Method and system for determining quality of radar system based on combination weighting grey cloud model

Abstract

The application provides a radar system quality determination method and system based on a combination weighting grey cloud model, and belongs to the technical field of radar systems. The radar system quality determination method comprises the following steps: obtaining the measurement values of multiple index factors of a measured radar system; for any index factor, calculating the grey cloud whitening weight values of each grey class corresponding to the index factor according to the measurement value of the index factor based on the whitening weight function of the normal grey cloud model; calculating the comprehensive grey class coefficients of the measured radar system belonging to each grey class according to the grey cloud whitening weight values of each grey class corresponding to each index factor, the comprehensive weights and the measurement values; the comprehensive weights of each index factor are determined in advance based on the analytic hierarchy process and the entropy weight method, so that the distribution of the weights is more scientific; and the quality of the measured radar system is determined according to the comprehensive grey class coefficients of the measured radar system belonging to each grey class, so that the fuzziness, randomness and incompleteness of the index factors are considered, and the reliability of the quality of the radar system is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar systems, and in particular to a radar system quality determination method and system based on a combined weighted grey cloud model. Background Art

[0002] As a crucial component of aerospace measurement, control, navigation, and tracking systems, radar plays an increasingly prominent role in both military and civilian applications. Conducting a rational and scientific assessment of radar quality, ensuring timely understanding of the system's operational status, and providing a scientific basis for radar production, design, operation, and maintenance has become a pressing need for radar equipment support in the new era. Modern radar systems are technologically advanced, characterized by complex structures, intensive technology, and intelligent control. Assessing their quality is a multi-factor, multi-attribute, comprehensive decision-making process.

[0003] Common equipment quality assessment methods include ADC effectiveness evaluation, analytic hierarchy process (AHP), fuzzy comprehensive evaluation, Bayesian network, and grey theory. In recent years, there has been extensive research on radar system evaluation. While existing evaluation methods provide some insight into the quality and effectiveness of radar systems under certain circumstances, they struggle to comprehensively address the randomness, ambiguity, and uncertainty of indicator data. The application of new technologies has significantly altered the structure and performance of radars. These systems now incorporate not only traditional transmitters and receivers but also data and signal processing units for target detection and identification, speed and ranging, tracking, and positioning. This has led to a complex operating mechanism for radar systems, resulting in a degree of uncertainty in the evaluation and decision-making information.

[0004] Based on the above problems, a new radar system quality determination method is urgently needed to improve the accuracy of radar system quality assessment. Summary of the Invention

[0005] The purpose of the present invention is to provide a radar system quality determination method and system based on a combined weighted grey cloud model, which can improve the accuracy of evaluating the quality of the radar system.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A radar system quality determination method based on a combined weighted grey cloud model comprises:

[0008] Obtaining measurement values ​​of multiple indicator factors of the radar system under test;

[0009] For any indicator factor, based on the whitening weight function of the normal gray cloud model, according to the measured value of the indicator factor, the gray cloud whitening weight of each gray class corresponding to the indicator factor is calculated; each gray class is a pre-set radar quality level;

[0010] Calculate the comprehensive gray class coefficient of the radar system under test belonging to each gray class according to the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor, and the measured value of each indicator factor; the comprehensive weight of each indicator factor is determined in advance based on the hierarchical analysis method and the entropy weight method;

[0011] The quality of the radar system under test is determined according to the comprehensive gray class coefficient of each gray class to which the radar system under test belongs.

[0012] To achieve the above object, the present invention also provides the following solution:

[0013] A radar system quality determination system based on a combined weighted grey cloud model, comprising:

[0014] A measurement value acquisition unit, used to obtain measurement values ​​of multiple indicator factors of the radar system under test;

[0015] a gray cloud whitening weight determination unit connected to the measurement value acquisition unit, for calculating, for any index factor, the gray cloud whitening weight of each gray class corresponding to the index factor based on the whitening weight function of the normal gray cloud model and the measured value of the index factor; each gray class is a pre-set radar quality level;

[0016] a comprehensive gray class coefficient determination unit, connected to the measurement value acquisition unit and the gray cloud whitening weight determination unit, respectively, for calculating the comprehensive gray class coefficient of the measured radar system belonging to each gray class according to the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor, and the measurement value of each indicator factor; the comprehensive weight of each indicator factor is pre-determined based on the hierarchical analysis method and the entropy weight method;

[0017] The quality determination unit is connected to the comprehensive gray class coefficient determination unit and is used to determine the quality of the radar system under test according to the comprehensive gray class coefficient of each gray class to which the radar system under test belongs.

[0018] According to the specific embodiment provided by the present invention, the present invention discloses the following technical effects: based on the whitening weight function of the normal gray cloud model, the gray cloud whitening weight of each gray class corresponding to the index factor is calculated according to the measured value of the index factor; each gray class is a pre-set radar quality level; based on the gray cloud whitening weight of each index factor corresponding to each gray class, the comprehensive weight of each index factor and the measured value of each index factor, the comprehensive gray class coefficient of each gray class of the tested radar system is calculated; finally, based on the comprehensive gray class coefficient of each gray class of the tested radar system, the quality of the tested radar system is determined, effectively taking into account the fuzziness, randomness and incompleteness of the radar index factor. Among them, the comprehensive weight of each index factor is pre-determined based on the hierarchical analysis method and the entropy weight method, taking into account both subjectivity and objectivity, making the weight distribution more scientific and improving the credibility of the radar system quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 Flowchart of the radar system quality determination method based on the combined weighted grey cloud model of the present invention;

[0021] Figure 2 Schematic diagram of a typical whitening weight function;

[0022] Figure 3 Schematic diagram of the lower limit measure whitening weight function;

[0023] Figure 4 Schematic diagram of moderate measure whitening weight function;

[0024] Figure 5 Schematic diagram of upper limit measure whitening weight function;

[0025] Figure 6 It is a schematic diagram of the one-dimensional normal cloud model;

[0026] Figure 7 This is a schematic diagram of the radar system evaluation index system;

[0027] Figure 8 The overall flow chart for radar system quality assessment;

[0028] Figure 9 A bar chart comparing the weights of radar system indicators;

[0029] Figure 10 This is a schematic diagram of the distribution of ash clouds with poor transmitter power indicators;

[0030] Figure 11 This is a schematic diagram of ash cloud distribution at a general level of transmitter power index;

[0031] Figure 12 This is a schematic diagram of the distribution of ash clouds with a good transmitter power index level;

[0032] Figure 13 This is a schematic diagram of the gray cloud distribution with excellent transmitter power index;

[0033] Figure 14 It is a schematic diagram of the normal gray cloud model of qualitative indicators;

[0034] Figure 15The figure is a schematic diagram of the module structure of the radar system quality determination system based on the combined weighted grey cloud model of the present invention.

