Characteristic parameter evaluation method for working mode recognition of multifunctional radar

Abstract

The invention discloses a multifunctional radar working mode recognition-oriented characteristic parameter evaluation method, which comprises the following steps of: firstly, constructing a characteristic parameter selection evaluation hierarchical structure of multifunctional radar working mode recognition, then constructing an interval intuition fuzzy language measurement table, confirming expert selection, obtaining evaluation information of experts on characteristic parameters, and finally, evaluating the characteristic parameters according to the evaluation information. An expert preference scale is constructed through summarizing information, then according to a language measurement comparison result, a normalized priority possibility degree is obtained through calculation, finally, according to the obtained priority possibility degrees of a criterion layer, an index layer and a feature layer, the influence degree of the feature layer on a target layer is obtained through calculation, size sorting is carried out, and a feature parameter sorting result is obtained. According to the method, the interval intuitionistic fuzzy-analytic hierarchy process is utilized to evaluate the working mode recognition characteristic parameters of the multifunctional radar, theoretical basis and rule guidance are provided for parameter selection of working mode recognition of the multifunctional radar in different scenes, accurate parameter selection is facilitated, and the reliability of a recognition result is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of radar characteristic parameter evaluation, and in particular relates to a characteristic parameter evaluation method for multi-function radar working mode recognition. Background Art

[0002] Multi-function radar working mode recognition is a key part of multi-function radar cognitive technology. This technology can help accurately grasp the target situation. Through this technology, we can quantitatively understand its type and operating status, providing a key basis for us to formulate efficient response strategies and decisions. Multi-function radar working mode recognition and prediction is a relatively cutting-edge direction.

[0003] In the early research field of multi-function radar working mode recognition, the method of associating and matching the pulse description words obtained by the detection equipment with the features in the template database was generally used. The pulse description words include carrier frequency, pulse width, pulse amplitude, arrival time, azimuth, etc. As the research deepens, more methods of deep learning are involved. The document "Li Y, Zhu M, Ma Y, et al. Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM. IETradar, sonar & navigation, 2020, 14(9): 1343-1353" adopted an end-to-end long short-term memory network for supervised learning, and used carrier frequency, pulse width, and pulse repetition interval to complete the recognition of the multifunction radar working mode; the document "He C, Zhang L, Wei S, et al. Multifunction Radar Working Mode Recognitionwith Unsupervised Hierarchical Modeling and Functional Semantics EmbeddingBased LSTM. IEEE Sensors Journal, 2024" added bandwidth as the fourth feature parameter on the basis of carrier frequency, pulse width, and pulse repetition interval, and completed the unsupervised hierarchical modeling and functional semantic embedding recognition of the multifunction radar working mode based on LSTM.

[0004] With the deepening of research and the increasing complexity of the electromagnetic environment, the emergence of distributed cooperative reverse detection systems has significantly improved the generalization and recognition capabilities of multi-function radar working mode recognition, and more parameters have been introduced into the recognition of multi-function radar working modes. The literature "Yu Wang, Shi Yan, Song Jiye, et al. Multi-station cooperative multi-function radar working mode recognition method based on CNN and DS evidence theory. Electronic Information Countermeasure Technology, 2024, 39(2): 33-39" proposed a multi-station cooperative multi-function radar working mode recognition method based on convolutional neural network and DS evidence theory, and proposed to simulate multi-station detection sites and use waveform data and pulse amplitude data under different working modes of multi-function radar to construct data for the recognition method.

[0005] It can be seen that with the deepening of research and the increasing complexity of the electromagnetic environment, many new features have been proposed in the research to compensate for the traditional parameters, such as the number of antenna scanning cycles, signal data rate, signal bandwidth, etc. However, most of the current research only explains which feature parameters need to be adopted, but rarely focuses on the performance of so many feature parameters in working mode recognition and how to choose when faced with multiple available feature parameters.

[0006] In summary, it is of great value to study a method for evaluating characteristic parameters of multi-function radar working mode recognition. The results of this study are helpful for the selection of characteristic parameters under multi-function radar working mode recognition, provide rule guidance for the selection of characteristic parameters of multi-function radar working mode, refine the quantitative analysis process, and provide theoretical support for subsequent data. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a characteristic parameter evaluation method for multi-function radar working mode recognition, which can provide a theoretical basis and rule guidance for parameter selection for multi-function radar working mode recognition in different scenarios.

[0008] The technical solution adopted by the present invention is: a characteristic parameter evaluation method for multi-function radar working mode identification, the specific steps are as follows:

[0009] S1. Construct a hierarchical structure for selecting and evaluating feature parameters for multi-function radar working mode recognition;

[0010] S2. Construct an interval-valued intuitionistic fuzzy language scale for multi-function radar working mode recognition;

[0011] S3. Confirm the experts in the relevant fields, obtain the experts' evaluation information on the characteristic parameters of the multi-function radar working mode recognition, and construct the expert preference scale by summarizing the information;

[0012] Experts in the field of multifunctional radar are identified, and the experts are asked to evaluate the influence of multiple characteristic parameters on the recognition of the working mode of the multifunctional radar through a questionnaire. The evaluation degree is shown in the content of the language measurement table in step S2. Finally, the language measurement comparison results of multiple characteristic parameters for the recognition of the working mode of the multifunctional radar at the criterion layer, the indicator layer, and the characteristic layer are obtained, and the information is summarized to obtain the expert preference scale.

[0013] S4, according to the language measurement comparison result obtained in step S3, the interval intuitionistic fuzzy weighted average operator is calculated, the summary decision information matrix is ​​constructed and the consistency check of the decision information matrix is ​​completed;

[0014] The language measurement comparison results include: language measurement comparison results at the criterion layer, the indicator layer, and the feature layer.

[0015] S5. Calculate the interval score matrix and interval index matrix of the decision information, construct the priority weight vector, construct the decision possibility matrix, and calculate the normalized priority possibility;

[0016] The normalized priority possibility includes: the normalized priority possibility of the criterion layer, the indicator layer, and the feature layer.

[0017] S6. According to the priority possibilities of the criterion layer, indicator layer, and feature layer obtained in step S5, the influence of the feature layer on the target layer is calculated, and the feature parameter sorting result can be obtained by sorting the size, thereby completing the feature parameter evaluation of the multi-function radar working mode recognition.

