A Multi-Objective Optimization-Based Cold-Air Calibration Attitude Planning Method for Spaceborne Microwave Radiometers

Abstract

This invention relates to a multi-objective optimization-based attitude planning method for cold-space calibration of a spaceborne microwave radiometer, belonging to the field of satellite microwave remote sensing. It establishes a satellite attitude kinematics model and attitude dynamics model using a geocentric equatorial inertial coordinate system, an orbital coordinate system, and a satellite body coordinate system; and establishes a model of the total brightness temperature received by the microwave radiometer during cold-space calibration. Taking into account the total brightness temperature received by the microwave radiometer, brightness temperature fluctuations, platform maneuverability, energy, and the field of view of the satellite sensor, a multi-objective optimization model considering constraints is established for cold-space calibration of the microwave radiometer. This model is then solved, constraints are established, and a solution or solution set is obtained. This invention uses multi-objective optimization to achieve optimal total brightness temperature and brightness temperature fluctuations within the microwave radiometer observation area during cold-space calibration, improving the on-orbit cold-space calibration effect and system reliability of the spaceborne microwave radiometer.

Description

Technical Field

[0001] This invention belongs to the field of satellite microwave remote sensing, specifically involving a cold-space calibration attitude planning method for spaceborne microwave radiometers based on multi-objective optimization. Background Technology

[0002] The radiometric calibration accuracy of spaceborne microwave radiometers has a significant impact on the accuracy and stability of remote sensing data. Traditional calibration methods mainly rely on internal calibration sources or ground equipment, which makes it difficult to achieve comprehensive calibration of large-size antennas, resulting in insufficient calibration accuracy. Due to the stable brightness temperature and uniform spatial distribution of the cold space background, it is widely used for the on-orbit absolute calibration of spaceborne microwave radiometers. By maneuvering the satellite attitude to point the microwave radiometer antenna towards the cold space, the entire system of the microwave radiometer can be calibrated, improving calibration accuracy and consistency.

[0003] During cold-space calibration, microwave radiometers need to complete effective observations of the cold-space region within a limited time window. Existing cold-space calibration methods typically first determine the preset external calibration area, and then use the antenna pattern corresponding to the microwave radiometer to perform a weighted summation of the preset observation area to obtain the total brightness temperature value. This method cannot guarantee the optimal summation and fluctuation of the load brightness temperature during calibration. Furthermore, existing methods are mostly focused on single-objective optimization to ensure the maximum observation time or minimum energy consumption, or are designed under simple geometric constraints, making it difficult to meet the optimization requirements of multiple performance indicators and complex state constraints. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-objective optimization-based attitude planning method for cold-space calibration of a spaceborne microwave radiometer. This method considers the impact of interference information such as the cosmic background on cold-space observations, as well as constraints such as the satellite's on-orbit attitude maneuverability, energy, and the field of view of the satellite's sensors. By employing multi-objective optimization, the method aims to optimize the total brightness temperature and brightness temperature fluctuation within the observation area of ​​the microwave radiometer during the cold-space calibration process, thereby improving the on-orbit cold-space calibration effect and system reliability of the spaceborne microwave radiometer.

[0005] The above-mentioned objectives of the present invention are mainly achieved through the following technical solutions:

[0006] A multi-objective optimization-based cold-space calibration attitude planning method for spaceborne microwave radiometers includes the following steps:

[0007] (1) Establish the satellite attitude kinematics model and attitude dynamics model based on the geocentric equatorial inertial coordinate system, orbital coordinate system and satellite body coordinate system;

[0008] (2) Establish a model of the sum of brightness temperatures received by the microwave radiometer during the cold air calibration process;

[0009] (3) Based on the satellite attitude kinematics model and attitude dynamics model obtained in step (1), and the total brightness temperature model received by the microwave radiometer during the cold air calibration process obtained in step (2), a multi-objective optimization model considering constraints for cold air calibration of the microwave radiometer is established based on multiple constraints.

[0010] (4) Solve the multi-objective optimization model obtained in step (3), set the constraint boundary, and obtain the solution or solution set.

