CN112162286B - Radar detection environment estimation method based on artificial intelligence - Google Patents
Radar detection environment estimation method based on artificial intelligence Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
- G01S13/958—Theoretical aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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Abstract
The invention relates to an artificial intelligence-based radar detection environment estimation method, and belongs to the field of radar detection. The invention provides an artificial intelligence-based radar detection environment estimation method, which relates to an inversion estimation framework based on a priori knowledge base, correlation analysis based on a gray correlation analysis method and radar detection environment inversion estimation based on a Kmeans algorithm.
Description
Technical Field
The invention relates to a radar detection environment estimation method, and belongs to the field of radar detection.
Background
The radar detection environment estimation comprises the estimation of the type, grade and characteristic parameters (height and intensity) of the marine atmosphere waveguide, is a precondition of effective radar detection performance estimation, radar self-adaptive environment detection and the like, and is usually carried out by a direct measurement method, a model method and an inversion method at present. The direct measurement method is to directly measure the distribution of the refractive index on the height by using a microwave refraction rate meter, or measure the temperature, humidity and pressure profile by using a sounding balloon, a sounding rocket and the like, and then calculate the distribution of the refractive index on the height by using a formula; the model method is to measure atmospheric parameters such as air temperature, air pressure, relative humidity, wind speed, sea water surface temperature and the like at a certain reference height, and calculate and obtain an evaporation waveguide refractive index profile through a theoretical model by utilizing the ocean atmospheric boundary layer similarity theory; the inversion method comprises a sea clutter inversion method and a GNSS occultation inversion method, and the refractive index profile of the evaporation waveguide is calculated through an inversion process by utilizing radar echoes or received satellite signals respectively. The direct measurement method is expensive and has strict requirements on the field; the model method is sensitive to input meteorological parameters, requires to accurately measure the atmospheric temperature, humidity, wind speed and sea surface temperature at a certain reference height, has higher requirements on measuring instruments and measuring environments, and has the problems that meteorological detection equipment is difficult to erect and non-uniform sea areas cannot be estimated; the clutter inversion method is still in theoretical research stage at present, and has no engineering practicability yet; the sea clutter inversion method has the problems of low inversion accuracy and low accuracy of the GNSS occultation inversion method, and in summary, the engineering realization feasibility of the radar detection environment estimation method needs to be enhanced, and the estimation accuracy is ensured.
Disclosure of Invention
Aiming at the problems that the inversion accuracy is low or continuous inversion cannot be performed in the existing inversion method radar detection environment estimation, the invention provides an artificial intelligence-based radar detection environment estimation method, which realizes the radar detection environment accurate estimation by establishing a priori knowledge base and utilizing a Kmeans algorithm in combination with a sea clutter inversion method, and provides effective support for effective radar detection efficiency estimation and adaptive generation of radar adaptive detection environment strategies.
The technical solution of the present invention mainly relates to three aspects: and (3) carrying out correlation analysis on the radar detection environment and the sea clutter, establishing a radar detection environment and a sea clutter priori knowledge base, and carrying out inversion estimation on the radar detection environment based on a Kmeans algorithm.
Firstly, establishing an intelligent inversion estimation framework of a radar detection environment based on a priori knowledge base;
secondly, establishing a radar detection environment and sea clutter association mapping decision model, calculating association factors and association weights between the radar detection environment and the sea clutter by a gray association analysis method and an improved entropy method, carrying out association inspection, and making a conflict resolution strategy to realize association mining of the radar detection environment and the sea clutter;
thirdly, a radar detection environment priori knowledge base is established, and the data base of the radar detection environment priori knowledge base is provided with two types of simulation and actual measurement data;
and finally, carrying out the estimation of the existence of the atmospheric waveguide, the type of the atmospheric waveguide and the characteristic parameters of the atmospheric waveguide by utilizing a Kmeans algorithm based on the actual radar echo data and combining with a priori knowledge base, realizing the accurate estimation of the radar detection environment, and carrying out correction compensation on the influence analysis of the atmospheric waveguide characteristics by establishing sea conditions and sea states, thereby further improving the accuracy.
