Middle and low orbit optical load thermal deformation sight line error correction method

Abstract

The invention relates to the technical field of optical sensor calibration and deep learning, and discloses a medium and low orbit optical load thermal deformation sight line error correction method, which comprises the following steps: S1, constructing an in-orbit camera imaging model based on a fixed star, and obtaining a camera sight line vector according to the imaging model; s2, analyzing reasons of errors caused by sun illumination to camera sight vectors and constructing characteristic parameters; s3, constructing a camera thermal deformation error model, and calculating a thermal deformation error matrix RSTD under different characteristic parameters; s4, the BP neural network is improved, a Newton-Raphson optimizer is used for adjusting the network structure of the BP neural network, and a rime optimization algorithm is used for initializing network parameters of the BP neural network; and S5, taking the characteristic parameters and the thermal deformation error matrix RSTD as input data, training by using the improved BP neural network, and predicting a thermal deformation error. According to the invention, camera sight line offset errors can be reduced, and the problem of frequent on-orbit camera sight line correction is solved.

Description

Technical Field

[0001] The present invention relates to the field of optical sensor calibration and deep learning technology, and in particular to a method for correcting thermal deformation line of sight errors of optical payloads in medium and low orbits. Background Art

[0002] In recent years, medium and low orbit cameras have been widely used in remote sensing, earth observation, astronomical observation and space surveillance. Medium and low orbit optical payloads provide new functions that are not covered by geostationary orbit observation missions, such as the ability to continuously monitor moving targets and wide-area detection, which is an important means of early warning and monitoring of space targets. The target monitoring and positioning accuracy of space optical payloads depends on the line of sight pointing accuracy of the camera. Due to the short operation cycle of medium and low orbits and the influence of sunlight, the incident light of the optical payload and the space thermal environment change dramatically and irregularly in a short period of time. This causes optical distortion inside the camera, and the installation matrix based on the satellite also changes. These changes ultimately affect the imaging process and line of sight accuracy of the camera, which is greatly unfavorable to the high-precision indication and positioning of the target. Correcting the line of sight accuracy problem of optical payloads caused by space thermal deformation is of great significance for the detection, tracking, positioning and indication of space targets.

[0003] The orbital period of medium and low orbit satellites is short, and the operating temperature changes are relatively stable compared to high orbit satellites, but the regularity is poor. Therefore, it is more difficult to correct the line of sight error caused by space thermal deformation on the optical payload of medium and low orbit satellites. At present, there are few studies on the correction of line of sight pointing errors of medium and low orbit cameras. In 1996, a six-state extended Kalman filter algorithm based on star observation data was proposed to correct the attitude error of spacecraft. In 2008, Leprince et al. proposed a line of sight correction method for medium and low orbit push-broom cameras. This method is based on the Earth Observation Satellite (SPOT) 4-HRV1 sensor for modeling, but it mainly solves the mechanical strain caused by satellite launch rather than the influence of space thermal deformation. For the narrow field of view tracking sensor of the space-based optoelectronic system, Clemons et al. proposed an integrated target tracking and positioning algorithm. While tracking the target, the background star observation is used to correct the line of sight pointing in real time. This method can eliminate the influence of dynamic changes in line of sight pointing error in real time, effectively improving the timeliness of the entire system and the accuracy of target tracking and positioning. In addition, in order to solve the camera distortion problem caused by the jitter and attitude fluctuation of the medium and low orbit satellite platform, the geometric pointing accuracy of the bundle method regional block adjustment of the Resource-3 (ZY-3) satellite can reach 3.5 pixels and 1.8 pixels in plane measurement and elevation respectively in the absence of ground control points (GCPs); in the presence of high-precision GCPs, the plane measurement and elevation can reach 1.5 pixels and 0.8 pixels respectively. However, these correction methods for camera geometric positioning errors are based on the premise that there are certain laws in the error changes, and are not very suitable for solving the camera line of sight pointing offset caused by complex space thermal deformation. There is an obvious gap in the field of thermal deformation line of sight error correction of medium and low orbit optical payloads. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, a method for correcting thermal deformation line of sight errors of medium and low orbit optical payloads provided by the present invention can use an improved BP neural network to map the relationship between the spatial thermal environment and the thermal deformation error matrix, reduce the camera line of sight offset, greatly improve the camera's line of sight correction accuracy, and solve the problem of frequent on-orbit correction.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for correcting the thermal deformation line of sight error of medium and low orbit optical payloads, comprising the following steps:

