Calibration method and system of sun sensor, electronic equipment and storage medium

Abstract

The invention provides a calibration method and system of a sun sensor, electronic equipment and a storage medium. The calibration method comprises the following steps: determining a target installation matrix of the sun sensor on three-axis turntable equipment; determining an initial rotation angle of three axes of the three-axis turntable equipment and a conversion relation between the initial rotation angle and the attitude matrix; after the user adjusts the rotation angles of at least two axes in the three axes according to the preset value every time, an imaging image corresponding to the current rotation angle of the sun sensor and the three axes is obtained; preprocessing each frame of imaging image to obtain a target measurement value set corresponding to each aperture; determining a first target parameter value of the first target aperture according to the target installation matrix, the conversion relation and the target measurement value set; performing optimization solution based on the first target parameter value and a first preset linear equation to obtain a scale factor; determining a second target parameter value of a second target aperture according to the scale factor and a second preset linear equation, wherein the second target aperture is all apertures except the first target aperture in the plurality of apertures; and determining a target calibration parameter of the sun sensor according to the first target parameter value of the first target aperture and the second target parameter value of the second target aperture.

Description

Technical Field

[0001] The present application relates to the technical field of solar sensor calibration, and in particular to a solar sensor calibration method and system, electronic equipment and storage medium. Background Art

[0002] A solar sensor is a photoelectric sensor used in spacecraft attitude control systems. It determines the spacecraft's orientation relative to the sun by measuring the angle between sunlight and a certain axis or coordinate plane of the spacecraft.

[0003] Traditional solar sensors include single-aperture solar sensors and slit-type solar sensors, which use the principle of pinhole imaging to image parallel incident light, and then calculate the position information of the spacecraft relative to the sun based on the position of the aperture imaging point or the slit imaging spot. However, the inventors of the present application have found that such solar sensors have limited applications such as low accuracy and small observation field angle because they only contain a single aperture or a single slit.

[0004] Compound eye structure mask array solar sensors with multiple apertures, no lenses, large field of view and high precision have a wider range of application scenarios. However, the inventors also found that the accuracy of the traditional solar sensor calibration method is not enough to meet the accuracy requirements of compound eye structure mask array solar sensors. The inventors believe that how to provide a high-precision compound eye structure mask array solar sensor calibration method is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a solar sensor calibration method and system, electronic equipment and storage medium, which are used to solve the problem of insufficient calibration accuracy in manual calibration of solar sensors of compound eye structure mask arrays.

[0006] According to one aspect of the present application, the present application provides a calibration method for a solar sensor, including: determining a target installation matrix of the solar sensor on a three-axis turntable device; determining the initial rotation angles of the three axes of the three-axis turntable device, and the conversion relationship between the initial rotation angles and the attitude matrix; obtaining an imaging image corresponding to the current rotation angle of the solar sensor and the three axes after the user adjusts the rotation angles of at least two of the three axes according to preset values ​​each time; preprocessing each frame of the imaging image to obtain a target measurement value set corresponding to each aperture; determining a first target parameter value of a first target aperture according to the target installation matrix, the conversion relationship and the target measurement value set; performing an optimization solution based on the first target parameter value and a first preset linear equation to obtain a scale factor; determining a second target parameter value of a second target aperture according to the scale factor and the second preset linear equation, wherein the second target aperture is all apertures except the first target aperture among multiple apertures; determining the target calibration parameters of the solar sensor according to the first target parameter value of the first target aperture and the second target parameter value of the second target aperture.

[0007] According to some embodiments of the present application, each frame of imaging image is preprocessed to obtain a target measurement value set corresponding to each aperture, including: based on each frame of imaging image, obtaining an imaging light spot corresponding to the aperture; determining the measurement value of each aperture in each frame of imaging image according to the centroid coordinates of the imaging light spot and the rotation angle of the three axes; combining the measurement values ​​of each aperture in all imaging images into a measurement value set; and determining the measurement value set whose elements in the measurement value set are greater than a preset threshold as the target measurement value set.

[0008] According to some embodiments of the present application, determining a first target parameter value of a first target aperture according to a target installation matrix, a conversion relationship, and a set of measured values ​​includes: determining an evaluation function of the first target parameter value according to the target installation matrix, the conversion relationship, and the set of measured values; initializing an initial value of a value envelope space of the first target parameter value to obtain an initial value envelope space; initializing a global historical optimal solution set to an empty set to obtain an initial global historical optimal solution set; optimizing the evaluation function multiple times based on a genetic algorithm framework to multiple times update the initial value envelope space and the initial global historical optimal solution set to obtain a target value envelope space. The target value envelope space is gridded in six dimensions so that each dimension includes a preset number of unit grids, wherein each unit grid corresponds to a first target parameter value; the first target parameter values ​​of all unit grids are traversed to calculate the evaluation function value of the evaluation function, and when the evaluation function value is greater than the maximum evaluation value in the target global historical optimal solution set, the corresponding first target parameter value is incorporated into the target global historical optimal solution set; the evaluation function value corresponding to the optimal first target parameter value in the target global historical optimal solution set is determined as the first target parameter value.

[0009] According to some embodiments of the present application, the calculation formula of the evaluation function for determining the first target parameter value according to the target installation matrix, the conversion relationship and the measurement value set is:

[0010]

[0011] g k =(x k ,y k , f k , r1, r2, r3)

[0012] θ i =(a i , b i , c i , x i ,y i )∈Θ k

[0013] R i =R abc (a i , b i , c i )

[0014]

[0015]

[0016] v i =R i ·(R r1 R r2 R r3 )·A·w i

[0017] Where fit = fit(g k , Θ k ) is the evaluation function, g k is the first target parameter value, Θ k is the set of measured values, N k =card(Θ k ), N k is θ k The number of elements in θ i is θ k The i-th measurement value in (a i ,b i ,c i ) are the rotation angles of the three axes of the three-axis turntable device contained in the i-th measurement value, (x i ,y i ) are the centroid coordinates of the first target aperture imaging point contained in the i-th measurement value, Ri is the conversion relationship, k is the aperture index in the first target aperture, (x k ,y k ) is the centroid coordinate of the foot of the kth aperture in the imaging plane, f k is the distance between the kth aperture and its perpendicular foot in the imaging plane, (r1, r2, r3) is the three-axis Euler angle error between the target installation matrix and the actual installation matrix of the sun sensor, A is the target installation matrix, w i To express the three-dimensional direction vector of the beam corresponding to the i-th measurement value in the imaging observation reference system, v i To express the three-dimensional direction vector of the light beam corresponding to the i-th measurement value in the ground inertial reference system, R r1 , R r2 , R r3 are the rotation matrix components around the three axes.

[0018] According to some embodiments of the present application, an optimization solution is performed based on the first target parameter value and the first preset linear equation to obtain a calculation formula for the scale factor:

[0019]

[0020]

[0021] Where μ is the scale factor, N SEL is the aperture number of the first target aperture, g * k is the first target parameter value, (X * k , Y * k ) are the physical horizontal coordinate and physical vertical coordinate of the first target aperture labeled k on the mask array, N SEL The i-th row of the ×3-dimensional matrix H, N SEL The i-th row of the ×1-dimensional column vector S.

[0022] According to some embodiments of the present application, a calculation formula for determining a second target parameter value of a second target aperture according to the scale factor and the second preset linear equation is:

[0023] x * m =u1

[0024] y * m =u2

[0025] f * m =s1u1+s2u2+s3

[0026] u=(J T J) -1 J T Q

[0027]

[0028] Among them, x * m ,y * m 、f * m is the second target parameter value, u1 and u2 are the two components of the two-dimensional column vector u, and J is (N SEL -1)×2 dimensional matrix, Q is (N SEL -1)×1-dimensional column vector, m is the index of the second target aperture, (X m , Y m ) are the physical horizontal coordinates and physical vertical coordinates of the aperture labeled m on the mask array, and (s1, s2, s3) are the three components of the column vector S respectively.