[0035] Explanation of symbols:

[0036] Measurement value acquisition unit-1, gray cloud whitening weight determination unit-2, comprehensive gray class coefficient determination unit-3, quality determination unit-4, indicator classification unit-5, initial weight determination unit-6, subjective weight determination unit-7, evaluation value acquisition unit-8, objective weight determination unit-9, comprehensive weight determination unit-10. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The purpose of the present invention is to provide a radar system quality determination method and system based on a combined weighted gray cloud model. The normal gray cloud model is used to determine the comprehensive gray class coefficient of the measured radar system belonging to each gray class according to the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor and the measured value of each indicator factor. The comprehensive weight of each indicator factor is determined in advance based on the hierarchical analysis method and the entropy weight method, which effectively takes into account the fuzziness, randomness and incompleteness of the radar indicator factors, and at the same time takes into account both subjectivity and objectivity, making the weight distribution more scientific and improving the credibility of the radar system quality.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] like Figure 1 As shown, the radar system quality determination method based on the combined weighted grey cloud model of the present invention includes:

[0041] S1: Obtain measurement values ​​of multiple indicator factors of the radar system under test.

[0042] In this embodiment, the multiple indicator factors are the transmitter power, noise figure, amplitude-frequency characteristics, improvement factor, antenna gain, transmitter high voltage, transmission pulse width, receiving field discharge current, antenna rotation speed, power supply voltage, switch status, operating parameters, fault information, equipment availability, equivalent service time, mean time between failures, rain and snow information, salt fog information, dust information, and temperature and humidity information of the surrounding environment.

[0043] S2: For any indicator factor, based on the whitening weight function of the normal gray cloud model and the measured value of the indicator factor, calculate the gray cloud whitening weight for each gray class corresponding to the indicator factor. Each gray class represents a pre-defined radar quality level. In this embodiment, the gray classes include: excellent, good, fair, and poor.

[0044] S3: Calculate the comprehensive gray class coefficient of the radar system under test for each gray class based on the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor, and the measured value of each indicator factor. The comprehensive weight of each indicator factor is pre-determined based on the analytic hierarchy process and the entropy weight method.

[0045] In the comprehensive evaluation process, the AHP (Analytic Hierarchy Process)-entropy weight method is introduced to determine the indicator weights, which takes into account the subjectivity of expert knowledge and the objectivity of test data, making the weight distribution more scientific and improving the credibility of radar system quality.

[0046] Specifically, the following formula is used to calculate the comprehensive gray class coefficient of the radar system under test belonging to the kth gray class:

[0047]

[0048] Among them, σ k is the comprehensive gray class coefficient of the radar system under test belonging to the kth gray class, n is the total number of indicator factors, is the gray cloud whitening weight of the kth gray class corresponding to the i-th index factor, x i is the measured value of the i-th indicator factor, ξ i is the comprehensive weight of the i-th indicator factor.

[0049] S4: Determine the quality of the radar system under test based on the comprehensive gray class coefficient of each gray class to which the radar system under test belongs. The gray class with the largest comprehensive gray class coefficient is defined as the final gray class of the radar system under test, and determine the quality level of the radar system under test based on the final gray class.

[0050] Specifically, according to the comprehensive gray class coefficient of each gray class of the radar system under test, the comprehensive clustering vector σ=(σ 1 ,σ 2 ,σ 3 ,...,σ s ), where σ s is the comprehensive gray class coefficient of the radar system under test belonging to the sth gray class. The final gray class to which the radar system under test belongs is determined according to the following formula: Among them, s is the total number of gray classes. From this, it can be determined that the final gray class to which the radar system under test belongs is k * .

[0051] Traditional radar quality assessments rely primarily on design history information, such as service life, failure time, and operating environment, but underutilize actual test data, leading to a degree of partiality. This invention, based primarily on monitoring data, simultaneously integrates radar BIT internal test information, historical data, and environmental data. While taking into account radar historical parameters and environmental factors, it focuses on collecting monitoring data such as radar performance parameters and operating status, constructing a radar system evaluation index system. This enables a comprehensive assessment of radar quality, addressing the lack of quantitative evaluation methods due to radar's intensive technology and complex structure.

[0052] Grey system theory is used to reflect the incompleteness of information, that is, greyness, and provides a new method for decision-making problems in uncertain systems. In the grey system, grey numbers are basic elements, which are used to represent numbers whose value range is known but whose specific values ​​are uncertain. The grey number can be regarded as a set of numbers. The possible values ​​in the set of numbers are called whitening values, and the size of the whitening value is represented by the whitening weight. The function that reflects the whitening weight coefficient is called the whitening weight function, or whitening function for short, recorded as f(x). In order to facilitate engineering calculations and applications, the commonly used whitening weight function is simplified to a linear triangular whitening weight function. There are four basic types of triangular whitening weight functions: classical type, lower limit measure type, moderate measure type and upper limit measure type, which represent different grey concepts respectively. The four whitening weight functions are as follows: Figure 2-Figure 5 As shown, the horizontal axis represents the value of the gray number, and the vertical axis represents the size of the whitening weight coefficient.

[0053] The cloud model is a method model for converting qualitative and quantitative information. It is very practical for processing random and fuzzy information. The cloud model has the following definition: Let U be a quantitative domain represented by a certain precise value, and C be a qualitative concept on U. If x is a quantitative value on the domain, and x is a random realization of C, the certainty of x with respect to C, μ(x)∈[0,1], is a random number with a stable tendency. It is denoted as μ:U→[0,1], x→μ(x), then the distribution of x on U is called a cloud, and each μ(x) is called a cloud drop, denoted as drop(x,μ(x)).