Compared with the prior art, the invention has the remarkable advantages that:
compared with the prior art, the method adjusts the implementation architecture of the inversion method estimated radar detection environment estimation, increases the judgment based on the priori knowledge base before the inversion method estimation, is used as the input of the subsequent inversion radar detection environment based on the sea clutter, combines the historical data of the priori knowledge base with the artificial intelligence algorithm in the sea clutter inversion process, establishes the influence analysis correction compensation of sea conditions and sea states on the atmospheric waveguide characteristics, and improves the accuracy of the inversion method estimated radar detection environment.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of an architecture of the present invention. Wherein, 1 is history rule information, 2 is information warehouse entry, 3 is the existence of an atmospheric waveguide, the atmospheric waveguide grade prejudgment result and sea clutter power, 4 is the atmospheric waveguide grade-meteorological parameter characteristic-hydrological parameter characteristic-echo power-low-altitude electromagnetic transmission attenuation characteristic, and the mapping relation priori information.
FIG. 2 is a flow chart of the radar detection environment and sea clutter correlation mining of the present invention.
Detailed Description
The invention provides an artificial intelligence-based radar detection environment estimation method, which comprises the following specific implementation steps:
(1) Establishing a radar detection environment intelligent inversion estimation framework based on a priori knowledge base;
(2) Realizing the correlation analysis of the radar detection environment and the sea clutter based on a gray correlation analysis method and an improved entropy method;
(3) Establishing a radar detection environment and sea clutter priori knowledge base according to the correlation analysis result of the step (2);
(4) And realizing the inversion estimation of the radar detection environment based on the priori knowledge base and the sea clutter by using a Kmeans algorithm.
The intelligent inversion estimation architecture in the radar detection environment based on the priori knowledge base comprises five parts: the method comprises the steps of real-time inversion basic data receiving processing, radar detection environment and radar echo characteristic knowledge base, inversion resource model, artificial intelligent inversion calculation and correction compensation. The real-time inversion basic data receiving process is used for receiving echo data in real time through an optical fiber, wherein the echo data comprises radar echoes and satellite echoes, after data validity judgment (comprising sea clutter validity judgment and low elevation satellite data judgment) is carried out, the judgment of the existence and the grade of an atmospheric waveguide is carried out by combining with 'the prior information of the mapping relation of the grade of the atmospheric waveguide, weather parameter characteristic, hydrological parameter characteristic, echo power and low-altitude electromagnetic transmission attenuation characteristic' in a radar echo characteristic knowledge base; based on the atmospheric waveguide prejudgement result and the sea clutter power sent by the real-time inversion basic data receiving process, the artificial intelligent inversion calculation module is combined with the prior information in the inversion resource model and the knowledge base, and the radar detection environment inversion estimation based on the prior knowledge base and the sea clutter is realized by using a Kmeans algorithm; and finally, correcting and compensating the influence analysis of the sea state and the sea state on the atmospheric waveguide characteristics.
The method for realizing the correlation analysis of the radar detection environment and the sea clutter based on the gray correlation analysis method and the improved entropy method comprises the following steps: and respectively establishing mapping relations between sea clutter multi-domain and multi-dimensional characterization in radar echo and ocean environment parameters, carrying out weight comparison on each parameter of the ocean environment, screening out correlation factors, and finally considering the comprehensive situation of multi-domain and multi-dimension.
Step 1: carrying out large sample storage on marine environment information of each group of sea clutter K at T moments (corresponding to different sea conditions) in the K groups of sea clutter; the data for each cycle includes the following marine environmental parameters: atmospheric temperature (AtmoT), sea surface temperature (SeaT), atmospheric humidity (Hum), atmospheric pressure (AtmoP), wind direction (WindD), wind speed (WindS), wave height (WaveH), wave flow rate (SeaV), and the like;
step 2: establishing a target signal sample matrix A for marine environment parameters corresponding to T moments of sea clutter k in a certain sea area; amp denotes the amplitude at the i-th time of the kth set of sea clutter, where k=1, 2, …, K, i=1, 2, …, T, j=1, 2, …, N correspond to parameters such as atmospheric temperature (AtmoT), sea surface temperature (SeaT), atmospheric humidity (Hum), atmospheric pressure (AtmoP), wind direction (WindD), wind speed (WindS), wave height (WaveH), wave current flow rate (SeaV), etc., respectively.
