A multi-task remote sensing satellite orbit integrated design method and device

Abstract

The present invention discloses a method and device for integrated design of the orbits of multi-mission remote sensing satellites, including the following steps: Step S1, obtaining an integrated optimization model for the return orbits of multi-mission remote sensing satellites; Step S2, performing multi-objective optimization on the integrated optimization model for the return orbits of multi-mission remote sensing satellites through a parameter adaptive genetic algorithm. By adopting the technical solution of the present invention, the problem that current remote sensing satellites are limited by the observation performance of the payload and cannot balance the multi-mission requirements of long-period global seamless coverage and short-period rapid revisit tomography imaging is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing satellites and is a method and device for integrated design of multi-task remote sensing satellite orbits based on a parameter adaptive genetic algorithm. Background Art

[0002] Remote sensing satellites are widely used in areas such as terrestrial natural resource surveys, marine ecological and environmental protection, and natural disaster forecasting. Current remote sensing satellites can achieve global coverage by designing long-period return orbits, or they can achieve rapid revisit tomographic imaging of key areas by designing short-period return orbits. However, due to limitations in payload observation performance, current remote sensing satellites cannot simultaneously meet the requirements for both global coverage and rapid revisit tomographic imaging of key areas, and can only meet the needs of a single mission. With the advancement of satellite orbit design technology, it has become feasible to achieve multi-task switching on a single satellite through the integrated design of long- and short-period return orbits.

[0003] The integrated design of multi-mission remote sensing satellite orbits requires comprehensive consideration of the application requirements for global, leak-free coverage and rapid revisit tomography in key areas, enabling the integrated design of multi-mission long- and short-period return orbits. However, there is a conflict between the requirements for a long-period return orbit that achieves global, leak-free coverage and a short-period return orbit that achieves rapid revisit tomography in key local areas. Furthermore, the orbit switching fuel consumption caused by multi-mission switching can affect the satellite's operational lifespan. The three optimization objectives of long-period global coverage overlap, short-period rapid revisit performance, and orbit switching fuel consumption cannot be simultaneously optimized, creating difficulties in the integrated design of multi-mission long- and short-period return orbits. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a multi-task remote sensing satellite orbit integrated design method and device to solve the problem that current remote sensing satellites are limited by payload observation performance and cannot take into account the multi-task requirements of long-period global leak-free coverage and short-period rapid revisit tomography imaging. The optimal solution set of orbital parameters with the best comprehensive performance that meets the long-period global coverage overlap rate, short-period revisit days and orbit switching fuel consumption can be obtained, thereby achieving the purpose of single-satellite multi-task switching.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A multi-mission remote sensing satellite orbit integrated design method, comprising:

[0007] Step S1, obtaining a multi-mission remote sensing satellite regression orbit integrated optimization model;

[0008] Step S2: performing multi-objective optimization on the integrated optimization model of the multi-mission remote sensing satellite regression orbit.

[0009] Preferably, in step S1, the integrated optimization model for the return orbit of the multi-task remote sensing satellite includes: setting decision variables, constraint conditions, and an optimization objective; among them, the constraint conditions include: sun-synchronous return constraint, orbital altitude constraint, maximum revisit period constraint, and global non-overlapping coverage constraint; the optimization objective is where η(x) is the long-period global non-overlapping coverage overlap rate, f(x) is the increment of the orbital transfer fuel consumption rate, and p(x) is the short-period revisit time.

[0010] Preferably, in step S2, a multi-objective optimization is performed on the integrated optimization model for the return orbit of the multi-task remote sensing satellite by using a parameter adaptive genetic algorithm.

[0011] Preferably, in step S2, a multi-objective optimization is performed on the long-period global non-overlapping coverage overlap rate, the orbital transfer fuel consumption, and the short-period revisit time by using a parameter adaptive genetic algorithm based on the constraint conditions.

[0012] The present invention also provides a device for integrated design of the orbit of a multi-task remote sensing satellite, including:

[0013] a construction module, configured to obtain an integrated optimization model for the return orbit of the multi-task remote sensing satellite;

[0014] an optimization module, configured to perform multi-objective optimization on the integrated optimization model for the return orbit of the multi-task remote sensing satellite.