[0018] Furthermore, the step S1 is specifically as follows:

[0019] The characteristic parameter selection evaluation hierarchy structure is constructed to achieve a comprehensive evaluation of the characteristic parameters to be evaluated. The characteristic parameter selection evaluation hierarchy structure includes: a target layer, a criterion layer, an indicator layer, and a characteristic layer.

[0020] The target layer is the impact assessment of characteristic parameters for multi-function radar working mode identification;

[0021] The criteria layers include: generality C1, accuracy C2, reliability C3, and complexity C4;

[0022] The indicator layer constructs 12 specific factors as the indicator layer based on the criterion layer, as follows:

[0023] 1) Feature clear relevance C11: Whether the feature parameter has a clear physical meaning or relevance for the working mode recognition is used as a measure. If there is a clear physical meaning or relevance, then there is a clear feature relevance;

[0024] 2) Result interpretability C12: The different working modes described by the characteristic parameters are easy to explain and interpret as a measure. The easier it is to explain, the higher the interpretability of the result;

[0025] 3) Feature platform adaptability C13: This is measured by whether the feature parameters can be effectively used on different detection platforms, including single detection sites and distributed detection sites. The wider the platform that can be used, the higher the feature platform adaptability.

[0026] 4) Feature accuracy C21: The mean square error of the feature parameter extracted by the detection system under a fixed receiving signal-to-noise ratio is used as a metric. The fixed receiving signal-to-noise ratio is 13dB. The smaller the mean square error, the higher the feature accuracy.

[0027] 5) Feature discrimination C22: It is measured by whether the feature parameter can successfully obtain the working mode results when describing different working modes. The more accurate the result, the higher the discrimination;

[0028] 6) Feature difference C23: Whether the feature parameter can generate multiple working mode recognition results when describing different working modes is used as a measure. The more results are generated, the lower the difference;

[0029] 7) Feature stability C31: The error rate of the feature parameter in the recognition of the working mode under different typical working environments is used as a measure. The higher the error rate, the lower the stability.

[0030] 8) Feature noise robustness C32: The variance of the mean square error of the feature parameter extracted by the detection system under complex conditions with low signal-to-noise ratio is used as a metric. The higher the variance, the lower the feature noise robustness;

[0031] 9) Feature interference robustness C33: The variance of the mean square error of the feature parameter extracted by the detection system under complex external interference conditions is used as a metric. The higher the variance, the lower the feature interference robustness;

[0032] 10) Feature degradation robustness C34: The variance of the mean square error of the feature parameter extracted by the detection system under complex feature degradation conditions is used as a metric. The higher the variance, the lower the feature degradation robustness.

[0033] 11) Time occupied by feature calculation C41: The average time required by the detection system to extract the feature parameter under different receiving signal-to-noise ratio conditions is used as a metric. The less time occupied, the higher the evaluation;

[0034] 12) Resource occupancy of feature calculation C42: The resource occupancy required by the detection system to extract the feature parameters under different receiving signal-to-noise ratio conditions is used as a measure. The less the occupancy, the higher the evaluation.

[0035] The feature layer lists detailed feature parameters that need to be evaluated, including: carrier frequency RF, pulse repetition interval PRI, and pulse width PW.

[0036] Among them, the specific parameters required for the feature layer are selected according to actual conditions.

[0037] Furthermore, the step S2 is specifically as follows:

[0038] Suppose there is a characteristic parameter set X = [x1, x2, ..., x n ].

[0039] Among them, x i , i=1,2,...,n represents the i-th characteristic parameter, and n represents the total number of characteristic parameters.

[0040] For the characteristic parameter x i There is an interval intuitionistic fuzzy set IVIFS A, which is expressed as follows:

[0041]

[0042] in, Represent the characteristic parameters x i For the membership interval and non-membership interval of the feature parameter set X, They represent the lower and upper limits of the membership degree, respectively. They represent the lower and upper limits of non-membership degree respectively.

[0043] The ambiguity interval π is calculated by formula (2): A (x i ), the expression is as follows:

[0044]

[0045] in, represent the lower and upper limits of the fuzziness, respectively.

[0046] For IVIFSA in formula (1), when X = [x] has only one element, A is called interval intuitionistic fuzzy number IVIFN. At this time, the score function S(A) and the exact function H(A) are calculated as follows:

[0047]

[0048] Then the interval intuitive fuzzy language measurement needs to correspond to three quantitative results: membership μ, non-membership υ, and fuzziness π. The interval intuitive fuzzy language measurement table for the multi-function radar working mode recognition feature parameters is constructed, including:

[0049] When the language measure A is extremely low (EL), the membership μ = [0.10, 0.25], and the non-membership υ = [0.65, 0.75];

[0050] When the language measure A is low (L), the membership μ = [0.15, 0.30], and the non-membership υ = [0.60, 0.70];

[0051] When the language measure A is low (ML), the membership μ = [0.20, 0.35], and the non-membership υ = [0.55, 0.65];

[0052] When the language measure A is low (SL), the membership μ = [0.25, 0.40], and the non-membership υ = [0.50, 0.60];

[0053] When the language measure A is general (AE), the membership μ = [0.45, 0.55], and the non-membership υ = [0.30, 0.45];

[0054] When the language measure A is equivalence (EE), the membership μ = [0.50, 0.50], and the non-membership υ = [0.50, 0.50];

[0055] When the language measure A is high (SH), the membership μ = [0.50, 0.60], and the non-membership υ = [0.25, 0.40];

[0056] When the language measure A is high (MH), the membership μ = [0.55, 0.65], and the non-membership υ = [0.20, 0.35];

[0057] When the language measure A is high (H), the membership μ = [0.60, 0.70], and the non-membership υ = [0.15, 0.30];

[0058] When the language measure A is extremely high (EH), the membership μ = [0.65, 0.75] and the non-membership υ = [0.10, 0.25].

[0059] In the language measurement table, the membership and non-membership are set to have no extreme values ​​of 0 or 1, and the fuzziness of the language measurement A is fixedly controlled at π=[0.00,0.25].