Signal sample matrix a:
for the convenience of calculation, let
Step 3: aiming at the marine environment sample matrix A of the sea clutter k, the following improved information entropy method is adopted for calculation;
(1) Calculating the parameter value x of the jth index at the ith moment ij Specific gravity of (2);
to make lnp ij Meaning, it is generally assumed that when p ij When=0, p ij lnp ij =0. But when p ij When=1, p is also present ij lnp ij =0, which obviously is not practical,contrary to the meaning of entropy, p is needed ij And (5) correcting again.
The index data is transformed by a standardized method:
wherein, the liquid crystal display device comprises a liquid crystal display device,the mean value of the j-th index value; s is(s) j : standard deviation of the j-th index. Thus, the first and second light sources are connected,
(2) Calculating entropy value e of jth index of kth group of sea clutter j ；
Let->Obtaining:
wherein, the liquid crystal display device comprises a liquid crystal display device,
(3) Calculating the difference coefficient g of the jth index of the kth group of sea clutter j ；
Wherein, the liquid crystal display device comprises a liquid crystal display device,the larger the index, the more important.
(4) Determining the weight of the jth index of the kth set of sea clutter
Step 4: for a first set of sea clutter, computing a weight vector:
step 5: and applying the method to calculate the associated weight of the second group of sea clutter, and executing steps Step2 to Step 3 to obtain:
step 6: and so on, obtaining the weight vector of the K group of sea clutter:
step 7: according to the associated weight matrix W of K groups of different sea clutter:
by formula (6):
calculating to obtain marine environment parameter weight, and obtaining an associated weight vector:
step 8: matrix the weightsThe method is applied to the correlation consistency effect test of the amplitude of the sea clutter in the original K groups of sea clutter at different moments and the sea environment, and a final correlation weight vector is obtained if consistency is met; if the consistency is not met, adopting a conflict resolution strategy for manual fine adjustment based on the weight vector until the consistency is met, and finally obtaining the associated weight vector.
The research method of the correlation between the sea clutter power, the spectrum characteristics and the scattering characteristics and the marine environment is the same as the research method of the correlation between the amplitude and the correlation.
Sea clutter feature similarity calculation under different sea conditions in the same sea area
According to the association theory, association=weight×similarity, and the mechanism of the similarity calculation formula is as follows:
to calculate the similarity of different sea conditions in the same sea area, it is assumed that the expected distribution interval of a certain feature (such as amplitude) is [ a, b]A and b represent the minimum and maximum values of the value, respectively, and the similarity d between a certain moment and the next moment of the feature i (i) corresponds to the parameters of atmospheric temperature, sea surface temperature, atmospheric humidity, atmospheric pressure, wind direction, wind speed, wave height, wave flow velocity, etc., respectively.
Calculated according to the following formula:
a and b are selected by the distribution range of the corresponding factors, wherein X represents the value of the radiation source parameter i at a certain point.
After calculating the similarity of the parameters, obtaining a final association degree A by weighting:
wherein w is i The weight value representing the ith parameter can be obtained by manual experience or a data mining method; and finally judging whether the association is successful or not according to the association degree and the association threshold value.
The construction of the radar detection environment and the sea clutter priori knowledge base and the implementation of the radar detection environment inversion estimation based on the priori knowledge base and the sea clutter by using the Kmeans algorithm:
step 1: according to the analysis result of the correlation analysis of radar detection environment and sea clutter, an environment and radar transmission characteristic priori knowledge base is established, and the base establishes correlation tables corresponding to sea clutter power (change along with distance), atmosphere correction refractive index (change along with altitude), electromagnetic wave propagation path loss (change along with distance) and corresponding meteorological hydrologic parameters (atmospheric temperature, atmospheric pressure, relative humidity, wind direction, wind speed, wave height and the like) according to different combat tasks (remote warning, low altitude burst prevention and the like), different environment situations (sea area, weather conditions, sea conditions and the like).
Step 2: and adopting a normalization processing method of the multivariate data, unifying dimensions of the historical actual measurement data, carrying out data validity judgment and classification by combining a PJ model, calculating electromagnetic wave propagation path loss by combining electromagnetic propagation models such as a parabolic equation method and the like, calculating sea clutter power by combining radar system parameters and a radar equation, and warehousing the actual measurement data and simulation data into a priori knowledge base.