[0029] According to another aspect of the present application, the present application provides a solar sensor calibration system, which includes: a data processing module, an image processing module, a first target parameter value processing module, a second target parameter value processing module and a target calibration parameter processing module. The data processing module determines the target installation matrix of the sun sensor on the three-axis turntable device, and determines the initial rotation angle of the three axes of the three-axis turntable device, and the conversion relationship between the initial rotation angle and the attitude matrix; the image processing module obtains the imaging image corresponding to the current rotation angle of the sun sensor and the three axes after adjusting the rotation angle of at least two axes of the three axes each time; the first target parameter value processing module preprocesses each frame of the imaging image to obtain a target measurement value set corresponding to each aperture, and determines the first target parameter value of the first target aperture according to the target installation matrix, the conversion relationship and the measurement value set; the second target parameter value processing module performs optimization solution based on the first target parameter value and the first preset linear equation to obtain a scale factor, and determines the second target parameter value of the second target aperture according to the scale factor and the second preset linear equation, wherein the second target aperture is all apertures except the first target aperture among the multiple apertures; the target calibration parameter processing module determines the target calibration parameter of the sun sensor according to the first target parameter value of the first target aperture and the second target parameter value of the second target aperture.

[0030] According to another aspect of the present application, an electronic device is provided, which includes: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors can implement the calibration method described above.

[0031] According to another aspect of the present application, a non-volatile computer-readable storage medium is provided, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the calibration method described above can be implemented.

[0032] In the calibration process of the solar sensor, the present application can determine the first target parameter value of the first target aperture according to the target installation matrix, the conversion relationship and the target measurement value set by preprocessing the imaging images corresponding to multiple rotation angles; and determine the second target parameter value of the second target aperture according to the first target parameter value, the first preset linear equation and the second preset linear equation. The present application can determine the target calibration parameters of the solar sensor through the first target parameter value and the second target parameter value, thereby completing the calibration of the solar sensor.

[0033] The calibration method provided in the present application can solve the problem of insufficient calibration accuracy in the current manual calibration of compound eye structure mask array solar sensors; and the calibration method can be fully automated by the calibration system through the calculation process and framework, and has the characteristics of high automation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flow chart showing a calibration method according to an embodiment of the present application;

[0036] Figure 2 Another flow chart showing the calibration method of the embodiment of the present application;

[0037] Figure 3 Another flow chart showing the calibration method of the embodiment of the present application;

[0038] Figure 4 A schematic diagram of a calibration system according to an embodiment of the present application is shown.

[0039] Description of reference numerals:

[0040] Calibration system 1; data processing module 10; image processing module 20; first target parameter value processing module 30; second target parameter value processing module 40; target calibration parameter processing module 50. DETAILED DESCRIPTION

[0041] The following is a clear and complete description of the technical solution of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0042] According to one aspect of the present application, a solar sensor calibration method is provided to solve the problem of insufficient calibration accuracy in manual calibration of solar sensors of a compound eye structure mask array.

[0043] According to example embodiments, the solar sensor may be a solar sensor having a compound-eye structure mask array, on which a mask array including a plurality of apertures is disposed.

[0044] During the calibration process of the sun sensor, the sun sensor is placed on the three-axis turntable device based on user operation. For example, based on user operation, the sun sensor is fixed on the three-axis turntable device in a manner of aligning a preset coordinate axis.

[0045] Figure 1 A flow chart of the calibration method of the embodiment of the present application is shown. Figure 1 As shown, the calibration method includes steps S100-S800. Exemplarily, the calibration method may be executed by a calibration system for a solar sensor having computing capability.

[0046] According to an example embodiment, in step S100 , the calibration system determines a target installation matrix of a sun sensor on a three-axis turntable device.

[0047] For example, the target installation matrix may be the estimated installation matrix A of the sun sensor calculated by the calibration system in response to user instructions based on the selected preset coordinate axis alignment. The installation matrix of the sun sensor refers to a mathematical model used to determine the attitude and position of the satellite, which can characterize its position and orientation in the satellite reference coordinate system.

[0048] Since the sun sensor is installed manually, during the installation process, it is necessary to ensure that the error of the three-axis Euler angles (r1, r2, r3) (such as roll angle Roll, pitch angle Pitch and yaw angle Yaw) between the estimated installation matrix A and the actual installation matrix is ​​controlled within a certain threshold.

[0049] Exemplarily, the error range may be: -1.5°≤r1≤1.5°, -1.5°≤r2≤1.5°, -1.5°≤r3≤1.5°.

[0050] As an embodiment, the estimated installation matrix A may be:

[0051]

[0052] As an embodiment, r1 = 1.4°, r2 = 1.4°, and r3 = 1.4°.

[0053] In step S200, the calibration system determines the initial rotation angles of the three axes of the three-axis turntable device, and the conversion relationship between the initial rotation angles and the posture matrix.

[0054] For example, after completing the fixed installation of the solar sensor, based on the user operation, the initial reference is selected, and the initial posture of the three-axis turntable device is adjusted while the parallel light beam emitted by the collimator is kept pointing to the three-axis turntable device, so that the light beam of the collimator can pass through the aperture in the central area of ​​the mask array and be projected onto the CMOS plane (Complementary Metal-Oxide Semiconductor) in an approximately vertical direction.

[0055] For example, the approximate verticality can be compared with the true verticality, and a certain degree of deviation can be allowed between the axes. When the aperture of the central area of ​​the mask array can be imaged at a relatively central position of the CMOS plane, it can be considered to be approximately vertical. In this way, in the subsequent imaging calibration process, the aperture of the central area of ​​the mask array can be imaged, rather than the aperture of the edge area of ​​the mask array, so that the error of the final linear difference link can be reduced.

[0056] According to an exemplary embodiment, the calibration system determines the rotation angle parameters of the three axes corresponding to the initial posture of the three-axis turntable device as the initial rotation angles of the three axes, and determines the initial rotation angles of the three axes and the posture matrix R abc The conversion relationship R abc (a, b, c).

[0057] For example, the conversion relationship (or conversion relationship function) R abc (a, b, c) are determined based on the real physical model of the three-axis turntable equipment.

[0058] For example, the conversion relationship function of the three-axis turntable device with Yew-Pitch-Roll as the parameter can be:

[0059]

[0060] In step S300, the calibration system obtains an imaging image corresponding to the current rotation angle of the sun sensor and the three axes each time the user adjusts the rotation angle of at least two axes of the three axes according to preset values.

[0061] For example, based on the user's operation, relative to the initial reference of the three axes, the rotation angle of one of the axes is kept unchanged, and the rotation angles of the other two axes are adjusted multiple times according to preset values ​​(such as keeping the Roll axis unchanged and adjusting the rotation angles of the Yew and Pitch axes).

[0062] The calibration system obtains the image of the sun sensor after each user adjusts the rotation angle of the other two axes. i .

[0063] Exemplarily, the rotation angles of the other two axes are adjusted no less than 200 times, and the angle of each rotation adjustment may be about 1°.

[0064] For example, during the i-th rotation, the calibration system can record the rotation angles of the three axes (a i ,b i ,c i ). During the rotation of the other two axes, the non-rotating axis may also be slightly disturbed, and the current rotation angle value is recorded at the same time.

[0065] As an example, i=3000. Then the calibration system collects 3000 imaging images and 3000 groups (a i ,b i ,c i ).

