Air target threat degree comprehensive evaluation method based on minimum violation ranking

Abstract

The invention discloses an air target threat degree comprehensive evaluation method, and adopts an air target threat degree comprehensive evaluation method based on minimum violation ranking. Through the method, the problem of evaluating the threat degree of the aerial target under the condition that observation information has uncertainty and fuzziness is solved. According to the method, the threat degrees of the targets in the air are reasonably ranked, good error interference resistance is achieved, and when the attributes of the targets calculated by a detection system have errors, the threat degree ranking of the targets does not change.

Description

Technical Field

[0001] The invention relates to an aerial target threat level estimation technology, in particular to an aerial target threat level comprehensive evaluation method. Background Art

[0002] In the context of the information age, air situation information is complex and changeable. Threat assessment of air targets helps our ground air defense weapons to make reasonable weapon configuration and resource management, so as to effectively intercept air targets and defend our positions. In addition, due to the complexity and changeability of situation information, it is usually impossible to accurately describe the analytical relationship between various influencing factors and air target threats, which has caused great difficulties in air target threat assessment.

[0003] In this complex combat context, it is very important to quickly obtain the original data of enemy targets from numerous and complex detection systems, and obtain key target attribute information such as target type, position, speed, distance, etc. by preprocessing the data, so as to judge the threat level of the target based on these key information. The target threat level is based on the current situation information, and the threat intention and combat capability of the enemy target are evaluated to indicate the threat level of the enemy target. Generally speaking, the greater the target threat level, the greater the importance of the target. There is currently no accurate internationally recognized definition of threat assessment. The most widely recognized statement came from a general information fusion processing model proposed by the Joint Command Laboratory for Data Fusion established by the US Department of Defense in the mid-1980s - the JDL (Joint Directors of Laboratories) model. Threat assessment is the third-level processing in the information fusion system, and its function is to infer the threat level posed by the enemy target to our side and infer the enemy's combat intention.

[0004] At present, there are roughly the following methods for aerial target threat assessment: multi-attribute target decision-making, fuzzy mathematics, Bayesian networks, neural network methods, etc.

[0005] Table 0 Summary of the ranking method of aerial target threat level

[0006]

[0007]

[0008] The glossaries involved in the present invention are explained as follows:

[0009] Threat level: The threat level of an aerial target describes the potential threat level of an aerial target (such as an aircraft, drone, etc.) based on the current situation information. This threat level can be determined based on a variety of factors, such as the type of aerial target: different types of aerial targets have different threat levels. A fighter jet has a higher threat level than a civilian aircraft because it has stronger strike capability and maneuverability. The threat level of an aerial target is a dynamic indicator that can change with various factors such as time and target attributes. Therefore, accurately ranking the threat level of aerial targets is crucial to protecting the security of our area.

[0010] Definition of fuzzy sets: Assume that the elements in the domain U are represented by x, that is, U = {x}. Fuzzy sets on U It can be expressed as:

[0011]

[0012] express The membership function of Represents x The membership degree of . When it is 1, it means that x completely belongs to the fuzzy set when The closer the value is to 1, the more likely x belongs to The greater the degree of When it is {0,1}, it means has the same characteristic function as the ordinary set, then degenerates into a normal set.

[0013] Intuitionistic fuzzy sets (IFS)

[0014] Definition: Assume a domain U, then the intuitionistic fuzzy set on U It can be expressed as:

[0015]

[0016] For x in U The membership degree of For x in U The non-membership degree of U→[0,1], and satisfies For x in U The degree of hesitation.

[0017] Interval valued intuitionistic fuzzy sets (IVIFS)

[0018] Definition: Assume a non-empty set X = {x1, x2, ..., x n}, then the interval intuitionistic fuzzy It can be expressed as:

[0019]

[0020] Number of intervals Indicates that x in X belongs to The membership degree, interval number Indicates that x in X belongs to The non-membership degree of And meet int([0,1]) is the set of all closed subintervals in [0,1]. For the convenience of writing, interval intuitionistic fuzzy sets can be expressed as:

[0021]

[0022] Satisfy the conditions in Indicates that x in X belongs to The degree of hesitation. Summary of the invention

[0023] The technical problem to be solved by the present invention is to provide a comprehensive evaluation method for the threat degree of aerial targets in view of the shortcomings of the existing technology, so as to reasonably rank the threat degree of aerial targets and obtain an objective ranking result.

[0024] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for comprehensively evaluating the threat level of aerial targets, comprising the following steps:

[0025] S1. Establish an evaluation index system based on air targets;

[0026] S2, preprocessing the original data of each evaluation index to obtain the interval intuitionistic fuzzy number of the target attribute;

[0027] S3, according to the interval intuitionistic fuzzy number, calculate the interval intuitionistic fuzzy set information entropy of each attribute of the target;

[0028] S4, calculate attribute weights based on the principle of minimizing interval intuitionistic fuzzy information entropy;

[0029] S5, compare the interval intuitive fuzzy values ​​under each attribute in pairs, and obtain a comprehensive attribute comparison matrix by combining the attribute weights;

[0030] S6. Use the Minimum Violations Rating (MVR) method to rank the threat level of aerial targets.

