Ground-based radar target characteristic data product acquisition method

Abstract

The invention provides a ground-based radar target characteristic data product acquisition method. The method comprises the following steps: dividing ground-based radar target characteristics into three levels; classifying the target characteristics into static characteristics and capability characteristics based on the characteristic element names; acquiring data related to target characteristics through different information sources to form a target characteristic data set; according to the information generation unit, the information content and the consistency between the information generation unit and the information content, determining the confidence coefficient of the obtained data related to the static characteristics, and determining the weight and the confidence coefficient of the data obtained through different information sources and related to the capability characteristics; obtaining the comprehensive confidence coefficient of the obtained data related to the static characteristics based on the determined confidence coefficient, and obtaining the comprehensive confidence coefficient of the obtained data related to the capability characteristics based on the determined weight and confidence coefficient; and on the basis of the formed data set and the obtained comprehensive confidence, synthesizing the static characteristics and the capability characteristics to obtain a ground-based radar target characteristic data product containing the comprehensive confidence.

Description

Technical Field

[0001] The present invention relates to the fields of radar signal processing and ground-based radar applications, and in particular to a method for acquiring target characteristic data products of ground-based radar. Background Art

[0002] Currently, research on ground-based radar target characteristics primarily focuses on studying capabilities such as detection range, accuracy, and resolution, as well as analyzing static characteristics such as application type and system composition. Existing research methods include theoretical calculations using radar equations, digital simulations, and experimental verification. Through these methods, researchers can gradually build a comprehensive database of ground-based radar target characteristics.

[0003] Traditionally, ground-based radar target characterization research often relies on a single data source or analysis method, limiting the accuracy and reliability of the final results. With technological advancements, researchers have begun to integrate multiple methods, including information collection, data screening and mining, digital simulation, and experimental testing, in order to obtain more comprehensive and accurate ground-based radar target characterization data.

[0004] However, existing technologies have certain shortcomings. First, the diversity and reliability of data sources used in ground-based radar target characteristic analysis still need to be improved. The information quality of different sources varies greatly, making it difficult to form a unified target characteristic evaluation standard. Second, relying solely on theoretical calculations and simulations cannot fully simulate the complex factors in real-world application environments, limiting the applicability and generalization of the analysis results. To overcome these shortcomings, it is urgent to propose a systematic ground-based radar target characteristic analysis method that can combine multiple data sources and verification methods to generate accurate and reliable target characteristic data products. Summary of the Invention

[0005] The present invention addresses several major problems in the prior art:

[0006] 1. Single data source and lack of authority: Current ground-based radar target characteristic analysis mostly relies on data from a single source and lacks multi-dimensional information integration.

[0007] 2. The target characteristics of ground-based radar lack a systematic system construction: the current target characteristics of ground-based radar lack the classification of target characteristics and the division of information sources under different classifications, and lack an architecture.

[0008] 3. The reliability of ground-based radar target characteristics is not high: Ground-based radar target characteristic data lacks a scientific comprehensive evaluation method, and the reliability of some target characteristic data is low.

[0009] 4. There is a gap between the simulation environment and actual applications: The simulation model cannot fully simulate the actual application scenario, resulting in insufficient applicability of ground-based radar target characteristic analysis.

[0010] 5. Insufficient data verification process: Research on target characteristics of ground-based radar usually remains at the theoretical analysis level and lacks sufficient experimental testing and verification.