[0060] Furthermore, the step S4 is specifically as follows:

[0061] Set the IVIFS obtained based on the language measurement comparison results The xth C Intuitionistic fuzzy numbers The expression is as follows:

[0062]

[0063] in, Respectively represent the lower limit of membership, the upper limit of membership, the lower limit of non-membership, and the upper limit of non-membership of the IVIFN; n C Indicates that the interval intuitionistic fuzzy set contains a total of n C Factor.

[0064] Defining IVIFS The interval intuitionistic fuzzy weighted IVIFW operator expression is as follows:

[0065]

[0066] in, express The weight vector of and

[0067] The IVIFS operation criteria are as follows:

[0068] There are two IVIFS settings. The operation rule expression is as follows:

[0069]

[0070] Here, λ represents a constant coefficient.

[0071] Then formula (6) can be expressed as formula (9), which is as follows:

[0072]

[0073] like All are considered to have the same weight, then The IVIFW operator is the interval intuitionistic fuzzy weighted average IVIFWA operator. In this case, equation (6) can be expressed as equation (10), which is as follows:

[0074]

[0075] By comparing the language measurement results obtained in step S3 with equation (10), the summary decision information matrix can be constructed: The expression is as follows:

[0076]

[0077] in, Indicates the i C and jth C The IVIFWA operator constructed by IVIFN.

[0078] Then, we can get Hesitation The expression is as follows:

[0079]

[0080] Then get The consistency test of hesitation is performed, and the expression is as follows:

[0081]

[0082] in, Represents the result of the consistency test. It is considered to have passed the consistency test; Indicates that the matrix size is n C The consistency check threshold is as follows:

[0083] When the matrix size is 1-2, When the matrix size is 3,

[0084] When the matrix size is 4, When the matrix size is 5,

[0085] When the matrix size is 6, When the matrix size is 7,

[0086] When the matrix size is 8, When the matrix size is 9,

[0087] Based on step S3, the language measurement comparison results are respectively substituted into the language measurement comparison results of the criterion layer, the indicator layer, and the feature layer, and equations (5)-(13) are repeated to calculate the interval intuitionistic fuzzy weighted average operator of the criterion layer, the indicator layer, and the feature layer, and a summary decision information matrix is ​​constructed to complete the consistency check of the decision information matrix.

[0088] Furthermore, the step S5 is specifically as follows:

[0089] When the decision information matrix summarized in step S4 If the consistency test conditions are met, the decision information interval score matrix is ​​calculated The expression is as follows:

[0090]

[0091] Among them, the i C and jth C The interval score of the IVIFWA operator constructed by IVIFN is

[0092] The decision information interval score matrix Exponentiation to obtain the interval indexation matrix The expression is as follows:

[0093]

[0094] Among them, the i C and jth C The interval indexation score of the IVIFWA operator constructed by IVIFN is

[0095] Calculate the i-th C Interval priority weights of factors Get interval priority weight vector The expression is as follows:

[0096]

[0097]

[0098] By interval priority weight vector Constructing a decision possibility matrix The expression is as follows:

[0099]

[0100] in, Calculated by formula (19), i C =1,2,...,n C , j C =1,2,...,n C , the calculation expression is as follows:

[0101]

[0102] Among them, max(·) means taking the maximum value. and The size relationship can be calculated by formula (3) and And the exact function is calculated by equation (4) and The specific evaluation rules are as follows:

[0103] when It is believed that when It is believed that when and It is believed that when and It is believed that when and It is believed that

[0104] Then the decision possibility matrix constructed by the interval priority weight vector Calculate the priority possibility, the expression is as follows:

[0105]

[0106] It can be known from step S2 that when the language measure is "equivalent", the membership and non-membership are 0.5, that is, the middle value of the language measure conversion, and 0.5 is added as a balance value.

[0107] Then the priority possibility is normalized, and the expression is as follows:

[0108]

[0109] Based on step S4, the summarized decision information matrix is ​​substituted into the summarized decision information matrices of the criterion layer, the indicator layer, and the feature layer respectively, and equations (14)-(21) are repeated to calculate the normalized priority possibilities of the criterion layer, the indicator layer, and the feature layer.

[0110] Further, the step S6 is specifically as follows:

[0111] The priority probability and normalized priority probability of the criterion layer obtained in step S5 are respectively and The priority possibility and normalized priority possibility of the indicator layer are and Then construct the weight information of the indicator layer-target layer.

[0112] According to the calculated weight information of index layer-target layer ω target And the normalized priority probability of the feature layer Calculate the influence weight I of each feature of feature-purpose target,feature , the expression is as follows:

[0113]

[0114] Finally, the obtained influence weight is normalized to obtain the normalized influence weight The characteristic parameter sorting results are obtained by size sorting, and the characteristic parameter evaluation of the multi-function radar working mode recognition is completed.

[0115] Beneficial effects of the present invention: The method of the present invention first constructs a characteristic parameter selection evaluation hierarchy for multi-function radar working mode recognition, then constructs an interval intuitive fuzzy language measurement table for multi-function radar working mode recognition, confirms the experts in the relevant fields, obtains the evaluation information of the experts on the characteristic parameters of the multi-function radar working mode recognition, and constructs the expert preference scale by summarizing the information, and then calculates the interval intuitive fuzzy weighted average operator according to the language measurement comparison result, constructs the summary decision information matrix and completes the consistency test of the decision information matrix, and obtains the interval score matrix and interval index matrix of the decision information by calculation, constructs the priority weight vector, constructs the decision possibility matrix, calculates the normalized priority possibility, and finally calculates the influence of the characteristic layer on the target layer according to the obtained criterion layer, indicator layer, and characteristic layer priority possibility, and sorts the size to obtain the characteristic parameter sorting result. The method of the present invention uses the interval intuitive fuzzy-hierarchy analysis method to evaluate the characteristic parameters of the multi-function radar working mode recognition, provides a theoretical basis and rule guidance for the parameter selection of the multi-function radar working mode recognition in different scenarios, is conducive to accurate parameter selection, improves the reliability of the recognition results, and provides data support for subsequent research. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 The present invention is a flow chart of a characteristic parameter evaluation method for multi-function radar working mode recognition.