[0031] The present invention takes into account that the target attribute values ​​observed by the detection system are not necessarily precise values ​​and there are certain error range fluctuations. The interval intuitive fuzzy number method is used for processing, which conforms to the actual situation in real life, and the resulting ranking is reasonable.

[0032] In the present invention, the evaluation indicators include: target type, target speed, target distance, and interference capability.

[0033] In the present invention, the target x i The interval intuitionistic fuzzy number X of the attribute i The expression is:

[0034]

[0035] in, They represent the lower bound of the interval of membership of the j-th attribute value of the ith target and the upper bound of the interval of membership; They respectively represent the lower bound of the non-membership interval of the j-th attribute value of the ith target and the upper bound of the non-membership interval, j = 1, 2, ..., m, where m represents the number of evaluation indicators.

[0036] The information entropy of interval intuitionistic fuzzy set of the jth attribute is expressed as:

[0037]

[0038] in, They represent the upper and lower bounds of the membership interval of the j-th attribute of the ith target respectively; They represent the upper and lower bounds of the non-membership interval of the j-th attribute of the ith target respectively;

[0039] They represent the upper and lower bounds of the hesitation of the j-th attribute of the ith target.

[0040] The weight ω of the jth attribute j It is expressed as: Represents the inverse sum of the information entropy of all target attributes.

[0041] Attribute comparison matrix d ij The expression is: in, It indicates the comparison result of the attributes of target i and target j under the kth attribute.

[0042] The minimum violation ranking method is used to rank the threat level of the aerial targets. The threat level ranking expression is as follows:

[0043]

[0044]

[0045] Among them, c ij =#{k|d ik <d jk}+#{k|d ki >d kj},#{k|d ik <d jk} indicates that in the comprehensive attribute comparison matrix, the number of attribute comparison results of target j is greater than that of target i; #{k|d ki >d kj} indicates the number of target i with a larger attribute comparison result than target j in the comprehensive attribute comparison matrix compared with target k.

[0046] The present invention adopts an MVR method based on an attribute comparison matrix to rank the threat levels of aerial targets, solves the ranking problem of target attribute values ​​being interval numbers, and is a ranking obtained based on comparison results of target attribute values, which is objective.

[0047] Considering that in the situational awareness system, the observed data may have a certain range of fluctuations or errors. At the same time, due to the complexity of the situation and the limitations of information perception, uncertainty and ambiguity are inevitable. In order to make accurate judgments in time for complex situations, reasonably use the observed target attribute values, and estimate the threat level of aerial targets, the present invention adopts a comprehensive evaluation method for the threat level of aerial targets based on interval intuitionistic fuzzy numbers. This method is used to solve the ranking method of the threat level of aerial targets when the observed information has uncertainty and ambiguity.

[0048] Compared with the prior art, the present invention has the following beneficial effects: the present invention reasonably ranks the threat levels of aerial targets and obtains objective ranking results. At the same time, it has good error resistance. When the attributes of each target observed by the detection system have errors, the threat level ranking of each target will not change. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The method adopted in the embodiment of the present invention belongs to the category of fuzzy mathematics. Most of the existing methods are aimed at threat assessment and analysis when the target attributes are precise values, but this attribute data is not very consistent with the actual situation. Some attributes such as speed and distance will be affected by the observation errors of the detection system, which will cause error fluctuations in the attribute values. The embodiment of the present invention proposes a comprehensive ranking method for the threat level of aerial targets based on interval intuitive fuzzy numbers, with the aim of effectively handling the situation where such data has range fluctuations, uniformly processing and representing qualitative and quantitative indicators in the form of interval intuitive fuzzy numbers, and ranking the threat level of the target in an objective method.

[0052] Considering that few existing threat ranking methods can rank interval data, in order to make full use of the interval attribute values ​​of the target, we used the MVR method based on the attribute comparison matrix to realize the ranking of interval intuitive fuzzy numbers, reducing the influence of human subjectivity and obtaining a comprehensive ranking of the threat degree of aerial targets.

[0053] The method flow chart of the embodiment of the present invention is as follows Figure 1 As shown in the figure, firstly, an evaluation index system is established for aerial targets. After data preprocessing, the attributes of the targets are processed into interval intuitionistic fuzzy numbers. Then, according to the attribute values ​​of the aerial targets, a comprehensive attribute comparison matrix is ​​obtained, and MVR ranking is performed to obtain the threat ranking of the aerial targets.