[0011] To this end, the present invention provides a method for obtaining ground-based radar target characteristic data products, the method comprising:

[0012] Step 1: Based on the cascade relationship between target characteristics, ground-based radar target characteristics are divided into three levels of target characteristics; each third-level target characteristic includes at least one characteristic element name; based on the characteristic element name, the ground-based radar third-level target characteristics are classified into static characteristics and capability characteristics;

[0013] Step 2: Obtain data related to static characteristics and capability characteristics through different information sources to form a ground-based radar target characteristic data set;

[0014] Step 3: Determine the confidence level of the acquired data related to static characteristics based on the information generating unit, information content, and consistency between the two, and determine the weight and confidence level of the data related to capability characteristics acquired from different information sources;

[0015] Step 4: obtaining a first comprehensive confidence level of the acquired data related to the static characteristics based on the determined confidence level, and obtaining a second comprehensive confidence level of the acquired data related to the capability characteristics based on the determined weight and confidence level;

[0016] Step 5: Based on the formed ground-based radar target characteristic data set and the obtained first and second comprehensive confidence levels, the static characteristics and capability characteristics of the ground-based radar target are integrated to obtain a ground-based radar target characteristic data product including the first and second comprehensive confidence levels.

[0017] Furthermore, in step 1, the three-level target characteristics are specifically:

[0018] The first-level target characteristics are the overall target characteristics;

[0019] Secondary target characteristics include: equipment type, scale deployment, system composition, integrated support, and radar emitter;

[0020] The third-level target characteristics are specific target characteristics under each second-level target characteristic type, specifically:

[0021] The device type target characteristics include: target type, and target physical properties;

[0022] The characteristics of scale deployment targets include: equipment quantity, organization and combat deployment;

[0023] The target characteristics of the system components include: platform, and system;

[0024] The comprehensive support target characteristics include: mobility, deployment capability, operational maintenance capability, environmental adaptability, and economy;

[0025] Radar emitter target characteristics include: tactical parameters, technical parameters, working mode, resource scheduling, waveform design, and information output and interconnection with other systems.

[0026] Furthermore, in step 1, the static characteristics refer to the basic information characteristics of the ground-based radar, and the capability characteristics refer to the quantitative characteristics related to the real-time working conditions of the ground-based radar.

[0027] Furthermore, the capability characteristics include: detection range, detection accuracy, and resolution in tactical parameters, and array resource scheduling, time resource scheduling, and hardware resource scheduling in resource scheduling.

[0028] Furthermore, in step 2, the information sources of the data related to static characteristics include at least: news reports, scientific literature, and official websites; the information sources of the data related to capability characteristics include at least: document materials, theoretical calculations, digital simulations, and experimental tests.

[0029] Furthermore, in step 3, the static characteristic confidence is divided into five levels, namely 0.2, 0.4, 0.6, 0.8, and 1.

[0030] Furthermore, in step 4, the first comprehensive confidence of the acquired data related to the static characteristics is obtained based on the determined confidence, specifically: based on the determined confidence, the confidence value of the acquired data related to the static characteristics is adjusted according to the authority of the information source to obtain the first comprehensive confidence of the acquired data related to the static characteristics.

[0031] Furthermore, in step 4, based on the determined weight W c , and confidence Q c The second comprehensive confidence level of the acquired data related to the capability characteristics is specifically obtained as follows:

[0032]

[0033] Among them, Q cij and W cij are the confidence and weight of the i-th information source of the j-th dynamic characteristic data product, n is the number of information sources, Q cj is the comprehensive confidence of the j-th capability characteristic, and the sum of all weights is 1.

[0034] Furthermore, in step 5, for static characteristics, the information content with high weight and confidence evaluation is used as the main body, and the information content with low weight and confidence evaluation is used as a supplement or reference; when the information content is a parameter value, the information result with the highest weight and confidence evaluation is used.

[0035] Furthermore, in step 5, for capability characteristics, the characteristic data D obtained from different information sources are c According to its weight value W c Perform weighted summation to generate capability characteristic data products:

[0036]

[0037] Among them, D cij and W cij The data D of the i-th information source of the j-th capability characteristic data product are respectively c and weight values, n is the number of information sources, the sum of all weights is 1, D cj is the j-th capability characteristic data product.

[0038] The method of the present invention systematically constructs a ground-based radar target characteristic structure system and target characteristic data set for the first time, effectively solving the problem of a single source and insufficient authority of ground-based radar target characteristic data. It lays the foundation for accurate evaluation of the target characteristic degree of various ground-based radar systems, helps to improve the reliability and accuracy of ground-based radar target characteristic data, and provides strong data support for application decision-making.