[0066] In step S400, the calibration system preprocesses each frame of imaging image to obtain a set of target measurement values ​​corresponding to each aperture.

[0067] Figure 2 Another flow chart of the calibration method of the embodiment of the present application is shown. Figure 2 As shown, step S400 may also include S410-S440.

[0068] In step S410, the calibration system obtains the imaging light spot corresponding to the aperture based on each frame of the imaging image.

[0069] For example, for each frame of imaging image, the calibration system obtains the correspondence between the aperture and the imaging light spot based on the image recognition technology matching and identification, and each imaging light spot corresponds to an aperture.

[0070] In step S420, the calibration system determines the measurement value of each aperture in each frame of the imaging image according to the centroid coordinates of the imaging light spot and the rotation angles of the three axes.

[0071] For example, for each frame of imaging, the calibration system determines the centroid coordinates (x, y) of the imaging light point corresponding to the aperture, as well as the rotation angles (a, b, c) associated with the current three axes, and each imaging light point obtains a set of 5-element measurement values ​​θ = (x, y, a, b, c) corresponding to the aperture.

[0072] In step S430, the calibration system combines the measurement values ​​of each aperture in all imaging images into a measurement value set.

[0073] For example, according to the aperture identity classification, all the images belong to the same aperture M k The five-element measurement values ​​form the measurement value set Θ k .

[0074] In step S440 , the calibration system determines a measurement value set whose elements in the measurement value set are greater than a preset threshold as a target measurement value set.

[0075] For example, the calibration system only retains elements whose number is greater than N MES The set of measurements Θ k With such a configuration, the present application can obtain a plurality of target measurement value sets of measurable apertures by preprocessing each frame of imaging images.

[0076] As an embodiment, due to the change in the angle of the incident light, the number of imaging light spots in each frame of the imaging image is different. There are 550 apertures set on the mask array, 325 imaging light spots are extracted from the first frame of the imaging image, corresponding to 325 apertures; 328 imaging light spots are extracted from the second frame of the imaging image, corresponding to 328 apertures.

[0077] The centroid coordinates (x, y) extracted from the image and the corresponding three-axis rotation angles (a, b, c) are combined into a 5-element measurement value θ = (x, y, a, b, c). Then, the 5-element measurement values ​​of the aperture with the same number in all the imaged images are summarized to obtain the target measurement value set Θ k Then the target measurement value set Θ k The sets with less than 2000 elements were discarded, and finally a set of target measurement values ​​of 212 apertures was obtained.

[0078] In step S500, the calibration system determines a first target parameter value of a first target aperture according to a target installation matrix, a conversion relationship, and a target measurement value set.

[0079] For example, the first target aperture is N selected by the user from all apertures according to requirements. SEL (The number is greater than or equal to 4) apertures.

[0080] The calibration system obtains the NSEL The target measurement value set corresponding to the apertures and calculate these N SEL The first target parameter value corresponding to the aperture.

[0081] For example, the first target parameter value may be a 6-tuple parameter g k .

[0082] g k =(x k ,y k , f k , r1, r2, r3).

[0083] Figure 3 Another flow chart of the calibration method of the embodiment of the present application is shown. Figure 3 As shown, step S500 may also include S510-S570.

[0084] In step S510, the calibration system determines an evaluation function of a first target parameter value according to a target installation matrix, a conversion relationship, and a set of measurement values.

[0085] Optionally, the calculation formula of the evaluation function is:

[0086]

[0087] g k =(x k ,y k , f k , r1, r2, r3)

[0088] θ i =(a i , b i , c i , x i ,y i )∈Θ k

[0089] R i =R abc (a i , b i , c i )

[0090]

[0091]

[0092] v i =R i ·(R r1 R r2 R r3 )·A·w i

[0093] Where fit = fit(g k , Θ k ) is the evaluation function, g k is the first target parameter value, Θ k is the set of measured values, N k =card(Θ k ), N k is θ k The number of elements in θ i is θ k The i-th measurement value in (a i ,b i ,c i ) are the rotation angles of the three axes of the three-axis turntable device contained in the i-th measurement value, (x i ,y i ) are the centroid coordinates of the first target aperture imaging point contained in the i-th measurement value, R i is the conversion relationship, k is the aperture index in the first target aperture, (x k ,y k ) is the centroid coordinate of the foot of the kth aperture in the imaging plane, f k is the distance between the kth aperture and its perpendicular foot in the imaging plane, (r1, r2, r3) is the three-axis Euler angle error between the target installation matrix and the actual installation matrix of the sun sensor, A is the target installation matrix, w i To express the three-dimensional direction vector of the beam corresponding to the i-th measurement value in the imaging observation reference system, v i To express the three-dimensional direction vector of the light beam corresponding to the i-th measurement value in the ground inertial reference system, R r1 , R r2 , R r3 are the rotation matrix components around the three axes.

[0094] In step S520, the calibration system initializes the initial value of the value envelope space of the first target parameter value to obtain an initial value envelope space.

[0095] For example, to initialize the 6-tuple parameter g k =(x k ,y k , f k , r1, r2, r3) is the initial value of the envelope space VarSpac e. For example, each parameter has a set of envelope upper and lower bounds, and there are 6 sets of envelope upper and lower bounds, which are:

[0096] x L ≤x k ≤x U

[0097] y L ≤y k ≤y U

[0098] f L ≤f k ≤f U

[0099] r 1L ≤r1≤r 1U

[0100] r 2L ≤r2≤r 2U

[0101] r 3L ≤r3≤r 3U

[0102] 6-tuple parameter g k The value envelope space of is expressed as:

[0103] VarSpace=(x L ,x U )×(y L ,y U )×(f L ,f U )×(r 1L ,r 1U )×(r 2L ,r 2U )×(r 3L ,r 3U ).

[0104] Then initialize the value envelope space according to the following initial values:

[0105] x L =y L =f L =r 1L =r 2L =r 3L =+∞;

[0106] x U =y U =f U =r 1U =r 2U =r 3U =-∞.

[0107] In step S530, the calibration system initializes the global historical optimal solution set to an empty set to obtain an initial global historical optimal solution set.

[0108] For example, the calibration system initializes the global historical optimal solution set Solutions to an empty set, that is,

[0109] As an example, take N SEL =4, from the 212 apertures obtained above, take 5 apertures and a target measurement value set, the 5 apertures are numbered 35, 40, 50, 60, 70 respectively.

[0110] In step S540, the calibration system optimizes the evaluation function multiple times based on the genetic algorithm framework to update the initial value envelope space and the initial global historical optimal solution set multiple times to obtain the target value envelope space and the target global historical optimal solution set.

[0111] For example, the calibration system independently optimizes N based on the genetic algorithm framework GA After each round of optimization, the value envelope space VarSpace and the global historical optimal solution set Solutions are updated to obtain the final target value envelope space and the target global historical optimal solution set.

[0112] Exemplarily, each round of optimization process includes:

[0113] (S1) The calibration system uses the 6-element parameter itself as the gene chain and sets the population size to N GROUP , set the crossover probability to q c ∈(0, 1), the mutation probability is q v ∈(0, 1), the probability of reselection of the historical gene chain q r ∈(0,1), set the feasible solution domain to:

[0114]

[0115] And set the reprojection method to:

[0116]

[0117] Among them, Project(g k ,ValValidSpace) represents a 6-tuple parameter g k A mapping function that maps back to the interval ValValidSpace.

