar side, it is necessary to accurately perceive the jamming environment, scientifically and reasonably evaluate the threat degree of the enemy in the battlefield environment, and make effective and timely anti-jamming strategies, which is an inevitable trend to adapt to the future strong confrontation game combat environment. Therefore, in-depth and detailed research on threat evaluation has a very important position in future electronic warfare and will be the key to our effective defense, attack and victory.

[0003] In traditional electronic warfare, after the radar receives the time-domain echo signal of the jamming, a series of signal processing such as pulse compression and anti-jamming are performed, and then the threat degree of the jamming signal of the jammer can be known to some extent, so that the subsequent jamming countermeasure adjustment can be made. In radar countermeasures, if the radar receives the jamming echo signal, the threat degree of the jamming can be accurately known according to the characteristics of the time-domain signal without subsequent signal processing, so that when multiple jamming is encountered, the anti-jamming means can be adjusted more timely and accurately, the resources can be reasonably allocated, and the influence of jamming can be minimized to seize the initiative in the game.

[0004] The current research on threat evaluation mainly focuses on threat targets, and the commonly used threat evaluation methods include multi-attribute decision method, analytic hierarchy process, ideal solution approximation method, etc. The threat evaluation method based on fuzzy set is a research hotspot in the direction of target threat, and the fuzzy set method introduces the membership function to describe the fuzziness of semantic description, which avoids the deviation of qualitative evaluation results, and then obtains the threat evaluation metric through comprehensive judgment and fuzzy measure, and finally converts it into a threat evaluation value. However, there is no method for evaluating the threat degree of intermittent sampling and retransmission jamming, and the existing jamming threat evaluation method applied to the threat evaluation of intermittent sampling and retransmission jamming is easily affected by subjective factors, resulting in slow recognition speed and low accuracy of the threat degree of intermittent sampling and retransmission jamming. SUMMARY

[0005] The present application aims to solve the problem of slow recognition speed and low accuracy of the threat degree of intermittent sampling and retransmission jamming when the existing jamming threat evaluation method is applied to the threat evaluation of intermittent sampling and retransmission jamming, and proposes a method for evaluating the threat of intermittent sampling and retransmission jamming.

[0006] The specific process of the intermittent sampling retransmission interference threat evaluation method is: obtaining intermittent sampling retransmission interference data to be evaluated, extracting evaluation parameter indexes in the intermittent sampling retransmission interference data to be evaluated by using time-frequency analysis and deconvolution method, obtaining an evaluation parameter index set corresponding to the intermittent sampling retransmission interference data to be evaluated, denoted as an evaluation parameter index set to be evaluated; inputting the evaluation parameter index to be evaluated into an evaluation system to obtain an interference threat level evaluation result;

[0007] The evaluation parameter index set to be evaluated includes: interference power, interference pattern, interference slice width duty cycle, and interference retransmission times;

[0008] The evaluation system includes: an evaluation parameter index set acquisition module, an evaluation parameter index set fuzzy processing module, an inference rule base construction module, an interference threat level intuitionistic fuzzy subset acquisition module, and a defuzzification module;

[0009] The evaluation parameter index set acquisition module: establishes the evaluation parameter index set according to the simulation result of the intermittent sampling retransmission interference;

[0010] The evaluation parameter index set includes: interference power, interference pattern, interference slice width duty cycle, and interference retransmission times;

[0011] The evaluation parameter index set fuzzy processing module: performs fuzzy processing on the evaluation parameter index set based on the membership function of the evaluation parameter index to obtain the intuitionistic fuzzy subset of the evaluation parameter index;

[0012] The inference rule base construction module: establishes the inference rule base by using the direct fuzzy subset of the evaluation parameter index based on the mapping relationship between the intermittent sampling retransmission interference data and the interference threat level;

[0013] The interference threat level intuitionistic fuzzy subset acquisition module: obtains the total intuitionistic fuzzy relationship according to the membership function and the inference rule base by using the intuitionistic fuzzy reasoning synthesis algorithm, and obtains the relationship between the interference threat level intuitionistic fuzzy subset and the total intuitionistic fuzzy relationship and the evaluation parameter index, so as to obtain the interference threat level intuitionistic fuzzy subset;

[0014] The defuzzification processing module: performs defuzzification processing on the interference threat level intuitionistic fuzzy subset to obtain the clear quantity of the intermittent sampling retransmission interference threat;

[0015] The evaluation parameter index set to be evaluated is input into the evaluation system, and the following processing is performed to obtain the interference threat level evaluation result:

[0016] S101, input the evaluation parameter index set to be evaluated into the interference threat level intuitionistic fuzzy subset acquisition module to obtain the interference threat level intuitionistic fuzzy subset of the intermittent sampling retransmission interference data to be evaluated;

[0017] S102, input the threat level of the intermittent sampling retransmission interference data into the defuzzification processing module, and obtain the threat clear amount of the intermittent sampling retransmission interference data.

[0018] Further, the membership function based on the evaluation parameter index set is fuzzy processed to obtain the intuitionistic fuzzy subset of the evaluation parameter index, including the following steps:

[0019] Step one, obtain the membership function and non-membership degree function of each evaluation parameter index in the evaluation parameter index set:

[0020]

[0021]

[0022] Wherein, sigma = sigma1, sigma2, sigma3, sigma4 is the width, c = c1, c2, c3, c4 is the center, x = n, m, f, r is the evaluation parameter index value, n is the value of interference power, that is, the value of jam to noise ratio, m is the value of interference pattern, f is the value of retransmission times, and r is the value of slice width duty ratio;

[0023] Step two, the evaluation parameter index set is fuzzy processed according to the membership function to obtain the intuitionistic fuzzy subset of the evaluation parameter index.