[0039] The method of the present invention introduces an information evaluation method based on weight and confidence, based on the ground-based radar target characteristic structure system and target characteristic data set. The method performs weight and confidence analysis on each information source of the target characteristic, and integrates the target characteristic data according to the category to which the target characteristic belongs, ultimately forming a reliable and accurate ground-based radar target characteristic data product.

[0040] The method of the present invention uses a multi-dimensional data verification approach, integrating multiple verification methods such as experimental testing, digital simulation, and theoretical calculations to ensure the accuracy and comprehensiveness of the data. The experimental data is verified and calibrated multiple times, avoiding the bias caused by a single data source. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the specific embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1This is a flowchart of the method for obtaining target characteristic data products of ground-based radar according to the present invention;

[0043] Figure 2 Schematic diagram of the static characteristics analysis process of the ground-based radar of the present invention;

[0044] Figure 3 Schematic diagram of the ground-based radar capability characteristics analysis process of the present invention. DETAILED DESCRIPTION

[0045] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0046] The method for obtaining ground-based radar target characteristic data products of the present invention constructs a ground-based radar target characteristic system architecture and data set, introduces an information evaluation method based on weights and confidence levels, performs weight and confidence level analysis on each target characteristic information source, and integrates target characteristic data according to the target characteristic category, thereby ensuring the reliability and accuracy of ground-based radar target characteristic data.

[0047] Figure 1 The figure shows the process of the method for obtaining target characteristic data products of ground-based radar of the present invention. Figure 1 As shown, the method includes the following steps:

[0048] Step S1: constructing a ground-based radar target characteristic system and a ground-based radar target characteristic dataset.

[0049] First, a ground-based radar target characteristic architecture was constructed, dividing ground-based radar target characteristics into static characteristics and capability characteristics. Static characteristics refer to the basic information characteristics of the ground-based radar, such as device attributes, basic parameters, and signal waveforms. Capability characteristics refer to quantitative characteristics related to the ground-based radar's real-time operating conditions, such as detection range, detection accuracy, and resolution. Based on this ground-based radar target characteristic architecture, the sources of static characteristic information were divided into news reports, scientific literature, official websites, and other materials. By collecting, screening, and summarizing ground-based radar target characteristic information from various sources, a ground-based radar static characteristic dataset was constructed. The sources of capability characteristic information were divided into documentation, theoretical calculations, digital simulations, and experimental tests. Through collection, calculation, and verification, a ground-based radar capability characteristic dataset was constructed.

[0050] Preferably, static characteristic information can be divided into the following categories:

[0051] News reports: such as reports on radar-related technologies by mainstream media at home and abroad.

[0052] Scientific and technological literature: relevant research published in important journals, conference papers, etc.

[0053] Official website: information released by equipment manufacturers or national government departments.

[0054] Other information: such as signal waveform parameters collected by the equipment.

[0055] Preferably, capability characteristic information can be divided into the following categories:

[0056] Documentation: Based on extensive information collection and analysis, directly obtain corresponding capability characteristics;

[0057] Theoretical calculation: Based on the acquired basic parameter information and calculation methods such as radar equations, theoretical calculation results of corresponding capability characteristics are obtained; the target characteristic evaluation foundation is established through radar electromagnetic wave propagation theory and tactical requirement models;

[0058] Digital simulation: Establish a digital simulation model of the radar and construct a digital simulation test environment, conduct digital simulation tests, and obtain digital simulation results of corresponding capability characteristics; combine a large amount of simulation data, and generate characteristic curves of the radar in various environments through computer simulation;

[0059] Experimental testing: Conduct actual equipment testing to obtain test results of corresponding capabilities and characteristics. Data collection, verification, and calibration of simulation models are performed in real-world environments to ensure the credibility and practicality of the evaluation results.