[0118] As an example, set N GA =15, that is, 15 rounds of genetic algorithm optimization are performed on the five apertures selected above, and q c =0.1,q v =0.1,q r =0.2. Since the plane resolution of CMOS is 2560*2560, Valid xkSet to the interval [0, 2560] pixels, Valid yk Set to [0, 2560] pixels. Due to the mechanical structure, the height between the mask and the CMOS is roughly estimated to be between 1300 and 1400 pixels, so Valid fk Set to [1300, 1400] pixels. Since the installation error is the sales volume, set Valid xk =Valid yk =Valid fk The mapping function Project(g k ,ValValidSpace) is set to return to the nearest value in the interval according to the element, such as for a 6-element parameter g that is beyond the feasible solution domain k If the x value is 2800, it exceeds the Valid range. xk , then the mapping function will map x to the interval Valid xk The nearest value within, that is, x=2560.

[0119] (S2) The calibration system creates a table of historical gene chains to record the solution with the highest evaluation function value during the solution process, and the table is initially an empty set. In ValValidSpace, for populations 1, 2..., N GROUP Initialize a random gene chain, a total of N GROUP Articles (such as 64).

[0120] (S3) The calibration system sets a maximum number of iterations L (eg, the maximum number can be set to 10,000) and starts this round of iterations.

[0121] (S4) The calibration system first traverses each historical solution in the Table in the lth iteration (1≤l≤L), and then reselects the solution according to the reselection probability q r Add it to the current population gene chain; then perform crossover mutation on the population gene chain with a crossover probability q c The genes at the same position of each two gene chains in the population are exchanged with a mutation probability q v The gene at each position in each gene chain in the population is mutated. The mutation method is: add a small random amount to the original value of the gene at that position to form a new value. Then, for all the changed gene chains, the gene chains are reprojected to the feasible solution domain of ValValidSpace according to the set reprojection method.

[0122] (S5) The calibration system fits all current gene chains in the population according to the evaluation function fit(g k , Θ k ) Obtain the evaluation function value, sort the evaluation function values ​​from large to small, and retain the top N with the largest evaluation value GROUPgene chains, which serve as the initial gene chains of the l+1th iteration of the population.

[0123] (S6) The calibration system maintains the table, specifically: GROUP Add the gene chains to the Table, and then sort all the gene chains in the Table again from large to small according to the evaluation function value, and keep the top N GROUP Gene chains form a new Table.

[0124] (S7) Repeat S4-S6 until L iterations are completed, and take the gene chain g with the highest evaluation function value from the Table BEST =(x BEST ,y BEST ,f BEST ,r 1BEST ,r 2BEST ,r 3BEST ), and its corresponding evaluation function value fit BEST , if fit BEST <fit 阈值 (the evaluation function threshold is set, for example, 0.999), the optimization result of this round is invalid, and returns to S1 to restart this round of optimization; if fit BEST ≥ fit 阈值 , then the optimization result of this round is valid, and g BEST Add Solutions and follow the g BEST Update VarSpace and enter the next round of optimization.

[0125] The update method is:

[0126] x L =min(x BEST ,x L );

[0127] y L =min(y BEST ,y L );

[0128] f L =min(f BEST ,f L );

[0129] f L =min(f BEST ,f L );

[0130] r 2L =min(r 2BEST ,r 2L );

[0131] r 3L =min(r 3BEST ,r 3L );

[0132] x U =max(x BEST ,x U );

[0133] y U =max(y BEST ,y U );

[0134] f U =max(f BEST ,f U );

[0135] r 1U =max(r 1BEST ,r 1U );

[0136] r 2U =max(r 2BEST ,r 2U );

[0137] r 3U =max(r 3BEST ,r 3U ).

[0138] In step S550, the calibration system grids the target value envelope space in six dimensions so that each dimension includes a preset number of unit grids, wherein each unit grid corresponds to a first target parameter value.

[0139] For example, when all N GA After the round of genetic algorithm optimization, the 6-element parameter value envelope space VarSpace is obtained, which is divided into N GRID Each unit grid corresponds to a 6-element parameter, so VarSpace is divided into N GRID 6 6 parameters.

[0140] As an embodiment, taking aperture 50 numbered as 50 as an example, its value envelope space VarSpace is finally:

[0141] [29.3,32.1]×[98.1,102.3]×[148.5,151.0]×[1.33,1.45]×[1.32,1.45]×[-1.50,-1.31]

[0142] Divide N in each dimensionGRID =10 unit grids, then there are 1,000,000 unit grids in total after division in 6 dimensions.

[0143] In step S560, the calibration system traverses the first target parameter values ​​of all unit grids to calculate the evaluation function value of the evaluation function. When the evaluation function value is greater than the maximum evaluation value in the target global historical optimal solution set, the corresponding first target parameter value is incorporated into the target global historical optimal solution set.

[0144] For example, the calibration system traverses the 6-element parameter g corresponding to all unit grids k , calculate the evaluation function value fit(g k , Θ k ). If fit(g k , Θ k ) is greater than the current most evaluated value in the Solutions set, then the current 6-element parameter g k Added to Solutions.

[0145] In step S570, the calibration system determines the evaluation function value corresponding to the optimal first target parameter value in the target global historical optimal solution set as the first target parameter value.

[0146] For example, after the calibration system has traversed all the unit grids, the 6-element parameter g with the best evaluation function value in Solutions is taken out. k , as the final aperture M k The 6-element parameter.

[0147] As an embodiment, taking aperture 50 as an example, the six-dimensional parameters of aperture 50 are:

[0148] The corresponding evaluation function value is 0.999657349695369.

[0149] In step S600, the calibration system performs an optimization solution based on the first target parameter value and the first preset linear equation to obtain a scale factor.

[0150] For example, let N SEL The apertures are numbered as follows: N SEL The six parameters corresponding to the apertures are For any number 6-element parameter It can be expressed as

[0151] Optionally, the scale factor is calculated as:

[0152]

[0153]

[0154] Where μ is the scale factor, N SEL is the aperture number of the first target aperture, g * k is the first target parameter value, (X * k , Y * k ) are the physical horizontal coordinate and physical vertical coordinate of the first target aperture labeled k on the mask array, N SEL The i-th row of the ×3-dimensional matrix H, N SEL The i-th row of the ×1-dimensional column vector S.

[0155] As an embodiment, H is a 5×3 dimensional matrix, S is a 5×1 dimensional vector, and the finally calculated vector S is [0.002825837, 0.006169453, 1349.2249], (s1, s2, s3) are s1=0.002825837, s2=0.006169453, s3=1349.2249, and the scale factor μ=5.99837.

[0156] In step S700, the calibration system determines a second target parameter value of a second target aperture according to the scale factor and a second preset linear equation, wherein the second target aperture is all apertures among the plurality of apertures except the first target aperture.

[0157] For example, the second target aperture is all the apertures divided by the above N SEL The remaining m apertures of the apertures, The calibration system responds to user instructions, constructs preset linear equations, and optimizes and solves three-dimensional parameters.

[0158] Optionally, the calibration system determines a calculation formula for a second target parameter value of a second target aperture according to the scale factor and the second preset linear equation as follows:

[0159] x * m =u1

[0160] y * m =u2

[0161] f * m =s1u1+s2u2+s3

[0162] u=(J T J) -1 JT Q

[0163]

[0164] Among them, x * m ,y * m 、f * m is the second target parameter value, u1 and u2 are the two components of the two-dimensional column vector u, and J is (N SEL -1)×2 dimensional matrix, Q is (N SEL -1)×1-dimensional column vector, m is the index of the second target aperture, (X m , Y m ) are the physical horizontal coordinates and physical vertical coordinates of the aperture labeled m on the mask array, and (s1, s2, s3) are the three components of the column vector S respectively.

[0165] As an embodiment, the 3-dimensional parameter of the aperture 52 numbered 52 obtained in step S7 is:

[0166]

[0167] In step S800, the calibration system determines a target calibration parameter of the sun sensor according to a first target parameter value of the first target aperture and a second target parameter value of the second target aperture.