[0006] S1, construct an imaging model of an on-orbit camera based on stars, and obtain the camera sight vector according to the imaging model;

[0007] S2, analyzing the cause of the error caused by sunlight to the camera sight vector and constructing characteristic parameters;

[0008] S3, build the camera thermal deformation error model and calculate the thermal deformation error matrix R under different characteristic parameters STD ;

[0009] S4, improve the BP neural network, use the Newton-Raphson optimizer to adjust the network structure of the BP neural network, and use the rime optimization algorithm to initialize the network parameters of the BP neural network;

[0010] S5: The characteristic parameters in S2 and the thermal deformation error matrix R in S3 STD As input data, the input data is trained using the improved BP neural network in S4 to predict the thermal deformation error and complete the correction of the camera line of sight error.

[0011] Furthermore, the above-mentioned method for correcting the thermal deformation line of sight error of the medium and low orbit optical payload, S1 is specifically as follows:

[0012] S11: Construct an internal positioning model of the low-medium orbit camera imaging model, use the internal positioning model to transform the pixel coordinate system into the camera coordinate system, and obtain the camera exit vector in the camera coordinate system;

[0013] S12: Construct an external positioning model for the low- and medium-orbit camera imaging model, use the external positioning model to transform the camera coordinate system into the celestial coordinate system, and obtain the camera exit vector in the celestial coordinate system. The camera exit vector in the celestial coordinate system is the camera line of sight vector.

[0014] Furthermore, the above-mentioned method for correcting the thermal deformation line of sight error of the medium and low orbit optical payload, S2 is specifically as follows:

[0015] S21: Calculate the angle θ between the sun vector and the satellite vector in the celestial coordinate system Sun-Sat , angle θ Sun-Sat It is the characteristic parameter of the influence of the change of space thermal environment temperature caused by sunlight on the thermal deformation of the load;

[0016] S22: Calculate the angle θ between the sun vector and the camera sight vector in the celestial coordinate system Sun-LOS , angle θ Sun-LOS It is the characteristic parameter of the effect of sunlight on the camera optical distortion model.

[0017] Furthermore, in the above-mentioned method for correcting the thermal deformation line of sight error of the medium and low orbit optical payload, the method for constructing the camera thermal deformation error model in S3 is specifically as follows:

[0018] S31, thermal deformation error matrix R under assumed space thermal environment STD Only with two characteristic parameters θ Sun-Sat and θ Sun-LOS The thermal deformation error matrix R under a fixed spatial thermal environment is related to STD same;

[0019] S32, construct characteristic parameters and thermal deformation error matrix R STD The mapping relationship between them.

[0020] Furthermore, the improved method in S4 of the above-mentioned method for correcting thermal deformation line of sight errors of medium and low orbit optical payloads is specifically as follows:

[0021] S41: Use Newton-Raphson optimizer to adjust the learning rate, convergence error and number of hidden layer nodes in BP neural network;

[0022] S42: using the rime optimization algorithm to initialize the weights and biases of the BP neural network, the weight values ​​include the weight values ​​from the input layer to the hidden layer and between each hidden layer, and the bias values ​​include the bias values ​​of each hidden layer.

[0023] Furthermore, the above-mentioned method for correcting the thermal deformation line of sight error of the medium and low orbit optical payload, S5 is specifically as follows:

[0024] S51: The two characteristic parameters θ Sun-Sat and θ Sun-LOS As an independent variable, the thermal deformation error matrix R STD As the dependent variable, a data set was made;

[0025] S52: dividing the data set into a training set and a test set in a ratio of 7:3, inputting the training set into the improved BP neural network for training, and obtaining a network representing the mapping relationship between the characteristic parameters and the thermal deformation error matrix;

[0026] S53: Input the independent variable part of the test set into the trained network and predict the dependent variable thermal deformation error matrix R STD ;

[0027] S54: Using the thermal deformation error matrix R STD Correct the camera sight vector. The correction formula is (1):

[0028] LOS J-correct =R STD ·LOS J-cal (1);

[0029] Among them, LOS J-cal Calculate the value for the camera's sight vector, LOS J-correct is the correction value of the camera's sight vector.