[0060] Step S2: Ground-based radar target characteristic data information evaluation. For a specific target characteristic, determine whether it is a static characteristic or a capability characteristic. If it is a static characteristic, use the static target characteristic information evaluation method to perform confidence evaluation and grading. If it is a capability characteristic, use the dynamic target characteristic information evaluation method to perform weight analysis and confidence evaluation and grading. Figure 2 、 Figure 3 shown.

[0061] The static characteristic confidence evaluation grading is to divide the confidence evaluation of information into five grades according to the specific information source and content, namely 0.2, 0.4, 0.6, 0.8, and 1. When evaluating the confidence of each piece of information, it is necessary to make specific judgments based on the information generation unit, information content and mutual consistency.

[0062] Preferably, to ensure the accuracy and credibility of static feature information, information from different sources is classified according to their confidence level. Each piece of information is assigned a corresponding confidence value based on its authority, and its credibility is evaluated through confidence scoring.

[0063] Capability characteristic weight analysis refers to assigning corresponding weights to information based on different information sources, mainly including four categories: document data, theoretical calculations, digital simulations, and experimental tests. The sum of all weight values should be 1.

[0064] Preferably, to ensure the accuracy and credibility of capability and characteristic information, a weighted analysis is performed on information from different sources. Each piece of information is assigned a corresponding weight W according to its authority. c , and evaluate its credibility Q through confidence scoring c The formula is as follows:

[0065]

[0066] Among them, Q cij and W cij are the confidence evaluation score and weight value of the i-th information source of the j-th dynamic characteristic data product, n is the number of information sources, Q cj is the comprehensive confidence evaluation of the j-th capability characteristic.

[0067] Step S3: ground-based radar target characteristic data integration.

[0068] If the target characteristic is a static characteristic, the information content is synthesized by combining the weights and confidence evaluation scores of information from different sources. The information content with high weight and confidence evaluation is used as the main body, and the information content with low weight and confidence evaluation is used as supplement or reference. When the information content is a parameter value, the information result with the highest weight and confidence evaluation is directly used. Figure 2 、 Figure 3 shown.

[0069] If the target characteristic is a capability characteristic, the characteristic data D obtained from different information sources c , according to its weight W c Perform weighted summation to generate the jth capability characteristic data product. The calculation formula is as follows:

[0070]

[0071] Among them, D cij and W cij are the data and weight values of the i-th information source of the j-th capability characteristic data product, D cj is the j-th capability characteristic data product.

[0072] Preferably, the analysis process of the static characteristics and capability characteristics of the ground-based radar is as follows, taking a certain radar as an example:

[0073] A.Main functions

[0074] (1) Information analysis

[0075] The main functions of the example radar are described below from different information sources.

[0076] Information source 1: A foreign radar knowledge education website

[0077] “It is an air traffic control radar used for detecting aviationaircraft.”

[0078] An example radar is an air traffic control radar used for aircraft detection.

[0079] Information source 2: A foreign website

[0080] "The phased array radar is primarily used to detect aerodynamic target."

[0081] The example radar is a phased array radar, which is mainly used for aerodynamic target detection.

[0082] Information source 3: other information.

[0083] The example radar is mainly used for civil aircraft detection.

[0084] Information source 4: A domestic scientific and technological literature

[0085] The article states that the example radar, produced by Raytheon in the United States, is a large, solid-state, phased array radar operating in the UHF band. Its primary mission is to detect aircraft.

[0086] Information source 5: A domestic scientific and technological literature

[0087] The article states that the example radar is mainly used to detect aerodynamic targets.

[0088] (2) Data integration

[0089] The weight and confidence evaluation of each information are determined according to the above research method, as shown in Table 1.

[0090] Table 1 Evaluation of main functional parameters of radar

[0091]

[0092]

[0093] It can be seen that the descriptions of the radar's primary functions are largely consistent across different sources. Information 1-2 originates from foreign websites, information 3 from other sources, and information 4-5 from domestic journal articles. Based on the weights and confidence ratings in the table above, the confidence level for the example radar's primary function description is 0.8.