[0011] In step (1), the posture kinematic model is as follows:

[0012]

[0013] Where t is time and q is the attitude quaternion in the reference inertial frame. q0 is the scalar part of q. q is the vector part; ω is the satellite's three-axis attitude angular velocity. × This is the cross product matrix of the attitude angular velocities;

[0014] The attitude dynamics model is as follows:

[0015]

[0016] Where J is the rotational inertia matrix of the spacecraft, u is the three-axis control torque during the spacecraft's attitude maneuver, and d is the sum of the torques exerted on the spacecraft by external disturbances.

[0017] In step (2), the total brightness temperature model received by the microwave radiometer during cold-space calibration includes the total brightness temperature model received by the radiometer and the total brightness temperature model of the brightness temperature sphere with region segmentation in the celestial coordinate system, wherein,

[0018] Model of total brightness temperature received by radiometer:

[0019]

[0020] Where G is the antenna gain, T a T is the total brightness temperature received by the radiometer. ap Let represent the brightness temperature of the cold space region and the brightness temperature of the surface region, and p represent the satellite's radius vector in the J2000 system. Let Ω be the angular coordinate of the antenna beam direction, and let Ω be the domain of the angular coordinates. φ is the latitude of the satellite's nadir point, λ is the longitude of the satellite's nadir point, and δ d δ is the declination of the celestial sphere. r Right ascension of the celestial sphere;

[0021] This leads to the overall brightness temperature model of the brightness temperature sphere with region segmentation in the celestial coordinate system:

[0022]

[0023] Among them, T ap earth T ap sky Brightness temperatures of Earth and cold space, respectively, Ω sky Ω earth These are the angular coordinate domains for the cold air region and the Earth's surface region, respectively.

[0024] In step (3), the method for establishing the multi-objective optimization model is as follows:

[0025] (1) During the satellite's on-orbit cold-space calibration attitude maneuver, the goal is to optimize the sum of the integrated brightness temperature values ​​and brightness temperature fluctuations of the microwave radiometer pattern, while minimizing the satellite's attitude angular velocity and angular acceleration, i.e.:

[0026] min F(p,q)=[T a ,||ω||,||α]

[0027] Where α represents the satellite's three-axis attitude angular acceleration;

[0028] (2) Considering the overall structural safety of the satellite during attitude maneuvers, platform maneuver constraints, and requirements for the total integrated brightness temperature and brightness temperature fluctuation of the antenna pattern, namely:

[0029] ||ω||≤ω max

[0030] ||α||≤α max

[0031] T a ≤T amax

[0032]

[0033] in, The root mean square error of the sum of brightness temperatures received by the radiometer during the tp time period;

[0034] (3) During the satellite's attitude maneuver, ensure that the Earth, Moon, and Sun are not visible within the field of view of at least one star sensor, i.e.:

[0035]

[0036] in, τ k The angle between the optical axis of the star sensor and the Earth, Sun, and Moon. For the j-th star-sensitive optical axis direction, the field of view of the Earth, Sun, and Moon must not be visible. h(τ) k ) is τk The semi-cone angle of the visible area of ​​Earth relative to the satellite at any given time satisfies:

[0037]

[0038] Wherein, the Earth's radius is R e Atmospheric thickness is R a ,r(τ k ) is τ k The radius vector from the Earth's center of mass to the satellite's center of mass at any given moment;

[0039] (4) To ensure satellite energy security, the normal direction of the satellite's solar panels must be as parallel as possible to the solar vector, that is:

[0040]

[0041] in, For τ k The angle between the solar panel normal and the solar vector at any given moment, β S This is the maximum allowable angle between the solar panel normal and the solar vector;

[0042] (5) Based on the constraints and optimization objectives described in steps (1) to (4), consider the time window [t0,t] for satellite cold space calibration. f The following multi-objective optimization model is obtained.