[0024] Further, the intuitionistic fuzzy subset of the evaluation parameter index obtained by fuzzy processing the evaluation parameter index set according to the membership function in step two includes the following steps:

[0025] Step two, the interference power is fuzzy processed according to the membership function to obtain the intuitionistic fuzzy subset N1, N2, N3, N4, N5, N6, N7 of the interference power, that is, the jam to noise ratio, converted to the range of [0, 1]:

[0026]

[0027] Wherein, the [sigma1, c1] of the jam to noise ratio n is divided into: minimum [0.084, 0], smaller [0.084, 0.1667], small [0.084, 0.3333], medium [0.084, 0.5], large [0.084, 0.6667], larger [0.084, 0.8333], maximum [0.084, 1], and N is the value in the intuitionistic fuzzy subset of the jam to noise ratio;

[0028] Step two, the interference pattern is fuzzy processed according to the membership function to obtain the intuitionistic fuzzy subset M1, M2 of the interference pattern converted to the range of [0, 1]:

[0029] When the interference pattern is repeated forwarding, [σ2, c2] = [0.213, 0]; when the interference pattern is cyclic forwarding, [σ2, c2] = [0.213, 1], and the corresponding intuitive fuzzy set of the interference pattern is M1, M2;

[0030] Step two three, fuzzy processing the forwarding number according to the membership function, obtaining the forwarding number converted to the range of [0, 1] that is the intuitive fuzzy subset F1, F2, F3, F4, F5 of the forwarding number:

[0031] The forwarding number f = 1, 2, 3, 4, 5 times; when the forwarding number f = 1 time, [σ3, c3] = [0.125, 0]; when the forwarding number f = 2 times, [σ3, c3] = [0.125, 0] = [0.125, 0.25]; when the forwarding number f = 3 times, [σ3, c3] = [0.125, 0] = [0.125, 0.5]; when the forwarding number f = 4 times, [σ3, c3] = [0.125, 0] = [0.125, 0.75]; when the forwarding number f = 5 times, [σ3, c3] = [0.125, 0] = [0.125, 1]; and the corresponding intuitive fuzzy set of the forwarding number obtained is F1, F2, F3, F4, F5;

[0032] Step two four, fuzzy processing the slice width duty cycle according to the membership function, obtaining the slice width duty cycle converted to the range of [0, 1] that is the intuitive fuzzy subset R1, R2, R3, R4, R5, R6, R7 of the slice width duty cycle:

[0033]

[0034] Wherein, [σ4, c4] of the slice width duty cycle is divided into: minimum [0.084, 0], smaller [0.084, 0.1667], small [0.084, 0.3333], medium [0.084, 0.5], large [0.084, 0.6667], larger [0.084, 0.8333], maximum [0.084, 1], and the corresponding intuitive fuzzy set is R1, R2, R3, R4, R5, R6, R7, and R is the value in the intuitive fuzzy subset of the slice width duty cycle.

[0035] Further, the mapping relationship between the intermittent sampling forwarding interference data and the threat level of the interference data utilizes the direct fuzzy subset of the evaluation parameter index to establish a reasoning rule base, including the following steps:

[0036] S1, establishing an intermittent sampling forwarding interference data set;

[0037] First, the theoretical number of intermittent sampling forwarding interference data is obtained:

[0038] The number of intermittent sampling retransmission interference data is D=D1*D2*D3*D4;

[0039] D1 is the number of intuitionistic fuzzy subsets of interference power, D2 is the number of intuitionistic fuzzy subsets of interference pattern, D3 is the number of intuitionistic fuzzy subsets of interference slice width duty cycle, and D4 is the number of intuitionistic fuzzy subsets of interference retransmission times.

[0040] Then, the intermittent sampling retransmission interference data that cannot be obtained is removed, and an intermittent sampling retransmission interference data set is established.

[0041] S2, the membership function of the interference threat level is used to convert the interference threat to [0-1], and the intuitionistic fuzzy subsets U1, U2, U3 and U4 of the interference threat level are obtained.

[0042] When the interference threat level is [σ5, c5]=[0.084, 0.143], the intuitionistic fuzzy subset U1 of the interference threat level is obtained.

[0043] When the interference threat level is [σ5, c5]=[0.084, 0.429], the intuitionistic fuzzy subset U2 of the interference threat level is obtained.

[0044] When the interference threat level is [σ5, c5]=[0.084, 0.715], the intuitionistic fuzzy subset U3 of the interference threat level is obtained.

[0045] When the interference threat level is [σ5, c5]=[0.084, 1], the intuitionistic fuzzy subset U4 of the interference threat level is obtained.

[0046] The membership function of the interference threat level is the same as the membership function of the evaluation parameter index.

[0047] S3, the intuitionistic fuzzy subsets of the interference threat level are mapped with the intermittent sampling retransmission interference signal data.

[0048] S4, the mapping relationship between the intuitionistic fuzzy subsets of the interference threat and the intermittent sampling retransmission interference signal data is used to establish the inference rule of the interference threat evaluation, and a reasoning rule library is constructed.

[0049] Further, the intuitionistic fuzzy subsets of the interference threat level are mapped with the intermittent sampling retransmission interference signal data in S3, as follows:

[0050] When the number of false targets is less than 30 and the threat value U is less than 0.143, it is level 1, i.e. almost no threat level.

[0051] When the number of false targets is [30, 100] and 0.143≤U<0.429, it is set to level 2, i.e. small threat level.

