CN112162286A - 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
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- 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
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Abstract
The invention relates to a radar detection environment estimation method based on artificial intelligence, and belongs to the field of radar detection. At present, a direct measurement model method and an inversion method are generally adopted for estimating the radar detection environment, the direct measurement model method has the problems that meteorological detection equipment is difficult to erect and estimation cannot be carried out on non-uniform sea areas, the inversion method can solve the defects, but the problem that the inversion accuracy is not high or the inversion can not be continuously carried out exists, aiming at the problem, the invention provides a radar detection environment estimation method based on artificial intelligence, the method relates to an inversion estimation framework based on a priori knowledge base, correlation analysis based on a grey correlation analysis method, and radar detection environment inversion estimation based on a Kmeans algorithm, the judgment based on the prior knowledge base is added in the conventional inversion method evaluation framework, the accuracy of the inversion method for estimating the radar detection environment is improved, and effective support is provided for effective radar detection efficiency evaluation and adaptive generation of radar adaptive detection environment strategies.
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 estimation of the type, the grade and the characteristic parameters (height and strength) of the marine atmospheric waveguide, which is the premise of effective radar detection performance evaluation, radar adaptive environment detection and the like, and currently, a direct measurement method, a model method and an inversion method are generally adopted for the estimation of the radar detection environment. The direct measurement method comprises the steps of directly measuring the distribution of the refractive index on the height by using a microwave refractometer, or measuring temperature, humidity and pressure profiles by using an air sounding balloon, an air sounding small rocket and the like, and then calculating by using a formula to obtain the distribution of the refractive index on the height; the model method is that the evaporation waveguide refractive index profile is calculated by measuring atmospheric parameters such as air temperature, air pressure, relative humidity, wind speed, seawater surface temperature and the like at a certain reference height and utilizing the ocean atmospheric boundary layer similarity theory and a theoretical model; 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 respectively utilizing radar echoes or received satellite signals. The direct measurement method is expensive and has strict requirements on the field; the model method is sensitive to input meteorological parameters, requires accurate measurement of atmospheric temperature, humidity, wind speed and sea surface temperature at a certain reference height, has high requirements on a measuring instrument and a measuring environment, and has the problems that meteorological detection equipment is difficult to erect and estimation cannot be carried out on non-uniform sea areas; the clutter inversion method is still in a theoretical research stage at present, and has no engineering practicability; the sea clutter inversion method has the problems that the inversion accuracy is not high, the GNSS occultation inversion method has the problems that continuous inversion cannot be performed and the accuracy is not high, and in conclusion, the engineering implementation feasibility of the radar detection environment estimation method needs to be enhanced, and meanwhile, the estimation accuracy is guaranteed.
Disclosure of Invention
The invention provides a radar detection environment estimation method based on artificial intelligence aiming at the problem that the inversion accuracy is not high or continuous inversion is impossible in the radar detection environment estimation of the current inversion method.
The technical solution of the present invention mainly relates to three aspects: the method comprises the steps of correlation analysis of a radar detection environment and sea clutter, establishment of a radar detection environment and sea clutter prior knowledge base, and inversion estimation of the radar detection environment based on a Kmeans algorithm.
Firstly, establishing a radar detection environment intelligent inversion estimation framework 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 through a grey association analysis method and an improved entropy method, performing association check, and making a conflict resolution strategy to realize association mining of the radar detection environment and the sea clutter;
thirdly, establishing a priori knowledge base of the radar detection environment, wherein the data base of the priori knowledge base has two types of simulation data and actual measurement data;
and finally, estimating the existence of the atmospheric waveguide, the type of the atmospheric waveguide and the atmospheric waveguide characteristic parameters by using a Kmeans algorithm based on actual radar echo data and combining a priori knowledge base, realizing accurate estimation of a radar detection environment, and performing correction compensation by establishing sea conditions and sea states to analyze the influence of the atmospheric waveguide characteristics, thereby further improving the accuracy.
Compared with the prior art, the invention has the following remarkable advantages:
compared with the prior art, the method adjusts the implementation framework of the radar detection environment estimation estimated by the inversion method, adds the judgment based on the prior knowledge base before the estimation of the inversion method, and uses the judgment as the input of the subsequent radar detection environment based on sea clutter inversion, and meanwhile, the artificial intelligence algorithm is adopted in the sea clutter inversion process to combine the historical data of the prior knowledge base and establish the influence analysis, correction and compensation of sea conditions and sea states on the atmospheric waveguide characteristics, so that the accuracy of the radar detection environment estimation by the inversion method is improved.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is an architectural design of the present invention. Wherein, 1 is historical rule information, 2 is information storage, 3 is atmospheric waveguide existence, atmospheric waveguide level prejudgment results and sea clutter power, 4 is atmospheric waveguide level-meteorological parameter characteristics-hydrological parameter characteristics-echo power-low altitude electromagnetic transmission attenuation characteristics, and mapping relation prior information.
FIG. 2 is a flow chart of the mining process of the relevance between the radar detection environment and the sea clutter.