[0015] Preferably, the integrated optimization model for the return orbit of the task remote sensing satellite includes: setting decision variables, constraint conditions, and an optimization objective; among them, the constraint conditions include: sun-synchronous return constraint, orbital altitude constraint, maximum revisit period constraint, and global non-overlapping coverage constraint; the optimization objective is where η(x) is the long-period global non-overlapping coverage overlap rate, f(x) is the increment of the orbital transfer fuel consumption rate, and p(x) is the short-period revisit time.

[0016] Preferably, the optimization module performs multi-objective optimization on the integrated optimization model for the return orbit of the multi-task remote sensing satellite by using a parameter adaptive genetic algorithm.

[0017] Preferably, the optimization module performs multi-objective optimization on the long-period global non-overlapping coverage overlap rate, the orbital transfer fuel consumption, and the short-period revisit time by using a parameter adaptive genetic algorithm based on the constraint conditions.

[0018] Aiming at the problem that traditional remote sensing satellites are limited by payload performance and cannot balance the multi-task requirements of global seamless coverage and rapid revisit tomography imaging in key areas, the present invention adopts an integrated design of long- and short-period regression orbits, and can obtain an optimal solution set of comprehensive performance of orbital parameters that meets the long-period global coverage overlap rate, short-period revisit days, and optimal fuel consumption for orbit switching, so as to achieve the purpose of single-satellite multi-task switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0020] Figure 1 It is a schematic flow chart of the integrated design method of the multi-task remote sensing satellite orbit in the embodiment of the present invention;

[0021] Figure 2 It is a schematic diagram of the principle of payload earth observation;

[0022] Figure 3 It is a flow chart of the integrated design of the multi-task regression orbit based on the parameter adaptive genetic algorithm. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0024] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0025] Embodiment 1:

[0026] As Figure 1 、 2 shown, the embodiment of the present invention provides a method for integrated design of a multi-task remote sensing satellite orbit, including the following steps:

[0027] Step S1, obtaining an integrated optimization model of the multi-task remote sensing satellite regression orbit;

[0028] Step S2, performing multi-objective optimization on the integrated optimization model of the multi-task remote sensing satellite regression orbit.

[0029] As an implementation manner of an embodiment of the present invention, the integrated optimization model of the multi - mission remote - sensing satellite's return orbit includes: setting decision variables, constraint conditions, and optimization objectives.

[0030] Decision variables: The decision variables are set as the orbital semi - major axes and orbital inclinations of the long - period and short - period continuous variables, \(x = [a long i long a short i short \), where \(a long ,a short are respectively the orbital semi - major axis of the long - period return orbit and the orbital semi - major axis of the short - period return orbit, and \(i long ,i short are respectively the orbital inclinations of the long - period return orbit and the short - period return orbit. Based on the constraints of the sun - synchronous characteristics, it can be simplified to \(x = [a long a short \).

[0031] Constraint conditions: Sun - synchronous return constraint, orbital altitude constraint, maximum revisit period constraint, and global non - leakage coverage constraint.

[0032] Sun - synchronous return constraint:

[0033] Due to the limited ground - observation ability of the satellite payload, in order to achieve global coverage, multiple - day return orbits are usually used, that is, the semi - major axis and inclination of the designed orbit are such that the nodal period satisfies the following formula.

[0034]

[0035] where \(\omega e is the angular velocity of the Earth's rotation, \(R\) represents the number of circles around the Earth during the return period, \(N\) represents the number of days of the return period, and the two are relatively prime, and \(T N is the nodal period of the satellite orbit.

[0036] Under the sun - synchronous constraint, the rate of change of the right ascension of the ascending node is equal to the angular velocity of the Earth's revolution, satisfying the following relational expression.

[0037]

[0038] When the orbital semi - major axis \(a\) and the orbital inclination \(i\) satisfy the following relational expression, the sun - synchronous constraint is satisfied.

[0039]

[0040] where \(R e is the Earth's radius, \(\mu\) is the Earth's gravitational constant, and \(J2 = 0.001082\).