[0052] When the number of false targets is [100, 300] and 0.429≤U<0.715, it is set to level 3, i.e. moderate threat level;

[0053] When the number of false targets is above 300 and U≥0.715, it is set to level 4, i.e. major threat level;

[0054] Wherein, U is the value in the intuitionistic fuzzy subset of the jamming threat level, the number of false targets is consistent with the number of intermittently sampled and forwarded jamming signal data.

[0055] Further, the jamming threat assessment inference rule is as follows:

[0056] IF N is N i

[0057] AND M is M m

[0058] AND F is F f

[0059] AND R is R i

[0060] THEN U is U j

[0061] i=1,2,…,7

[0062] m=1,2,f=1,2,…,5

[0063] j=1,2,…,4

[0064] Wherein, N i ,M m ,F f ,R i are intuitionistic fuzzy subsets defined on the jamming-to-noise ratio domain, the jamming pattern domain, the number of forwarding times domain and the slice width duty cycle domain, N, M, F, R are evaluation parameter index values, U j is the intuitionistic fuzzy subset of the jamming threat level, M is the value in M1, M2, and F is the value in F1, F2, F3, F4, F5.

[0065] Further, the jamming adopts an intuitionistic fuzzy inference synthesis algorithm to obtain a total intuitionistic fuzzy relationship according to a membership function and an inference rule library, and to obtain a relationship between the jamming threat level intuitionistic fuzzy subset and the total intuitionistic fuzzy relationship and the evaluation parameter index, including the following steps:

[0066] First, the synthesis operation of the intuitionistic fuzzy rule is used to obtain the total intuitionistic fuzzy relationship R' by using the jamming threat assessment inference rule, as follows:

[0067]

[0068] Then, the membership function and the non-membership function of the total intuitionistic fuzzy relation are obtained;

[0069] Finally, the relationship between the interference threat level intuitionistic fuzzy subset and the total intuitionistic fuzzy relation and the evaluation parameter index is obtained according to the total intuitionistic fuzzy relation and the membership function and the non-membership function of the total intuitionistic fuzzy relation.

[0070] Further, the membership function and the non-membership function of the total intuitionistic fuzzy relation are as follows:

[0071]

[0072]

[0073] Wherein, u R' is the membership function of the total intuitionistic fuzzy relation, and γ R' is the non-membership function of the total intuitionistic fuzzy relation.

[0074] Further, the relationship between the total interference threat level intuitionistic fuzzy subset and the total intuitionistic fuzzy relation and the evaluation parameter index is as follows:

[0075]

[0076] Wherein, is a composition operator.

[0077] Further, the defuzzification processing of the interference threat level intuitionistic fuzzy subset is realized by using the barycenter method.

[0078] The present application has the following beneficial effects:

[0079] The present application enables the radar party to not perform subsequent signal processing after receiving the intermittent sampling retransmission interference echo signal, to realize the grade evaluation of the interference threat degree according to the index characteristics in the time domain, and to further take or adjust the anti-interference measures in the subsequent action in time, so that the radar can accurately and timely track and detect the target and seize the opportunity in the radar countermeasure game. The present application takes the number of false targets of the intermittent sampling retransmission interference signal in the range domain as an intermediary, establishes the mapping relationship between the time domain data of the intermittent sampling retransmission interference and the interference threat level, and improves the cognition speed and accuracy of the threat degree of the intermittent sampling retransmission interference. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 It is a general flowchart of the present application;

[0081] Figure 2 It is a schematic diagram of the evaluation system;

[0082] Figure 3(a) is a time-domain diagram of intermittent sampling and forwarding interference sample 1;

[0083] Figure 3(b) is the range domain diagram of intermittent sampling and forwarding interference sample 1;

[0084] Figure 4 The parameter extraction results are for intermittent sampling and forwarding interference sample 1;

[0085] Figure 5(a) is a time-domain diagram of intermittent sampling and forwarding interference sample 2;

[0086] Figure 5(b) is the range domain diagram of intermittent sampling and forwarding interference sample 2;

[0087] Figure 6 The parameter extraction results are for the intermittent sampling and forwarding interference sample 2;

[0088] Figure 7(a) is a time-domain diagram of intermittent sampling and forwarding interference sample 3;

[0089] Figure 7(b) is the range domain diagram of intermittent sampling and forwarding interference sample 3;

[0090] Figure 8 The parameter extraction results are for intermittent sampling and forwarding interference sample 3. Detailed Implementation

[0091] Specific implementation method one: as follows Figure 1 As shown, the specific process of the intermittent sampling forwarding interference threat assessment method in this embodiment is as follows: acquire the intermittent sampling forwarding interference data to be assessed, extract the assessment parameter indicators from the intermittent sampling forwarding interference data to be assessed using time-frequency analysis and deconvolution method, and obtain the assessment parameter indicator set corresponding to the intermittent sampling forwarding interference data to be assessed, which is denoted as the assessment parameter indicator set; input the assessment parameter indicators to be assessed into the assessment system to obtain the interference threat level assessment result;

[0092] The set of parameters to be evaluated includes: interference power, interference pattern, interference slice width duty cycle, and interference forwarding count.