[0043] min F(p,q)=[T a ,||ω||,||α||]

[0044]

[0045] In step (4), a multi-objective optimization method is used to solve the multi-objective, multi-constraint attitude planning problem during cold air calibration. For this type of constrained multi-objective optimization problem, it is necessary to simultaneously satisfy multiple optimal performance indicators and complex constraints. The solution methods mainly include the following categories:

[0046] The first category is penalty function-based methods, which introduce penalty terms to infeasible solutions, transforming the original problem into an equivalent unconstrained multi-objective problem, and then combining it with conventional evolutionary algorithms for solution. The second category is feasibility-priority selection strategies, widely used in typical multi-objective algorithms such as NSGA-II and MOEA / D. These methods typically employ the Deb criterion, prioritizing feasible solutions during evolution and sorting infeasible solutions according to the degree of constraint violation, ensuring the population converges to the feasible region. The third category is transformation methods based on ε-constraints, which select a primary optimization objective, transforming other objectives into constraints, setting allowable thresholds for each objective, and guiding the search process by adjusting the value of ε, flexibly controlling the distribution characteristics of the Pareto front. The fourth category is decomposition-based optimization methods, which decompose the original problem into multiple constrained single-objective subproblems for individual solution, exhibiting good parallelism and scalability, and are particularly suitable for high-dimensional objective systems. The fifth category comprises methods based on repair mechanisms. These methods guide infeasible solutions to feasible regions through projection, heuristic adjustments, and local search, avoiding the loss of high-quality solutions. They are particularly suitable for optimization problems with explicit geometric or physical constraints. Finally, intelligent optimization methods based on learning or adaptive strategies have also gained increasing attention in recent years. By introducing machine learning models to model and predict constraint structures, feasible regions, or search strategies, they achieve dynamic guidance of the search direction and adjustment of strategies.

[0047] In step (3), the constraints include the total brightness temperature received by the microwave radiometer, brightness temperature fluctuation, platform maneuverability, energy, and the field of view of the satellite sensor.

[0048] A computer device includes a memory, a processor, and a computer program stored in the memory, characterized in that: the processor executes the computer program to implement the steps of the above-described method.

[0049] A computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0050] A multi-objective optimization-based cold-space calibration attitude planning system for a spaceborne microwave radiometer includes a first model establishment system, a second model establishment system, a third model establishment system, and a computation system.

[0051] The first model establishment system is used to establish satellite attitude kinematics and attitude dynamics models based on the geocentric equatorial inertial coordinate system, orbital coordinate system, and satellite body coordinate system.

[0052] The second model establishment system is used to establish a model of the sum of brightness temperatures received by the microwave radiometer during the cold air calibration process;

[0053] The third model establishment system is used to establish a multi-objective optimization model for cold-space calibration of microwave radiometers based on multiple constraints, according to the satellite attitude kinematics model, attitude dynamics model and brightness temperature summation model received by microwave radiometers during cold-space calibration.

[0054] A computing system is used to solve multi-objective optimization models, set constraint boundaries, and obtain solutions or solution sets.

[0055] Compared with the prior art, the present invention has at least the following beneficial effects:

[0056] (1) This invention takes into account the impact of interference information such as the cosmic background on cold space observation, as well as the constraints such as the satellite's on-orbit attitude maneuverability, energy, and the field of view of the satellite sensor. It adopts multi-objective optimization to achieve the optimal sum of brightness temperature and brightness temperature fluctuation in the observation area of ​​the microwave radiometer during the cold space calibration process, thereby improving the on-orbit cold space calibration effect and system reliability of the satellite microwave radiometer. It can be widely applied to cold space calibration tasks of satellites carrying microwave radiometers.

[0057] (2) The preferred embodiment of the present invention takes into account the constraints of the satellite platform to achieve the optimal sum of brightness temperature and brightness temperature fluctuation during the calibration process of microwave radiometer, which can improve the accuracy and stability of on-orbit cold space calibration of microwave radiometer. Attached Figure Description

[0058] Figure 1 This is the attitude angular velocity curve of Embodiment 1 of the present invention;

[0059] Figure 2 This is the attitude angular acceleration curve of Embodiment 1 of the present invention;

[0060] Figure 3 This is the sum of the brightness temperatures of the microwave radiometer in Embodiment 1 of the present invention;

[0061] Figure 4 This is the root mean square error of the average brightness temperature value every 100 seconds in Embodiment 1 of the present invention;

[0062] Figure 5 The angle between the optical axis of the star sensor in Embodiment 1 of the present invention and the Earth, the Sun, and the Moon;

[0063] Figure 6 This refers to the angle between the normal of the solar panel and the solar vector in Embodiment 1 of the present invention. Detailed Implementation

[0064] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments:

[0065] This invention relates to a multi-objective optimization-based cold-space calibration attitude planning method for spaceborne microwave radiometers, comprising the following steps:

[0066] (1) In order to describe the position and attitude changes of the satellite in space, the coordinate system definitions of the geocentric equatorial inertial coordinate system, the orbital coordinate system, and the satellite body coordinate system are clarified, and on this basis, the satellite attitude kinematic model and attitude dynamic model are established.