[0168] For example, the calibration system determines the target calibration parameters of the sun sensor according to the six-dimensional parameters of the first target aperture and the three-dimensional parameters of the second target aperture, and completes the calibration of the sun sensor with the target calibration parameters.

[0169] Through the above exemplary embodiments, in the calibration process of the solar sensor, the present application can determine the first target parameter value of the first target aperture according to the target installation matrix, the conversion relationship and the target measurement value set by preprocessing the imaging images corresponding to multiple rotation angles; and determine the second target parameter value of the second target aperture according to the first target parameter value, the first preset linear equation and the second preset linear equation. The present application can determine the target calibration parameters of the solar sensor through the first target parameter value and the second target parameter value, thereby completing the calibration of the solar sensor.

[0170] The calibration method provided in the present application can solve the problem of insufficient calibration accuracy in the current manual calibration of compound eye structure mask array solar sensors; and the calibration method can be fully automated by the calibration system through the calculation process and framework, and has the characteristics of high automation performance.

[0171] The calibration method provided in this application can model multiple error factors, and can eliminate small installation errors in the calibration process by combining the errors in the calibration process itself. The calibration method can also consider the three-axis measurement transfer error between the mask and the CMOS plane, and achieve unified optimization of all apertures through a hybrid architecture of multiple optimization algorithms.

[0172] Exemplarily, as an embodiment, when the centroid extraction accuracy is 0.01 pixel, the calibration accuracy of the multi-aperture 3D parameters can reach 0.05 pixel, which is at least 2 orders of magnitude higher than the current manual calibration accuracy of the compound eye structure mask array solar sensor, and can ensure the arc second level solar azimuth measurement accuracy of the compound eye structure mask array solar sensor.

[0173] According to another aspect of the present application, the present application provides a solar sensor calibration system for solving the problem of insufficient calibration accuracy in manual calibration of solar sensors of a compound eye structure mask array.

[0174] Figure 4 A schematic diagram of a calibration system according to an embodiment of the present application is shown. Figure 4 As shown, the calibration system 1 includes a data processing module 10 , an image processing module 20 , a first target parameter value processing module 30 , a second target parameter value processing module 40 and a target calibration parameter processing module 50 .

[0175] According to an exemplary embodiment, the data processing module 10 determines a target mounting matrix of the sun sensors on a three-axis turntable apparatus.

[0176] For example, the target installation matrix may be an estimated installation matrix A of the sun sensor calculated by the data processing module 10 in response to a user instruction based on the selected preset coordinate axis alignment method. The installation matrix of the sun sensor refers to a mathematical model for determining the attitude and position of the satellite, which can characterize its position and orientation in the satellite reference coordinate system.

[0177] Since the sun sensor is installed manually, during the installation process, it is necessary to ensure that the error of the three-axis Euler angles (r1, r2, r3) (such as roll angle Roll, pitch angle Pitch and yaw angle Yaw) between the estimated installation matrix A and the actual installation matrix is ​​controlled within a certain threshold.

[0178] Exemplarily, the error range may be: -1.5°≤r1≤1.5°, -1.5°≤r2≤1.5°, -1.5°≤r3≤1.5°.

[0179] As an embodiment, the estimated installation matrix A may be:

[0180]

[0181] As an embodiment, r1 = 1.4°, r2 = 1.4°, and r3 = 1.4°.

[0182] The data processing module 10 determines the initial rotation angles of the three axes of the three-axis turntable device, and the conversion relationship between the initial rotation angles and the posture matrix.

[0183] For example, after completing the fixed installation of the solar sensor, based on the user operation, the initial reference is selected, and the initial posture of the three-axis turntable device is adjusted while the parallel light beam emitted by the collimator is kept pointing to the three-axis turntable device, so that the light beam of the collimator can pass through the aperture in the central area of ​​the mask array and be projected onto the CMOS plane (Complementary Metal-Oxide Semiconductor) in an approximately vertical direction.

[0184] For example, the approximate verticality can be compared with the true verticality, and a certain degree of deviation can be allowed between the axes. When the aperture of the central area of ​​the mask array can be imaged at a relatively central position of the CMOS plane, it can be considered to be approximately vertical. In this way, in the subsequent imaging calibration process, the aperture of the central area of ​​the mask array can be imaged, rather than the aperture of the edge area of ​​the mask array, so that the error of the final linear difference link can be reduced.

[0185] According to the exemplary embodiment, the data processing module 10 determines the rotation angle parameters of the three axes corresponding to the initial posture of the three-axis turntable device as the initial rotation angles of the three axes, and determines the initial rotation angles of the three axes and the posture matrix R abc The conversion relationship R abc (a, b, c).

[0186] For example, the conversion relationship (or conversion relationship function) R abc (a, b, c) are determined based on the real physical model of the three-axis turntable equipment.

[0187] For example, the conversion relationship function of the three-axis turntable device with Yew-Pitch-Roll as the parameter can be:

[0188]

[0189] The image processing module 20 obtains an imaging image corresponding to the current rotation angle of the sun sensor and the three axes each time the user adjusts the rotation angle of at least two axes of the three axes according to the preset value.

[0190] For example, based on the user's operation, relative to the initial reference of the three axes, the rotation angle of one of the axes is kept unchanged, and the rotation angles of the other two axes are adjusted multiple times according to preset values ​​(such as keeping the Roll axis unchanged and adjusting the rotation angles of the Yew and Pitch axes).

[0191] The image processing module 20 obtains the image corresponding to the sun sensor after the user adjusts the rotation angle of the other two axes. i .

[0192] Exemplarily, the rotation angles of the other two axes are adjusted no less than 200 times, and the angle of each rotation adjustment may be about 1°.

[0193] For example, during the i-th rotation process, the data processing module 10 can record the rotation angles of the three axes (a i ,b i ,c i ). During the rotation of the other two axes, the non-rotating axis may also be slightly disturbed, and the current rotation angle value is recorded at the same time.

[0194] As an example, i=3000. Then the data processing module 10 collects 3000 imaging images and 3000 groups (a i ,b i ,c i ).

[0195] The first target parameter value processing module 30 pre-processes each frame of imaging image to obtain a set of target measurement values ​​corresponding to each aperture.

[0196] Optionally, the first target parameter value processing module 30 obtains an imaging light spot corresponding to the aperture based on each frame of imaging image.

[0197] For example, for each frame of imaging image, the first target parameter value processing module 30 obtains the corresponding relationship between the aperture and the imaging light spot by matching and identifying based on the image recognition technology, and each imaging light spot corresponds to an aperture.

[0198] The first target parameter value processing module 30 determines the measurement value of each aperture in each frame of the imaging image according to the centroid coordinates of the imaging light spot and the rotation angles of the three axes.

[0199] For example, for each frame of imaging image, the first target parameter value processing module 30 determines the centroid coordinates (x, y) of the imaging light point corresponding to the aperture, and the rotation angle (a, b, c) associated with the current three axes, and each imaging light point obtains a set of 5-element measurement values ​​θ=(x, y, a, b, c) corresponding to the aperture.

[0200] The first target parameter value processing module 30 combines the measurement values ​​of each aperture in all imaging images into a measurement value set.

[0201] For example, according to the aperture identity classification, all the images belong to the same aperture M k The 5-element measurement values ​​form the measurement value set Θ k .

[0202] The first target parameter value processing module 30 determines a measurement value set whose elements in the measurement value set are greater than a preset threshold as a target measurement value set.

[0203] For example, the first target parameter value processing module 30 only retains the number of elements greater than N MES The set of measurements Θ k With such a configuration, the present application can obtain a plurality of target measurement value sets of measurable apertures by preprocessing each frame of imaging images.