[0054] Step 1: Establish an evaluation index system based on aerial targets, such as target type, target speed, target distance, jamming capability, etc. The original data of each target attribute is obtained through detection by the detection system software (aerial target detection software DroneOptID).

[0055] Step 2: Preprocess the original data of each indicator to form the interval intuitionistic fuzzy number matrix of the target attribute.

[0056] Step 3: According to the interval-valued intuitionistic fuzzy numbers, calculate the information entropy of interval-valued intuitionistic fuzzy sets (IVIFS) of each target attribute.

[0057] Step 4: Obtain attribute weights based on the principle of minimizing interval intuitionistic fuzzy information entropy.

[0058] Step 5: Compare the interval intuitionistic fuzzy values ​​of each attribute pairwise, and obtain the comprehensive attribute comparison matrix based on the attribute weights.

[0059] Step 6: Use the Minimum Violations Rating (MVR) method to rank the threat level of aerial targets.

[0060] The specific implementation of step 2 is as follows:

[0061] To conduct threat assessment on aerial targets, it is necessary to normalize the attributes of the targets. For different types of indicators, namely benefit-type or cost-type, they are unified into benefit-type indicators, and the membership and non-membership of each attribute of the target are obtained by solving the membership and non-membership functions. Assuming that there are n targets and each target has m attributes, after data preprocessing, the interval intuitive fuzzy value of the jth attribute value of the i-th target is:

[0062] They represent the lower bound of the interval of membership of the j-th attribute value of the ith target and the upper bound of the interval of membership; They represent the lower bound of the non-membership interval of the j-th attribute value of the ith target and the upper bound of the non-membership interval respectively. i The attribute value of is an interval intuitionistic fuzzy number, which can be expressed as:

[0063]

[0064] The specific calculation process of step 3 is as follows:

[0065] After preprocessing, the interval intuitionistic fuzzy number matrix of each target attribute is obtained. In order to reasonably rank and evaluate the threat level of aerial targets, the establishment of target attribute weights is the key. The entropy method is a common method for determining weights, which can be used to represent the relative importance of target attributes.

[0066] The calculation expression of IVIFS information entropy we use is shown in (1) to calculate the attribute weight of the target:

[0067]

[0068] In the above formula, E j Represents the information entropy of the j-th attribute of the target. They represent the upper and lower bounds of the membership interval of the j-th attribute of the ith target respectively; They represent the upper and lower bounds of the non-membership interval of the j-th attribute of the ith target respectively; They represent the upper and lower bounds of the hesitation of the j-th attribute of the ith target.

[0069] The specific implementation of step 4 is as follows:

[0070] In order to make full use of the attribute values ​​of the target, based on the principle of minimizing the interval intuitionistic fuzzy information entropy, the attribute weight ω of the target is obtained. j , as shown in expression (2).

[0071]

[0072]

[0073] The attribute weight of the target is shown in expression (3):

[0074]

[0075] E j 、E k The solution of is shown in expression (1). Represents the inverse sum of all attribute information entropy of the target. j Represents the j-th attribute weight of the target.

[0076] The specific implementation of step 5 is as follows:

[0077] After obtaining the target's attribute weights, in order to reasonably rank the threat levels of aerial targets, for interval intuitionistic fuzzy numbers, we first compare the target's attributes in pairs, and then combine the attribute weights to obtain the comprehensive attribute comparison matrix D = (d ij ) n×n , which represents the attribute comparison matrix of target i and target j obtained after integrating various attributes:

[0078]

[0079]

[0080] in It represents the attribute comparison result between target i and target j under the kth attribute.

[0081] The specific implementation of step 6 is as follows:

[0082] After obtaining the comprehensive attribute comparison matrix D, the MVR method is used to rank the threat degree of aerial targets, and the threat assessment problem is solved by binary integer linear programming to obtain the threat degree ranking:

[0083]

[0084]

[0085] Matrix C = (c ij ) n×n The number of inconsistencies between targets i and j is counted, that is,

[0086] c ij =#{k|d ik <d jk}+#{k|d ki >d kj}

[0087] In the formula #{k|d ik <d jk} indicates that in the comprehensive attribute comparison matrix, target i has a larger number of attribute comparison results than target i; #{k|d ki >d kj} indicates the number of attribute comparison results of target i that are greater than target j in the comprehensive attribute comparison matrix compared with target k.

[0088] Decision matrix X = (x ij ) n×n Indicates whether the ranking of target i is above the ranking of target j.

[0089]

[0090] The present invention is further described in detail below in conjunction with implementation examples.

[0091] Assuming that there are four air raid targets X = {x1, x2, ..., x4}, through observation, we can get the six decision attribute values ​​of each target G = {target type, distance, route angle, speed, altitude, jamming capability}, expressed as G = {g1, g2, ..., g6}. Through the data preprocessing in step 2, the interval intuitionistic fuzzy attribute matrix of the air target at a certain time is obtained as shown in Table 1.