[0067] (2) Establish a model of the sum of brightness temperatures received by the microwave radiometer during the cold air calibration process;

[0068] (3) Based on the satellite dynamics and kinematics model and the total brightness temperature model of the radiometer received in steps 1) and 2), and considering the constraints such as the total brightness temperature of the microwave radiometer received, brightness temperature fluctuation, platform maneuverability, energy and the field of view of the satellite sensor, a multi-objective optimization model for cold air calibration of microwave radiometer with constraints is established.

[0069] 4) Use a multi-objective optimization method to solve the constrained multi-objective optimization model established in step 3), and obtain the solution or solution set of the problem in step 3) while satisfying the constraints.

[0070] Specifically as follows:

[0071] Step 1: To describe the changes in the satellite's position and attitude in space, define the coordinate system and establish the satellite's attitude kinematics model and attitude dynamics model.

[0072] (1) Geocentric equatorial inertial coordinate system f i (C i X i Y i Z i )

[0073] An inertial frame of reference, centered on the Earth's center of mass, X i Along the direction of the vernal equinox; Z i Perpendicular to the Earth's equatorial plane; Y i Located in the equatorial plane, it satisfies the right-hand rule.

[0074] (2) Orbital coordinate system f o (C o X o Y o Z o )

[0075] Origin C o Fixed to the satellite's center of mass, Z o The axis points from the origin to the center of the Earth; X o The axis lies in the orbital plane, perpendicular to the axis and pointing in the direction of system motion; Y o The axial direction satisfies the right-hand rule.

[0076] (3) Satellite body coordinate system f b (C b X bY b Z b )

[0077] Origin C b It is fixed to the satellite's center of mass, and the three coordinate axes are respectively along the satellite's principal inertial axes.

[0078] The attitude of the satellite relative to its orbital system is described using quaternions, and its attitude kinematic equations are as follows:

[0079]

[0080] Where q represents the attitude quaternion in the reference inertial frame, satisfying the normalization constraint. q0 is the scalar part of q. Let q be the vector part, ω represent the satellite's three-axis attitude angular velocity, and I3 = diag(1,1,1) be a third-order identity matrix. For any 3×1 array x = [x1 x2 x3], ... T Its cross product matrix x × Represents an antisymmetric oblique matrix, that is:

[0081]

[0082] The satellite attitude dynamics equations can be written as:

[0083]

[0084] Where J represents the spacecraft's moment of inertia matrix, which is a positive definite symmetric matrix, ω × Let be the cross product matrix of attitude angular velocities, u represent the three-axis control torques during spacecraft attitude maneuvers, and d represent the sum of torques exerted on the spacecraft by external disturbances.

[0085] Step 2: Establish a model for the sum of brightness temperatures received by the radiometer.

[0086] The expression for the total brightness temperature received by the radiometer is:

[0087]

[0088] Where G represents the antenna gain, T a T is the total brightness temperature received by the radiometer. ap It includes brightness temperature in the cold space region and brightness temperature in the surface region, where p represents the satellite's radius vector in the J2000 series. This represents the angular coordinates indicating the direction of the antenna beam, where Ω is the domain of the angular coordinates. φ and λ are the latitude and longitude of the satellite's nadir point, respectively, and δ d and δ r These represent the declination and right ascension of the celestial sphere, respectively.

[0089] For a brightness temperature sphere with region division in the celestial coordinate system, the total brightness temperature can be calculated as:

[0090]

[0091] Among them, T ap earth T ap sky Brightness temperatures of Earth and cold space, respectively, Ω sky Ω earth These are the angular coordinate domains for the cold air region and the Earth's surface region, respectively.

[0092] Step 3: Establish a multi-objective optimization model for cold air calibration of microwave radiometers, taking constraints into account.