[0041] Orbital altitude constraint:

[0042] In the integrated design of the return orbit of a multi - mission remote - sensing satellite, it is necessary to constrain the orbital altitude range of the orbit design. Let the upper and lower limits of the orbital altitude be [h min h max . Then the semi - major axis in the decision variables needs to satisfy:

[0043] a long ,a short ∈[R e +h min R e +h max (4)

[0044] Maximum revisit period constraint

[0045] The long - period return orbit and the short - period return orbit of the multi - mission remote - sensing satellite should have a maximum revisit period constraint. During the orbit design process, the revisit period of the return orbit should be less than or equal to the maximum revisit period N max . That is:

[0046] N short ,N long ≤N max (5)

[0047] Global non - overlapping coverage constraint

[0048] The long - period return orbit of the multi - mission remote - sensing satellite needs to meet the constraint condition of global non - overlapping coverage. Let the width of the satellite's ground - observation coverage strip be L, the return period of the orbit be N long days, and it rotates R long circles within the return period. Then the following relationship needs to be satisfied:

[0049]

[0050] Among them, R e is the radius of the earth.

[0051] Optimization objective: Among them, η(x) is the long - period global non - overlapping coverage overlap rate, f(x) is the increment of the orbital transfer fuel consumption rate, and p(x) is the short - period revisit time. The calculation method of the optimization objective is as follows.

[0052] Calculation method of global non - overlapping coverage overlap rate

[0053] The width of the satellite's coverage strip L is affected by the semi - major axis a of the satellite's long - period return orbit and the range of the incident angle α. Based on the principle of the payload's ground observation, the ground observation conditions of the payload based on the minimum and maximum incident angles are shown in the figure. Among them, Re is the radius of the earth, a is the semi - major axis of the satellite orbit, the minimum incident angle is α1, and the maximum incident angle is α2.

[0054] The calculation formula for the width L of the coverage strip is as follows:

[0055]

[0056] Among them, a long is the semi-major axis of the long-period return orbit. If the long-period return orbit orbits the Earth R long times in N long days, within one return period, the equator is equally divided into R long segments, and the calculation formula for the arc length δ corresponding to each segment is:

[0057]

[0058] The calculation formula for the global seamless coverage overlap rate is as follows.

[0059]

[0060] Calculation method for fuel consumption in the switching between long and short period orbits

[0061] The fuel consumption for orbit switching is represented by the velocity increment of orbit switching. Δa represents the change in the semi-major axis of the long and short period return orbits.

[0062] Δa = a long - a short (10)

[0063] a represents the semi-major axis of the current operating orbit of the satellite (either the long-period return orbit or the short-period return orbit). v is the velocity of the satellite in the current operating orbit, and the expression is:

[0064]

[0065] Since the correction efficiency of the radial velocity pulse is half of that of the track velocity pulse, therefore, the track velocity pulse Δv T is usually used for correction. On the premise that both the long and short period return orbits are circular orbits, the formulas for calculating the three track pulses are as follows:

[0066]

[0067] Therefore, the fuel consumption in the orbital plane can be expressed as:

[0068]

[0069] Control outside the orbital plane: The normal velocity pulse Δv N can change the orbital inclination, and the expression is as follows:

[0070] ζ N = v·Δi(14)

[0071] where, Δi is the inclination error, Δi = |i long - i short |.

[0072] The total fuel consumption is:

[0073] f(x) = ζ T + ζ N (15)

[0074] Short - period revisit period

[0075] Taking the number of days of return of the short - period return orbit as the evaluation index, the function is established as follows:

[0076] p(x) = N short (16)

[0077] where, N short is the number of days of return of the short - period orbit.

[0078] As an implementation manner of the embodiment of the present invention, in step S2, a multi - objective optimization is performed on the integrated optimization model of the multi - task remote - sensing satellite return orbit through a parameter - adaptive genetic algorithm, as Figure 3 shown, and the specific operation steps are as follows:

[0079] The first step: Parameter initialization. Given the initial population size N P , the crossover probability P C , the mutation probability P D , the natural selection ratio m, the semi - major axis value range [a min a max , the load incident angle value range [α1α2], the maximum number of days of return period N max .

[0080] The second step: Initial population creation. Based on the given upper and lower bounds of the decision variables, the most primitive population is generated.