Step 3: and clustering radar echo data, sea state information and sea state information received in real time by using a Kmeans algorithm, and determining the level of the atmospheric waveguide to judge whether the atmospheric waveguide exists or not by using the correlation elements and weight proportion of the atmospheric waveguide characteristics and the attenuation rule of the electromagnetic wave propagation path loss.
Step 4: and carrying out radar detection environment inversion estimation based on a sea clutter inversion flow by combining the inversion resource model.
Step 5: the sea condition is calculated by using the wave height, and the amplitude of the sea clutter is corrected (the backscattering coefficient is influenced).
Step 6: and carrying out fusion analysis processing on the inversion calculation result and the result obtained through the prior analysis of the historical data to obtain final atmospheric waveguide characteristics, and giving out the atmospheric waveguide grade.
Claims (2)
1. The radar detection environment estimation method based on artificial intelligence is characterized by comprising the following steps of:
step one: establishing a radar detection environment intelligent inversion estimation framework based on a priori knowledge base, wherein the framework comprises five parts: receiving and processing real-time inversion basic data, a radar detection environment and radar echo characteristic knowledge base, an inversion resource model, and performing inversion calculation and correction compensation based on artificial intelligence;
step two: establishing a radar detection environment and sea clutter association mapping decision model, calculating association factors and association weights between the radar detection environment and the sea clutter by a gray association analysis method and an improved entropy method, carrying out association test, and making a conflict resolution strategy to realize association mining of the radar detection environment and the sea clutter;
step three: establishing a radar detection environment priori knowledge base, wherein the feature base is a mapping relation of atmospheric waveguide grade, meteorological parameter feature, hydrological parameter feature, echo power and low-altitude electromagnetic transmission attenuation feature, and the data base comprises two types of simulation and actual measurement data;
step four: before inversion, based on actual measurement echo data, a priori knowledge base is combined, and whether an atmospheric waveguide exists, the atmospheric waveguide type and the atmospheric waveguide grade are judged through cluster analysis, so that the atmospheric waveguide is used as the priori information of the subsequent radar detection environment based on sea clutter inversion; and then, carrying out atmospheric waveguide characteristic parameter estimation by utilizing a Kmeans algorithm based on actual radar echo data and combining prior knowledge information, realizing accurate estimation of radar detection environment, and carrying out correction compensation on the influence analysis of atmospheric waveguide characteristics by establishing sea conditions and sea states, thereby further improving the accuracy.
2. The artificial intelligence based radar detection environment estimation method according to claim 1, wherein: the second step further comprises: and analyzing radar detection environment parameters including atmospheric temperature, sea surface temperature, atmospheric humidity, atmospheric pressure, wind direction, wind speed, wave height, flow speed and sea clutter power relevance by adopting a gray correlation analysis method, establishing a sample matrix of the sea clutter power and other elements, calculating a difference coefficient by utilizing an entropy value, obtaining a weight vector, and calculating the correlation element weight between the sea clutter power and the radar detection environment.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101915943A (en) * | 2010-08-10 | 2010-12-15 | 中南大学 | Joint inversion method of dielectric constant and concealed target parameters of homogeneous background media |
| CN102540162A (en) * | 2011-12-12 | 2012-07-04 | 中国船舶重工集团公司第七二四研究所 | Method for estimating low-altitude electromagnetic wave propagation characteristic on basis of sea clutter |