[0094] Conclusion: The main function of the example radar is to detect aircraft, aerodynamic targets, etc.

[0095] B. Price

[0096] (1) Information analysis

[0097] Information source 1: A domestic scientific and technological literature

[0098] The document states that "in 2004, the United States officially decided to sell two sample radars and related equipment worth $1.776 billion."

[0099] Information source 2: other materials.

[0100] The article states that "in 2013, the company agreed to sell a large phased array radar, and after paying $1.38 billion, the radar was successfully installed at Site A."

[0101] Information source 3: A domestic website

[0102] The article states that "in 2004, the price of two radars with four arrays in the north and south was US$1.776 billion, and in 2012, the price of one radar in Site A was US$1.42 billion."

[0103] (2) Data integration

[0104] The weight and confidence evaluation of each information are determined according to the above research method, as shown in Table 2.

[0105] Table 2 Purchase price parameter evaluation

[0106]

[0107] As can be seen, the descriptions of the radar system's composition vary somewhat between sources. Information 1 comes from a domestic journal article, Information 2 from other sources, and Information 3 from a domestic website. Information 1 and 3 indicate that the original plan for 2004 was to procure two of a specific radar system for a total of $1.776 billion. Information 2 and 3 indicate that the actual price of the radars purchased was approximately $1.4 billion.

[0108] According to the weighted summation formula in the method, the comprehensive confidence evaluation of this information is 0.7.

[0109] Conclusion: The actual price of the sample radar purchased is about $1.4 billion.

[0110] C. Theoretical calculation of distance resolution

[0111] According to the data obtained, the linear frequency modulation bandwidth is 1.3MHz and the resolution factor K R Take it as 1.5, then

[0112]

[0113] ΔR = kτ = 1.5 × 115.4 = 173.1 m (resolution factor is 1.5, signal bandwidth is 1.3 MHz);

[0114] D. Experimental testing

[0115] The simulation scenario is the same as the detection range scenario.

[0116] Ground-based radar target characteristic data products cover a variety of performance parameters, such as:

[0117] Detection range: the maximum effective distance that the radar can detect;

[0118] Angular resolution: The ability of a radar to distinguish the direction of a target;

[0119] Velocity resolution: the ability to detect the target's speed relative to the radar;

[0120] Beamwidth and scanning pattern: The impact of the radar's spatial coverage capability for different targets and its scanning pattern.

[0121] Statistics of the resolution probability at different distance intervals:

[0122] Table 3 Distance resolution probability statistics (dual-target scenario)

[0123]

[0124]

[0125] Conclusion: Through theoretical calculation and experimental testing, two sets of resolution values were obtained, which have high relative consistency and small error.

[0126] Table 4 Summary of the resolution of the two methods

[0127] method Theoretical calculations Experimental testing Distance (m) 173.1m (1.3MHz) 165m (1.3MHz)

[0128] Resolution is based on theoretical calculations and experimental testing based on this information. The theoretical calculations were based on radar technical parameters such as beamwidth and target signal-to-noise ratio, derived from antenna aperture simulations using other data. The theoretical calculation model was empirical, with a confidence level of 0.9. Experimental testing, based on the theoretical calculations, further refined details and targets, achieving a confidence level of 0.8, equivalent to the theoretical calculations.

[0129] According to the above description, the weights of the two methods can be taken as 0.5 and 0.5.

[0130] The final result of detection accuracy is calculated according to the following formula:

[0131] I=I 理论 ×W 理论 +I 类比 ×W 类比 +I 测试 ×W 测试

[0132] The confidence level is calculated as follows:

[0133] Q=Q 理论 ×W 理论 +Q 类比 ×W 类比 +Q 测试 ×W 测试

[0134] Table 5 is a summary of the calculation results of the radar detection resolution confidence level.