Detailed Description
The invention provides a radar detection environment estimation method based on artificial intelligence, which comprises the following concrete implementation steps:
(1) establishing a radar detection environment intelligent inversion estimation framework based on a priori knowledge base;
(2) the relevance analysis of the radar detection environment and the sea clutter is realized based on a grey relevance analysis method and an improved entropy method;
(3) establishing a radar detection environment and sea clutter prior knowledge base according to the correlation analysis result of the step (2);
(4) and (3) utilizing a Kmeans algorithm to realize radar detection environment inversion estimation based on a priori knowledge base and sea clutter.
The intelligent inversion estimation framework in the radar detection environment based on the prior knowledge base comprises five parts: the method comprises the steps of real-time inversion of basic data receiving and processing, radar detection environment and radar echo characteristic knowledge base, inversion of resource models, inversion calculation based on artificial intelligence and correction compensation. The real-time inversion basic data receiving and processing method comprises the steps of receiving and processing echo data in real time through optical fibers, wherein the echo data comprises radar echoes and satellite echoes, and after data effectiveness judgment (comprising sea clutter effectiveness judgment and low elevation satellite data judgment), judging whether atmospheric waveguides exist or not and grade judgment is carried out by combining mapping relation prior information of 'atmospheric waveguide grade-meteorological parameter characteristic-hydrological parameter characteristic-echo power-low-altitude electromagnetic transmission attenuation characteristic' in a radar detection environment and radar echo characteristic knowledge base; based on an artificial intelligence inversion calculation module, receiving and processing the sent atmosphere waveguide pre-judgment result and the sea clutter power according to real-time inversion basic data, combining an inversion resource model and prior information in a knowledge base, and utilizing a Kmeans algorithm to realize radar detection environment inversion estimation based on the prior knowledge base and the sea clutter; and finally, correcting and compensating by establishing sea conditions and sea states to analyze the influence of the atmospheric waveguide characteristics.
The method for realizing the relevance analysis of the radar detection environment and the sea clutter based on the grey relevance analysis method and the improved entropy method comprises the following steps: the method comprises the steps of establishing mapping relations between sea clutter multi-domain and multi-dimensional representations in radar echoes and marine environment parameters, carrying out weight comparison on all the parameters of the marine environment, screening out association factors, and finally considering multi-domain and multi-dimensional comprehensive conditions.
Step 1: carrying out large sample storage on marine environment information of K moments (corresponding to different sea conditions) of each group of sea clutter in 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 velocity (SeaV), and the like;
step 2: establishing a target signal sample matrix A for marine environment parameters corresponding to T moments of a sea clutter k in a certain sea area; amp represents the amplitude of the ith time of the kth group of sea clutter, where K is 1,2, …, K, i is 1,2, …, T, j is 1,2, …, and N corresponds 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), and wave flow velocity (SeaV), respectively.
Signal sample matrix a:
for convenience of calculation, order
Step 3: aiming at a marine environment sample matrix A of a sea clutter k, calculating by adopting the following improved information entropy method;
(1) calculating the parameter value x of the j index at the ith momentijSpecific gravity of (a);
to make lnpijSignificantly, it is generally desirable to assume p isijWhen equal to 0, pij lnpij0. But when p isijWhen 1, also has pijlnpijWhen p is 0, it is obviously not practical, contrary to the entropy meaning, p is requiredijAnd then the correction is performed again.
The index data is transformed by a standardization method:
wherein the content of the first and second substances,
the mean value of the jth index value; sj: standard deviation of j-th index. As a result of this, the number of the,
(2) calculating the entropy e of the jth index of the kth group of sea clutterj；
wherein the content of the first and second substances,
(3) calculating j index of k group of sea clutterCoefficient of difference g ofj；
Wherein the content of the first and second substances,
the larger the index, the more important the index.
(4) Determining weight of j index of k group 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 correlation weight of the second group of sea clutter, and executing the steps of Step 2-Step 3 to obtain:
step 6: and analogizing in turn to obtain a weight vector of the K group of sea clutter:
step 7: according to the correlation weight matrix W of K groups of different sea clutters:
by formula (6):
calculating to obtain marine environment parameter weight, and obtaining an associated weight vector:
step 8: the weight matrix is formed
The method is applied to the sea clutter amplitude and sea environment correlation consistency effect test in the original K groups of sea clutter at different moments, and if the consistency is met, a final correlation weight vector is obtained; and if the consistency is not met, adopting a conflict resolution strategy of carrying out manual fine adjustment on the basis of the weight vector until the consistency is met, and finally obtaining the associated weight vector.
The method for researching the relevance of the sea clutter power, the spectrum characteristic and the scattering characteristic to the marine environment and the method for researching the amplitude and the relevance of the sea clutter power, the spectrum characteristic and the scattering characteristic.