[0054] Cloud models are characterized numerically by expectation (Ex), entropy (En), and hyperentropy (He). Ex, which best reflects qualitative concepts, is represented by the location of cloud peaks in cloud maps. En, a measure of Ex's uncertainty, represents the range of values ​​within the domain that can be accepted by a certain qualitative concept and is represented by the cloud's width. He, a measure of En's uncertainty, is the entropy of entropy and reflects the degree of cloud droplet dispersion, and is represented by cloud thickness in cloud maps.

[0055] Based on the cloud model definition, if x satisfies: x~N(Ex,En' 2), where En'~N(En,He 2 ), and the degree of certainty of x on C satisfies: Then x is said to belong to the normal cloud distribution on the universe U. The one-dimensional normal cloud model is as follows Figure 6 shown.

[0056] Normal gray cloud model: Let U be a domain, T be the linguistic value associated with U, and an element x∈U. The whitening weight of the gray concept expressed by x in T is a random number with a stable tendency. The distribution of whitening weights on the domain U is called the gray cloud whitening weight function, often referred to as the gray cloud. The gray cloud is a mapping from the domain U to the interval [0,1], that is, U(x)=GL(x):U→[0,1], x∈U, x→GL(x).

[0057] The digital features of the gray cloud model include the peak value Cx, the left and right boundary values ​​(Lx, Rx), the entropy En, and the super entropy He, which can be expressed as: GC = (Cx; Lx, Rx; En; He). In this embodiment, Lx is the minimum boundary value of the indicator factor, and Rx is the maximum boundary value of the indicator factor. The peak value Cx represents the value of the whitening weight equal to 1, which is the value that best reflects the qualitative concept; the left and right boundary values ​​(Lx, Rx) represent the value range of the gray concept in the domain U; ​​the larger the entropy value En, the stronger the fuzziness of the evaluation level boundary; and the larger the super entropy He, the stronger the randomness of the whitening weight coefficient.

[0058] The normal distribution is universal in scientific research, and its numerical characteristics are generally expressed using mean and variance. If the curve of a gray cloud model conforms to the normal distribution, it is called a normal gray cloud model. In combination with the characteristics of radar systems, this paper establishes a point-peak normal gray cloud model to evaluate its quality. The relationship between the various numerical characteristics is as follows:

[0059]

[0060]

[0061]

[0062] Where α is a given constant, and the whitening weight function of the point peak normal gray cloud model is:

[0063]

[0064] Among them, En' obeys E n For the expectation, 2 is the normal distribution of variance, and x is the measured value of the indicator factor. Assuming that there are n indicator factors in the radar system, and the radar system has s kinds of gray classes, a normal gray cloud model is established in which the i-th indicator factor belongs to the k-th gray class, and its corresponding whitening weight function is recorded as f k (xi ), x i is the measured value of the i-th indicator factor. According to the classification of the whitening weight function, the normal grey cloud model can also be divided into upper limit measurement type, moderate measurement type and lower limit measurement type. The formula is as follows:

[0065] Upper limit measure cloud model:

[0066]

[0067] Moderate measure cloud model:

[0068]

[0069] Lower limit measure cloud model:

[0070]

[0071] Specifically, if the elements in a gray class are larger, better, and more specific, the upper limit measure whitening weight function is used. If the elements in a gray class are smaller, better, and more specific, the lower limit measure whitening weight function is used. If the likelihood of the elements in a gray class taking whitening values ​​revolves around a point, the moderate measure whitening weight function is used.

[0072] Furthermore, step S2 specifically includes:

[0073] S21: Determine the peak value and entropy of the normal gray cloud model according to the minimum boundary value and the maximum boundary value of the indicator factor.

[0074] S22: using a whitening weight function, and determining the whitening weight of each gray class corresponding to the index factor according to the peak value, entropy, and the measured value of the index factor.

[0075] The whitening weight reflects the degree of membership of the index factor to each gray class. k There are random variables in (x). To increase the credibility of the whitening weight, the present invention adopts the method of multiple calculations and averaging to determine the whitening weight of each gray class corresponding to the index factor. Assuming that q calculations are performed, each calculation generates a cloud droplet, and the mean value of the whitening weight of the i-th index factor is obtained. k (x i ), the more times, the more stable the whitening right is.

[0076]

[0077] in, The cloud droplets generated by the qth operation.

[0078] S23: Determine the gray cloud whitening weight corresponding to each gray class according to the whitening weight corresponding to each gray class of the indicator factor.

[0079] Specifically, the whitening weights of the index factors corresponding to the gray classes are normalized to obtain the gray cloud whitening weights of the index factors corresponding to the gray classes.

[0080] The following formula is used to determine the gray cloud whitening weight of the i-th index factor:

[0081] In this embodiment, the numerical characteristics of each indicator factor are calculated based on the relevant theories of the gray cloud model. First, the value range of the quantitative indicator is determined and actual measurements are performed. Then, the peak value Cx, entropy value En, and excess entropy He are calculated. Second, for the qualitative indicator, an expert scoring method is used to determine the degree of membership of the indicator factor to each gray category and calculate its numerical characteristics. Qualitative and quantitative indicators are distinguished based on the numerical characteristics, and a normal gray cloud model is constructed for the indicator factor. Based on the normal gray cloud model, the gray cloud whitening weight of each level corresponding to the indicator factor is calculated.

[0082] Furthermore, in terms of determining the comprehensive weight of each indicator factor, the radar system quality determination method based on the combined weighted grey cloud model of the present invention further includes:

[0083] S101: Classify multiple indicator factors and determine multiple criterion factors. First, analyze the underlying indicator factors that affect radar quality and determine multiple indicator factors for quality detection, including 20 underlying indicators such as transmitter power and noise figure. Rationally classify multiple indicator factors, distinguish between the target layer, criterion layer, and indicator layer, and establish a three-level indicator system for radar system quality detection, such as Figure 7 As shown in the figure, the target layer represents radar system quality, the criterion layer comprises multiple criterion factors, and the indicator layer comprises multiple indicator factors. Taking a radar as an example, this indicator system rationally categorizes the indicator factors into five criterion indicators and 20 underlying indicators, aiming to provide a comprehensive and objective evaluation of radar system quality.

