CN116702092A - Reconnaissance satellite threat degree evaluation method for comprehensive meteorological and geographic elements - Google Patents
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Abstract
The invention discloses a reconnaissance satellite threat degree evaluation method for comprehensive meteorological and geographic elements, which is based on the target sizeAnd optical satellite resolutionCalculating detection probabilityThe method comprises the steps of carrying out a first treatment on the surface of the Calculating the detection probability of the illumination influence probability fb, the weather influence probability fa and the forest shielding influence probability fcThe influence of the detection probability P' n is acquired; acquiring a target detection probability P 'n-1, a target recognition probability P' n-2 and a target recognition probability P 'n-3 by using the corrected detection probability P' n; the weights of the target detection probability P ' n-1, the target identification probability P ' n-2 and the target identification probability P ' n-3 are adjusted to obtain satellite threat degreesThe method comprises the steps of carrying out a first treatment on the surface of the The invention integrates meteorological and geographic elements with detection probability, and comprises the satellite detection probability of meteorological influence probability fa, illumination influence probability fb and forest shielding influence probability fcThe accuracy of the acquisition correction detection probability P' n is high, the detection probability of the satellite to the target is calculated more accurately, and the accurate judgment of the target to the threat degree of the satellite is realized.
Description
Technical Field
The invention relates to the technical field of satellite detection probability and threat degree, in particular to a reconnaissance satellite threat degree evaluation method for comprehensive meteorological and geographic elements.
Background
The optical satellite imaging has obvious dependence on geographical environment elements and meteorological environment elements, the existing analysis algorithm considers visual factors such as solar dip angle, illumination intensity, cloud quantity and the like, and mainly analyzes the factors, such as an influence analysis method of space weather on satellite imaging, an influence analysis method of illumination conditions on satellite imaging and the like are mainly carried out, academic documents with numbers of 1001-2486 (2006) 04-0014-04 disclose object detection probability analysis of optical reconnaissance satellites, and the literature is from volume 28, page 4 and page 14-17 of national defense science and technology university report 2006, the literature proposes that the thickness and coverage of cloud layer are main factors which directly influence imaging tasks, however, the imaging of the optical satellite is influenced by the cloud layer thickness, water fall, visibility and other meteorological conditions, the influence of illumination intensity, vegetation coverage and other geographic conditions on satellite imaging is huge, and the influence of the meteorological elements and the geographic elements on satellite detection probability is comprehensively considered, and the detection probability of the satellite can be calculated more accurately, and the threat detection probability of the object can be found, recognized and calculated.
Disclosure of Invention
The invention aims to provide a reconnaissance satellite threat degree evaluation method for comprehensive meteorological elements and geographic elements, which solves the problems of low satellite detection probability precision and poor satellite threat degree judgment caused by the fact that the influence of the meteorological elements and the geographic elements on the detection probability is not comprehensively considered when the satellite detection probability is calculated.
The aim of the invention can be achieved by the following technical scheme: a reconnaissance satellite threat degree evaluation method for integrating meteorological and geographic elements comprises the following steps:
s1, according to the target sizeAnd optical satellite resolutionCalculating detection probability；
S2, calculating the detection probability of the illumination influence probability fb, the meteorological influence probability fa and the forest shielding influence probability fcThe influence of the detection probability P' n is acquired;
s3, acquiring a target detection probability P 'n-1, a target recognition probability P' n-2 and a target recognition probability P 'n-3 by using the corrected detection probability P' n;
s4, adjusting weight values of the target detection probability P ' n-1, the target recognition probability P ' n-2 and the target recognition probability P ' n-3 to obtain satellite threat degree。
Further: the S1 obtains and calculates the detection probabilityThe method comprises the following steps:
s11, calculating the target sizeThe formula is:
wherein W is the target width and H is the target height;
s12, according to the satellite resolutionAnd target sizeThe number of pairs N of crossing target feature size lines is calculated as:
wherein the satellite resolutionThe acquisition formula of (1) is:
wherein Is the spacing of the picture elements,is the focal length of the lens,is the range between the satellite and the target,is a side view.
S13, calculating detection probability according to the number N of the line pairs crossing the target feature sizeThe formula is:
wherein ,the number of pairs crossing the target feature size at a target detection probability P' n-1 of 80%.
Further: the formula for obtaining the corrected detection probability P' n is as follows:
P’n=d1*P n *fa*fb*fc
wherein To adjust the coefficients.