| CN102902977A (en) * | 2012-06-21 | 2013-01-30 | 中国人民解放军电子工程学院 | Aerial target classification method based on wind field disturbance characteristics |
| CN102955902A (en) * | 2012-10-09 | 2013-03-06 | 中国人民解放军63892部队 | Method and system for evaluating reliability of radar simulation equipment |
| CN103135108A (en) * | 2011-11-23 | 2013-06-05 | 北京雷音电子技术开发有限公司 | Method and terminal for detecting and diagnosing atmospheric duct in real time |
| CN103323816A (en) * | 2013-05-24 | 2013-09-25 | 南京信息工程大学 | Navigation X-band radar wave height inverse calculation method based on information entropy |
| CN103344957A (en) * | 2013-06-14 | 2013-10-09 | 张永刚 | Waveguide beyond-visual-range wave monitoring radar |
| CN104240202A (en) * | 2014-09-09 | 2014-12-24 | 西北工业大学 | Sea wave three-dimensional visualization effect evaluation method |
| CN105022792A (en) * | 2015-06-25 | 2015-11-04 | 中国船舶重工集团公司第七二四研究所 | Passive radar signal sorting association weight calculation method based on data mining |
| CN106291491A (en) * | 2015-05-29 | 2017-01-04 | 中国人民解放军信息工程大学 | A kind of sea clutter power calculation algorithms for inverting evaporation waveguide and device |
| CN106707256A (en) * | 2015-07-27 | 2017-05-24 | 中国人民解放军信息工程大学 | Tropospheric waveguide inversion method and device based on radar sea clutter |
| CN106772300A (en) * | 2016-12-02 | 2017-05-31 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | A kind of microwave over-the-horizon radar reflectogram computational methods |
| CN107656250A (en) * | 2017-11-13 | 2018-02-02 | 浙江大学 | A kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm |
| CN110610308A (en) * | 2019-09-09 | 2019-12-24 | 清华大学 | Method for evaluating environmental technology based on benchmarking method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11340345B2 (en) * | 2015-07-17 | 2022-05-24 | Origin Wireless, Inc. | Method, apparatus, and system for wireless object tracking |
-
2020
- 2020-09-29 CN CN202011048352.6A patent/CN112162286B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101915943A (en) * | 2010-08-10 | 2010-12-15 | 中南大学 | Joint inversion method of dielectric constant and concealed target parameters of homogeneous background media |
| CN103135108A (en) * | 2011-11-23 | 2013-06-05 | 北京雷音电子技术开发有限公司 | Method and terminal for detecting and diagnosing atmospheric duct in real time |
| CN102540162A (en) * | 2011-12-12 | 2012-07-04 | 中国船舶重工集团公司第七二四研究所 | Method for estimating low-altitude electromagnetic wave propagation characteristic on basis of sea clutter |
| CN102902977A (en) * | 2012-06-21 | 2013-01-30 | 中国人民解放军电子工程学院 | Aerial target classification method based on wind field disturbance characteristics |
| CN102955902A (en) * | 2012-10-09 | 2013-03-06 | 中国人民解放军63892部队 | Method and system for evaluating reliability of radar simulation equipment |
| CN103323816A (en) * | 2013-05-24 | 2013-09-25 | 南京信息工程大学 | Navigation X-band radar wave height inverse calculation method based on information entropy |
| CN103344957A (en) * | 2013-06-14 | 2013-10-09 | 张永刚 | Waveguide beyond-visual-range wave monitoring radar |
| CN104240202A (en) * | 2014-09-09 | 2014-12-24 | 西北工业大学 | Sea wave three-dimensional visualization effect evaluation method |
| CN106291491A (en) * | 2015-05-29 | 2017-01-04 | 中国人民解放军信息工程大学 | A kind of sea clutter power calculation algorithms for inverting evaporation waveguide and device |
| CN105022792A (en) * | 2015-06-25 | 2015-11-04 | 中国船舶重工集团公司第七二四研究所 | Passive radar signal sorting association weight calculation method based on data mining |
| CN106707256A (en) * | 2015-07-27 | 2017-05-24 | 中国人民解放军信息工程大学 | Tropospheric waveguide inversion method and device based on radar sea clutter |
| CN106772300A (en) * | 2016-12-02 | 2017-05-31 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | A kind of microwave over-the-horizon radar reflectogram computational methods |
| CN107656250A (en) * | 2017-11-13 | 2018-02-02 | 浙江大学 | A kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm |
| CN110610308A (en) * | 2019-09-09 | 2019-12-24 | 清华大学 | Method for evaluating environmental technology based on benchmarking method |
Non-Patent Citations (2)
| Title |
|---|
| Radar Target Detection Using Target Features and Artificial Intelligence;Hai Denget al.;《2018 International Conference on Radar》;全文 * |
| 基于海杂波的雷达探测效能估计技术;王玲玲;申宏亚;王向敏;;雷达与对抗;第31卷(第01期);全文 * |
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