[0030] The beneficial effects of the present invention are as follows: the present invention proposes to use the angular relationship between the sun vector, the satellite vector and the camera sight line vector to characterize the thermal environment in which the payload is located, which provides the possibility for quantitative analysis of the complex factor of the space thermal environment, and overcomes the problem that the sight line errors of medium and low orbit payloads are irregular and require frequent correction.

[0031] The present invention uses a deep learning network to participate in the camera's line of sight correction problem, and uses the complexity of the neural network to solve the mapping relationship between the spatial thermal environment and the camera's line of sight offset, thereby greatly improving the camera's line of sight correction accuracy.

[0032] The present invention improves the BP neural network, uses a Newton-Raphson optimizer network structure for optimization, and simultaneously initializes network parameters using a rime optimization algorithm, thereby improving network performance and reducing the possibility of overfitting. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flowchart of the method is shown in FIG.

[0034] Figure 2 Schematic diagram of the camera's internal orientation model;

[0035] Figure 3 Schematic diagram of focal plane coordinate system and ideal focal plane coordinate system;

[0036] Figure 4 Schematic diagram of the camera's exterior orientation model;

[0037] Figure 5 Schematic diagram of the angle between the sun vector and the satellite vector;

[0038] Figure 6 Schematic diagram of the angle between the sun vector and the camera sight vector. DETAILED DESCRIPTION

[0039] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0040] like Figure 1-6 As shown, this embodiment provides a method for correcting thermal deformation line of sight errors of medium and low orbit optical payloads, including the following steps:

[0041] S1, construct an on-orbit camera imaging model based on stars. The camera imaging model includes an internal positioning model and an external positioning model. The camera sight vector is obtained according to the imaging model.

[0042] S11: Construct an internal positioning model of the low-medium orbit camera imaging model, use the internal positioning model to transform the pixel coordinate system into the camera coordinate system, and obtain the camera exit vector in the camera coordinate system;

[0043] like Figure 2 As shown in the figure, it is a schematic diagram of the internal positioning model, where O1-ij is the pixel coordinate system, O2-uv is the focal plane coordinate system, O4-xyz represents the ideal space coordinate system, and O5-X cp Y cp Z cp is the camera coordinate system. The pixel coordinate system O1-ij directly corresponds to the output image data of the camera. We define its origin at the upper left corner of the image, the j coordinate axis is downward along the image column direction, and the i coordinate axis is to the right along the image row direction. The pixel coordinate system is in pixels, and the coordinate origin is (1, 1). The focal plane coordinate system O2-uv is established on the basis of the pixel coordinate system. Its function is to calculate the physical distance of each pixel relative to the center of the focal plane. The u and v coordinate axes of the focal plane coordinate system are parallel to the i and j coordinate axes of the pixel coordinate system, respectively, and the origin is at the physical position corresponding to the center of the pixel coordinate system.

[0044] Optical distortion is ubiquitous in the design, manufacture and assembly of optical instruments. Therefore, a virtual coordinate system for distortion correction is introduced, namely the ideal focal plane coordinate system O3-u'v'. Assuming that the camera distortion model is D(u, v), the root distortion theory, the corresponding relationship between the coordinate systems O3-u'v' and O2-uv is as follows:

[0045]

[0046] Where (u, v) and (u′, v′) represent the image point coordinates in the focal plane coordinate system and the actual image point coordinates in the ideal focal plane coordinate system, respectively. Figure 3 It is a schematic diagram of the focal plane coordinate system and the ideal focal plane coordinate system.

[0047] Assume that the coordinates of the origin O3 in the O2-uv coordinate system are (u0, v0), which represents the center of the ideal image plane of the camera, that is, the position of the camera principal point. The distortion correction parameters corresponding to a certain pixel are expressed as (Δu(u, v), Δv(u, v)), then the distortion correction model of the pixel is expressed as

[0048]

[0049] The ideal spatial coordinate system O4-xyz is a three-dimensional coordinate system with its origin O4 located at the camera projection center. It is used to convert the two-dimensional coordinates (u', v') in the ideal focal plane into the three-dimensional coordinate system (u', v', f). Among them, f is the camera principal distance. The x and y axes are parallel to u' and v' respectively.