[0084] In this embodiment, the criteria factors include performance indicator data, working status data, radar internal measurement information, historical information and environmental information.

[0085] The performance indicator data includes transmitter power, noise figure, amplitude-frequency characteristics, improvement factor, and antenna gain. The operating status data includes transmitter high voltage, transmit pulse width, receiver field discharge current, antenna rotation speed, and power supply voltage. The radar internal measurement information includes switch status, operating parameters, and fault information. The historical information includes equipment availability, equivalent service time, and mean time between failures. The environmental information includes rain, snow, salt spray, sand and dust, and temperature and humidity information.

[0086] S102: Calculate the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs using the hierarchical analysis method.

[0087] Specifically, the calculation of the weight of each criterion factor using the hierarchical analysis method in step S102 specifically includes:

[0088] S1021: Initialize the initial importance between every two criterion factors.

[0089] S1022: For the b-th iteration, according to the b-th importance between every two criterion factors, a 1-9 scaling method is used to determine a first evaluation matrix A; b≥0; the 0th importance is the initial importance.

[0090]

[0091] Among them, a 12 It represents the relative importance of criterion factor a1 compared with criterion factor a2, and m is the total number of criterion factors.

[0092] S1023: Calculate the maximum eigenvalue of the first evaluation matrix and the eigenvector corresponding to the maximum eigenvalue. The eigenvector includes the initial weight of each criterion factor. By calculating the maximum eigenvalue of the first evaluation matrix and its corresponding eigenvector, the importance ranking of each criterion factor can be obtained, that is, the weight vector of the criterion factor w=(w1,w2,...,w m ) T .

[0093] S1024: Determine a consistency check index CI according to the maximum eigenvalue and the number of the criterion factors.

[0094] Specifically, Among them, λ max is the largest characteristic root.

[0095] S1025: Determine an average consistency index RI according to the dimension of the first evaluation matrix. As a specific implementation, the values ​​of the average consistency index are shown in Table 1.

[0096] Table 1 Average random consistency index values

[0097] Dimension of the first judgment matrix 3 4 5 6 7 8 9 RI 0.58 0.90 1.12 1.24 1.32 1.41 1.45

[0098] S1026: Determine a consistency check ratio CR based on the consistency check index and the average consistency index:

[0099] S1027: Determine the difference between the consistency check ratio and a preset ratio threshold.

[0100] S1028: If the consistency check ratio is less than a preset ratio threshold, the initial weight of each criterion factor in the feature vector is the weight of each criterion factor. Specifically, the preset ratio threshold is 0.1.

[0101] S1029: If the consistency check ratio is greater than or equal to the preset ratio threshold, the importance between every two criterion factors is adjusted to obtain the b+1th importance between every two criterion factors, and the b+1th iteration is performed.

[0102] It should be noted that the specific method of using the hierarchical analysis method to calculate the weight of each indicator factor in the criterion factor to which it belongs in step S102 is the same as the method of using the hierarchical analysis method to calculate the weight of each criterion factor and each indicator factor, which will not be repeated here.

[0103] S103: Determine the subjective weight of each indicator factor based on the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs.

[0104] S104: Obtain evaluation values ​​of multiple reference radar systems under various indicator factors.

[0105] According to the evaluation value of the reference radar system under each index factor, the entropy weight matrix Z = [z ij ] n×d , where z ij represents the evaluation value of the j-th reference radar system under the i-th index factor, and d is the total number of reference radar systems.

[0106] S105: Determine the objective weight of each indicator factor using an entropy weight method based on the evaluation value of each reference radar system under each indicator factor.

[0107] Specifically, step S105 includes:

[0108] S1051: For any reference radar system, determine the weight of the reference radar system under each indicator factor according to the evaluation value of the reference radar system under each indicator factor.

[0109] Specifically, the following formula is used to calculate the weight p of the jth reference radar system under the i-th indicator factor: ij :

[0110]

[0111] S1052: Determine the entropy value of each indicator factor according to the weight of each reference radar under each indicator factor.

[0112] Specifically, the entropy value E of the i-th indicator factor is calculated using the following formula: i :

[0113]

[0114] S1053: Determine the entropy weight of each indicator factor based on the entropy value of each indicator factor. The entropy weight of each indicator factor is the objective weight corresponding to each indicator factor.

[0115] Specifically, the entropy weight w′ of the i-th indicator factor is determined using the following formula: i :

[0116]

[0117] S106: For any indicator factor, determine the comprehensive weight of the indicator factor according to the subjective weight and objective weight of the indicator factor.

[0118] The final comprehensive weight vector w' is:

[0119] w′=(w′1,w′2,…,w′ n ) T ,

[0120] Entropy is used to characterize the degree of disorder in radar systems. The entropy weighting method comprehensively quantifies and weights the information contained in the index factors. The smaller the entropy value, the less disorder the index factor represents, the greater the amount of information represented, and the greater the weight. The entropy weighting method minimizes subjective human intervention and makes the weighting more objective and reliable.

[0121] Specifically, the following formula is used to determine the comprehensive weight of the i-th indicator factor:

[0122]

[0123] Among them, ξ i is the comprehensive weight of the i-th indicator factor, w i is the subjective weight of the i-th indicator factor, w′ i is the objective weight of the ith indicator factor, n is the total number of indicator factors, and w′ is the vector composed of the objective weights of n indicator factors.

[0124] Weights are a quantitative reflection of the importance of an indicator factor to the evaluation object (radar system). A single weighting method fails to balance subjective experience with objective information. Scientifically determining indicator weights ensures the reliability of the evaluation results. Radar is a complex system in which precision mechanical, optical, and electrical components work together. Its indicators include both qualitative indicators and quantitative data. Therefore, determining indicator weights requires considering both subjective factors and objectivity. Therefore, this paper uses a combination of AHP and entropy weighting to determine the comprehensive weights of each indicator factor.