[0093] like Figure 2 As shown, the evaluation system includes: an evaluation parameter index set acquisition module, an evaluation parameter index set fuzzy processing module, an inference rule base construction module, an interference threat level intuition fuzzy subset acquisition module, and a defuzzification module;

[0094] The evaluation parameter index set acquisition module: establishes an evaluation parameter index set based on the simulation results of intermittent sampling forwarding interference;

[0095] The evaluation metric set includes: interference power, interference pattern, interference slice width duty cycle, and interference forwarding count;

[0096] The evaluation index set fuzzy processing module: based on the membership function of the evaluation parameter index, the evaluation parameter index set is fuzzy processed to obtain the intuitionistic fuzzy subset of the evaluation parameter index;

[0097] The reasoning rule base construction module: based on the mapping relationship between the intermittent sampling forwarding interference data and the interference threat level, the direct fuzzy subset of the evaluation parameter index is used to establish the reasoning rule base;

[0098] The interference threat level intuitionistic fuzzy subset acquisition module: using the intuitionistic fuzzy reasoning synthesis algorithm, the total intuitionistic fuzzy relationship is obtained according to the membership function and the reasoning rule base, and the relationship between the interference threat level intuitionistic fuzzy subset and the total intuitionistic fuzzy relationship and the evaluation parameter index is obtained;

[0099] The de-fuzzification module: the interference threat level intuitionistic fuzzy subset is de-fuzzification processed to obtain the clear quantity of the intermittent sampling forwarding interference threat.

[0100] The evaluation parameter index set to be evaluated is input into the evaluation system, and the following processing is performed to obtain the interference threat level evaluation result:

[0101] S101, the evaluation parameter index set to be evaluated is input into the interference threat level intuitionistic fuzzy subset acquisition module to obtain the interference threat level intuitionistic fuzzy subset of the intermittent sampling forwarding interference data to be evaluated;

[0102] S102, the interference threat level intuitionistic fuzzy subset of the intermittent sampling forwarding interference data to be evaluated is input into the de-fuzzification processing module to obtain the threat clear quantity of the intermittent sampling forwarding interference data to be evaluated.

[0103] Specific implementation method two: the evaluation parameter index set includes: interference power, interference pattern, interference slice width duty cycle, and interference forwarding times, specifically:

[0104] Interference power: for intermittent sampling forwarding interference, the greater the interference power, the greater the echo signal power of the false target compared with the true target, so that the probability of radar detecting the false target is greatly increased. The influence of interference power on interference threat degree is the most essential influence, and the evaluation index uses jam-to-noise ratio to represent the interference power.

[0105] Interference pattern: the intermittent sampling retransmission interference pattern includes direct retransmission, repeated retransmission and cyclic retransmission, and the repeated retransmission interference and the cyclic retransmission interference can make the radar generate a large number of false target groups before and after the true target echo; the cyclic retransmission interference has a wider range of dense false targets and more numbers, and thus has a greater threat degree. Generally, the interference pattern factor cannot be quantitatively described by a fixed value or a mathematical expression. According to the mechanism of the intermittent sampling retransmission jammer, the direct retransmission is the repeated retransmission with the retransmission number being 1, and thus the direct retransmission is regarded as a kind of repeated retransmission.

[0106] Interference slice width duty cycle: in the case that other parameters of the interference remain unchanged, the greater the slice width duty cycle, the more the number of false targets in the distance domain, and the greater the threat degree of the interference, and the slice width duty cycle is the ratio of the intermittent sampling retransmission interference slice width to the pulse width.

[0107] Interference retransmission number: for the intermittent sampling retransmission interference, the greater the retransmission number, the more the number of false targets formed in the distance domain, the more complex the distribution, and the greater the threat degree of the interference.

[0108] Specific implementation three: the evaluation index set fuzzy processing module is used for fuzzy processing of the evaluation parameter index set based on the membership function of the evaluation parameter index, and obtaining an intuitive fuzzy subset of the evaluation parameter index, specifically:

[0109] Step one, obtaining the membership function and the non-membership function of each evaluation parameter index in the evaluation parameter index set:

[0110] The membership function and the non-membership function of the four evaluation parameter indexes are determined, and the membership function of the index element is generally determined according to the actual demand. In the application, the Gaussian function is selected as the membership function μ A (x), and the non-membership function is γ A (x). In order to simplify the operation, let π A (x) = 0. When π A (x) = 0, the accuracy of the result is not affected, that is:

[0111]

[0112] In the formula, σ = σ1, σ2, σ3, σ4 represents the width, c = c1, c2, c3, c4 represents the center, x = n, m, f, r is the evaluation parameter index value, μ A (x) is the membership function, γ A (x) is the non-membership function, n is the value of the interference power, that is, the value of the jamming noise ratio, m is the value of the interference pattern, f is the value of the retransmission number, and r is the value of the slice width duty cycle.

[0113] Step two, the evaluation parameter index set is fuzzily processed according to the membership function to obtain an intuitive fuzzy subset of the evaluation parameter index:

[0114] In the aspect of fuzzy strategy, the evaluation index is quantified and range transformed because the input space is nonlinear. After the processing, the membership function and the non-membership function of each input state variable are defined in the interval [0, 1], which is convenient for unified processing.

[0115] Step two, the evaluation parameter index set is fuzzily processed according to the membership function to obtain an intuitive fuzzy subset of the evaluation parameter index:

[0116]

[0117] Wherein, the change range of the dry noise ratio n is generally (-5, 30] dB, [σ1, c1] is divided into: minimum [0.084, 0], smaller [0.084, 0.1667], small [0.084, 0.3333], medium [0.084, 0.5], large [0.084, 0.6667], larger [0.084, 0.8333], maximum [0.084, 1], σ1=0.084, N∈{N1, N2, N3, N4, N5, N6, N7}.

[0118] Step two, the evaluation parameter index set is fuzzily processed according to the membership function to obtain an intuitive fuzzy subset of the evaluation parameter index:

[0119] The interference pattern m is divided into two modes of repeated forwarding and cyclic forwarding.

[0120] When the interference pattern is repeated forwarding, [σ2, c2]=[0.213, 0]; when the interference pattern is cyclic forwarding, [σ2, c2]=[0.213, 1], and the corresponding intuitive fuzzy set of the interference pattern is M1, M2, σ2=0.213.