[0093] During the satellite's on-orbit cold-space calibration attitude maneuver, it is necessary to optimize the sum of the integrated brightness temperature values ​​and brightness temperature fluctuations of the microwave radiometer pattern, while minimizing the satellite's attitude angular velocity and angular acceleration. The objective function described above can be described as follows:

[0094] min F(p,q)=[T a ,||ω||,||α||]

[0095] Where α represents the satellite's three-axis attitude angular acceleration.

[0096] Considering the overall structural safety of the satellite during attitude maneuvers, platform maneuver constraints, and requirements for the total integrated brightness temperature and brightness temperature fluctuation of the antenna pattern, the above constraints can be described as follows:

[0097] ||ω||≤ω max

[0098] ||α||≤α max

[0099] T a ≤T amax

[0100]

[0101] in, This is the root mean square error of the sum of brightness temperatures received by the radiometer during the time period tp.

[0102] During satellite attitude maneuvers, to ensure the availability of star sensors, it is necessary to ensure that the Earth (including the atmosphere), the Moon, and the Sun are not visible within the field of view of at least one star sensor. This constraint can be expressed as:

[0103]

[0104] in, τ kThe angle between the optical axis of the star sensor and the Earth, Sun, and Moon. For the j-th star-sensitive optical axis direction, the field of view of the Earth, Sun, and Moon must not be visible. h(τ) k ) is τ k The semi-cone angle of the Earth's visible area relative to the satellite at any given time can be expressed as:

[0105]

[0106] Wherein, the Earth's radius is R e Atmospheric thickness is R a ,r(τ k ) is τ k The radius vector from the Earth's center of mass to the satellite's center of mass at any given moment.

[0107] To ensure satellite energy security, the normal direction of the satellite's solar panels should be as parallel as possible to the solar vector. This constraint can be described as follows:

[0108]

[0109] in, For τ k The angle between the solar panel normal and the solar vector at any given moment, β S This is the maximum allowable angle between the solar panel normal and the solar vector.

[0110] Considering the time window [t0,t] for satellite cold space calibration f Based on the optimization objectives and constraints during the calibration process, this multi-objective optimization problem can be described as follows:

[0111] min F(p,q)=[T a ,||ω||,||α||]

[0112]

[0113] Step 4: Solve the multi-objective, multi-constraint attitude planning problem during cold-air calibration using multi-objective optimization methods. For this type of constrained multi-objective optimization problem, it is necessary to simultaneously satisfy multiple optimal performance indicators and complex constraints. The solution methods mainly include the following categories:

[0114] The first category is penalty function-based methods, which introduce penalty terms to infeasible solutions, transforming the original problem into an equivalent unconstrained multi-objective problem, and then combining it with conventional evolutionary algorithms for solution. The second category is feasibility-priority selection strategies, widely used in typical multi-objective algorithms such as NSGA-II and MOEA / D. These methods typically employ the Deb criterion, prioritizing feasible solutions during evolution and sorting infeasible solutions according to the degree of constraint violation, ensuring the population converges to the feasible region. The third category is transformation methods based on ε-constraints, which select a primary optimization objective, transforming other objectives into constraints, setting allowable thresholds for each objective, and guiding the search process by adjusting the value of ε, flexibly controlling the distribution characteristics of the Pareto front. The fourth category is decomposition-based optimization methods, which decompose the original problem into multiple constrained single-objective subproblems for individual solution, exhibiting good parallelism and scalability, and are particularly suitable for high-dimensional objective systems. The fifth category comprises methods based on repair mechanisms. These methods guide infeasible solutions to feasible regions through projection, heuristic adjustments, and local search, avoiding the loss of high-quality solutions. They are particularly suitable for optimization problems with explicit geometric or physical constraints. Finally, intelligent optimization methods based on learning or adaptive strategies have also gained increasing attention in recent years. By introducing machine learning models to model and predict constraint structures, feasible regions, or search strategies, they achieve dynamic guidance of the search direction and adjustment of strategies.

[0115] Example 1

[0116] In this embodiment, a dynamic constraint non-dominated sorting genetic algorithm is used to solve the problem.