[0117] Figure 2 This is a diagram of the evaluation hierarchy for selecting characteristic parameters for multi-function radar working mode recognition in an embodiment of the present invention. DETAILED DESCRIPTION

[0118] The method of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0119] like Figure 1 As shown, a flow chart of a characteristic parameter evaluation method for multi-function radar working mode recognition of the present invention is shown, and the specific steps are as follows:

[0120] S1. Construct a hierarchical structure for selecting and evaluating feature parameters for multi-function radar working mode recognition;

[0121] S2. Construct an interval-valued intuitionistic fuzzy language scale for multi-function radar working mode recognition;

[0122] S3. Confirm the experts in the relevant fields, obtain the experts' evaluation information on the characteristic parameters of the multi-function radar working mode recognition, and construct the expert preference scale by summarizing the information;

[0123] Experts in the field of multifunctional radar are identified, and the experts are asked to evaluate the influence of multiple characteristic parameters on the recognition of the working mode of the multifunctional radar through a questionnaire. The evaluation degree is shown in the content of the language measurement table in step S2. Finally, the language measurement comparison results of multiple characteristic parameters for the recognition of the working mode of the multifunctional radar at the criterion layer, the indicator layer, and the characteristic layer are obtained, and the information is summarized to obtain the expert preference scale.

[0124] S4, according to the language measurement comparison result obtained in step S3, the interval intuitionistic fuzzy weighted average operator is calculated, the summary decision information matrix is ​​constructed and the consistency check of the decision information matrix is ​​completed;

[0125] The language measurement comparison results include: language measurement comparison results at the criterion layer, the indicator layer, and the feature layer.

[0126] S5. Calculate the interval score matrix and interval index matrix of the decision information, construct the priority weight vector, construct the decision possibility matrix, and calculate the normalized priority possibility;

[0127] The normalized priority possibility includes: the normalized priority possibility of the criterion layer, the indicator layer, and the feature layer.

[0128] S6. According to the priority possibilities of the criterion layer, indicator layer, and feature layer obtained in step S5, the influence of the feature layer on the target layer is calculated, and the feature parameter sorting result can be obtained by sorting the size, thereby completing the feature parameter evaluation of the multi-function radar working mode recognition.

[0129] In this embodiment, the step S1 is specifically as follows:

[0130] The key to establishing an evaluation index system is to construct a suitable evaluation hierarchy to achieve a comprehensive evaluation of the characteristic parameters to be evaluated. The evaluation hierarchy is generally divided into three layers: target layer, criterion layer, and indicator layer.

[0131] like Figure 2 As shown, the characteristic parameter selection evaluation hierarchy structure described in this embodiment includes: a target layer, a criterion layer, an indicator layer, and a characteristic layer.

[0132] The target layer is the impact assessment of characteristic parameters for multi-function radar working mode identification;

[0133] The criteria layers include: generality C1, accuracy C2, reliability C3, and complexity C4;

[0134] The indicator layer constructs 12 specific factors as the indicator layer based on the criterion layer, as follows:

[0135] 1) Feature clear relevance C11: Whether the feature parameter has a clear physical meaning or relevance for the working mode recognition is used as a measure. If there is a clear physical meaning or relevance, then there is a clear feature relevance;

[0136] 2) Result interpretability C12: The different working modes described by the characteristic parameters are easy to explain and interpret as a measure. The easier it is to explain, the higher the interpretability of the result;

[0137] 3) Feature platform adaptability C13: This is measured by whether the feature parameters can be effectively used on different detection platforms, including single detection sites and distributed detection sites. The wider the platform that can be used, the higher the feature platform adaptability.

[0138] 4) Feature accuracy C21: The mean square error of the feature parameter extracted by the detection system under a fixed receiving signal-to-noise ratio is used as a metric. The fixed receiving signal-to-noise ratio is 13dB. The smaller the mean square error, the higher the feature accuracy.

[0139] 5) Feature discrimination C22: It is measured by whether the feature parameter can successfully obtain the working mode results when describing different working modes. The more accurate the result, the higher the discrimination;

[0140] 6) Feature difference C23: Whether the feature parameter can generate multiple working mode recognition results when describing different working modes is used as a measure. The more results are generated, the lower the difference;

[0141] 7) Feature stability C31: The error rate of the feature parameter in the recognition of the working mode under different typical working environments is used as a measure. The higher the error rate, the lower the stability.

[0142] 8) Feature noise robustness C32: The variance of the mean square error of the feature parameter extracted by the detection system under complex conditions with low signal-to-noise ratio is used as a metric. The higher the variance, the lower the feature noise robustness;

[0143] 9) Feature interference robustness C33: The variance of the mean square error of the feature parameter extracted by the detection system under complex external interference conditions is used as a metric. The higher the variance, the lower the feature interference robustness;

[0144] 10) Feature degradation robustness C34: The variance of the mean square error of the feature parameter extracted by the detection system under complex feature degradation conditions is used as a metric. The higher the variance, the lower the feature degradation robustness.

[0145] 11) Time occupied by feature calculation C41: The average time required by the detection system to extract the feature parameter under different receiving signal-to-noise ratio conditions is used as a metric. The less time occupied, the higher the evaluation;

[0146] 12) Resource occupancy of feature calculation C42: The resource occupancy required by the detection system to extract the feature parameters under different receiving signal-to-noise ratio conditions is used as a measure. The less the occupancy, the higher the evaluation.

[0147] The feature layer lists detailed feature parameters that need to be evaluated, including: carrier frequency RF, pulse repetition interval PRI, and pulse width PW.

[0148] The specific parameters required for the feature layer are selected according to the actual situation. The specific parameters to be selected need to be determined according to environmental factors such as the working environment and working background and the task objectives.

[0149] In this embodiment, step S2 is specifically as follows:

[0150] Suppose there is a characteristic parameter set X = [x1, x2, ..., x n ].

[0151] Among them, x i , i=1,2,...,n represents the i-th characteristic parameter, and n represents the total number of characteristic parameters.

[0152] For the characteristic parameter x i There is an interval-valued intuitionistic fuzzy set IVIFS (Interval-Valued Intuitionistic Fuzzy Set) A, which is expressed as follows:

[0153]

[0154] in, Represent the characteristic parameters x i For the membership interval and non-membership interval of the feature parameter set X, They represent the lower and upper limits of the membership degree, respectively. They represent the lower and upper limits of non-membership degree respectively.

[0155] The ambiguity interval π is calculated by formula (2): A (x i ), the expression is as follows:

[0156]

[0157] in, represent the lower and upper limits of the fuzziness, respectively.