[0121] Step two, the evaluation parameter index set is fuzzily processed according to the membership function to obtain an intuitive fuzzy subset of the evaluation parameter index:

[0122] The number of forwarding times f = 1, 2, 3, 4, 5. When the number of forwarding times f is 1, [σ3, c3] = [0.125, 0] = [0.125, 0]; when the number of forwarding times f is 2, [σ3, c3] = [0.125, 0] = [0.125, 0.25]; when the number of forwarding times f is 3, [σ3, c3] = [0.125, 0] = [0.125, 0.5]; when the number of forwarding times f is 4, [σ3, c3] = [0.125, 0] = [0.125, 0.75]; when the number of forwarding times f is 5, [σ3, c3] = [0.125, 0] = [0.125, 1]; the corresponding number of forwarding times obtained is the intuitionistic fuzzy set F1, F2, F3, F4, F5, σ3 = 0.125.

[0123] Step two four, according to the membership function, the slice width duty cycle is fuzzy processed, the slice width duty cycle converted to [0, 1] range is obtained, that is, the intuitionistic fuzzy subset R1, R2, R3, R4, R5, R6, R7 of slice width duty cycle:

[0124]

[0125] Among them, [σ4, c4] of slice width duty cycle is divided into: minimum [0.084, 0], smaller [0.084, 0.1667], small [0.084, 0.3333], medium [0.084, 0.5], large [0.084, 0.6667], larger [0.084, 0.8333], maximum [0.084, 1], and the corresponding intuitionistic fuzzy set is R1, R2, R3, R4, R5, R6, R7, σ4 = 0.084, R ∈ {R1, R2, R3, R4, R5, R6, R7}.

[0126] Specific implementation four: the mapping relationship between the intermittent sampling forwarding interference data and the interference threat level utilizes the direct fuzzy subset of the evaluation parameter index to establish the inference rule base, including the following steps:

[0127] S1, an intermittent sampling forwarding interference signal data set is established:

[0128] According to the number of intuitionistic fuzzy subsets of evaluation parameter indexes, the number of rules in the inference rule base is determined to be 7 × 7 × 5 × 2 = 490. According to the principle of intermittent sampling forwarding interference and the value range of each evaluation parameter index, the parameters samples that cannot be taken are removed, and finally q = 441 intermittent sampling forwarding interference signals are used to set the inference rules.

[0129] S2, the membership function of the interference threat level is utilized to convert the interference threat level to [0-1], and the intuitionistic fuzzy subset U1, U2, U3, U4 of the interference threat level is obtained:

[0130] [σ5, c5] = [0.084, 0.143], an intuitive fuzzy subset U1 of jamming threat is obtained;

[0131] [σ5, c5] = [0.084, 0.429], an intuitive fuzzy subset U2 of jamming threat is obtained;

[0132] [σ5, c5] = [0.084, 0.715], an intuitive fuzzy subset U3 of jamming threat is obtained;

[0133] [σ5, c5] = [0.084, 1], an intuitive fuzzy subset U4 of jamming threat is obtained;

[0134] The membership function of the jamming threat level is the same as the membership function of the evaluation parameter index;

[0135] S3, mapping relationship between the intuitive fuzzy subset of jamming threat level and the intermittent sampling and forwarding jamming signal data is established:

[0136] The jamming threat evaluation level is divided, and the mapping relationship between the jamming signal data and the threat level is established. The reasoning rule is derived from the analysis of a large number of simulation experiment results of jamming signals. By changing the size of each evaluation parameter index of the jamming signal data, the intermittent sampling and forwarding jamming signal data and the number of false targets have a one-to-one mapping relationship in the distance domain. The number and distribution of false targets will affect the threat degree of jamming. The more and denser the number of false targets, the greater the threat degree of jamming. Therefore, according to the number of false targets, the threat level is divided into four levels. When the number of false targets is less than 30 and the threat value U is less than 0.143, it is set to level 1, i.e. almost no threat level. When the number of false targets is in the range of [30, 100] and 0.143≤U<0.429, it is set to level 2, i.e. small threat level. When the number of false targets is in the range of [100, 300] and 0.429≤U<0.715, it is set to level 3, i.e. moderate threat level. When the number of false targets is more than 300 and U≥0.715, it is set to level 4, i.e. major threat level. Therefore, by taking the number of false targets as an intermediary, the mapping relationship between the time domain data and the intuitive fuzzy subset of jamming threat level is established, and U is the value in the intuitive fuzzy subset of jamming threat level.

[0137] S4, the jamming threat evaluation reasoning rule is established by using the mapping relationship between the intuitive fuzzy subset of jamming threat and the intermittent sampling and forwarding jamming signal data, and a reasoning rule library is constructed;

[0138] The number of membership functions of jamming threat is N u = 4, the reasoning rule is multiple and multi-dimensional, and the jamming threat evaluation reasoning rule is as follows:

[0139] IF N is N i

[0140] AND M is M m

[0141] AND F is F f

[0142] AND R is R i

[0143] THEN U is U j

[0144] i = 1, 2, …, 7

[0145] m = 1, 2, f = 1, 2, …, 5

[0146] j = 1, 2, …, 4

[0147] Specifically, if N is the data in N i , M is the data in M m , F is the data in F f , R is the data in R i , then U is the data in U j .