[0125] Based on the combined weighting, the present invention organically combines the cloud model with the grey theory. Grey clustering can effectively deal with poor information and uncertainty decision-making problems. The cloud model can well realize the mutual conversion between qualitative concepts and quantitative indicators, integrate the fuzziness and randomness of evaluation information, optimize the grey clustering information, and effectively take into account the fuzziness, randomness and incompleteness of radar indicator factors.

[0126] In order to better understand the solution of the present invention, it is further described below with reference to specific embodiments.

[0127] Taking a radar system as an example, a normal gray cloud model is established and the comprehensive gray class coefficient is calculated according to the radar system quality determination method based on the combined weighted gray cloud model of the present invention. The specific process is as follows Figure 8 The value range and actual measurement value of the quantitative index factors that can be measured in the index system are determined. The results are shown in Table 2. Among them, X min is the minimum boundary value of the indicator factor, X max is the maximum boundary value of the indicator factor.

[0128] Table 2 Boundary values ​​and measured values ​​of the underlying index factors of a certain type of radar system

[0129]

[0130]

[0131] Determine the comprehensive weight of the indicators. Based on the indicator system of the radar system, use AHP to calculate the subjective weight of the indicator factors, use the entropy weight method to calculate the objective weight of the indicator factors, and use the multiplication integration method to calculate the comprehensive weight of the indicator factors. Taking the five indicators of the criterion layer as an example, establish the importance evaluation matrix of the target layer according to the 1-9 scaling method:

[0132]

[0133] Calculate the maximum eigenvalue and eigenvector of the evaluation matrix, and we can get

[0134] λ max =5.0264;

[0135] w=(w1,w2,w3,w4,w5) T =(0.3505,0.3505,0.1374,0.0808,0.0808) T ;

[0136] Testing the consistency of this evaluation matrix yields test indices CI = 0.0066, RI = 1.12, and CR = 0.0059. Therefore, the evaluation matrix A passes the consistency test, and the required w is the weight vector of the criterion layer. Similarly, we can construct an evaluation matrix for the indicator layer relative to the criterion layer and calculate the weight vector for the indicator layer relative to the criterion layer. By performing layer-by-layer calculations, we can obtain the subjective weight of each indicator factor relative to the target layer.

[0137] Then, using the entropy weight method formula, the mathematical tool MATLAB was used to calculate the entropy value and entropy weight of each indicator factor, thereby determining the objective weight. Finally, each indicator factor was combined and weighted. Taking the first indicator, transmitter power, as an example, the subjective weight w1 = 0.1463 and the objective weight w1' = 0.1415 were calculated. Using the multiplication integration method to combine and weight, the comprehensive weight of the first indicator factor was obtained:

[0138]

[0139] By analogy, the combined weights of the underlying indicator factors for the evaluation target can be obtained. The calculation results of the weights of all indicator factors are shown in Table 3. Taking the five underlying indicator factors in the performance indicator data as an example, the comparison of their subjective and objective weights and comprehensive weights is shown in Table 3. Figure 9 As shown in the figure, it can be seen that the comparison of indicator weights after combined weighting is more obvious, and it is easier to distinguish the importance of indicators.

[0140] Table 3 Radar system evaluation index weights

[0141]

[0142]

[0143] Constructing a normal gray cloud model

[0144] First, the gray class level for the radar system quality is determined. Combining the actual application of radar and the habit of equipment quality assessment, the present invention determines four gray classes for the radar system, namely "poor, general, good, and excellent". According to the evaluation index system, quantitative indicators and qualitative indicators are distinguished to construct gray cloud models respectively. For quantitative indicators, according to their value range and actual measurement values ​​(as shown in Table 2), the left and right boundaries of the four levels are delineated, and α=6 is taken to calculate the peak value Cx, entropy value En and super entropy He. Taking the transmitter power as an example, its value range is 130-160W, and the actual measurement is 150W. Its gray class classification and digital characteristics are shown in Table 4. According to the value range and digital characteristics of its four levels, a gray cloud model is constructed. With the help of MATLAB mathematical tools, 2000 whitening weight calculations are performed to generate the normal gray cloud model corresponding to each level as shown below. Figure 10-13 shown.

[0145] Table 4 Transmitter power index levels and digital characteristics

[0146] Index level Poor generally good excellent Left and right limits [130,140] [136,146] [143,153] [150,160] Cx 135 141 148 155 En 1.67 1.67 1.67 1.67 He 0.28 0.28 0.28 0.28

[0147] Comprehensive clustering calculation

[0148] (1) Calculate the whitening weight mean of the gray class index. Based on the 2000 cloud droplets, calculate the whitening weight mean of each gray class level of the transmitter power f according to the following formula k (x i ):

[0149]

[0150]

[0151]

[0152]

[0153] (2) Calculate the gray cloud whitening weight. Normalize the above whitening weights to obtain the final gray cloud whitening weights for each transmitter power level:

[0154]

[0155]

[0156]

[0157]

[0158] Similarly, repeating the above steps can calculate the final gray cloud whitening weights of each level of the remaining quantitative indicators.

[0159] (3) Construct a gray cloud model for qualitative indicators. The 10 qualitative indicators in the indicator system do not have specific values ​​and unified dimensions. In view of this, the qualitative evaluation scores are given by experts. In order to facilitate the use of the cloud model method to measure these indicators, after fully consulting experts, four gray evaluation levels of "poor, general, good, and excellent" are uniformly set for the qualitative indicators, and the left and right boundaries of each level interval are set between 0 and 1. Take α = 6 and calculate the peak value, entropy value and super entropy to obtain the gray level and digital characteristics of the qualitative indicators. The results are shown in Table 5, and the gray cloud model is generated as follows: Figure 14 shown.

[0160] Table 5 Qualitative indicator evaluation levels and numerical characteristics

[0161] Index level Left and right limits Cx En He Poor [0.00,0.25] 0.125 0.0417 0.00695 generally [0.25,0.50] 0.375 0.0417 0.00695 good [0.50,0.75] 0.625 0.0417 0.00695 excellent [0.75,1.00] 0.875 0.0417 0.00695

[0162] Five experts were invited to quantify the scores of the 10 qualitative indicators assigned to each gray level. The average score was used to determine the final membership degree, as shown in Table 6. This membership degree, like the whitening weight coefficient, reflects the degree of preference for an indicator assigned to a certain evaluation level, thereby determining the whitening weights of all indicators.