[0158] For IVIFS A in formula (1), when X = [x] has only one element, A is called interval-valued intuitionistic fuzzy number IVIFN (Interval-Valued Intuitionistic Fuzzy Number). At this time, the score function S(A) and the exact function H(A) are calculated as follows:

[0159]

[0160] Then the interval intuitive fuzzy language measurement needs to correspond to three quantitative results: membership μ, non-membership υ, and fuzziness π. The interval intuitive fuzzy language measurement table for the multi-function radar working mode recognition feature parameters is constructed, including:

[0161] When the language measure A is extremely low (EL), the membership μ = [0.10, 0.25], and the non-membership υ = [0.65, 0.75];

[0162] When the language measure A is low (L), the membership μ = [0.15, 0.30], and the non-membership υ = [0.60, 0.70];

[0163] When the language measure A is low (ML), the membership μ = [0.20, 0.35], and the non-membership υ = [0.55, 0.65];

[0164] When the language measure A is low (SL), the membership μ = [0.25, 0.40], and the non-membership υ = [0.50, 0.60];

[0165] When the language measure A is general (AE), the membership μ = [0.45, 0.55], and the non-membership υ = [0.30, 0.45];

[0166] When the language measure A is equivalence (EE), the membership μ = [0.50, 0.50], and the non-membership υ = [0.50, 0.50];

[0167] When the language measure A is high (SH), the membership μ = [0.50, 0.60], and the non-membership υ = [0.25, 0.40];

[0168] When the language measure A is high (MH), the membership μ = [0.55, 0.65], and the non-membership υ = [0.20, 0.35];

[0169] When the language measure A is high (H), the membership μ = [0.60, 0.70], and the non-membership υ = [0.15, 0.30];

[0170] When the language measure A is extremely high (EH), the membership μ = [0.65, 0.75] and the non-membership υ = [0.10, 0.25].

[0171] In the language measurement table, in order to prevent the occurrence of information loss and other phenomena that lead to unreasonable results, the membership and non-membership are set not to have extreme values ​​of 0 or 1, and the fuzziness of the above language measurement A is fixed at π = [0.00, 0.25]. Through reasonable language measurement settings, the expert's evaluation of the impact of characteristic parameters on the recognition of multi-function radar working modes is refined, making the evaluation results more accurate.

[0172] In this embodiment, step S3 is specifically as follows:

[0173] In this embodiment, a questionnaire survey is conducted on two experts (Expert 1 and Expert 2) using a Likert scale to obtain their evaluation information on the characteristic parameters of the multi-function radar working mode recognition under a single detection platform. The characteristic parameters selected in the characteristic layer include 7 characteristic parameters: carrier frequency (RF), pulse width (PW), pulse repetition interval (PRI), angle of arrival (AOA), pulse amplitude (PA), bandwidth (BW), and data rate (DR). The above parameters are generally known to people in the industry. If there are other signal characteristic parameters that need to be added, they can be selected and evaluated according to the data characteristics detected in actual situations.

[0174] The preliminary results of the questionnaire in this embodiment are shown in Tables 1-6, where Table 1 is the language measurement results of expert 1's evaluation criterion layers, Table 2 is the language measurement results of expert 1's evaluation indicator layers, Table 3 is the language measurement results of expert 1's evaluation feature-indicator layer C11, Table 4 is the language measurement results of expert 2's evaluation criterion layers, Table 5 is the language measurement results of expert 2's evaluation indicator layers, and Table 6 is the language measurement results of expert 2's evaluation feature-indicator layer C11.

[0175] Table 1

[0176]

[0177] Table 2

[0178]

[0179] Table 3

[0180]

[0181] Table 4

[0182]

[0183] Table 5

[0184]

[0185] Table 6

[0186]

[0187] Among them, the results of language measurement in Table 1, Table 2, Table 4, and Table 5 represent the influence of the criteria / indicators on the radar working mode recognition results relative to the comparison criteria / indicators; Table 3 and Table 6 represent the performance of each feature parameter in the indicator layer C11. There are the same number of such matrices here as the indicator layers, that is, 12, which represent the performance of each feature parameter in each indicator, and will not be repeated here.

[0188] In addition, the preliminary results of the questionnaire are only the evaluation results of the characteristic parameters of the multi-function radar working mode recognition by two experts. If there are other needs, it is necessary to re-conduct a multi-expert evaluation survey and a multi-detection platform survey according to the actual situation. In the survey, in order to simulate the situation where information is difficult to obtain and the uncertainty is high, only part of the simulated working environment and simulated external conditions were informed to the two experts, resulting in a large number of intermediate values ​​in the survey. For the existing hierarchical analysis method, this situation is extremely difficult to distinguish.

[0189] In this embodiment, step S4 is specifically as follows:

[0190] Set the IVIFS obtained based on the language measurement comparison results The xth C Intuitionistic fuzzy numbers The expression is as follows:

[0191]

[0192] in, They represent the lower limit of membership, the upper limit of membership, the lower limit of non-membership, and the upper limit of non-membership of the IVIFN respectively; n C Indicates that the interval intuitionistic fuzzy set contains a total of n C Factor.

[0193] Defining IVIFS The interval valued intuitionistic fuzzy weighted IVIFW (Interval Valued Intuitionistic Fuzzy Weighted) operator expression is as follows:

[0194]

[0195] in, express The weight vector of and

[0196] The IVIFS operation criteria are as follows:

[0197] There are two IVIFS settings. The operation rule expression is as follows:

[0198]

[0199] Here, λ represents a constant coefficient.

[0200] Then formula (6) can be expressed as formula (9), which is as follows:

[0201]

[0202] like All are considered to have the same weight, then The IVIFW operator is the Interval Valued Intuitionistic Fuzzy Weighted Averaging (IVIFWA) operator. In this case, equation (6) can be expressed as equation (10), which is as follows:

[0203]

[0204] By comparing the language measurement results obtained in step S3 with equation (10), the summary decision information matrix can be constructed: The expression is as follows:

[0205]

[0206] in, Indicates the i C and jth C The IVIFWA operator constructed by IVIFN.