[0148] Wherein, N i , M m , F f , R i are premise part language items, N, M, F, R are evaluation parameter index values, U is the value of output quantity interference threat, U j is the intuitionistic fuzzy subset of interference threat level. Detailed description five: the interference threat level intuitionistic fuzzy subset acquisition module is used to acquire total intuitionistic fuzzy relationship and the relationship between the interference threat level intuitionistic fuzzy subset and total intuitionistic fuzzy relationship and evaluation parameter index according to the membership function and the reasoning rule base by using the intuitionistic fuzzy reasoning composition algorithm:

[0149] The "minimum-maximum" composition operator is used, the reasoning rule and the intermittent sampling forwarding interference data set and the intuitionistic fuzzy subset output relationship of interference threat level are one-to-one corresponding, and the synthesis operation relationship of the system can be obtained from the reasoning rule of the intuitionistic fuzzy relationship.

[0150] Firstly, the total intuitionistic fuzzy relationship R' of the system is obtained from the synthesis operation of the intuitionistic fuzzy rule by using the reasoning rule in the reasoning rule base as follows:

[0151]

[0152] Then, the membership function and the non-membership function of the total intuitionistic fuzzy relationship of the system are acquired as follows:

[0153]

[0154]

[0155] In the formula, N i ,M m ,F f ,R i are respectively intuitionistic fuzzy subsets defined on the domain of signal-to-noise ratio, the domain of jamming pattern, the domain of retransmission times and the domain of slice width duty cycle, U j is an intuitionistic fuzzy subset defined on the output domain U. Wherein i=1, 2, …7; m=1, 2; f=1, 2, …5; j=1, 2, …4.

[0156] The above process can be represented by the following formula:

[0157]

[0158] In the above formula, R is a synthetic operator, and R' represents a total intuitionistic fuzzy relation.

[0159] Specific implementation method six: the defuzzification module is used for defuzzification processing of the jamming threat level intuitionistic fuzzy subset to obtain an intermittent sampling retransmission jamming threat evaluation clear quantity.

[0160] The output obtained by using the rules in the rule base for reasoning is in a fuzzy domain and is a fuzzy quantity, which does not conform to the accurate quantity output requirement of the fuzzy reasoning system, so it needs to be mapped to an accurate basic domain through calculation. The present application adopts the barycenter method, and the barycenter of the area surrounded by the true value function curve of the membership function and the non-membership function synthesis of the jamming threat level intuitionistic fuzzy subset and the abscissa is the final output value of the intuitionistic fuzzy reasoning.

[0161] Embodiment:

[0162] The following embodiment is used to verify the beneficial effects of the present application:

[0163] In order to illustrate the effectiveness of the present application for intermittent sampling retransmission jamming threat evaluation, MATLAB simulation experiments are performed on intermittent sampling retransmission jamming data. The key parameters in the simulation experiment are as follows: the radar transmitted signal form is an LFM signal, the pulse width is 30us, the bandwidth is 10MHz, and the sampling rate is 30MHz.

[0164] Suppose that in a certain electronic warfare, three kinds of intermittent sampling retransmission jamming with different parameters are implemented by the jamming party as the radar party. The measured parameter values of the intermittent sampling retransmission jamming of our party are shown in Table 1:

[0165] Table 1 Jamming parameter measurement values

[0166]

[0167] Fig. 3(a), Figure 4 The time domain graph and parameter extraction graph of the interference sample 1 are shown in Fig. 3(a) and Fig. 3(b), and the parameter extraction results are 0.061, 4 times and 24.9 dB respectively. Figure 6 The time domain graph and parameter extraction graph of the interference sample 2 are shown in Fig. 5(a) and Fig. 5(b), and the parameter extraction results are 0.101, 2 times and 20.2 dB respectively. Figure 8 The time domain graph and parameter extraction graph of the interference sample 3 are shown in Fig. 7(a) and Fig. 7(b), and the parameter extraction results are 0.082, 3 times and 29.9 dB respectively.

[0168] Fig. 3(b), Fig. 5(b) and Fig. 7(b) are the range domain pulse compression graphs of the interference sample 1, sample 2 and sample 3. The number and distribution of the false targets of the three interference samples are analyzed. It can be preliminarily judged that the interference sample 3 actually has the largest threat, the largest number of false targets and the most complex distribution, so it has the highest threat level. The interference sample 2 has the least number of false targets and the lowest threat level.

[0169] The extracted values are intuitively fuzzy measured, and are brought into the membership function to obtain a parameter input vector. The three input vectors are respectively taken as inputs of the intuitive fuzzy inference machine. According to the inference rule, the interference threat level value is output. The final evaluation result is shown in Table 2.

[0170] Table 2: Interference threat evaluation result

[0171]

[0172] As shown in Table 2, the threat value of the interference sample 1 is 0.63927, and the threat level is moderate threat level. The threat value of the interference sample 2 is 0.35785, and the threat level is small threat level. The threat value of the interference sample 3 is 0.79393, and the threat level is major threat level. The threat degree of the three samples is ranked as: sample 3> sample 1> sample 2, which is consistent with the actual situation described above. When anti-interference processing is performed, the interference sample 3 should be selected first to maximize the interests of the radar side. The threat evaluation result shows that the threat evaluation system based on the intuitive fuzzy inference established by the present application is effective and reasonable. The present application establishes a mapping relationship between the intermittent sampling and forwarding interference time domain data and the interference threat level, so that the radar side can more quickly and accurately recognize the threat degree of the interference, and thus more quickly adjust the anti-interference strategy to seize the opportunity in the game confrontation.

[0173] The present application can also be used for other various data and scenes. Those skilled in the art can process different data in different scenes according to the present application without departing from the spirit and essence of the present application. However, these should all belong to the protection scope of the claims attached to the present application.