[0135] Table 5 Calculation of radar detection resolution confidence

[0136]

[0137]

[0138] Conclusion: The weighted calculation result of detection distance resolution is 169m.

[0139] E. Data Product Summary

[0140] The target characteristic data are summarized in Table 6.

[0141] Table 6: Example radar target characteristic data products

[0142]

[0143]

[0144]

[0145] As can be seen from the above introduction, the method for obtaining ground-based radar target characteristic data products presented in this paper offers several significant advantages over traditional ground-based radar target characteristic research techniques. This method, for the first time, constructs a ground-based radar target characteristic architecture, dividing ground-based radar target characteristics into static characteristics and capability characteristics. Static characteristics refer to the basic information characteristics of the ground-based radar, such as device attributes, basic parameters, and signal waveforms. Capability characteristics refer to quantitative characteristics related to the ground-based radar's real-time operating conditions, such as detection range, detection accuracy, and resolution. Based on this ground-based radar target characteristic architecture, static characteristic information sources are divided into news reports, scientific literature, official websites, and other materials. A ground-based radar static characteristic dataset is constructed by collecting, screening, and summarizing ground-based radar target characteristic information from various sources. Capability characteristic information sources are divided into document materials, theoretical calculations, digital simulations, and experimental tests. A ground-based radar capability characteristic dataset is constructed through collection, calculation, and verification. Based on this ground-based radar target characteristic dataset, a weighted and confidence-based information evaluation method is introduced. The weighted and confidence-based analysis is performed on each source of target characteristic information. The target characteristic data is then integrated according to the target characteristic category. Ultimately, a reliable and accurate ground-based radar target characteristic data product is formed, ensuring the reliability and accuracy of the ground-based radar target characteristic data product. Specifically:

[0146] 1. For the first time, a ground-based radar target characteristic architecture and a ground-based radar target characteristic dataset were constructed. Ground-based radar target characteristics are divided into static characteristics and capability characteristics. Static characteristics refer to the basic information characteristics of the ground-based radar, such as equipment attributes, basic parameters, and signal waveforms. Capability characteristics refer to quantitative characteristics related to the real-time working conditions of the ground-based radar, such as detection range, detection accuracy, and resolution. Based on this ground-based radar target characteristic architecture, the sources of static characteristic information are divided into news reports, scientific literature, official websites, and other materials. By collecting, screening, and summarizing radar target characteristic information from different sources, a ground-based radar static characteristic dataset is constructed. The sources of capability characteristic information are divided into document materials, theoretical calculations, digital simulations, and experimental tests. Through collection, calculation, and verification, a ground-based radar capability characteristic dataset is constructed.

[0147] 2. Highly Accurate Ground-Based Radar Target Characteristic Analysis: This invention integrates information from multiple sources to form a comprehensive description of ground-based radar target characteristics. Compared to traditional single-source data analysis methods, this invention can more accurately reflect the detection range, accuracy, resolution, and other capabilities of ground-based radar. By carefully evaluating the weighting and confidence levels of information sources, the ground-based radar characteristic data products generated by this invention have high accuracy and reliability, providing stable analysis results, especially in low signal-to-noise ratio environments.

[0148] 3. High-Confidence Data Products: This invention utilizes multi-source information fusion technology to perform weighted analysis and confidence assessment on information from different sources, generating high-confidence ground-based radar target characteristic data products. This method utilizes a weighted summation approach, combining the strengths of various data sources to ensure the final data product is highly representative and reliable. This data product not only accurately reflects the static and capability characteristics of ground-based radars, but also possesses strong scalability to adapt to future technological developments and changing needs.