[0204] As an embodiment, due to the change in the angle of the incident light, the number of imaging light spots in each frame of the imaging image is different. There are 550 apertures set on the mask array, 325 imaging light spots are extracted from the first frame of the imaging image, corresponding to 325 apertures; 328 imaging light spots are extracted from the second frame of the imaging image, corresponding to 328 apertures.

[0205] The centroid coordinates (x, y) extracted from the image and the corresponding three-axis rotation angles (a, b, c) are combined into a 5-element measurement value θ = (x, y, a, b, c). Then, the 5-element measurement values ​​of the aperture with the same number in all the imaged images are summarized to obtain the target measurement value set Θ k Then the target measurement value set Θ k The sets with less than 2000 elements were discarded, and finally a set of target measurement values ​​of 212 apertures was obtained.

[0206] The first target parameter value processing module 30 determines a first target parameter value of a first target aperture according to the target installation matrix, the conversion relationship and the target measurement value set.

[0207] For example, the first target aperture is N selected by the user from all apertures according to requirements. SEL (The number is greater than or equal to 4) apertures.

[0208] The first target parameter value processing module 30 obtains the N SEL The target measurement value set corresponding to the apertures and calculate these N SEL The first target parameter value corresponding to the aperture.

[0209] For example, the first target parameter value may be a 6-tuple parameter g k .

[0210] g k =(x k ,y k , f k , r1, r2, r3).

[0211] Optionally, the first target parameter value processing module 30 determines an evaluation function of the first target parameter value according to the target installation matrix, the conversion relationship and the measurement value set.

[0212] Optionally, the calculation formula of the evaluation function is:

[0213]

[0214] g k =(x k ,y k , f k , r1, r2, r3)

[0215] θ i =(a i , b i , c i , x i ,y i )∈Θ k

[0216] R i =R abc (a i , b i , c i )

[0217]

[0218]

[0219] v i =R i ·(R r1 R r2 R r3 )·A·w i

[0220] Where fit = fit(g k , Θ k ) is the evaluation function, g k is the first target parameter value, Θ k is the set of measured values, N k =card(Θ k ), N k is θ k The number of elements in θ i is θ kThe i-th measurement value in (a i ,b i ,c i ) are the rotation angles of the three axes of the three-axis turntable device contained in the i-th measurement value, (x i ,y i ) are the centroid coordinates of the first target aperture imaging point contained in the i-th measurement value, R i is the conversion relationship, k is the aperture index in the first target aperture, (x k ,y k ) is the centroid coordinate of the foot of the kth aperture in the imaging plane, f k is the distance between the kth aperture and its perpendicular foot in the imaging plane, (r1, r2, r3) is the three-axis Euler angle error between the target installation matrix and the actual installation matrix of the sun sensor, A is the target installation matrix, w i To express the three-dimensional direction vector of the beam corresponding to the i-th measurement value in the imaging observation reference system, v i To express the three-dimensional direction vector of the light beam corresponding to the i-th measurement value in the ground inertial reference system, R r1 , R r2 , R r3 are the rotation matrix components around the three axes.

[0221] The first target parameter value processing module 30 initializes the initial value of the value envelope space of the first target parameter value to obtain an initial value envelope space.

[0222] For example, to initialize the 6-tuple parameter g k =(x k ,y k , f k , r1, r2, r3) is the initial value of the envelope space VarSpac e. For example, each parameter has a set of envelope upper and lower bounds, and there are 6 sets of envelope upper and lower bounds, which are:

[0223] x L ≤x k ≤x U

[0224] y L ≤y k ≤y U

[0225] f L ≤f k ≤f U

[0226] r 1L ≤r1≤r 1U

[0227] r 2L≤r2≤r 2U

[0228] r 3L ≤r3≤r 3U

[0229] 6-tuple parameter g k The value envelope space of is expressed as:

[0230] VarSpace=(x L ,x U )×(y L ,y U )×(f L ,f U )×(r 1L ,r 1U )×(r 2L ,r 2U )×(r 3L ,r 3U ).

[0231] Then initialize the value envelope space according to the following initial values:

[0232] x L =y L =f L =r 1L =r 2L =r 3L =+∞;

[0233] x U =y U =f U =r 1U =r 2U =r 3U =-∞.

[0234] The first target parameter value processing module 30 initializes the global historical optimal solution set to an empty set to obtain an initial global historical optimal solution set.

[0235] For example, the first target parameter value processing module 30 initializes the global historical optimal solution set Solutions to an empty set, that is,

[0236] As an example, take N SEL =4, from the 212 apertures obtained above, take 5 apertures and a target measurement value set, the 5 apertures are numbered 35, 40, 50, 60, 70 respectively.

[0237] The first target parameter value processing module 30 optimizes the evaluation function multiple times based on the genetic algorithm framework to update the initial value envelope space and the initial global historical optimal solution set multiple times to obtain the target value envelope space and the target global historical optimal solution set.

[0238] For example, the first target parameter value processing module 30 independently optimizes N based on the genetic algorithm framework. GA After each round of optimization, the value envelope space VarSpace and the global historical optimal solution set Solutions are updated to obtain the final target value envelope space and the target global historical optimal solution set.

[0239] Exemplarily, each round of optimization process includes:

[0240] (S1) The first target parameter value processing module 30 uses the 6-element parameter itself as the gene chain and sets the population size to N GROUP , set the crossover probability to q c ∈(0, 1), the mutation probability is q v ∈(0, 1), the probability of reselection of the historical gene chain q r ∈(0,1), set the feasible solution domain to:

[0241]

[0242] And set the reprojection method to:

[0243]

[0244] Among them, Project(g k ,ValValidSpace) represents a 6-tuple parameter g k A mapping function that maps back to the interval ValValidSpace.

[0245] As an example, set N GA =15, that is, 15 rounds of genetic algorithm optimization are performed on the five apertures selected above, and q c =0.1,q v =0.1,q r =0.2. Since the plane resolution of CMOS is 2560*2560, Valid xk Set to the interval [0, 2560] pixels, Valid yk Set to [0, 2560] pixels. Due to the mechanical structure, the height between the mask and the CMOS is roughly estimated to be between 1300 and 1400 pixels, so Valid fk Set to [1300, 1400] pixels. Since the installation error is the sales volume, set Valid xk=Valid yk =Valid fk The mapping function Project(g k ,ValValidSpace) is set to return to the nearest value in the interval according to the element, such as for a 6-element parameter g that is beyond the feasible solution domain k If the x value is 2800, it exceeds the Valid range. xk , then the mapping function will map x to the interval Valid xk The nearest value within, that is, x=2560.

[0246] (S2) The first target parameter value processing module 30 creates a table of historical gene chains to record the solution with the highest evaluation function value in the solution process, and initializes the table to an empty set. In ValValidSpace, for populations 1, 2..., N GROUP Initialize a random gene chain, a total of N GROUP Articles (such as 64).

[0247] (S3) The first target parameter value processing module 30 sets a maximum number of iterations L (eg, the maximum number can be set to 10,000) and starts this round of iterations.

[0248] (S4) The first target parameter value processing module 30 in the lth iteration (1≤l≤L) first traverses each historical solution in the Table and selects the solution according to the reselection probability q r Add it to the current population gene chain; then perform crossover mutation on the population gene chain with a crossover probability q c The genes at the same position of each two gene chains in the population are exchanged with a mutation probability q v The gene at each position in each gene chain in the population is mutated. The mutation method is: add a small random amount to the original value of the gene at that position to form a new value. Then, for all the changed gene chains, the gene chains are reprojected to the feasible solution domain of ValValidSpace according to the set reprojection method.