[0207] Then, we can get Hesitation The expression is as follows:

[0208]

[0209] Then get The consistency test of hesitation is performed, and the expression is as follows:

[0210]

[0211] in, Represents the result of the consistency test. It is considered to have passed the consistency test; Indicates that the matrix size is n CThe consistency check threshold is as follows:

[0212] When the matrix size is 1-2, When the matrix size is 3,

[0213] When the matrix size is 4, When the matrix size is 5,

[0214] When the matrix size is 6, When the matrix size is 7,

[0215] When the matrix size is 8, When the matrix size is 9,

[0216] Based on step S3, the language measurement comparison results are respectively substituted into the language measurement comparison results of the criterion layer, the indicator layer, and the feature layer, and equations (5)-(13) are repeated to calculate the interval intuitionistic fuzzy weighted average operator of the criterion layer, the indicator layer, and the feature layer, and a summary decision information matrix is ​​constructed to complete the consistency check of the decision information matrix.

[0217] In this embodiment, step S5 is specifically as follows:

[0218] When the decision information matrix summarized in step S4 If the consistency test conditions are met, the decision information interval score matrix is ​​calculated The expression is as follows:

[0219]

[0220] Among them, the i C and jth C The interval score of the IVIFWA operator constructed by IVIFN is

[0221] The decision information interval score matrix Exponentiation to obtain the interval indexation matrix The expression is as follows:

[0222]

[0223] Among them, the i C and jth C The interval indexation score of the IVIFWA operator constructed by IVIFN is

[0224] Calculate the i-th C Interval priority weights of factors Get interval priority weight vector The expression is as follows:

[0225]

[0226] By interval priority weight vector Constructing a decision possibility matrix The expression is as follows:

[0227]

[0228] in, Calculated by formula (19), i C =1,2,...,n C , j C =1,2,...,n C , the calculation expression is as follows:

[0229]

[0230] Among them, max(·) means taking the maximum value. and The size relationship can be calculated by formula (3) and And the exact function is calculated by equation (4) and The specific evaluation rules are as follows:

[0231] when It is believed that when It is believed that when and It is believed that when and It is believed that when and It is believed that

[0232] Then the decision possibility matrix constructed by the interval priority weight vector Calculate the priority possibility, the expression is as follows:

[0233]

[0234] Among them, based on step S2, when the language measure is "equivalent", the membership and non-membership are 0.5, that is, the intermediate value of the language measure conversion. For the subsequent normalization processing, it is necessary to prevent the calculated probability from being less than 0, and add 0.5 as a balance value.

[0235] Then the priority possibility is normalized, and the expression is as follows:

[0236]

[0237] Based on step S4, the summarized decision information matrix is ​​substituted into the summarized decision information matrices of the criterion layer, the indicator layer, and the feature layer respectively, and equations (14)-(21) are repeated to calculate the normalized priority possibilities of the criterion layer, the indicator layer, and the feature layer.

[0238] In this embodiment, according to the language measurement comparison results described in Table 1, a language measurement-value conversion table between criterion levels of expert 1 as shown in Table 7 can be obtained, and the calculation results are rounded to two decimal places for display.

[0239] Table 7

[0240]

[0241] Similarly, from Table 4, we can get the language measurement-value conversion table between the criterion layers of expert 2, as shown in Table 8. The calculation results are rounded to two decimal places for display.

[0242] Table 8

[0243]

[0244] According to the language measurement-value conversion table between the criterion layers of experts 1 and 2, the summarized criterion layer interval decision information table can be calculated by formula (11), as shown in Table 9. The calculation results are rounded to two decimal places for display.

[0245] Table 9

[0246]

[0247] The hesitancy of the summary criterion layer interval decision information table can be obtained by formula (12), as shown in Table 10. The calculation results are rounded to two decimal places for display.

[0248] Table 10

[0249]

[0250] In this embodiment, according to the obtained hesitation result and formula (13), the consistency test result of the criterion layer decision information can be calculated as CR criterion =0.0416<0.1, that is, it passes the consistency test.

[0251] In this embodiment, according to the results in Table 9, as well as equations (14) and (15), the criterion layer interval indexation matrix can be obtained, as shown in Table 11. The calculation results are rounded to two decimal places for display.

[0252] Table 11

[0253]

[0254] According to the interval indexation matrix of the criterion layer in Table 11 and equations (16)-(17), the interval priority weight vector of the criterion layer can be obtained: As shown in Table 12, the calculation results are rounded to two decimal places for display.

[0255] Table 12

[0256]

[0257] According to the criterion layer priority weight vector in Table 12 and equations (18) and (19), the decision possibility matrix is ​​constructed, and the priority possibility of the criterion layer is calculated according to equations (20) and (21): and normalized priority probability As shown in Table 13, the calculation results are rounded to two decimal places for display.

[0258] Table 13

[0259]

[0260] It can be seen from Table 13 that for the criterion layers C1-C4, their importance is ranked as follows: C2>C1>C3>>C4.

[0261] In summary, this embodiment also uses the comparison results of the criterion layer language measurements corresponding to each indicator layer in Table 2 and Table 5 to directly give the normalized priority possibility of the criterion layer corresponding to each indicator layer. The normalized priority probability of the indicator layer corresponding to each feature As shown in Table 14 and Table 15, the calculation results are rounded to two decimal places for display. (The specific corresponding steps are consistent with steps S4-S5, so the detailed calculation steps are not listed here).

[0262] Table 14

[0263]

[0264] Table 15

[0265]

[0266] In this embodiment, step S6 is specifically as follows:

[0267] In this embodiment, the priority probability and normalized priority probability of the criterion layer obtained in step S5 are respectively and The priority possibility and normalized priority possibility of the indicator layer are and Then construct the weight information of the indicator layer-target layer. As shown in Table 16, the calculation results are rounded to two decimal places for display.

[0268] Table 16

[0269]

[0270] According to the calculated weight information of index layer-target layer ω target And the normalized priority probability of the feature layer Calculate the influence weight I of each feature of feature-purpose target,feature , the expression is as follows:

[0271]

[0272] Finally, the obtained influence weight is normalized to obtain the normalized influence weight The characteristic parameter sorting results are obtained by sorting by size, and the characteristic parameter evaluation of the multi-function radar working mode recognition is completed. The possible degree of feature layer priority is shown in Table 17, and the calculation results are rounded to two decimal places for display.