Calculation of sea clutter feature similarity under different sea conditions in same sea area
According to the association theory, the association degree is weight × similarity, and the similarity calculation formula mechanism is as follows:
to calculate the similarity between different sea states in the same sea area, assume the expected distribution interval of a certain feature (such as amplitude) as [ a, b ]]A and b represent the minimum and maximum values of the value, respectively, the similarity d of the feature from one moment to the nexti(i corresponds to the atmospheric temperature, the sea surface temperature, the atmospheric humidity, the atmospheric pressure, the wind direction, the wind speed, the wave height, the wave flow velocity and other parameters 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 the similarity of the parameters is calculated, the final association degree A is obtained through weighting:
wherein wiThe 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 according to the association degree and the association threshold value.
Establishing a radar detection environment and a sea clutter prior knowledge base, and realizing radar detection environment inversion estimation based on the prior knowledge base and the sea clutter by using a Kmeans algorithm:
step 1: according to the analysis result of the relevance analysis of the radar detection environment and the sea clutter, an environment and radar transmission characteristic priori knowledge base is established, and the base establishes a corresponding relevance table of sea clutter power (changed along with distance), atmosphere correction refractive index (changed along with height) and electromagnetic wave propagation path loss (changed along with distance) according to different combat missions (remote warning, low altitude penetration protection and the like) and different environment situations (sea areas, weather conditions, sea conditions and the like), and corresponding meteorological hydrological parameters (atmospheric temperature, atmospheric pressure, relative humidity, wind direction, wind speed, wave height and the like).
Step 2: the method comprises the steps of adopting a multivariate data normalization processing method, unifying dimensions of historical measured data, carrying out data effectiveness judgment and classification by combining a PJ model, calculating electromagnetic wave propagation path loss by combining an electromagnetic wave propagation model such as a parabolic equation method, calculating sea clutter power by combining radar system parameters and a radar equation, and storing the measured data and simulation data in a prior knowledge base.
Step 3: and clustering radar echo data, sea conditions 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 and the level of the atmospheric waveguide according to the atmospheric waveguide characteristic and the electromagnetic wave propagation path loss attenuation rule associated elements and weight proportion.
Step 4: and performing radar detection environment inversion estimation based on a sea clutter inversion flow by combining an inversion resource model.
Step 5: the wave height is used to calculate the sea state and correct the amplitude of the sea clutter (influence the backscattering coefficient).
Step 6: and performing fusion analysis processing on the inversion calculation result and the result obtained by the prior analysis of the historical data to obtain the final atmospheric waveguide characteristic and give the atmospheric waveguide grade.
Claims (3)
1. A radar detection environment estimation method based on artificial intelligence is characterized by comprising the following steps:
the method comprises the following steps: establishing a radar detection environment intelligent inversion estimation framework based on a priori knowledge base;
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 through a grey association analysis method and an improved entropy method, performing association inspection, and making a conflict resolution strategy to realize association mining of the radar detection environment and the sea clutter;
step three: establishing a prior knowledge base of a radar detection environment, wherein the data base of the prior knowledge base has two types of simulation data and actual measurement data;
step four: and (3) estimating the existence of the atmospheric waveguide, the type of the atmospheric waveguide and the characteristic parameters of the atmospheric waveguide by using a Kmeans algorithm based on actual radar echo data and combining a priori knowledge base, realizing accurate estimation of a radar detection environment, and correcting and compensating by establishing sea conditions and sea states to analyze the influence of the atmospheric waveguide characteristics, thereby further improving the accuracy.
2. The artificial intelligence based radar detection environment estimation method according to claim 1, wherein: the first step further comprises: the method comprises five parts of real-time inversion basic data receiving and processing, radar detection environment and radar echo characteristic knowledge base, inversion resource model, and inversion calculation and correction compensation based on artificial intelligence; and adding a priori knowledge base, and judging whether the atmospheric waveguide exists, the type of the atmospheric waveguide and the grade of the atmospheric waveguide through cluster analysis based on the combination of actually measured echo data and the prior knowledge base before inversion to serve as the prior information of the detection environment of the radar based on sea clutter inversion subsequently.
3. An artificial intelligence based radar detection environment estimation method according to claim 1 or claim 2, characterized by: the second step further comprises: and analyzing radar detection environment parameters by adopting a grey correlation analysis method, wherein the parameters comprise the correlation of atmospheric temperature, sea surface temperature, atmospheric humidity, atmospheric pressure, wind direction, wind speed, wave height, flow velocity and sea clutter power, establishing a sample matrix of sea clutter power and other elements, calculating a difference coefficient by utilizing an entropy value to obtain a weight vector, and calculating the weight of a correlation element between the sea clutter power and the radar detection environment.
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Citations (15)
| 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 |
| US20200191943A1 (en) * | 2015-07-17 | 2020-06-18 | 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 (15)
| 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 |
| US20200191943A1 (en) * | 2015-07-17 | 2020-06-18 | Origin Wireless, Inc. | Method, apparatus, and system for wireless object tracking |
| 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 |
|---|
| HAI DENGET AL.: "Radar Target Detection Using Target Features and Artificial Intelligence", 《2018 INTERNATIONAL CONFERENCE ON RADAR》 * |
| 王玲玲;申宏亚;王向敏;: "基于海杂波的雷达探测效能估计技术", 雷达与对抗 * |
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