Further: the influence factors of the weather influence probability fa include: cloud impact factorFactor of influence of precipitationAnd visibility influencing factorThe expression formula of the weather influence probability fa is as follows:
fa=d2*fa1*fa2*fa3
wherein To adjust the coefficients.
Further: the cloud impact factorAccording to cloud level standards, the method is divided into four levels of sunny days (cloud deck numbers 0-1), less clouds (cloud deck numbers 1-3), more clouds (cloud deck numbers 3-7) and cloudy days (cloud deck numbers 7-10), wherein the formula is as follows:
wherein ,is a positive integer of 1-4;
the precipitation factorThe precipitation is divided into six grades of light rain (less than 10 mm), medium rain (10-15 mm), heavy rain (25-50 mm), heavy rain (50-100 mm), heavy rain (100-250 mm) and extra heavy rain (more than 250 mm), and the formula is as follows:
wherein ,is a positive integer of 1-6;
the visibility influencing factorThe visibility is classified into nine grades of excellent visibility (25-30 KM), good visibility (20-25 KM), general visibility (15-20 KM), poor visibility (10-15 KM), poor visibility (5-10 KM), poor visibility (1-5 KM), extremely poor visibility (heavy fog 0.5-1 KM), extremely poor visibility (heavy fog 0.1-0.5 KM) and worst visibility (strong fog 0-0.1 KM), and the formula is expressed as:
wherein ,is a positive integer of 1-9.
Further, the forest shielding influence probability fc is divided into four grades of a forest (10), a gap forest (50), a arbor forest (100) and a forest (200) according to a forest density grade standard, and the formula is as follows:
wherein ,is a positive integer of 1-4.
Further: the satellite threat level is obtainedThe method comprises the following steps:
s41, calculating target detection probability P ' n-1, target recognition probability P ' n-2 and target recognition probability P ' n-3, wherein the formula is as follows:
P’n-1=K 1 P’n
P’n-2=K 2 P’n
P’n-3=K 3 P’n
wherein ,、andfor the coefficient of variation, the、Andthe range of the value of (2) is 0-1.
S42, distributing the weight of the target detection probability P' n-1 asThe weight of the target recognition probability P' n-2 is assigned asAnd assigning the weight of the target recognition probability P' n-3 as；
S43, calculating satellite threat degreeThe formula is:
T=P’n-1*w 1 +P’n-2*w 2 +P’n-3*w 3
wherein Is satellite threat level.
The invention has the beneficial effects that:
1. the satellite detection probability of the invention integrates meteorological elements and geographic elements, and comprises the probability of satellite detection by meteorological influence probability fa, illumination influence probability fb and forest shielding influence probability fcThe obtained correction detection probability P' n has high precision, the detection probability of the satellite to the target can be calculated more accurately, and the accurate judgment of the target to the threat degree of the satellite is realized.
2. The invention refines the influence of the meteorological influence probability fa on the satellite detection probability and distinguishes the influence of the meteorological influence probability fa as a cloud amount influence factorFactor of influence of precipitationAnd visibility influencing factorThe influence of the meteorological influence probability fa on the satellite detection probability can be calculated more accurately, and the obtained satellite detection probability on the target is more accurate and has higher precision.
3. The cloud impact factor of the inventionDivided into four classes, precipitation influence factorsDivided into six classes, visibility influencing factorsThe method is divided into nine grades, the actual situation of clinging to meteorological factors is divided, a specific calculation formula is given, the quantized influence of cloud cover, precipitation and visibility on satellite discovery probability is realized, and the calculation accuracy of the satellite discovery probability is ensured.
4. According to the method, the satellite is used for finding, identifying and identifying the target, the weight of the satellite for finding, identifying and identifying the target is set, the acquisition of the threat degree of the satellite is close to the outline of the target, the acquired threat degree of the satellite is high in precision, and the threat degree of the satellite to the target can be accurately acquired.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating the threat level of a reconnaissance satellite integrating meteorological elements and geographic elements;
FIG. 2 is a schematic view of a satellite discovery identification target application environment according to the present invention;
FIG. 3 is a schematic view of coverage range of the satellite finding identification target according to the present invention;
FIG. 4 is a schematic diagram of threat level determination for integrated weather elements and geographic elements according to the present invention;
FIG. 5 is another schematic diagram of threat level determination for integrated weather elements and geographic elements according to the present invention;
FIG. 6 is another schematic diagram of threat level determination for integrated weather elements and geographic elements according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar symbols indicate like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
1-6, the invention discloses a reconnaissance satellite threat degree evaluation method for integrating meteorological and geographic elements, which comprises the following steps:
s1, according to the target sizeAnd optical satellite resolutionCalculating detection probability；
S2, calculating the detection probability of the illumination influence probability fb, the meteorological influence probability fa and the forest shielding influence probability fcThe influence of the detection probability P' n is acquired;
s3, acquiring a target detection probability P 'n-1, a target recognition probability P' n-2 and a target recognition probability P 'n-3 by using the corrected detection probability P' n;
s4, adjusting weight values of the target detection probability P ' n-1, the target recognition probability P ' n-2 and the target recognition probability P ' n-3 to obtain satellite threat degree。
The satellite detection probability is obtained and calculated in the S1The method comprises the following steps:
s11, calculating the target sizeThe formula is:
wherein W is the target width and H is the target height; for planar targets, target dimensionsFor the square root of the product of the target width W and the target height H, for irregularly shaped objects,the target can be considered as a contour, with the square root of the contour area being the feature size more accurate. W is an abbreviation of Width and indicates the Width of an object, and H is an abbreviation of english word Height.