[0273] Table 17

[0274]

[0275] It can be seen from Table 17 that in this embodiment, the importance ranking result of the feature parameters evaluated by experts is: RF>PW>PRI>BW>PA>AOA≈DR.

[0276] In summary, the method of the present invention uses interval intuitionistic fuzzy-hierarchy analysis method to evaluate the characteristic parameters of multi-function radar working mode recognition, provides a theoretical basis and rule guidance for the parameter selection of multi-function radar working mode recognition in different scenarios, is conducive to accurate parameter selection, improves the reliability of recognition results, and provides data support for subsequent research.

[0277] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A characteristic parameter evaluation method for multi-function radar working mode recognition, the specific steps are as follows: S1. Construct a hierarchical structure for selecting and evaluating feature parameters for multi-function radar working mode recognition; S2. Construct an interval-valued intuitionistic fuzzy language scale for multi-function radar working mode recognition; S3. Confirm the experts in the relevant fields, obtain the experts' evaluation information on the characteristic parameters of the multi-function radar working mode recognition, and construct the expert preference scale by summarizing the information; Confirm the experts in the field of multifunctional radar, and let the experts evaluate the influence of multiple characteristic parameters on the recognition of multifunctional radar working mode through questionnaires. The evaluation degree is shown in the language measurement table in step S2. Finally, the language measurement comparison results of multiple characteristic parameters for the recognition of multifunctional radar working mode at the criterion level, the indicator level, and the characteristic level are obtained, and the information is summarized to obtain the expert preference scale; S4, according to the language measurement comparison result obtained in step S3, the interval intuitionistic fuzzy weighted average operator is calculated, the summary decision information matrix is ​​constructed and the consistency check of the decision information matrix is ​​completed; The language measurement comparison results include: language measurement comparison results at the criterion level, indicator level, and feature level; S5. Calculate the interval score matrix and interval index matrix of the decision information, construct the priority weight vector, construct the decision possibility matrix, and calculate the normalized priority possibility; Among them, the normalized priority possibility includes: the normalized priority possibility of the criterion layer, the indicator layer, and the feature layer; S6. According to the priority possibilities of the criterion layer, indicator layer, and feature layer obtained in step S5, the influence of the feature layer on the target layer is calculated, and the feature parameter sorting result can be obtained by sorting the size, thereby completing the feature parameter evaluation of the multi-function radar working mode recognition.

2. The characteristic parameter evaluation method for multi-function radar working mode recognition according to claim 1 is characterized in that: The step S1 is specifically as follows: Constructing the characteristic parameter selection evaluation hierarchy structure to realize the comprehensive evaluation of the characteristic parameters to be evaluated, wherein the characteristic parameter selection evaluation hierarchy structure includes: a target layer, a criterion layer, an indicator layer, and a characteristic layer; The target layer is the impact assessment of characteristic parameters for multi-function radar working mode identification; The criteria layers include: generality C1, accuracy C2, reliability C3, and complexity C4; The indicator layer constructs 12 specific factors as the indicator layer based on the criterion layer, as follows: 1) Feature clear relevance C11: Whether the feature parameter has a clear physical meaning or relevance for the working mode recognition is used as a measure. If there is a clear physical meaning or relevance, then there is a clear feature relevance; 2) Result interpretability C12: The different working modes described by the characteristic parameters are easy to explain and interpret as a measure. The easier it is to explain, the higher the interpretability of the result; 3) Feature platform adaptability C13: This is measured by whether the feature parameters can be effectively used on different detection platforms, including single detection sites and distributed detection sites. The wider the platform that can be used, the higher the feature platform adaptability. 4) Feature accuracy C21: The mean square error of the feature parameter extracted by the detection system under a fixed receiving signal-to-noise ratio is used as a metric. The fixed receiving signal-to-noise ratio is 13dB. The smaller the mean square error, the higher the feature accuracy. 5) Feature discrimination C22: It is measured by whether the feature parameter can successfully obtain the working mode results when describing different working modes. The more accurate the result, the higher the discrimination; 6) Feature difference C23: Whether the feature parameter can generate multiple working mode recognition results when describing different working modes is used as a measure. The more results are generated, the lower the difference; 7) Feature stability C31: The error rate of the feature parameter in the recognition of the working mode under different typical working environments is used as a measure. The higher the error rate, the lower the stability. 8) Feature noise robustness C32: The variance of the mean square error of the feature parameter extracted by the detection system under complex conditions with low signal-to-noise ratio is used as a metric. The higher the variance, the lower the feature noise robustness; 9) Feature interference robustness C33: The variance of the mean square error of the feature parameter extracted by the detection system under complex external interference conditions is used as a metric. The higher the variance, the lower the feature interference robustness; 10) Feature degradation robustness C34: The variance of the mean square error of the feature parameter extracted by the detection system under complex feature degradation conditions is used as a metric. The higher the variance, the lower the feature degradation robustness. 11) Time occupied by feature calculation C41: The average time required by the detection system to extract the feature parameter under different receiving signal-to-noise ratio conditions is used as a metric. The less time occupied, the higher the evaluation; 12) Resource occupancy of feature calculation C42: The resource occupancy required by the detection system to extract the feature parameter under different receiving signal-to-noise ratio conditions is used as a measure. The less the occupancy, the higher the evaluation; The feature layer lists detailed feature parameters that need to be evaluated, including: carrier frequency RF, pulse repetition interval PRI, pulse width PW; Among them, the specific parameters required for the feature layer are selected according to actual conditions.