[0117] First, an initial population of size N is randomly generated. A dynamic constraint boundary is introduced, and the initial constraint boundary is relaxed so that all individuals in the initial population are ε-feasible solutions. The constraint boundary is then contracted to generate a descendant population. The parent and descendant populations are merged, and the population is divided into an ε-feasible set and an ε-infeasible set. If the number of individuals in the ε-feasible set is greater than or equal to N, individuals are selected to join the next generation of the parent population using non-dominated sorting and an elite selection mechanism based on reference points. If the number of individuals in the ε-feasible set is less than N, the ε-feasible set is directly added to the next generation of the parent population, and individuals are selected from the ε-infeasible set based on the degree of constraint violation. This process is repeated until the program terminates, yielding the optimal solution set in the final environment state. The detailed process is as follows:

[0118] (1) Randomly initialize the population individuals After normalizing the constraint violation rates, we take the average value as the objective function cv(x), which can be written as:

[0119]

[0120] in, Let q represent the initial population, q represent the number of hard constraints, and G represent the constraint violation degree of the i-th constraint. i (x) can be represented as G i (x)=max{g i (x), 0}, i = 1, 2, 3, 4, 5, 6, when Take 1 at the time.

[0121] (2) Introducing dynamic shrinkage boundary calculate Given the objective values ​​and constraint violation values ​​of all individuals in the initial population, relax the original constraint boundaries to make every individual in the entire initial population an ε-feasible solution.

[0122] Write about the attitude planning problem during cold air calibration:

[0123] min[F(x),cv(x)]

[0124]

[0125] Among them, t e Represents the state of the environment. For dynamic constraint boundaries, when a solution satisfies If the condition is met, the solution is called an ε-feasible solution; otherwise, it is called an ε-infeasible solution. The ε-constraint boundary conditions change with environmental variables. Dynamic contraction, in the final environmental state t e =t n The attitude planning problem can be written as follows:

[0126] min[F(x),0]

[0127] st:g(x)<0

[0128] Among them, the Pareto optimal solution under the final environmental state is the optimal solution to the attitude planning multi-objective optimization problem.

[0129] The dynamic constraint boundary model is as follows:

[0130]

[0131] Where δ is a given positive real number close to 0, taken as δ = 1 × 10 -8 cp controls the degree of change in the environment state, A i =ε i (0) +δ,

[0132] (3) Shrinking the dynamic boundary Offspring populations are obtained through selection, crossover, and mutation;

[0133] (4) Divide the merged parent and offspring population into two sets: ε-feasible set S1 and ε-infeasible set S2. Assume the population size is N. If |S1| < N, directly add S1 to the next generation of the parent population. The remaining individuals are selected from the (N-|S1|) solutions from S2, based on the minimum constraint violation value. If |S1|≥N, select N individuals from S1 using a non-dominated ranking and a reference-point-based elite selection mechanism.

[0134] (5) Enter the loop of steps (3) to (4). After each loop, the algebraic number is incremented by 1 until the number of iterations reaches the preset maximum value.

[0135] Set the initial population The scale N is 10000. See the solution results for details. Figures 1-6 All constraints are satisfied.

[0136] Figure 1 The image shows the satellite attitude angular velocity curve during cold air calibration, which satisfies the constraint that the attitude angular velocity is no greater than 0.12° / s. Figure 2 The figure shows the satellite attitude angular acceleration curve during cold air calibration, which meets the condition that the attitude angular acceleration is no greater than 0.0006° / s2. Figure 3 The curve shown is the sum of the brightness temperatures of the microwave radiometer antenna during cold air calibration, which satisfies the constraint that the sum of the brightness temperatures of the radiometer antenna does not exceed 10K within the calibration window. Figure 4 The figure shows the root mean square error curve of the total brightness temperature of the microwave radiometer antenna during cold air calibration, which satisfies the constraint that the root mean square error of the total brightness temperature of the radiometer antenna is no greater than 0.02K within the calibration window. Figure 5 The figure shows the angles between the optical axes of the three star sensors and the Earth, the Sun, and the Moon, respectively. During the calibration process, at least one star sensor must not be constrained by the Earth (including the atmosphere), the Moon, or the Sun within a field of view of ±35°. Figure 6 The figure shows the angle between the solar panel normal and the sun, which satisfies the constraint that the angle between the solar panel normal and the sun vector is less than 50° during the calibration process.