[0149] Please note that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several variations and improvements can be made, which all fall within the scope of protection of the present application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

Claims

1. A method for obtaining target characteristic data products of ground-based radar, characterized in that: The method includes: Step 1: Based on the cascade relationship between target characteristics, ground-based radar target characteristics are divided into three levels of target characteristics; each third-level target characteristic includes at least one characteristic element name; based on the characteristic element name, the ground-based radar third-level target characteristics are classified into static characteristics and capability characteristics; Step 2: Obtain data related to static characteristics and capability characteristics through different information sources to form a ground-based radar target characteristic data set; Step 3: Determine the confidence level of the acquired data related to static characteristics based on the information generating unit, information content, and consistency between the two, and determine the weight and confidence level of the data related to capability characteristics acquired from different information sources; Step 4: obtaining a first comprehensive confidence level of the acquired data related to the static characteristics based on the determined confidence level, and obtaining a second comprehensive confidence level of the acquired data related to the capability characteristics based on the determined weight and confidence level; Step 5: Based on the formed ground-based radar target characteristic data set and the obtained first and second comprehensive confidence levels, the static characteristics and capability characteristics of the ground-based radar target are integrated to obtain a ground-based radar target characteristic data product including the first and second comprehensive confidence levels.

2. The method according to claim 1, wherein In step 1, the three-level target characteristics are: The first-level target characteristics are the overall target characteristics; Secondary target characteristics include: equipment type, scale deployment, system composition, integrated support, and radar emitter; The third-level target characteristics are specific target characteristics under each second-level target characteristic type, specifically: The device type target characteristics include: target type, and target physical properties; The characteristics of scale deployment targets include: equipment quantity, organization and combat deployment; The target characteristics of the system components include: platform, and system; The comprehensive support target characteristics include: mobility, deployment capability, operational maintenance capability, environmental adaptability, and economy; Radar emitter target characteristics include: tactical parameters, technical parameters, working mode, resource scheduling, waveform design, and information output and interconnection with other systems.

3. The method according to claim 2, wherein In step 1, static characteristics refer to the basic information characteristics of the ground-based radar, and capability characteristics refer to the quantitative characteristics related to the real-time working conditions of the ground-based radar.

4. The method according to claim 3, wherein Capability characteristics include: detection range, detection accuracy, and resolution in tactical parameters, array resource scheduling, time resource scheduling, and hardware resource scheduling in resource scheduling.

5. The method according to claim 1, wherein In step 2, the information sources of data related to static characteristics include at least: news reports, scientific literature, and official websites; the information sources of data related to capability characteristics include at least: document materials, theoretical calculations, digital simulations, and experimental tests.

6. The method according to claim 1, wherein In step 3, the static characteristic confidence is divided into five levels, namely 0.2, 0.4, 0.6, 0.8, and 1.

7. The method according to claim 1 or 6, wherein: In step 4, the first comprehensive confidence of the acquired data related to the static characteristics is obtained based on the determined confidence. Specifically, based on the determined confidence, the confidence value of the acquired data related to the static characteristics is adjusted according to the authority of the information source to obtain the first comprehensive confidence of the acquired data related to the static characteristics.

8. The method according to claim 1, wherein In step 4, based on the determined weight W c , and confidence Q c The second comprehensive confidence level of the acquired data related to the capability characteristics is specifically obtained as follows: Among them, Q cij and W cij are the confidence and weight of the i-th information source of the j-th dynamic characteristic data product, n is the number of information sources, Q cj is the comprehensive confidence of the j-th capability characteristic, and the sum of all weights is 1.

9. The method according to claim 1, wherein In step 5, for static characteristics, the information content with high weight and confidence evaluation is taken as the main body, and the information content with low weight and confidence evaluation is used as supplement or reference; when the information content is a parameter value, the information result with the highest weight and confidence evaluation is used.

10. The method according to claim 1, wherein In step 5, for capability characteristics, the characteristic data D obtained from different information sources are c According to its weight value W c Perform weighted summation to generate capability characteristic data products: Among them, D cij and W cij The data D of the i-th information source of the j-th capability characteristic data product are respectively c and weight values, n is the number of information sources, the sum of all weights is 1, D cj is the j-th capability characteristic data product.

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