Claims

1. A method for assessing intermittent sampling and forwarding interference threats, characterized in that... The method includes the following steps: acquiring intermittent sampling forwarding interference data to be evaluated; extracting evaluation parameter indicators from the intermittent sampling forwarding interference data to be evaluated using time-frequency analysis and deconvolution method to obtain the evaluation parameter indicator set corresponding to the intermittent sampling forwarding interference data to be evaluated, denoted as the evaluation parameter indicator set; inputting the evaluation parameter indicators into the evaluation system to obtain the interference threat level evaluation result. The set of parameters to be evaluated includes: interference power, interference pattern, interference slice width duty cycle, and interference forwarding count. The evaluation system includes: an evaluation parameter index set acquisition module, an evaluation parameter index set fuzzy processing module, an inference rule base construction module, an interference threat level intuitive fuzzy subset acquisition module, and a defuzzification module; The evaluation parameter index set acquisition module is used to establish an evaluation parameter index set based on the simulation results of intermittent sampling forwarding interference. The set of evaluation parameters includes: interference power, interference pattern, interference slice width duty cycle, and interference forwarding count. The evaluation parameter index set fuzzy processing module performs fuzzy processing on the evaluation parameter index set based on the membership function of the evaluation parameter index to obtain an intuitive fuzzy subset of the evaluation parameter index. The inference rule base construction module: Based on the mapping relationship between intermittently sampled and forwarded interference data and interference threat level, an inference rule base is established using a direct fuzzy subset of evaluation parameter indicators; The interference threat level intuitive fuzzy subset acquisition module: uses an intuitive fuzzy reasoning synthesis algorithm to obtain the total intuitive fuzzy relation based on the membership function and reasoning rule base, and obtains the relationship between the interference threat level intuitive fuzzy subset and the total intuitive fuzzy relation and evaluation parameter indicators, thereby obtaining the interference threat level intuitive fuzzy subset; The deblurring module is used to deblur the intuitively ambiguous subset of the interference threat level to obtain a clear amount of intermittently sampled forwarding interference threat. The set of parameters to be evaluated is input into the evaluation system, and the interference threat level evaluation result is obtained after the following processing: S101. Input the set of parameters to be evaluated into the interference threat level intuition fuzzy subset acquisition module to obtain the interference threat level intuition fuzzy subset of the intermittent sampling forwarding interference data to be evaluated. S102. Input the intuitive fuzzy subset of the interference threat level of the intermittent sampling forwarding interference data to be evaluated into the defuzzification processing module to obtain the threat clarity quantity of the intermittent sampling forwarding interference data to be evaluated.

2. The intermittent sampling forwarding interference threat assessment method according to claim 1, characterized in that: The process of fuzzifying the evaluation parameter index set based on the membership function of the evaluation parameter index to obtain an intuitive fuzzy subset of the evaluation parameter index includes the following steps: Step 1: Obtain the membership function and non-membership function of each evaluation parameter in the evaluation parameter index set: Where σ = σ1, σ2, σ3, σ4 are the widths, c = c1, c2, c3, c4 are the centers, x = n, m, f, r are the evaluation parameter values, n is the interference power (i.e., the interference-to-noise ratio), m is the interference pattern, f is the number of forwards, and r is the slice width duty cycle. Step 2: Perform fuzzification processing on the evaluation parameter index set according to the membership function to obtain the intuitive fuzzy subset of the evaluation parameter index.

3. The intermittent sampling forwarding interference threat assessment method according to claim 2, characterized in that: Step two, which involves fuzzifying the evaluation parameter index set based on the membership function to obtain an intuitive fuzzy subset of the evaluation parameter indexes, includes the following steps: Step 2.

1. Perform fuzzification processing on the interference power according to the membership function to obtain the intuitive fuzzy subsets N1, N2, N3, N4, N5, N6, N7 of the interference power (i.e., the interference-to-noise ratio) transformed to the range [0,1]. Among them, the [σ1, c1] of the interference noise ratio n is divided into: very small [0.084, 0], relatively small [0.084, 0.1667], small [0.084, 0.3333], medium [0.084, 0.5], large [0.084, 0.6667], relatively large [0.084, 0.8333], and extremely large [0.084, 1]. N is the value of the interference noise ratio in the intuitive fuzzy subset. Step 22: Perform fuzzification on the interference patterns according to the membership function to obtain the interference patterns transformed to the range [0,1], i.e., the intuitive fuzzy subsets M1 and M2 of the interference patterns; When the interference pattern is repeated forwarding, [σ2, c2] = [0.213, 0]; when the interference pattern is cyclic forwarding, [σ2, c2] = [0.213, 1], and the corresponding intuitive fuzzy sets of the interference patterns are M1, M2; Steps 2 and 3: Fuzzyenize the forwarding counts according to the membership function to obtain the intuitive fuzzy subsets F1, F2, F3, F4, F5 of the forwarding counts transformed into the range [0,1]. The number of forwardings f = 1, 2, 3, 4, 5 times; when the number of forwardings f = 1 time, [σ3, c3] = [0.125, 0]; when the number of forwardings f = 2 times, [σ3, c3] = [0.125, 0.25]; when the number of forwardings f = 3 times, [σ3, c3] = [0.125, 0.5]; when the number of forwardings f = 4 times, [σ3, c3] = [0.125, 0.75]; when the number of forwardings f = 5 times, [σ3, c3] = [0.125, 1]; the corresponding intuitive fuzzy sets of forwarding counts are F1, F2, F3, F4, F5; Step 24: Fuzzyenize the slice width duty cycle according to the membership function to obtain the intuitive fuzzy subsets R1, R2, R3, R4, R5, R6, R7 of the slice width duty cycle transformed to the range [0,1]. Among them, the slice width duty cycle [σ4, c4] is divided into: minimum [0.084, 0], minimum [0.084, 0.1667], minimum [0.084, 0.3333], medium [0.084, 0.5], maximum [0.084, 0.6667], maximum [0.084, 0.8333], maximum [0.084, 1], and the corresponding intuitionistic fuzzy sets are R1, R2, R3, R4, R5, R6, R7, where R is the value in the intuitionistic fuzzy subset of the slice width duty cycle.