[0081] The third step: Based on the analytical design method of the sun - synchronous return orbit, the decision variables are corrected and the objective function is calculated. For the semi - major axes a long0 , a short0 of the initially generated long - and short - period return orbits, the constraints of sun - synchronous return need to be satisfied, so the following corrections are made.

[0082] Due to the constraints of the sun - synchronous characteristics, the corresponding orbit inclinations of the long - and short - period return orbits can be calculated by the following formula.

[0083]

[0084] However, the regression characteristics of this parameter cannot guarantee that in the non-spherical gravitational field perturbation, the J2 perturbation plays a dominant role. Therefore, the satellite orbital period T is calculated through the following formula long ,T short 。

[0085]

[0086] Since the orbit is a sun-synchronous regression orbit, the nodal day D N =86400s. The regression parameter Q is calculated through formulas (21) and (22) long ,Q short 。

[0087]

[0088] Furthermore, the number of orbits R of the long and short period regression orbits is obtained through formulas (23) and (24) long ,R short as well as the number of days N of the regression period long ,N short 。

[0089]

[0090] Based on the obtained R long ,R short , N long ,N short Through the analytical method, the regression orbit parameters a long ,a short ,i long ,i short are obtained. Based on these parameters, the values of the objective functions η(x), f(x), and p(x) are calculated.

[0091]

[0092]

[0093] Step 4: Calculate the fitness function of the objective function and perform the selection operation. In the embodiment of the present invention, the selection strategy uses a method combining elitist retention and roulette wheel method. First, the Pareto optimal solutions in each generation are retained in the population of the next generation through elitist retention. Secondly, through the roulette wheel method, a fixed proportion of individuals are selected and retained in the population of the next generation.

[0094] The calculation method of the fitness function is as follows: The initial fitness of each individual in the population is 0. Each individual in the population is compared with other individuals one by one. In each comparison, if it dominates other individuals, 3 points are added; if it is dominated by other individuals, 0 points are added; if they do not dominate each other, 1 point is added. Thus, the fitness function of each individual is obtained.

[0095] Step 5: Obtain offspring through crossover and mutation operations. The parent generation is selected through the roulette wheel method based on the fitness function, and the offspring generation is obtained through the single-point crossover method; the mutation operation selects single-point mutation.

[0096] Step 6: Adaptively modify the population size. In order to prevent the genetic algorithm from falling into a local optimal solution during the optimization process, for multi-objective optimization problems, at the end of each generation, it is determined whether the number of feasible solutions m on the Pareto front exceeds 1 / 3 of the population size. If it exceeds this range, the number of feasible solutions m is adjusted by N. P =3m updates the population size, retaining the Pareto frontier feasible solution while preventing the algorithm from falling into the local optimal solution.

[0097] Repeat steps 3 to 6 until the Pareto front converges or the maximum number of iterations is reached.

[0098] In order to solve the problem that traditional remote sensing satellites are limited by payload performance and cannot take into account the multi-task requirements of global leak-free coverage and rapid revisit tomography of key areas, the embodiment of the present invention adopts an integrated design of long and short period regression orbits. Under the premise of meeting the multi-task requirements, it achieves the optimal orbit switching fuel consumption and realizes the multi-task function of a single satellite. In order to solve the problem that the time required for strict regression orbit design in the optimization process is long, which affects the speed of iterative optimization, the embodiment of the present invention obtains the value of the optimization target based on the sun-synchronous regression orbit analytical design method. There is no need to design each strict regression orbit. On the basis of ensuring the accuracy of the objective function, the calculation speed of each iteration is accelerated. In order to solve the problem that it is easy to fall into the local optimal solution during the multi-objective optimization process, the parameter adaptive correction is introduced in the genetic algorithm part. According to the changing trend of the Pareto front, the population size parameter is adaptively corrected to prevent the genetic algorithm from falling into the local optimal solution during the optimization process, thereby improving the global search capability.

[0099] Example 2:

[0100] An embodiment of the present invention further provides a multi-mission remote sensing satellite orbit integrated design device, comprising:

[0101] Building a module for obtaining an integrated optimization model for the regression orbit of multi-mission remote sensing satellites;

[0102] The optimization module is used to perform multi-objective optimization on the integrated optimization model of the multi-mission remote sensing satellite regression orbit.