[0249] (S5) The first target parameter value processing module 30 calculates all current gene chains in the population according to the evaluation function fit(g k , Θ k ) Obtain the evaluation function value, sort the evaluation function values ​​from large to small, and retain the top N with the largest evaluation value GROUP gene chains, which serve as the initial gene chains of the l+1th iteration of the population.

[0250] (S6) The first target parameter value processing module 30 maintains the table Table, specifically: GROUPAdd the gene chains to the Table, and then sort all the gene chains in the Table again from large to small according to the evaluation function value, and keep the top N GROUP Gene chains form a new Table.

[0251] (S7) The first target parameter value processing module 30 repeatedly executes S4-S6 until L iterations are completed, and then takes out the gene chain g with the highest evaluation function value from the Table. BEST =(x BEST ,y BEST ,f BEST ,r 1BEST ,r 2BEST ,r 3BEST ), and its corresponding evaluation function value fit BEST , if fit BEST <fit 阈值 (the evaluation function threshold is set, for example, 0.999), the optimization result of this round is invalid, and returns to S1 to restart this round of optimization; if fit BEST ≥ fit 阈值 , then the optimization result of this round is valid, and g BEST Add Solutions and follow the g BEST Update VarSpace and enter the next round of optimization.

[0252] The update method is:

[0253] x L =min(x BEST ,x L );

[0254] y L =min(y BEST ,y L );

[0255] f L =min(f BEST ,f L );

[0256] f L =min(f BEST ,f L );

[0257] r 2L =min(r 2BEST ,r 2L );

[0258] r 3L =min(r 3BEST ,r 3L );

[0259] x U =max(x BEST ,x U );

[0260] y U =max(y BEST ,y U );

[0261] f U =max(f BEST ,f U );

[0262] r 1U =max(r 1BEST ,r 1U );

[0263] r 2U =max(r 2BEST ,r 2U );

[0264] r 3U =max(r 3BEST ,r 3U ).

[0265] The first target parameter value processing module 30 performs gridding processing on the target value envelope space in six dimensions, so that each dimension includes a preset number of unit grids, wherein each unit grid corresponds to a first target parameter value.

[0266] For example, when all N GA After the round of genetic algorithm optimization, the 6-element parameter value envelope space VarSpace is obtained, which is divided into N GRID Each unit grid corresponds to a 6-element parameter, so VarSpace is divided into N GRID 6 6 parameters.

[0267] As an embodiment, taking aperture 50 numbered as 50 as an example, its value envelope space VarSpace is finally:

[0268] [29.3,32.1]×[98.1,102.3]×[148.5,151.0]×[1.33,1.45]×[1.32,1.45]×[-1.50,-1.31]

[0269] Divide N in each dimension GRID =10 unit grids, then there are 1,000,000 unit grids in total after division in 6 dimensions.

[0270] The first target parameter value processing module 30 traverses the first target parameter values ​​of all unit grids to calculate the evaluation function value of the evaluation function. When the evaluation function value is greater than the maximum evaluation value in the target global historical optimal solution set, the corresponding first target parameter value is incorporated into the target global historical optimal solution set.

[0271] For example, the first target parameter value processing module 30 traverses the 6-element parameter g corresponding to all unit grids. k , calculate the evaluation function value fit(g k , Θ k ). If fit(g k , Θ k ) is greater than the current most evaluated value in the Solutions set, then the current 6-element parameter g k Added to Solutions.

[0272] The first target parameter value processing module 30 determines the evaluation function value corresponding to the optimal first target parameter value in the target global historical optimal solution set as the first target parameter value.

[0273] For example, after the first target parameter value processing module 30 traverses all unit grids, it takes out the 6-element parameter g with the best evaluation function value in Solutions. k , as the final aperture M k The 6-element parameter.

[0274] As an embodiment, taking aperture 50 as an example, the six-dimensional parameters of aperture 50 are:

[0275] The corresponding evaluation function value is 0.999657349695369.

[0276] The second target parameter value processing module 40 performs an optimization solution based on the first target parameter value and the first preset linear equation to obtain a scale factor.

[0277] For example, let N SEL The apertures are numbered as follows: N SEL The six parameters corresponding to the apertures are For any number 6-element parameter It can be expressed as

[0278] Optionally, the scale factor is calculated as:

[0279]

[0280] Where μ is the scale factor, N SELis the aperture number of the first target aperture, g * k is the first target parameter value, (X * k , Y * k ) are the physical horizontal coordinate and physical vertical coordinate of the first target aperture labeled k on the mask array, N SEL The i-th row of the ×3-dimensional matrix H, N SEL The i-th row of the ×1-dimensional column vector S.

[0281] As an embodiment, H is a 5×3 dimensional matrix, S is a 5×1 dimensional vector, and the finally calculated vector S is [0.002825837, 0.006169453, 1349.2249], (s1, s2, s3) are s1=0.002825837, s2=0.006169453, s3=1349.2249, and the scale factor μ=5.99837.

[0282] The second target parameter value processing module 40 determines a second target parameter value of a second target aperture according to the scale factor and the second preset linear equation, wherein the second target aperture is all apertures among the plurality of apertures except the first target aperture.

[0283] For example, the second target aperture is all the apertures divided by the above N SEL The remaining m apertures of the apertures, The second target parameter value processing module 40 responds to user instructions, constructs a preset linear equation, and optimizes and solves the three-dimensional parameters.

[0284] Optionally, the second target parameter value processing module 40 determines the calculation formula of the second target parameter value of the second target aperture according to the scale factor and the second preset linear equation as follows:

[0285] x * m =u1

[0286] y * m =u2

[0287] f * m =s1u1+s2u2+s3

[0288] u=(J T J) -1 J T Q

[0289]

[0290] Among them, x * m ,y * m 、f * m is the second target parameter value, u1 and u2 are the two components of the two-dimensional column vector u, and J is (N SEL -1)×2 dimensional matrix, Q is (N SEL -1)×1-dimensional column vector, m is the index of the second target aperture, (X m , Y m ) are the physical horizontal coordinates and physical vertical coordinates of the aperture labeled m on the mask array, and (s1, s2, s3) are the three components of the column vector S respectively.

[0291] As an embodiment, the 3-dimensional parameter of the aperture 52 numbered 52 obtained in step S7 is:

[0292]

[0293] The target calibration parameter processing module 50 determines the target calibration parameter of the sun sensor according to the first target parameter value of the first target aperture and the second target parameter value of the second target aperture.

[0294] For example, the target calibration parameter processing module 50 determines the target calibration parameters of the sun sensor according to the 6-dimensional parameters of the first target aperture and the 3-dimensional parameters of the second target aperture, and completes the calibration of the sun sensor with the target calibration parameters.

[0295] Through the above exemplary embodiments, in the calibration process of the solar sensor, the present application can determine the first target parameter value of the first target aperture according to the target installation matrix, the conversion relationship and the target measurement value set by preprocessing the imaging images corresponding to multiple rotation angles; and determine the second target parameter value of the second target aperture according to the first target parameter value, the first preset linear equation and the second preset linear equation. The present application can determine the target calibration parameters of the solar sensor through the first target parameter value and the second target parameter value, thereby completing the calibration of the solar sensor.

[0296] The calibration system provided in the present application can solve the problem of insufficient calibration accuracy in the current manual calibration of compound eye structure mask array solar sensors; and the calibration method can be fully automated by the calibration system through the calculation process and framework, and has the characteristics of high automation performance.

[0297] The calibration system provided in the present application can model multiple error factors, and can eliminate small installation errors in the calibration process by combining the errors in the calibration process itself. The calibration method can also consider the three-axis measurement transfer error between the mask and the CMOS plane, and achieve unified optimization of all apertures through a hybrid architecture of multiple optimization algorithms.