[0050] Camera coordinate system O5-X cp Y cp Z cpThe origin of is located at the geometric center of the camera reference prism. The transformation matrix R between the camera output vector and the camera coordinate system in the ideal space coordinate system is IS2C It is related to the reflection matrix of the pointing mirror inside the camera and its azimuth and elevation angles, and needs to be calibrated according to the theodolite station.

[0051] The conversion process of the internal positioning model is expressed as follows:

[0052]

[0053] Among them, LOS cam represents the camera exit vector corresponding to the image point (i, j) in the camera coordinate system; Du(u ij , v ij ) and Dv(u ij , v ij ) represents the distortion correction parameter of the pixel corresponding to the image point (i, j); dx and dy are the pixel sizes.

[0054] S12: Construct an external positioning model for the low- and medium-orbit camera imaging model, and use the external positioning model to transform the camera coordinate system into the celestial coordinate system (J2000.0) to obtain the camera exit vector in the celestial coordinate system. The camera exit vector in the celestial coordinate system is the camera line of sight vector.

[0055] like Figure 4 The figure shows a schematic diagram of the external positioning model, where O6-X c Y c Z c Represents the camera coordinate system, O7-X s Y s Z s Indicates the satellite body coordinate system, O8-X o Y o Z o is the satellite orbit coordinate system, O9-X J Y J Z J It is the celestial coordinate system. The camera is installed on the satellite platform, and the rotation axis of the camera turntable has installation angles of α, β and γ relative to the three coordinate axes of the satellite body coordinate system. Therefore, the camera coordinate system O6-X c Y c Z c Convert to the satellite coordinate system O7-X s Y s Z s The installation matrix is ​​denoted as R c2s (α, β, γ). Satellite body coordinate system O7-X s Y s Z s Transform to orbital coordinate system O8-X oY o Z o It is necessary to know the three-axis attitude of the satellite (pitch, roll and yaw), and its coordinate transformation matrix is ​​represented by R s2o (pitch, roll, yaw). The X-axis of the satellite orbital coordinate system points to the tangent direction of the satellite's motion, and the Z-axis points to the center of the earth. The coordinate axes conform to the right-hand spiral law. The orbital coordinate system and the celestial coordinate system O9-X J Y J Z J The conversion relationship between (J2000.0) depends on the orbital parameters of the satellite, that is, the instantaneous position vector and velocity vector of the satellite, and its conversion matrix is ​​expressed as R o2J .

[0056] According to the above description, the outgoing vector of the pixel in the detector array is expressed in the celestial coordinate system as follows:

[0057] LOS J =R o2J ·R s2o (pitch, roll, yaw)·R c2s (α, β, γ) LOS cam

[0058] Among them, LOS J It represents the camera exit vector corresponding to the image point (i, j) in the celestial coordinate system, that is, the camera sight vector at that moment.

[0059] S2, analyzing the cause of the error caused by sunlight to the camera sight vector and constructing characteristic parameters;

[0060] According to the camera imaging process, the line of sight pointing error is divided into internal orientation parameter error and external orientation parameter error. The internal orientation parameter error refers to the calibration error of the camera's principal point, principal distance, pixel distortion and other parameters caused by the change of the optical system distortion model after working in orbit. In addition, the internal orientation parameter error also includes the image centroid position extraction error and the measurement error of the camera's internal pointing mirror parameters. The external orientation parameter error mainly includes the camera installation error, satellite attitude and orbit parameter measurement error. In addition, there are errors caused by changes in the space thermal environment, vibration of the satellite platform and time asynchrony.

[0061] The measurement error in the process of determining the line of sight is a random error. This type of error usually has the characteristics of zero mean and fixed variance, and does not change much over time and remains basically unchanged. The effective correction method is to use more precise measuring instruments to reduce the impact on the line of sight.

[0062] Thermal deformation error and camera installation error are system errors, which will cause the corresponding measurement value to have a certain range of offset errors. Among them, due to the influence of spatial thermal deformation, the optical distortion model and camera installation matrix change dramatically, which has a huge impact on the line of sight accuracy.