The optical satellite is imaged by reflecting sunlight, and the resolution of the optical satelliteTarget sizeThe probability of detection of the target is greatly affected.
S12, according to the satellite resolutionAnd target sizeThe number of pairs N of crossing target feature size lines is calculated as:
wherein the satellite resolutionThe acquisition formula of (1) is:
wherein Is the spacing of the picture elements,is the focal length of the lens,is the range between the satellite and the target,is a side view.
S13, calculating detection probability according to the number N of the line pairs crossing the target feature sizeThe formula is:
wherein ,the number of pairs crossing the target feature size at a target detection probability P' n-1 of 80%.
If a satellite were to obtain a detection probability of 80%, the number of pairs contained within the target feature size would be required1 is shown in the specification; if the number of lines N contained in the feature size of the target is known, the detection probability of the imaging system for the target can be calculated by the target transfer probability function TTPF。
The detection probability obtained by the methodMainly considering satellite resolutionAnd target sizeThe probability of detecting meteorological elements and geographic elements is not consideredThe imaging of the optical satellite is also influenced by meteorological conditions such as cloud layer thickness, precipitation, visibility, illumination intensity and the like, and geographical conditions such as vegetation coverage and the like, and the detection probability is requiredAnd (5) performing correction.
The correction method can calculate the satellite detection probability of the weather influence probability fa, the illumination influence probability fb and the forest shielding influence probability fcThe influence of the correction detection probability P' n is obtained, and the formula is as follows:
P’n=d1*P n *fa*fb*fc
wherein ,in order to adjust the coefficient, the illumination influence probability fb can be obtained through calculation according to the illumination time and intensity of the transit satellite, and the forest shielding influence probability fc can be obtained through calculation according to the forest density data of the target area.
The influence factors of the weather influence probability fa include: cloud impact factorFactor of influence of precipitationAnd visibility influencing factorThe expression formula of the weather influence probability fa is as follows:
fa=d2*fa1*fa2*fa3
wherein To adjust the coefficient, the cloud impact factorAccording to cloud level standards, the method is divided into four levels of sunny days (cloud deck numbers 0-1), less clouds (cloud deck numbers 1-3), more clouds (cloud deck numbers 3-7) and cloudy days (cloud deck numbers 7-10), wherein the formula is as follows:
wherein ,for example, n=1 when the cloud level standard is a sunny day (cloud cover number 0-1), n=2 when the cloud level standard is a cloudy day (cloud cover number 1-3), and the like are correspondingly valued.
The precipitation factorThe precipitation is divided into six grades of light rain (less than 10 mm), medium rain (10-15 mm), heavy rain (25-50 mm), heavy rain (50-100 mm), heavy rain (100-250 mm) and extra heavy rain (more than 250 mm), and the formula is as follows:
wherein ,for example, n=1 when precipitation is small rain (less than 10 mm), n=2 when precipitation is medium rain (10-15 mm), and the like.
The visibility influencing factorThe visibility is classified into nine grades of excellent visibility (25-30 KM), good visibility (20-25 KM), general visibility (15-20 KM), poor visibility (10-15 KM), poor visibility (5-10 KM), poor visibility (1-5 KM), extremely poor visibility (heavy fog 0.5-1 KM), extremely poor visibility (heavy fog 0.1-0.5 KM) and worst visibility (strong fog 0-0.1 KM), and the formula is expressed as:
wherein ,n=1 when visibility is excellent (25 to 30 KM) and n=2 when visibility is excellent (20 to 25 KM) are positive integers of 1 to 9, respectivelyValues.