[0092] Table 1 Interval intuitionistic fuzzy matrix of target attributes

[0093]

[0094] According to step 3, the information entropy of the intuitionistic fuzzy sets of each attribute interval is shown in Table 2.

[0095] Table 2 Information entropy of each attribute of the target

[0096]

[0097]

[0098] According to step 4, the weights of the intuitionistic fuzzy sets of each attribute interval are shown in Table 3.

[0099] Table 3 Weights of target attributes

[0100]

[0101] According to step 5, the comprehensive attribute comparison matrix is ​​obtained as shown in Table 4.

[0102] Table 4 Comprehensive attribute comparison matrix

[0103] Target <![CDATA[x1]]> <![CDATA[x2]]> <![CDATA[x3]]> <![CDATA[x4 <!-- 8 -->]]> <![CDATA[x1]]> 0 0.4841 0.5953 0.9283 <![CDATA[x2]]> 0.2563 0 0.5364 0.7112 <![CDATA[x3]]> 0.2275 0.3171 0 0.3330 <![CDATA[x4]]> 0 0.1582 0.1838 0

[0104] According to step 6, the decision matrix of x is shown in Table 5.

[0105] Table 5 Decision matrix

[0106] Target <![CDATA[x1]]> <![CDATA[x2]]> <![CDATA[x3]]> <![CDATA[x4]]> <![CDATA[x1]]> 0 1 1 1 <![CDATA[x2]]> 0 0 1 1 <![CDATA[x3]]> 0 0 0 1 <![CDATA[x4]]> 0 0 0 0

[0107] After solving the X matrix using the MVR sorting method, the ranking results of each target can be obtained by sorting the columns and sums of the X matrix in ascending order. The ranking results of the targets obtained by solving are:

[0108] x1>x2>x3>x4.

[0109] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0110] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for comprehensive assessment of the threat level of aerial targets based on minimum violation ranking, characterized in that: The following steps are involved: S1. Establish an evaluation index system based on air targets; S2, preprocessing the original data of each evaluation index to obtain the interval intuitionistic fuzzy number of the target attribute; S3, according to the interval intuitionistic fuzzy number, calculate the interval intuitionistic fuzzy set information entropy of each attribute of the target; S4, calculate attribute weights based on the principle of minimizing interval intuitionistic fuzzy information entropy; S5, compare the interval intuitive fuzzy values ​​under each attribute in pairs, and obtain a comprehensive attribute comparison matrix by combining the attribute weights; S6. Use the minimum violation ranking method to rank the threat level of aerial targets.

2. The method for comprehensive assessment of the threat level of aerial targets according to claim 1, characterized in that: Evaluation metrics include: Target type, target speed, target distance, jamming capability.

3. The method for comprehensive assessment of the threat level of aerial targets according to claim 1, characterized in that: Target x i The interval intuitionistic fuzzy number X of the attribute i The expression is: in, They represent the lower bound of the interval of membership of the j-th attribute value of the ith target and the upper bound of the interval of membership; They respectively represent the lower bound of the non-membership interval of the j-th attribute value of the ith target and the upper bound of the non-membership interval, j = 1, 2, ..., m, where m represents the number of evaluation indicators.

4. The method for comprehensive assessment of the threat level of aerial targets according to claim 1, characterized in that: The information entropy of interval intuitionistic fuzzy set of the jth attribute is expressed as: in, They represent the upper and lower bounds of the membership interval of the j-th attribute of the ith target respectively; They represent the upper and lower bounds of the non-membership interval of the j-th attribute of the ith target respectively; They represent the upper and lower bounds of the hesitation of the j-th attribute of the ith target.

5. The method for comprehensive assessment of the threat level of aerial targets according to claim 4 is characterized in that: The weight ω of the jth attribute j It is expressed as: Represents the inverse sum of the information entropy of all target attributes.

6. The method for comprehensive assessment of the threat level of aerial targets according to claim 4 is characterized in that: Attribute comparison matrix d ij The expression is: in, It indicates the comparison result of the attributes of target i and target j under the kth attribute.

7. The method for comprehensive assessment of the threat level of aerial targets according to claim 6 is characterized in that: The threat level of the aerial targets is ranked using the minimum violation ranking method.

8. The method for comprehensive assessment of the threat level of aerial targets according to claim 6 is characterized in that: The threat ranking expression is as follows: Among them, c ij =#{k|d ik <d jk }+#{k|d ki >d kj },#{k|d ik <d jk } indicates that in the comprehensive attribute comparison matrix, the number of attribute comparison results of target j is greater than that of target i; #{k|d ki >d kj } indicates the number of target i with a larger attribute comparison result than target j in the comprehensive attribute comparison matrix compared with target k.

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