[0298] Exemplarily, as an embodiment, when the centroid extraction accuracy is 0.01 pixel, the calibration accuracy of the multi-aperture 3D parameters can reach 0.05 pixel, which is at least 2 orders of magnitude higher than the current manual calibration accuracy of the compound eye structure mask array solar sensor, and can ensure the arc second level solar azimuth measurement accuracy of the compound eye structure mask array solar sensor.

[0299] According to another aspect of the present application, an electronic device is provided, which includes: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors can implement the calibration method described above.

[0300] According to another aspect of the present application, a non-volatile computer-readable storage medium is provided, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the calibration method described above can be implemented.

[0301] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions of the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for calibrating a solar sensor, characterized in that: The sun sensor is arranged on a three-axis turntable device, and a mask array including a plurality of apertures is arranged on the sun sensor. The calibration method comprises: Determining a target mounting matrix of the sun sensor on the three-axis turntable device; Determine the initial rotation angles of the three axes of the three-axis turntable device, and the conversion relationship between the initial rotation angles and the attitude matrix; After the user adjusts the rotation angle of at least two of the three axes according to the preset value each time, acquiring an imaging image corresponding to the current rotation angle of the sun sensor and the three axes; Preprocessing each frame of the imaging image to obtain a set of target measurement values ​​corresponding to each aperture; Determine a first target parameter value of a first target aperture according to the target installation matrix, the conversion relationship and the target measurement value set; Performing an optimization solution based on the first target parameter value and the first preset linear equation to obtain a scale factor; Determine a second target parameter value of a second target aperture according to the scale factor and the second preset linear equation, wherein the second target aperture is all apertures of the plurality of apertures except the first target aperture; A target calibration parameter of the sun sensor is determined according to a first target parameter value of the first target aperture and a second target parameter value of the second target aperture.

2. The calibration method according to claim 1, characterized in that: The preprocessing of each frame of the imaging image to obtain a set of target measurement values ​​corresponding to each aperture includes: Based on each frame of the imaging image, acquiring an imaging light spot corresponding to the aperture; Determine the measurement value of each aperture in each frame of the imaging image according to the centroid coordinates of the imaging light spot and the rotation angles of the three axes; Combining the measured values ​​of each aperture in all the imaging images into a set of measured values; A measurement value set whose elements in the measurement value set are greater than a preset threshold is determined as the target measurement value set.

3. The calibration method according to claim 1, characterized in that: Determining a first target parameter value of a first target aperture according to the target installation matrix, the conversion relationship and the measurement value set comprises: Determine an evaluation function for the first target parameter value according to the target installation matrix, the conversion relationship and the measurement value set; Initializing the initial value of the value envelope space of the first target parameter value to obtain an initial value envelope space; Initialize the global historical optimal solution set to an empty set to obtain an initial global historical optimal solution set; Based on the genetic algorithm framework, the evaluation function is optimized multiple times to update the initial value envelope space and the initial global historical optimal solution set multiple times to obtain the target value envelope space and the target global historical optimal solution set; Gridding the target value envelope space in six dimensions so that each dimension includes a preset number of unit grids, wherein each unit grid corresponds to a first target parameter value; Traversing the first target parameter values ​​of all unit grids to calculate the evaluation function value of the evaluation function, and in the case where the evaluation function value is greater than the maximum evaluation value in the target global historical optimal solution set, incorporating the corresponding first target parameter value into the target global historical optimal solution set; The evaluation function value corresponding to the optimal first target parameter value in the target global historical optimal solution set is determined as the first target parameter value.

4. The calibration method according to claim 3, characterized in that: The calculation formula of the evaluation function for determining the first target parameter value according to the target installation matrix, the conversion relationship and the measurement value set is: g k =(x k ,y k ,f k ,r1,r2,r3) i i =(a i ,b i ,c i ,x i ,y i )∈Θ k R i =R abc (a i ,b i ,c i ) v i =R i ·(R r1 R r2 R r3 )·A·w i Where fit = fit(g k , Θ k ) is the evaluation function, g k is the first target parameter value, θ k is the set of measured values, N k =card(Θ k ), N k is θ k The number of elements in θ i is θ k The i-th measurement value in (a i ,b i ,c i ) are the rotation angles of the three axes of the three-axis turntable device contained in the i-th measurement value, (x i ,y i ) are the centroid coordinates of the first target aperture imaging point contained in the i-th measurement value, R i is the conversion relationship, k is the aperture index in the first target aperture, (x k ,y k ) is the centroid coordinate of the foot of the k-th aperture in the imaging plane, f k is the distance between the kth aperture and its perpendicular foot in the imaging plane, (r1, r2, r3) is the three-axis Euler angle error between the target installation matrix and the actual installation matrix of the sun sensor, A is the target installation matrix, w i To express the three-dimensional direction vector of the beam corresponding to the i-th measurement value in the imaging observation reference system, v i To express the three-dimensional direction vector of the light beam corresponding to the i-th measurement value in the ground inertial reference system, R r1 , R r2 , R r3 are the rotation matrix components around the three axes.

5. The calibration method according to claim 1, characterized in that: The optimization solution is performed based on the first target parameter value and the first preset linear equation to obtain the calculation formula of the scale factor: Where μ is the scale factor, N SEL is the aperture number of the first target aperture, g * k is the first target parameter value, (X * k , Y * k ) are the physical horizontal coordinates and physical vertical coordinates of the first target aperture labeled k on the mask array, N SEL The i-th row of the ×3-dimensional matrix H, f * ni N SEL The i-th row of the ×1-dimensional column vector S.

6. The calibration method according to claim 5, characterized in that: The calculation formula for determining the second target parameter value of the second target aperture according to the scale factor and the second preset linear equation is: x * m =u1 the * m =u2 f * m =s1u1+s2u2+s3 u=(J T J) -1 J T Q Among them, x * m ,y * m 、f * m is the second target parameter value, u1 and u2 are two components of the two-dimensional column vector u, and J is (N SEL -1)×2 dimensional matrix, Q is (N SEL -1)×1 dimensional column vector, m is the index of the second target aperture, (X m , Y m ) are the physical horizontal coordinates and physical vertical coordinates of the aperture labeled m on the mask array, and (s1, s2, s3) are the three components of the column vector S respectively.

7. A solar sensor calibration system, characterized in that: The sun sensor is arranged on a three-axis turntable device, and a mask array including a plurality of apertures is arranged on the sun sensor. The calibration system comprises: A data processing module is used to determine a target installation matrix of the sun sensor on the three-axis turntable device, and to determine an initial rotation angle of three axes of the three-axis turntable device, and a conversion relationship between the initial rotation angle and an attitude matrix; An image processing module, after adjusting the rotation angle of at least two axes of the three axes each time, obtains an imaging image corresponding to the current rotation angle of the sun sensor and the three axes; a first target parameter value processing module, which preprocesses each frame of the imaging image to obtain a target measurement value set corresponding to each aperture, and determines a first target parameter value of a first target aperture according to the target installation matrix, the conversion relationship and the measurement value set; a second target parameter value processing module, performing an optimization solution based on the first target parameter value and a first preset linear equation to obtain a scale factor, and determining a second target parameter value of a second target aperture according to the scale factor and the second preset linear equation, wherein the second target aperture is all apertures among the multiple apertures except the first target aperture; The target calibration parameter processing module determines the target calibration parameter of the sun sensor according to a first target parameter value of the first target aperture and a second target parameter value of the second target aperture.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the calibration method as described in any one of claims 1-6.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the calibration method according to any one of claims 1 to 6 is implemented.

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