[0063] Considering that sunlight is the main cause of thermal deformation of the payload, it has the greatest impact on the temperature environment and optical distortion model of the payload. The parameters that can directly reflect the impact of illumination are the real-time position of the satellite, the satellite attitude, and the corresponding camera attitude. However, the satellite orbit is actually constantly changing, and the orbital position of each operating cycle is different. It is almost impossible to complete the illumination analysis of the entire orbit. Moreover, the orbits of different satellites are different. If the satellite position parameters are used as the analysis object, it does not have the universality of low-orbit optical payloads. The analysis of the satellite attitude and the camera attitude at the corresponding moment is more complicated. It is necessary to analyze the camera attitude based on the installation matrix and other information when the satellite attitude is changing. The analysis of these parameters is essentially to explore the impact of the incident light of the camera on optical distortion.

[0064] In order to make the proposed method universal for low-orbit optical payloads and simplify the calculation and correction process, two parameters that can reflect the effect of sunlight on the payload are proposed for analysis: in the celestial coordinate system, the angle θ between the sun position vector and the satellite position vector Sun-Sat ; In the celestial coordinate system, the angle θ between the sun position vector and the camera sight vector Sun-LOS The former reflects the effect of the change of ambient temperature caused by sunlight on the thermal deformation of the load. The latter represents the effect of sunlight on the optical distortion model.

[0065] S21: Calculate the angle θ between the sun vector and the satellite vector in the celestial coordinate system (J2000.0) Sun-Sat , angle θ Sun-Sat It is the characteristic parameter of the influence of the change of space thermal environment temperature caused by sunlight on the thermal deformation of the load;

[0066] like Figure 5 As shown, it is the angle θ between the sun vector and the satellite vector Sun-Sat Schematic diagram of .

[0067] First, calculate the sun's position vector in the celestial coordinate system, that is, the vector from the center of the earth to the center of the sun. First, convert the current time t into the Julian century number T starting from j2000.0. Then, the current sun's geometric mean ecliptic longitude L can be calculated based on the Julian century number. sun and the solar anomaly M sun , we get the solar intermediate equation C sun expression.

[0068] C sun =(1.914600-0.004817·T-0.000014·T 2 )·sin(M sun )+(0.019993-0.000101·T)·sin(2·M sun )+0.000290·sin(3·M sun )

[0069] Θ sun =L sun +C sun

[0070] V sun =M sun +C sun

[0071] Among them, Θ sun 、V sun are the true ecliptic longitude and true anomaly of the sun respectively. sun The nutation and aberration corrections are performed to obtain the solar ecliptic longitude λ in the ecliptic coordinate system. sun :

[0072] λ sun =Θ sun -0.00569-0.00478·sin(125.04-1934.136·T)

[0073] Calculate the true obliquity of the ecliptic ε sun :

[0074] ε sun =ε0+Δε

[0075]

[0076] Among them, ε0 is the intersection angle of the mean ecliptic and obliquity, and Δε is the nutation correction of the intersection angle.

[0077] The apparent right ascension and declination of the Sun at time t (δ sun , σ sun ) is expressed as:

[0078]

[0079] Right ascension and declination (δ, σ) and the vector (X J , Y J , Z J ) are as follows:

[0080]

[0081] Combining the above formula, we can calculate the three-dimensional vector of the sun in the celestial coordinate system:

[0082] P sun =(X sun , Y sun , Z sun ).

[0083] Assume that the instantaneous position vector of the satellite at the corresponding moment is P sat =(X sat , Y sat , Z sat ), then the angle θ between the sun vector and the satellite vector Sun-Sat The expression is:

[0084]

[0085] Among them, θ Sun-Sat The range is (-180°~180°).

[0086] S22: Calculate the angle θ between the sun vector and the camera sight vector in the celestial coordinate system (J2000.0) Sun-LOS , angle θ Sun-LOS It is the characteristic parameter of the effect of sunlight on the camera optical distortion model.

[0087] like Figure 6 As shown, it is the angle θ between the sun vector and the camera sight vector Sun-LOS Schematic diagram.

[0088] In the celestial coordinate system (J2000.0), the sun vector P sun =(X sun , Y sun , Z sun ) and the camera sight vector LOS J The angle θ between Sun-LOS It is expressed as:

[0089]

[0090] Among them, θ Sun-LOS The angle range is (0°~180°).