The forest shading influence probability fc is divided into four grades of a forest (10), a gap forest (50), a arbor forest (100) and a forest (200) according to a forest density grade standard, and the formula is as follows:
wherein ,for example, n=1 when the wood density grade standard is divided into forests (10), n=2 when the wood density grade standard is divided into clearance forests (50), and the like are correspondingly valued.
The optical satellite finds, identifies and recognizes objects to comprehensively form threat degrees to ground targets (such as buildings, vehicles and markers on the ground), the ground targets are detection probabilities for the satellites, otherwise, the satellites have influence on the ground targets, particularly targets for executing tasks, the threat degrees are threat degrees, and the greater the threat degrees, the more the safety of the ground target tasks is affected.
The optical satellite finds, recognizes and recognizes the ground object to be three degrees of comprehensive perception of the object, the finding is that the satellite shoots the object, the recognition is that the object is what is known by comparative analysis, the recognition is that the object is specifically identified, and the object is specifically identified according to an experience library, such as the finding of an airplane, an automobile, a ship and even a specific model.
The target detection probability P' n-1=k can be generally calculated 1 P 'n, object recognition probability P' n-2=k 2 P 'n, object recognition probability P' n-3=k 3P’n, wherein The method is characterized in that the specific coefficient is 0-1, the threat of the satellite can be quantitatively calculated according to the corrected detection probability P' n of the satellite to the target through the calculation, and the detection capability of the satellite to the target is divided into different levels of discovery, identification and resolution, so that the threat degree analysis of the satellite is carried outThe specific level depends on the task requirement, so the threat level of the optical satellite to the ground target can be expressed by the following formula:
T=P’n-1*w 1 +P’n-2*w 2 +P’n-3*w 3
wherein T represents the threat degree of the satellite, P ' n-1, P ' n-2 and P ' n-3 are the discovery, identification and recognition probabilities of the satellite on the target respectively,、、the weight corresponding to the three probabilities is determined by task requirements, and the weight reflects the grade requirement of the identified target on the threat degree of the satellite in the target identification.
The target size is required to be known in satellite detection probability calculation, so that the satellite threat degree is closely related to the task target size, the method can be used for threat degree analysis under the condition that specific targets are known, and when the specific target conditions are unclear, threat degree is adopted when the threat degree conditions during satellite identification are to be predictedThe threat degree judgment is not easy to realize by the algorithm of the system, and threat degree prediction can be performed by adopting satellite threat degree grade by referring to NIIRS image quality grade concepts.
Fig. 2 is a schematic view of an application environment of a satellite discovery recognition target according to the present invention, and fig. 3 is a schematic view of coverage range of the satellite discovery recognition target according to the present invention.
FIG. 4 is a schematic diagram showing threat level determination of comprehensive meteorological elements and geographic elements, when cloud cover influences factorsMedium cloud quantity grade sunny day (cloud layer number 0-1), precipitation quantity influence factorPrecipitation of waterRain is small (less than 10 mm); visibility influencing factorIs excellent in visibility (25-30 KM); according to the forest density data of the target area, the forest shielding influence probability fc is calculated and obtained, the forest is classified as a tree (10) according to the forest density grade, the illumination influence probability fb is calculated and obtained according to the illumination time and intensity of a satellite detection target, and the satellite threat condition is calculated by adopting a threat degree evaluation model based on the corrected detection probability P' n. The threat of the acquired satellite to the target is 0.425.
FIG. 5 is a schematic diagram showing threat level determination of comprehensive meteorological elements and geographic elements, when cloud cover influences factorsMedium cloud quantity grade sunny day (cloud layer number 0-1), precipitation quantity influence factorPrecipitation heavy rain (25-50 mm); visibility influencing factorIs excellent in visibility (25-30 KM); according to the forest density data of the target area, the forest shielding influence probability fc is calculated and obtained, the forest is classified as a tree (10) according to the forest density grade, the illumination influence probability fb is calculated and obtained according to the illumination time and intensity of a satellite detection target, and the satellite threat condition is calculated by adopting a threat degree evaluation model based on the corrected detection probability P' n. The threat level of the acquired satellite to the target is reduced to 0.7600.
FIG. 6 is a schematic diagram showing threat level determination of comprehensive meteorological elements and geographic elements, when cloud cover influences factorsCloudiness (cloud layer number 3-7) of medium cloud quantity grade, precipitation quantity influence factorPrecipitation heavy storm (100-250 mm); visibility influencing factorPoor visibility (5-10 KM); according to the forest density data of the target area, the forest shielding influence probability fc is calculated and obtained, according to the forest density grade as a forest (200), the illumination influence probability fb is calculated and obtained according to the illumination time and intensity of a satellite when the target is detected, and the threat condition of the satellite is calculated by adopting a threat degree evaluation model based on the corrected detection probability P' n. The threat level of the acquired satellite to the target is reduced to 0.9948.