[0103] As an implementation method of an embodiment of the present invention, the integrated optimization model of the mission remote sensing satellite regression orbit includes: setting decision variables, constraints and optimization objectives; wherein the constraints include: sun-synchronous regression constraint, orbit altitude constraint, maximum revisit period constraint, global leak-free coverage constraint; the optimization objective is Among them, η(x) is the long-period global seamless coverage overlap rate, f(x) is the increment of the orbital transfer fuel consumption rate, and p(x) is the short-period revisit time.

[0104] As an implementation manner of an embodiment of the present invention, the optimization module performs multi-objective optimization on the integrated optimization model of the multi-task remote sensing satellite's regression orbit through a parameter adaptive genetic algorithm.

[0105] As an implementation manner of an embodiment of the present invention, the optimization module performs multi-objective optimization on the long-period global seamless coverage overlap rate, the orbital transfer fuel consumption, and the short-period revisit time based on the constraint conditions through a parameter adaptive genetic algorithm.

[0106] The above-described embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An integrated design method for the orbit of a multi-task remote sensing satellite, characterized in that, including: Step S1, obtaining an integrated optimization model for the return orbit of a multi-task remote sensing satellite; Step S2, performing multi-objective optimization on the integrated optimization model for the return orbit of a multi-task remote sensing satellite; In step S1, the integrated optimization model for the regression orbit of the multi-task remote sensing satellite includes: setting decision variables, constraint conditions, and optimization objectives; among them, the constraint conditions include: sun-synchronous regression constraint, orbit altitude constraint, maximum revisit period constraint, and global seamless coverage constraint; the optimization objective is where η(x) is the long-period global seamless coverage overlap rate, f(x) is the increment of the orbit transfer fuel consumption rate, p(x) is the short-period revisit time; the decision variables are the semi-major axis and the orbit inclination of the long-period orbit and the short-period orbit x = [a long i long a short i short . Through the sun-synchronous regression constraint in the constraint conditions, it can be simplified to x = [a long a short , where a long , a short are the semi-major axis of the long-period regression orbit and the semi-major axis of the short-period regression orbit respectively, and i long , i short are the orbit inclinations of the long-period regression orbit and the short-period regression orbit respectively; In step S2, performing multi-objective optimization on the integrated optimization model for the return orbit of a multi-task remote sensing satellite through a parameter adaptive genetic algorithm; In step S2, through a parameter adaptive genetic algorithm, based on the constraint conditions, performing multi-objective optimization on the long-period global seamless coverage overlap rate, orbit switching fuel consumption, and short-period revisit time.

2. An integrated design device for the orbit of a multi-task remote sensing satellite, characterized in that, including: A construction module for obtaining an integrated optimization model for the return orbit of a multi-task remote sensing satellite; An optimization module for performing multi-objective optimization on the integrated optimization model for the return orbit of a multi-task remote sensing satellite; The integrated optimization model for the return orbit of a mission remote sensing satellite includes: setting decision variables, constraint conditions, and optimization objectives; among them, the constraint conditions include: sun-synchronous return constraint, orbital altitude constraint, maximum revisit period constraint, global seamless coverage constraint; the optimization objective is where η(x) is the long-period global seamless coverage overlap rate, f(x) is the incremental fuel consumption rate for orbit switching, p(x) is the short-period revisit time; the decision variables are the semi-major axis and orbital inclination of the long-period orbit and the short-period orbit, x = [a long i long a short i short , through the sun-synchronous return constraint in the constraint conditions, it can be simplified to x = [a long a short , where a long , a short are the semi-major axis of the long-period return orbit and the semi-major axis of the short-period return orbit respectively, and i long , i short are the orbital inclinations of the long-period return orbit and the short-period return orbit respectively; The optimization module performs multi-objective optimization on the integrated optimization model for the return orbit of a multi-task remote sensing satellite through a parameter adaptive genetic algorithm; The optimization module performs multi-objective optimization on the long-period global seamless coverage overlap rate, orbit switching fuel consumption, and short-period revisit time through a parameter adaptive genetic algorithm based on the constraint conditions.

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