[0091] S3, build the camera thermal deformation error model and calculate the thermal deformation error matrix R under different characteristic parameters STD ;

[0092] In the coordinate transformation process, the error caused by small angles (<1000μrad) can usually be described using the following approximate relationship:

[0093] R(n+Δn)R(ξ+Δξ)=R(n)R(Δn)R(ξ)R(Δξ)≈R(Δn)R(Δξ)R(n)R(ξ)

[0094] ≈R(Δη+Δξ)R(η)R(ξ)

[0095] Based on the above description, the inaccurate load line of sight caused by thermal deformation can be equivalent to the thermal deformation error matrix R STD Problem. Assume that the thermal deformation error matrix in the space thermal environment is only related to the two characteristic parameters θ proposed in S2 Sun-Sat and θ Sun-LOS The thermal deformation error matrix under the fixed space thermal environment is the same. Based on the above assumptions, it is only necessary to construct the mapping relationship between the characteristic parameters and the error matrix to realize the correction of the thermal deformation error. Therefore, the correction problem of the thermal deformation error of the medium and low orbit load is transformed into the estimation problem of the thermal deformation error matrix under a specific environment.

[0096] The theoretical values ​​of right ascension and declination of stars determined from the star catalog are (δ star , σ star ), calculate the true value LOS of the camera's sight vector J-star :

[0097]

[0098] Thermal deformation error matrix R STD The calculation formula is:

[0099] LOS J-star =R STD (φ,ω)·LOS J-cal

[0100] Among them, LOS J-cal is the calculated value of the camera sight vector, which is calculated according to the imaging model in S1.

[0101] S4, improve the BP neural network, use the Newton-Raphson optimizer to adjust the network structure of the BP neural network, and use the rime optimization algorithm to initialize the network parameters of the BP neural network;

[0102] S41: Use Newton-Raphson optimizer to adjust the learning rate, convergence error and number of hidden layer nodes in BP neural network;

[0103] The Newton-Raphson optimizer is a new meta-heuristic algorithm that uses the Newton-Raphson search rule and the trap avoidance operator to complete the entire search process and explore the best results. The Newton-Raphson search method can improve the exploration ability and increase the convergence speed to reach an improved search space position. The trap avoidance operator helps avoid the local optimum trap.

[0104] S42: using the rime optimization algorithm to initialize the weights and biases of the BP neural network, the weight values ​​include the weight values ​​from the input layer to the hidden layer and between each hidden layer, and the bias values ​​include the bias values ​​of each hidden layer.

[0105] The rime optimization algorithm is an efficient optimization algorithm based on the physical phenomenon of ice formation. This algorithm simulates the soft atomization and hard atomization growth process of rime, constructs a soft atomization search strategy and a hard atomization penetration mechanism, and realizes the exploration and utilization behavior in the optimization method. At the same time, an active greedy selection mechanism is used, and the population is updated in the stage of selecting the optimal solution to try to avoid the algorithm from falling into the local optimum.

[0106] S5: The characteristic parameters in S2 and the thermal deformation error matrix R in S3 STD As input data, the input data is trained using the improved BP neural network in S4 to predict the thermal deformation error and complete the correction of the camera line of sight error.

[0107] S51: The two characteristic parameters θ Sun-Sat and θ Sun-LOS As an independent variable, the thermal deformation error matrix R STD As the dependent variable, a data set was made;

[0108] S52: dividing the data set into a training set and a test set in a ratio of 7:3, inputting the training set into the improved BP neural network for training, and obtaining a network representing the mapping relationship between the characteristic parameters and the thermal deformation error matrix;

[0109] S53: Input the independent variable part of the test set into the trained network and predict the dependent variable thermal deformation error matrix R STD ;

[0110] S54: Using the thermal deformation error matrix R STD Correct the camera sight vector. The correction formula is:

[0111] LOS J-correct =R STD ·LOS J-cal ;

[0112] Among them, LOS J-cal Calculate the value for the camera's sight vector, LOS J-correct is the correction value of the camera's sight vector.

[0113] In this embodiment, in order to verify the effectiveness of the proposed method for correcting the optical payload space thermal deformation line of sight error, 90 sets of on-orbit data were used for verification.

[0114] The total absolute error of the camera's line of sight is calculated as follows:

[0115]

[0116] The average total absolute error of the camera line of sight after correction is 0.0011 rad, which is 80.28% higher than 0.0056 rad before correction. The experimental results show that the proposed algorithm has a significant correction effect on the camera line of sight error caused by thermal deformation.