From the above illustrated examples, it can be derived that the cloud impact factorFactor of influence of precipitationVisibility influencing factorAnd the influence of the forest shading influence probability fc on the threat degree of the target satellite is obvious.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (7)
1. A reconnaissance satellite threat degree evaluation method for integrating meteorological elements and geographic elements is characterized by comprising the following steps:
s1, according to the target sizeAnd optical satellite resolution->Calculating detection probability->；
S2, calculating the detection probability of the illumination influence probability fb, the meteorological influence probability fa and the forest shielding influence probability fcThe influence of the detection probability P' n is acquired;
s3, acquiring a target detection probability P 'n-1, a target recognition probability P' n-2 and a target recognition probability P 'n-3 by using the corrected detection probability P' n;
s4, adjusting weight values of the target detection probability P ' n-1, the target recognition probability P ' n-2 and the target recognition probability P ' n-3 to obtain satellite threat degree。
2. The method for reconnaissance satellite threat assessment of integrated weather and geographic elements of claim 1, wherein: the S1 obtains and calculates the detection probabilityThe method comprises the following steps:
s11, calculating the target sizeThe formula is:
wherein W is the target width and H is the target height;
s12, according to the satellite resolutionAnd target size->The number of pairs N of crossing target feature size lines is calculated as:
wherein satellite resolution->The acquisition formula of (1) is:
wherein Is the spacing of the picture elements,is the focal length of the lens,is the range between the satellite and the target,is a side view angle;
s13, calculating detection probability according to the number N of the line pairs crossing the target feature sizeThe formula is:
wherein ,/>The number of pairs crossing the target feature size at a target detection probability P' n-1 of 80%.
3. The method for reconnaissance satellite threat assessment of integrated weather and geographic elements of claim 1, wherein: the formula for obtaining the corrected detection probability P' n is as follows:
P’n=d1*P n *fa*fb*fc
wherein To adjust the coefficients.
4. A method of reconnaissance satellite threat assessment for integrated weather and geographic elements according to claim 3 wherein: the influence factors of the weather influence probability fa include: cloud impact factorPrecipitation factor->And visibility influencing factor->The expression formula of the weather influence probability fa is as follows:
fa=d2*fa1*fa2*fa3
wherein To adjust the coefficients.
5. The method for reconnaissance satellite threat assessment of integrated weather and geographic elements of claim 4, wherein: the cloud impact factorAccording to cloud quantity grade standard, the method is divided into four grades of sunny days, cloudy days and cloudy days, and the formula is as follows:
wherein ,/>Is a positive integer of 1-4;
the precipitation factorThe precipitation is classified into six grades of light rain, medium rain, heavy rain and extra heavy rain, and the formula is as follows:
wherein ,/>Is a positive integer of 1-6;
the visibility influencing factorThe visibility is classified into nine grades of excellent visibility, good visibility, general visibility, poor visibility, very poor visibility, extremely poor visibility and the worst visibility according to the formula:
wherein ,/>Is a positive integer of 1-9.
6. The method for reconnaissance satellite threat assessment of integrated weather and geographic elements of claim 1, wherein: the forest shading influence probability fc is divided into four grades of a forest, a gap forest, a arbor forest and a forest according to a forest density grade standard, and the formula is as follows:
wherein ,/>Is a positive integer of 1-4.
7. The method for reconnaissance satellite threat assessment of integrated weather and geographic elements of claim 1, wherein: the satellite threat level is obtainedThe method comprises the following steps:
s41, calculating target detection probability P ' n-1, target recognition probability P ' n-2 and target recognition probability P ' n-3, wherein the formula is as follows:
P’n-1=K 1 P’n
P’n-2=K 2 P’n
P’n-3=K 3 P’n
wherein ,、/> and />For the coefficient of variation, said->、/> and />The value range of (2) is 0-1;
s42, distributing the weight of the target detection probability P' n-1 asThe weight of the assigned target recognition probability P' n-2 is +.>And assigning a weight of the target recognition probability P' n-3 as +.>；
S43, calculating satellite threat degreeThe formula is:
T=P’n-1*w 1 +P’n-2*w 2 +P’n-3*w 3
wherein Is satellite threat level.
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