[0163] Table 6 Membership scores of qualitative indicators of a radar system

[0164] Indicator factors Poor generally good excellent Switch status 0.0 0.1 0.2 0.7 Working parameters 0.1 0.1 0.3 0.5 Fault information 0.2 0.1 0.2 0.5 Equipment availability rate 0.0 0.1 0.3 0.6 Equivalent service time 0.1 0.1 0.5 0.3 Mean time between failures 0.1 0.2 0.4 0.3 Rain and snow 0.2 0.1 0.5 0.2 salt spray 0.0 0.0 0.6 0.4 sand and dust 0.1 0.2 0.1 0.6 Temperature and humidity 0.0 0.1 0.1 0.8

[0165] (4) Calculate the comprehensive gray class coefficient to determine the evaluation result. After obtaining the comprehensive weights of all indicators and the gray cloud whitening weight, calculate the comprehensive gray class coefficient of the radar system under test for the four gray class levels:

[0166] σ 1 =0.0471;

[0167] σ 2 =0.2154;

[0168] σ 3 =0.4552;

[0169] σ 4 =0.2823;

[0170] Thus, the comprehensive gray class vector of the radar system is obtained:

[0171] σ=(σ 1 ,σ 2 ,σ 3 ,σ 4 )=(0.0471,0.2154,0.4552,0.2823);

[0172] It can be seen that the radar's good-grade comprehensive grayscale coefficient is the largest, at 0.4552, significantly outperforming the other coefficients. The excellent-grade comprehensive grayscale coefficient is second only to the poor-grade comprehensive grayscale coefficient, while the poor-grade comprehensive grayscale coefficient is very small, consistent with the actual performance of the radar system. Therefore, the radar system's overall quality level is considered good.

[0173] In view of the fact that traditional radar quality assessment does not make sufficient use of test data and relies too much on design data and historical information, the present invention comprehensively considers radar monitoring data indicators, radar BIT information, environmental data and historical parameters to establish a three-level evaluation index system for radar systems, making the evaluation information more scientific and comprehensive.

[0174] The present invention determines the comprehensive weights of radar index factors based on the combined weighting method of AHP-entropy weight method, which effectively solves the problems of diverse types of index factors, difficult quantification and strong subjectivity in evaluation. It comprehensively considers the subjectivity of expert knowledge and experience and the objectivity of monitoring values, and increases the credibility of evaluation.

[0175] An evaluation method based on the normal gray cloud model applies the cloud model to the traditional gray whitening weight function, optimizing clustering information and effectively accounting for the fuzziness, randomness, and incompleteness of radar performance factors. The gray cloud model provides a scientific and practical approach for evaluating the quality of complex radar systems. The feasibility and effectiveness of the method are verified through analysis of a typical radar example, providing valuable insights into radar system quality assessment and equipment support operations.

[0176] like Figure 15 As shown, the radar system quality determination system based on the combined weighted grey cloud model of the present invention includes: a measurement value acquisition unit 1, a grey cloud whitening weight determination unit 2, a comprehensive grey class coefficient determination unit 3 and a quality determination unit 4.

[0177] The measurement value acquisition unit 1 is used to obtain the measurement values ​​of multiple index factors of the radar system under test.

[0178] The gray cloud whitening weight determination unit 2 is connected to the measurement value acquisition unit 1. The gray cloud whitening weight determination unit 2 is used to calculate the gray cloud whitening weight of each gray class corresponding to any index factor based on the whitening weight function of the normal gray cloud model and the measurement value of the index factor; each gray class is a pre-set radar quality level.

[0179] The comprehensive gray class coefficient determination unit 3 is connected to the measurement value acquisition unit and the gray cloud whitening weight determination unit 2 respectively. The comprehensive gray class coefficient determination unit 3 is used to calculate the comprehensive gray class coefficient of the measured radar system belonging to each gray class according to the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor and the measurement value of each indicator factor; the comprehensive weight of each indicator factor is determined in advance based on the hierarchical analysis method and the entropy weight method.

[0180] The quality determination unit 4 is connected to the comprehensive gray class coefficient determination unit 3, and is used to determine the quality of the radar system under test according to the comprehensive gray class coefficient of each gray class to which the radar system under test belongs.

[0181] Furthermore, the gray cloud whitening weight determination unit 2 includes: a peak value determination module, a whitening weight determination module and a gray cloud whitening weight determination module.

[0182] The peak value determination module is used to determine the peak value and entropy of the normal gray cloud model according to the minimum boundary value and the maximum boundary value of the indicator factor.

[0183] The whitening weight determination module is connected to the peak value determination module and the measurement value acquisition unit 1. The whitening weight determination module is used to use a whitening weight function to determine the whitening weight of each gray class corresponding to the index factor according to the peak value, entropy and the measurement value of the index factor.

[0184] The gray cloud whitening weight determination module is connected to the whitening weight determination module, and the gray cloud whitening weight determination module is used to determine the gray cloud whitening weight corresponding to each gray class according to the whitening weight of the indicator factor corresponding to each gray class.

[0185] In addition, the radar system quality determination system based on the combined weighted grey cloud model of the present invention also includes: an indicator classification unit 5, an initial weight determination unit 6, a subjective weight determination unit 7, an evaluation value acquisition unit 8, an objective weight determination unit 9 and a comprehensive weight determination unit 10.

[0186] The indicator classification unit 5 is used to classify multiple indicator factors and determine multiple criterion factors.

[0187] The initial weight determination unit 6 is connected to the indicator classification unit 5 and is used to calculate the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs by using the hierarchical analysis method.

[0188] Specifically, the initial weight determination unit 6 includes: an initialization module, a judgment matrix determination module, a characteristic root determination module, a consistency test index determination module, an average consistency index determination module, a consistency test ratio determination module, a judgment module, a weight determination module and an adjustment module.

[0189] The initialization module is connected to the indicator classification unit 5, and is used to initialize the initial importance between every two criterion factors.