3. The characteristic parameter evaluation method for multi-function radar working mode recognition according to claim 1 is characterized in that: The step S2 is specifically as follows: Suppose there is a characteristic parameter set X = [x1, x2, ..., x n ]; Among them, x i , i=1,2,...,n represents the i-th characteristic parameter, and n represents the total number of characteristic parameters; For the characteristic parameter x i There is an interval intuitionistic fuzzy set IVIFS A, which is expressed as follows: in, Represent the characteristic parameters x i For the membership interval and non-membership interval of the feature parameter set X, They represent the lower and upper limits of the membership degree, respectively. Respectively represent the lower and upper limits of non-membership degree; The ambiguity interval π is calculated by formula (2): A (x i ), the expression is as follows: in, Respectively represent the lower and upper limits of the fuzziness; For IVIFSA in formula (1), when X = [x] has only one element, A is called interval intuitionistic fuzzy number IVIFN. At this time, the score function S(A) and the exact function H(A) are calculated as follows: Then the interval intuitive fuzzy language measurement needs to correspond to three quantitative results: membership μ, non-membership υ, and fuzziness π; an interval intuitive fuzzy language measurement table for the characteristic parameters of multi-function radar working mode recognition is constructed, including: When the language measure A is extremely low (EL), the membership μ = [0.10, 0.25], and the non-membership υ = [0.65, 0.75]; When the language measure A is low (L), the membership μ = [0.15, 0.30], and the non-membership υ = [0.60, 0.70]; When the language measure A is low (ML), the membership μ = [0.20, 0.35], and the non-membership υ = [0.55, 0.65]; When the language measure A is low (SL), the membership μ = [0.25, 0.40], and the non-membership υ = [0.50, 0.60]; When the language measure A is general (AE), the membership μ = [0.45, 0.55], and the non-membership υ = [0.30, 0.45]; When the language measure A is equivalence (EE), the membership μ = [0.50, 0.50], and the non-membership υ = [0.50, 0.50]; When the language measure A is high (SH), the membership μ = [0.50, 0.60], and the non-membership υ = [0.25, 0.40]; When the language measure A is high (MH), the membership μ = [0.55, 0.65], and the non-membership υ = [0.20, 0.35]; When the language measure A is high (H), the membership μ = [0.60, 0.70], and the non-membership υ = [0.15, 0.30]; When the language measure A is extremely high (EH), the membership μ = [0.65, 0.75], and the non-membership υ = [0.10, 0.25]; In the language measurement table, the membership and non-membership are set to have no extreme values ​​of 0 or 1, and the fuzziness of the language measurement A is fixedly controlled at π=[0.00,0.25].

4. The characteristic parameter evaluation method for multi-function radar working mode recognition according to claim 1 is characterized in that: The step S4 is specifically as follows: Set the IVIFS obtained based on the language measurement comparison results The xth C Intuitionistic Fuzzy Number The expression is as follows: in, Respectively represent the lower limit of membership, the upper limit of membership, the lower limit of non-membership, and the upper limit of non-membership of the IVIFN; n C Indicates that the interval intuitionistic fuzzy set contains a total of n C Factors; Defining IVIFS The interval intuitionistic fuzzy weighted IVIFW operator expression is as follows: in, express The weight vector of and The IVIFS operation criteria are as follows: There are two IVIFS settings. The operation rule expression is as follows: Among them, λ represents the constant coefficient; Then formula (6) can be expressed as formula (9), which is as follows: like All are considered to have the same weight, then The IVIFW operator is the interval intuitionistic fuzzy weighted average IVIFWA operator. In this case, equation (6) can be expressed as equation (10), which is as follows: By comparing the language measurement results obtained in step S3 with equation (10), the summary decision information matrix can be constructed: The expression is as follows: in Indicates the i C and jth C IVIFWA operator constructed by IVIFN; Then, we can get Hesitation The expression is as follows: Then get The consistency test of hesitation is performed, and the expression is as follows: in, Represents the result of the consistency test. It is considered to have passed the consistency test; Indicates that the matrix size is n C The consistency check threshold is as follows: When the matrix size is 1-2, When the matrix size is 3, When the matrix size is 4, When the matrix size is 5, When the matrix size is 6, When the matrix size is 7, When the matrix size is 8, When the matrix size is 9, Based on step S3, the language measurement comparison results are respectively substituted into the language measurement comparison results of the criterion layer, the indicator layer, and the feature layer, and equations (5)-(13) are repeated to calculate the interval intuitionistic fuzzy weighted average operator of the criterion layer, the indicator layer, and the feature layer, and a summary decision information matrix is ​​constructed to complete the consistency check of the decision information matrix.

5. The characteristic parameter evaluation method for multi-function radar working mode recognition according to claim 1 is characterized in that: The step S5 is specifically as follows: When the decision information matrix summarized in step S4 If the consistency test conditions are met, the decision information interval score matrix is ​​calculated The expression is as follows: Among them, the i C and jth C The interval score of the IVIFWA operator constructed by IVIFN is The decision information interval score matrix Exponentiation to obtain the interval indexation matrix The expression is as follows: Among them, the i C and jth C The interval indexation score of the IVIFWA operator constructed by IVIFN is Calculate the i-th C Interval priority weights of factors Get interval priority weight vector The expression is as follows: By interval priority weight vector Constructing a decision possibility matrix The expression is as follows: in, Calculated by formula (19), i C =1,2,...,n C , j C =1,2,...,n C , the calculation expression is as follows: Among them, max(·) means taking the maximum value; and The size relationship can be calculated by formula (3) and And the exact function is calculated by equation (4) and The specific evaluation rules are as follows: when It is believed that when It is believed that when and It is believed that when and It is believed that when and It is believed that Then the decision possibility matrix constructed by the interval priority weight vector Calculate the priority possibility, the expression is as follows: Among them, based on step S2, it can be known that when the language measure is "equivalent", the membership and non-membership are 0.5, that is, the middle value of the language measure conversion, and 0.5 is added as a balance value; Then the priority possibility is normalized, and the expression is as follows: Based on step S4, the summarized decision information matrix is ​​substituted into the summarized decision information matrices of the criterion layer, the indicator layer, and the feature layer respectively, and equations (14)-(21) are repeated to calculate the normalized priority possibilities of the criterion layer, the indicator layer, and the feature layer.

6. The characteristic parameter evaluation method for multi-function radar working mode recognition according to claim 1 is characterized in that: The step S6 is specifically as follows: The priority probability and normalized priority probability of the criterion layer obtained in step S5 are respectively and The priority possibility and normalized priority possibility of the indicator layer are and Then construct the weight information of the indicator layer-target layer. According to the calculated weight information of index layer-target layer ω target And the normalized priority probability of the feature layer Calculate the influence weight I of each feature of feature-purpose target,feature , the expression is as follows: Finally, the obtained influence weight is normalized to obtain the normalized influence weight The characteristic parameter sorting results are obtained by size sorting, and the characteristic parameter evaluation of the multi-function radar working mode recognition is completed.

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