4. The intermittent sampling forwarding interference threat assessment method according to claim 3, characterized in that: The mapping relationship between intermittently sampled forwarding interference data and interference threat levels is established using a direct fuzzy subset of evaluation parameter indicators to build an inference rule base, including the following steps: S1. Establish an intermittent sampling forwarding interference dataset; First, obtain the theoretical number of intermittent sampling and forwarding interference data: The theoretical number of intermittent sampling and forwarding interference data is D = D1 * D2 * D3 * D4; Where D1 is the number of intuitive fuzzy subsets of interference power, D2 is the number of intuitive fuzzy subsets of interference pattern, D3 is the number of intuitive fuzzy subsets of interference slice width duty cycle, and D4 is the number of intuitive fuzzy subsets of interference forwarding count. Then, remove the unobtainable intermittent sampling forwarding interference data and establish an intermittent sampling forwarding interference dataset; S2. Using the membership function of the interference threat level, the interference threat is transformed to the range [0-1] to obtain the intuitive fuzzy subsets U1, U2, U3, and U4 of the interference threat level: When the interference threat level is [σ5, c5] = [0.084, 0.143], the intuitive fuzzy subset U1 of the interference threat level is obtained; When the interference threat level is [σ5, c5] = [0.084, 0.429], the intuitive fuzzy subset U2 of the interference threat level is obtained; When the interference threat level is [σ5, c5] = [0.084, 0.715], the intuitive fuzzy subset U3 of the interference threat level is obtained; When the interference threat level is [σ5, c5] = [0.084, 1], the intuitive fuzzy subset U4 of the interference threat level is obtained; The membership function of the interference threat level is the same as the membership function of the evaluation parameter index; S3. Establish a mapping relationship between the intuitive fuzzy subset of the interference threat level and the intermittently sampled and forwarded interference signal data; S4. Establish interference threat assessment inference rules by utilizing the mapping relationship between the intuitive fuzzy subset of interference threats and intermittently sampled and forwarded interference signal data, and construct an inference rule base.

5. The intermittent sampling forwarding interference threat assessment method according to claim 4, characterized in that: In S3, a mapping relationship is established between the intuitively ambiguous subset of the interference threat level and the intermittently sampled and forwarded interference signal data, as follows: When the number of false targets is less than 30 and the threat value U is less than 0.143, it is classified as Level 1, which means almost no threat. When the number of false targets is [30, 100], and 0.143 ≤ U < 0.429, it is set to level 2, i.e., a relatively small threat level; The number of false targets is [100, 300]. When 0.429 ≤ U < 0.715, it is set to level 3, which is the medium threat level. When the number of false targets exceeds 300 and U ≥ 0.715, it is set to Level 4, i.e., a major threat level; Where U is a value in the intuitive fuzzy subset of the interference threat level, and the number of false targets is consistent with the number of intermittently sampled and forwarded interference signal data.

6. The intermittent sampling forwarding interference threat assessment method according to claim 5, characterized in that: The interference threat assessment inference rule takes the following form: IF N is N i AND M is M m AND F is F f AND R is R i THEN U is U j i＝1,2,…,7 m = 1, 2, f = 1, 2, ..., 5 j＝1,2,…,4 Where, N i M m ,F f ,R i These are intuitionistic fuzzy subsets defined on the domains of interference-to-noise ratio, interference pattern, forwarding count, and slice width duty cycle, respectively. N, M, F, and R are evaluation parameter values, and U... j It is an intuitive fuzzy subset of the interference threat level, where M is the value among M1 and M2, and F is the value among F1, F2, F3, F4, and F5.

7. The intermittent sampling forwarding interference threat assessment method according to claim 6, characterized in that: The interference is synthesized using an intuitionistic fuzzy reasoning algorithm to obtain the total intuitionistic fuzzy relation based on the membership function and the reasoning rule base, and to obtain the relationship between the intuitionistic fuzzy subset of the interference threat level and the total intuitionistic fuzzy relation and evaluation parameter indicators, including the following steps: First, the total intuitionistic fuzzy relation R' is obtained by using the composition operation of intuitionistic fuzzy rules and the interference threat assessment reasoning rules, as shown in the following formula: Then, obtain the membership function and non-membership function of the total intuitionistic fuzzy relation; Finally, the relationship between the intuitive fuzzy subset of the interference threat level and the overall intuitive fuzzy relation and evaluation parameter indicators is obtained based on the membership function and non-membership function of the overall intuitive fuzzy relation.

8. The intermittent sampling forwarding interference threat assessment method according to claim 7, characterized in that: The membership function and non-membership function of the total intuitive fuzzy relation are as follows: Among them, u R' It is the membership function of the general intuitionistic fuzzy relation, γ R' It is the non-membership function of the general intuitive fuzzy relation.

9. The intermittent sampling forwarding interference threat assessment method according to claim 8, characterized in that: The relationship between the intuitive fuzzy subset of the interference threat level and the total intuitive fuzzy relationship, as well as the relationship between the evaluation parameter indicators, are as follows: in, This is a composition operator.

10. The intermittent sampling forwarding interference threat assessment method according to claim 9, characterized in that: The centroid method is used to obtain a clear measure of the intermittent sampling and forwarding interference threat by deblurring the intuitive fuzzy subset of the interference threat level.

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