[0190] The evaluation matrix determination module is connected to the initialization module. The evaluation matrix determination module is used to determine the first evaluation matrix for the bth iteration according to the bth importance between each two criterion factors using a 1-9 scaling method; b≥0; the 0th importance is the initial importance.

[0191] The eigenvalue determination module is connected to the evaluation matrix determination module and is used to calculate the maximum eigenvalue of the first evaluation matrix and the eigenvector corresponding to the maximum eigenvalue. The eigenvector includes the initial weights of the various criteria factors.

[0192] The consistency check index determination module is connected to the characteristic root determination module, and the consistency check index determination module is used to determine the consistency check index according to the maximum characteristic root and the number of the criterion factors.

[0193] The average consistency index determination module is connected to the evaluation matrix determination module, and the average consistency index determination module is used to determine the average consistency index according to the dimension of the first evaluation matrix.

[0194] The consistency check ratio determination module is connected to the consistency check index determination module and the average consistency index determination module. The consistency check ratio determination module is used to determine the consistency check ratio according to the consistency check index and the average consistency index.

[0195] The judgment module is connected to the consistency check ratio determination module, and the judgment module is used to judge the size of the consistency check ratio and a preset ratio threshold.

[0196] The weight determination module is connected to the judgment module, and is used to use the initial weight of each criterion factor in the feature vector as the weight of each criterion factor when the consistency check ratio is less than a preset ratio threshold.

[0197] The adjustment module is connected to the judgment module and the evaluation matrix determination module. The adjustment module is used to adjust the importance between each two criterion factors when the consistency check ratio is greater than or equal to the preset ratio threshold, obtain the b+1th importance between each two criterion factors, and perform the b+1th iteration.

[0198] The subjective weight determination unit 7 is connected to the initial weight determination unit 6 and is used to determine the subjective weight of each indicator factor according to the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs.

[0199] The evaluation value acquisition unit 8 is used to obtain the evaluation values ​​of multiple reference radar systems under various indicator factors.

[0200] The objective weight determination unit 9 is connected to the evaluation value acquisition unit 8 and is used to determine the objective weight of each index factor using an entropy weight method according to the evaluation value of each reference radar system under each index factor.

[0201] Specifically, the objective weight determination unit 9 includes: a weight determination module, an entropy value determination module and an entropy weight determination module.

[0202] Among them, the proportion determination module is connected to the evaluation value acquisition unit 8, and the proportion determination module is used to determine the proportion of any reference radar system under each index factor according to the evaluation value of the reference radar system under each index factor.

[0203] The entropy value determination module is connected to the proportion determination module, and the entropy value determination module is used to determine the entropy value of each index factor according to the proportion of each reference radar under each index factor.

[0204] The entropy weight determination module is connected to the entropy value determination module and is used to determine the entropy weight of each indicator factor based on the entropy value of each indicator factor. The entropy weight of each indicator factor is the objective weight corresponding to each indicator factor.

[0205] The comprehensive weight determination unit 10 is connected to the subjective weight determination unit 7, the objective weight determination unit 9 and the comprehensive gray coefficient determination unit 3. The comprehensive weight determination unit 10 is used to determine the comprehensive weight of any indicator factor based on the subjective weight and objective weight of the indicator factor.

[0206] Compared with the prior art, the radar system quality determination system based on the combined weighted grey cloud model of the present invention has the same beneficial effects as the above-mentioned radar system quality determination method based on the combined weighted grey cloud model, which will not be repeated here.

[0207] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0208] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A radar system quality determination method based on a combined weighted grey cloud model, characterized in that: The radar system quality determination method based on the combined weighted grey cloud model includes: Categorize multiple indicator factors and determine multiple criterion factors; The analytic hierarchy process is used to calculate the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs; Determine the subjective weight of each indicator factor based on the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs; Obtain evaluation values ​​of multiple reference radar systems under various index factors; According to the evaluation value of each reference radar system under each index factor, the objective weight of each index factor is determined by using the entropy weight method; For any indicator factor, the comprehensive weight of the indicator factor is determined based on the subjective weight and objective weight of the indicator factor; the comprehensive weight of the i-th indicator factor is determined using the following formula: Among them, ξ i is the comprehensive weight of the i-th indicator factor, w i is the subjective weight of the i-th indicator factor, w i ′ is the objective weight of the i-th indicator factor, n is the total number of indicator factors, and w′ is the vector composed of the objective weights of n indicator factors; Obtaining measurement values ​​of multiple indicator factors of the radar system under test; For any index factor, based on the whitening weight function of the normal gray cloud model, the gray cloud whitening weight of the index factor corresponding to each gray class is calculated according to the measured value of the index factor; each gray class is a pre-set radar quality level, including poor, general, good, and excellent. The digital characteristics of the gray cloud model include peak C x , left and right limits (L x , R x ), Entropy E n and super entropy H e ;L x is the minimum boundary value of the indicator factor, R x is the maximum boundary value of the indicator factor; the relationship between the digital features is as follows: Among them, α is a given constant; for quantitative indicators, according to their value range and actual measurement values, the left and right boundaries of four levels are delineated, and the peak value, entropy value and super entropy are calculated; Calculate the comprehensive gray class coefficient of the radar system under test belonging to each gray class according to the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor, and the measured value of each indicator factor; the comprehensive weight of each indicator factor is determined in advance based on the hierarchical analysis method and the entropy weight method; The quality of the radar system under test is determined according to the comprehensive gray class coefficient of each gray class to which the radar system under test belongs.

2. The radar system quality determination method based on the combined weighted grey cloud model according to claim 1 is characterized in that: The multiple indicator factors include the transmitter power, noise figure, amplitude-frequency characteristics, improvement factor, antenna gain, transmitter high voltage, transmission pulse width, receiving field discharge current, antenna rotation speed, power supply voltage, switch status, operating parameters, fault information, equipment availability, equivalent service time, mean time between failures, rain and snow information, salt fog information, sand and dust information, and temperature and humidity information of the surrounding environment.