[0117] The above specific embodiments are merely explanations of the present invention and are not limitations of the present invention. After reading this specification, those skilled in the art may make modifications to the embodiments without any creative contribution as needed. However, such modifications are protected by the patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A method for correcting thermal deformation line of sight errors of medium and low orbit optical payloads, characterized in that: The following steps are involved: S1, construct an imaging model of an on-orbit camera based on stars, and obtain the camera sight vector according to the imaging model; S2, analyzing the cause of the error caused by sunlight on the camera sight vector and constructing characteristic parameters; S3, build the camera thermal deformation error model and calculate the thermal deformation error matrix R under different characteristic parameters STD ; S4, improve the BP neural network, use the Newton-Raphson optimizer to adjust the network structure of the BP neural network, and use the rime optimization algorithm to initialize the network parameters of the BP neural network; S5: Combine the characteristic parameters in S2 and the thermal deformation error matrix R in S3 STD As input data, the input data is trained using the improved BP neural network in S4 to predict the thermal deformation error and complete the correction of the camera line of sight error.

2. The method for correcting thermal deformation line of sight errors of optical payloads in medium and low orbits according to claim 1, characterized in that: The S1 is specifically: S11: Construct an internal positioning model of the low- and medium-orbit camera imaging model, use the internal positioning model to transform the pixel coordinate system into the camera coordinate system, and obtain the camera exit vector in the camera coordinate system; S12: Construct an external positioning model for the low- and medium-orbit camera imaging model, use the external positioning model to transform the camera coordinate system into the celestial coordinate system, and obtain the camera exit vector in the celestial coordinate system. The camera exit vector in the celestial coordinate system is the camera line of sight vector.

3. The method for correcting thermal deformation line of sight errors of optical payloads in medium and low orbits according to claim 1, characterized in that: The S2 is specifically: S21: Calculate the angle θ between the sun vector and the satellite vector in the celestial coordinate system Sun-Sat , the angle θ Sun-Sat It is the characteristic parameter of the influence of the change of space thermal environment temperature caused by sunlight on the thermal deformation of the load; S22: Calculate the angle θ between the sun vector and the camera sight vector in the celestial coordinate system Sun-LOS , the angle θ Sun-LOS It is the characteristic parameter of the effect of sunlight on the camera optical distortion model.

4. The method for correcting thermal deformation line of sight errors of optical payloads in medium and low orbits according to claim 1, characterized in that: The method for constructing the camera thermal deformation error model in S3 is specifically as follows: S31, thermal deformation error matrix R under assumed space thermal environment STD Only with two characteristic parameters θ Sun-Sat and θ Sun-LOS The thermal deformation error matrix R under a fixed spatial thermal environment is related to STD same; S32, construct characteristic parameters and thermal deformation error matrix R STD The mapping relationship between them.

5. The method for correcting thermal deformation line of sight errors of optical payloads in medium and low orbits according to claim 1, characterized in that: The improved method in S4 is specifically as follows: S41: Use Newton-Raphson optimizer to adjust the learning rate, convergence error and number of hidden layer nodes in BP neural network; S42: Initialize the weights and biases of the BP neural network using the rime optimization algorithm, wherein the weight values ​​include weight values ​​from the input layer to the hidden layer and between each hidden layer, and the bias values ​​include bias values ​​of each hidden layer.

6. The method for correcting thermal deformation line of sight errors of optical payloads in medium and low orbits according to claim 1, characterized in that: The S5 is specifically: S51: The two characteristic parameters θ Sun-Sat and θ Sun-LOS As an independent variable, the thermal deformation error matrix R STD As the dependent variable, a data set was made; S52: dividing the data set into a training set and a test set in a ratio of 7:3, inputting the training set into the improved BP neural network for training, and obtaining a network representing the mapping relationship between the characteristic parameters and the thermal deformation error matrix; S53: Input the independent variable part of the test set into the trained network and predict the dependent variable thermal deformation error matrix R STD ; S54: Using the thermal deformation error matrix R STD Correct the camera sight vector, the correction formula is formula (1): THE J-correct =R STD ·THE J-cal (1); Among them, LOS J-cal Calculate the value for the camera's sight vector, LOS J-correct is the correction value of the camera's sight vector.

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