[0137] The above description is only the best specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

[0138] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A spaceborne microwave radiometer cold-space calibration attitude planning method based on multi-objective optimization, characterized in that: Includes the following steps: (1) Establish the satellite attitude kinematics model and attitude dynamics model based on the geocentric equatorial inertial coordinate system, orbital coordinate system and satellite body coordinate system; (2) Establish a model for the sum of brightness temperatures received by the microwave radiometer during cold air calibration; (3) Based on the satellite attitude kinematics model and attitude dynamics model obtained in step (1), and the total brightness temperature model received by the microwave radiometer during the cold air calibration process obtained in step (2), a multi-objective optimization model considering constraints for the cold air calibration of the microwave radiometer is established based on multiple constraints. (4) Solve the multi-objective optimization model obtained in step (3), set constraint boundaries, and obtain the solution or solution set; In step (1), the posture kinematic model is as follows: Where t is time, For the attitude quaternion in the reference inertial frame, , for The scalar part, ,for The vector part; The satellite's three-axis attitude angular velocity, This is the cross product matrix of the attitude angular velocities; The attitude dynamics model is as follows: in, Here is the rotational inertia matrix of the spacecraft. The three-axis control torque during spacecraft attitude maneuvers. This represents the sum of the torques exerted on the spacecraft by external disturbances. In step (2), the total brightness temperature model received by the microwave radiometer during cold-space calibration includes the total brightness temperature model received by the radiometer and the total brightness temperature model of the brightness temperature sphere with region segmentation in the celestial coordinate system, wherein, Model of total brightness temperature received by radiometer: in, For antenna gain, This is the sum of brightness temperatures received by the radiometer. Brightness temperature in the cold air region and brightness temperature in the surface region. The satellite's radius vector in the J2000 system. The angular coordinates of the antenna beam direction. Define the domain for angular coordinates. , The latitude of the point below the satellite. The longitude of the satellite's nadir point. The declination of the celestial sphere. Right ascension of the celestial sphere; This leads to the overall brightness temperature model of the brightness temperature sphere with region segmentation in the celestial coordinate system: in, , Brightness temperatures of Earth and cold space, respectively. , These are the angular coordinate domains for the cold air region and the Earth's surface region, respectively. In step (3), the method for establishing the multi-objective optimization model is as follows: (1) During the satellite's on-orbit cold-space calibration attitude maneuver, the goal is to optimize the sum of the integrated brightness temperature values ​​and brightness temperature fluctuations of the microwave radiometer pattern while minimizing the satellite's attitude angular velocity and angular acceleration, i.e.: in, This represents the satellite's three-axis attitude angular acceleration; (2) Considering the overall structural safety of the satellite, platform maneuver constraints, and requirements for the total integrated brightness temperature and brightness temperature fluctuation of the antenna pattern during attitude maneuvers, namely: in, In order to be in The root mean square error of the total brightness temperature received by the radiometer over a time period; (3) During the satellite's attitude maneuver, ensure that the Earth, Moon, and Sun are not visible within the field of view of at least one star sensor, i.e.: in, , , They are respectively The angle between the optical axis of the star sensor and the Earth, Sun, and Moon. The field of view of the Earth, Sun, and Moon must not appear in the direction of the j-th star-sensitive optical axis. for The semi-cone angle of the visible area of ​​Earth relative to the satellite at any given time satisfies: Wherein, the Earth's radius is Atmospheric thickness is , for The radius vector from the Earth's center of mass to the satellite's center of mass at any given moment; (4) To ensure satellite energy security, the normal direction of the satellite's solar panels must be parallel to the solar vector, that is: in, for The angle between the solar panel normal and the solar vector at any given moment. This is the maximum allowable angle between the solar panel normal and the solar vector; (5) Based on the constraints and optimization objectives described in steps (1) to (4), consider the time window for satellite cold space calibration. The following multi-objective optimization model is obtained; 。 2. The spaceborne microwave radiometer cold-space calibration attitude planning method based on multi-objective optimization according to claim 1, characterized in that: In step (3), the constraints include the total brightness temperature received by the microwave radiometer, brightness temperature fluctuation, platform maneuverability, energy, and the field of view of the satellite sensor.

3. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

4. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of the method of claim 1.

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