3. The radar system quality determination method based on the combined weighted grey cloud model according to claim 1, characterized in that: The whitening weight function based on the normal gray cloud model calculates the gray cloud whitening weight of each gray class corresponding to the index factor according to the measured value of the index factor, specifically including: Determining the peak value and entropy of the normal gray cloud model according to the minimum boundary value and the maximum boundary value of the indicator factor; Using a whitening weight function, according to the peak value, entropy and the measured value of the index factor, determine the whitening weight of the index factor corresponding to each gray class; According to the whitening weights of the index factors corresponding to the gray classes, the gray cloud whitening weights of the index factors corresponding to the gray classes are determined.

4. The radar system quality determination method based on the combined weighted grey cloud model according to claim 1, characterized in that: The weight of each criterion factor is calculated by using the hierarchical analysis method, which specifically includes: Initialize the initial importance between each two criterion factors; For the bth iteration, the first evaluation matrix is ​​determined using the 1-9 scaling method based on the bth importance between each two criteria factors; b≥0; the 0th importance is the initial importance; Calculating the maximum eigenvalue of the first evaluation matrix and the eigenvector corresponding to the maximum eigenvalue; the eigenvector includes the initial weight of each criterion factor; Determining a consistency test index according to the maximum characteristic root and the number of the criterion factors; Determining an average consistency index according to the dimension of the first evaluation matrix; Determining a consistency check ratio according to the consistency check index and the average consistency index; Determine the size of the consistency check ratio and the preset ratio threshold. If the consistency check ratio is less than the preset ratio threshold, the initial weight of each criterion factor in the feature vector is the weight of each criterion factor; if the consistency check ratio is greater than or equal to the preset ratio threshold, adjust the importance between each two criterion factors to obtain the b+1th importance between each two criterion factors, and perform the b+1th iteration.

5. The radar system quality determination method based on the combined weighted grey cloud model according to claim 1, characterized in that: The criteria factors include performance index data, working status data, radar internal measurement information, historical information and environmental information; The performance index data includes transmitter power, noise figure, amplitude-frequency characteristics, improvement factor and antenna gain; The working status data includes transmitter high voltage, transmission pulse width, receiver field discharge current, antenna speed and power supply voltage; The radar internal measurement information includes switch status, operating parameters and fault information; The historical information includes equipment availability, equivalent service time and mean time between failures; The environmental information includes rain and snow information, salt fog information, sand and dust information, and temperature and humidity information.

6. The radar system quality determination method based on the combined weighted grey cloud model according to claim 1, characterized in that: The objective weight of each index factor is determined by using the entropy weight method based on the evaluation value of each reference radar system under each index factor, specifically including: For any reference radar system, determining the weight of the reference radar system under each indicator factor according to the evaluation value of the reference radar system under each indicator factor; According to the proportion of each reference radar under each index factor, the entropy value of each index factor is determined; According to the entropy value of each indicator factor, the entropy weight of each indicator factor is determined; the entropy weight of each indicator factor is the objective weight corresponding to each indicator factor.

7. The radar system quality determination method based on the combined weighted grey cloud model according to claim 1, characterized in that: The following formula is used to calculate the comprehensive gray class coefficient of the radar system under test belonging to the kth gray class: Among them, σ k is the comprehensive gray class coefficient of the radar system under test belonging to the kth gray class, n is the total number of indicator factors, is the gray cloud whitening weight of the kth gray class corresponding to the i-th index factor, x i is the measured value of the i-th indicator factor, ξ i is the comprehensive weight of the i-th indicator factor.

8. A radar system quality determination system based on a combined weighted grey cloud model, characterized in that: The radar system quality determination system based on the combined weighted grey cloud model includes: An indicator classification unit is used to classify multiple indicator factors and determine multiple criterion factors; The initial weight determination unit is connected to the indicator classification unit. The initial weight determination unit 6 is used to calculate the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs by using the hierarchical analysis method; The subjective weight determination unit is connected to the initial weight determination unit and is used to determine the subjective weight of each indicator factor according to the weight of each criterion factor and the weight of each indicator factor in the criterion factor to which it belongs; An evaluation value acquisition unit, used to obtain evaluation values ​​of multiple reference radar systems under various index factors; The objective weight determination unit is connected to the evaluation value acquisition unit and is used to determine the objective weight of each index factor using an entropy weight method according to the evaluation value of each reference radar system under each index factor; The comprehensive weight determination unit is connected to the subjective weight determination unit, the objective weight determination unit, and the comprehensive gray coefficient determination unit, and is used to determine the comprehensive weight of any indicator factor based on the subjective weight and objective weight of the indicator factor; the comprehensive weight of the i-th indicator factor is determined using the following formula: Among them, ξ i is the comprehensive weight of the i-th indicator factor, w i is the subjective weight of the i-th indicator factor, w i ′ is the objective weight of the i-th indicator factor, n is the total number of indicator factors, and w′ is the vector composed of the objective weights of n indicator factors; A measurement value acquisition unit, used to obtain measurement values ​​of multiple indicator factors of the radar system under test; The gray cloud whitening weight determination unit is connected to the measurement value acquisition unit and is used to calculate the gray cloud whitening weight of each gray class corresponding to any index factor based on the whitening weight function of the normal gray cloud model according to the measurement value of the index factor; each gray class is a pre-set radar quality level, including poor, general, good, and excellent. The digital characteristics of the gray cloud model include peak C x , left and right limits (L x , R x ), Entropy E n and super entropy H e ;L x is the minimum boundary value of the indicator factor, R x is the maximum boundary value of the indicator factor; the relationship between the digital features is as follows: Among them, α is a given constant; for quantitative indicators, according to their value range and actual measurement values, the left and right boundaries of four levels are delineated, and the peak value, entropy value and super entropy are calculated; a comprehensive gray class coefficient determination unit, connected to the measurement value acquisition unit and the gray cloud whitening weight determination unit, respectively, for calculating the comprehensive gray class coefficient of the measured radar system belonging to each gray class according to the gray cloud whitening weight of each indicator factor corresponding to each gray class, the comprehensive weight of each indicator factor, and the measurement value of each indicator factor; the comprehensive weight of each indicator factor is pre-determined based on the hierarchical analysis method and the entropy weight method; The quality determination unit is connected to the comprehensive gray class coefficient determination unit and is used to determine the quality of the radar system under test according to the comprehensive gray class coefficient of each gray class to which the radar system under test belongs.

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