Multi-task remote sensing satellite orbit integrated design method and device

Abstract

The invention discloses a multi-task remote sensing satellite orbit integrated design method and device. The method comprises the following steps: S1, obtaining a multi-task remote sensing satellite regression orbit integrated optimization model; and S2, performing multi-target optimization on the multi-task remote sensing satellite regression orbit integrated optimization model through a parameter adaptive genetic algorithm. By adopting the technical scheme of the invention, the problem that the current remote sensing satellite is limited by the load observation performance and cannot give consideration to the multi-task requirements of long-period global leakage-free coverage and short-period rapid revisit tomography imaging is solved.

Description

Technical Field

[0001] The 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 parameter adaptive genetic algorithm. Background Art

[0002] Remote sensing satellites have been widely used in the fields of land natural resource surveys, marine ecological environment protection, and natural disaster forecasting. Current remote sensing satellites can achieve global leak-free coverage by designing long-period return orbits, and can also achieve rapid revisit tomographic imaging of key areas by designing short-period return orbits. However, due to the limitations of payload observation performance, current remote sensing satellites cannot simultaneously meet the needs of global leak-free coverage and rapid revisit tomographic imaging of key areas, and can only meet the needs of a single mission. With the development of satellite orbit design technology, it is feasible to achieve single-satellite multi-task switching through the integrated design of satellite long- and short-period return orbits.

[0003] In the integrated design of multi-task remote sensing satellite orbits, it is necessary to comprehensively consider the application requirements of global leak-free coverage and rapid revisit tomography in key areas to achieve the integrated design of multi-task long and short cycle return orbits. However, there is a conflict in demand between the long-period return orbit that completes the global leak-free full coverage mission and the short-period return orbit that completes the rapid revisit tomography in local key areas. At the same time, the orbit switching fuel consumption caused by multi-task switching will affect the satellite's operating life. The three optimization goals of long-period global coverage overlap rate, short-period rapid revisit performance, and orbit switching fuel consumption cannot be optimized at the same time, which brings difficulties to the integrated design of multi-task long and short cycle return orbits. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for integrated design of multi-task remote sensing satellite orbits, so as to solve the problem that current remote sensing satellites are limited by payload observation performance and cannot take into account both long-period global leak-free coverage and short-period rapid revisit tomography multi-task requirements. The optimal solution set of orbital parameter comprehensive performance that meets the optimal 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 solution:

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

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

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

[0009] Preferably, in step S1, the integrated optimization model of multi-task remote sensing satellite regression orbit includes: setting decision variables, constraints and optimization objectives; wherein the constraints include: sun-synchronous regression constraint, orbit height constraint, maximum revisit period constraint, global leak-free coverage constraint; the optimization objective is Among them, η(x) is the long-period global leak-free coverage overlap rate, f(x) is the orbit switching fuel consumption rate increment, 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 of the multi-task remote sensing satellite regression orbit by using a parameter adaptive genetic algorithm.

[0011] Preferably, in step S2, a parameter-adaptive genetic algorithm is used to perform multi-objective optimization on the long-period global leak-free coverage overlap rate, the orbit switching fuel consumption and the short-period revisit time based on constraint conditions.

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

[0013] Building modules for obtaining integrated optimization models of multi-mission remote sensing satellite regression orbits;

[0014] The optimization module is used to perform multi-objective optimization on the integrated optimization model of the multi-mission remote sensing satellite regression orbit.

[0015] As a preferred method, 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 constraints, orbit height constraints, maximum revisit period constraints, and global leak-free coverage constraints; the optimization objective is Among them, η(x) is the long-period global leak-free coverage overlap rate, f(x) is the orbit switching fuel consumption rate increment, and p(x) is the short-period revisit time.

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

[0017] Preferably, the optimization module uses a parameter-adaptive genetic algorithm to perform multi-objective optimization on the long-period global leak-free coverage overlap rate, orbit switching fuel consumption and short-period revisit time based on constraints.

[0018] In view of the problem that traditional remote sensing satellites are limited by payload performance and cannot take into account both global leak-free coverage and multi-task requirements of rapid revisit tomography of key areas, the present invention adopts an integrated design of long and short period regression orbits to obtain the optimal solution set of orbital parameter comprehensive performance that meets the long-period global coverage overlap rate, short-period revisit days and orbit switching fuel consumption, thereby achieving the purpose of single-satellite multi-task switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of a flow chart of a method for integrated design of multi-task remote sensing satellite orbits according to an embodiment of the present invention;

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

[0022] Figure 3 Design a flow chart for multi-task regression track integration based on parameter adaptive genetic algorithm. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Embodiment 1:

[0026] like Figure 1 , 2 As shown, an embodiment of the present invention provides a multi-mission remote sensing satellite orbit integrated design method, comprising the following steps:

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

[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 method of an embodiment of the present invention, a multi-mission remote sensing satellite regression orbit integrated optimization model includes: setting decision variables, constraints and optimization objectives.

[0030] Decision variables: Set the decision variables to continuous variables: the semi-major axis of the long-period and short-period orbits and the orbital inclination x = [a long i long a short i 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, i long ,i short are the orbital inclinations of the long-period regression orbit and the short-period regression orbit, respectively. Based on the constraint of the sun-synchronous characteristics, it can be simplified to x = [a long a short ].

[0031] Constraints: sun-synchronous return constraint, orbit altitude constraint, maximum revisit period constraint, and global leak-free coverage constraint.

[0032] Sun-synchronous regression constraints:

[0033] Due to the limited earth observation capability of satellite payloads, a multi-day regression orbit is usually used to achieve global coverage, that is, the semi-major axis and inclination of the orbit are designed so that the node period satisfies the following formula.

[0034]

[0035] Among them, ω e is the angular velocity of the Earth's rotation, R represents the number of circles around the Earth in the regression period, N represents the number of days in the regression period, and the two are mutually prime, T N is the satellite orbit node period.

[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 relationship.

[0037]

[0038] The sun-synchronous constraint is met when the orbital semi-major axis a and the orbital inclination i satisfy the following relationship.

[0039]

[0040] Among them, R e is the radius of the earth, μ is the gravitational constant of the earth, J2=0.001082.

[0041] Track height constraint:

[0042] In the integrated design of multi-mission remote sensing satellite return orbits, it is necessary to constrain the orbital altitude range of the orbit design. The upper and lower limits of the orbital altitude are set as [h min h max ], then the semi-major axis in the decision variable must 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 short-period return orbit of 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 .Right now:

[0046] N short ,N long ≤N max (5)

[0047] Global leak-free coverage constraints

[0048] The long-period return orbit of a multi-mission remote sensing satellite needs to meet the constraint of global leak-free coverage. Suppose the width of the satellite's earth observation coverage strip is L and the return period of the orbit is N. long days, and rotates R in the regression period long Circle. Then the following relationship needs to be satisfied:

[0049]

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

[0051] Optimization goal: Among them, η(x) is the long-term global leak-free coverage overlap rate, f(x) is the orbit switching fuel consumption rate increment, and p(x) is the short-term revisit time. The optimization target calculation method is as follows.

[0052] Calculation method of global leak-free coverage overlap rate

[0053] The satellite's coverage strip width L is affected by the semi-major axis a of the satellite's long-period regression orbit and the range of the incident angle α. According to the principle of payload earth observation, the payload earth observation based on the minimum and maximum incident angles is 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 coverage strip width L is:

[0055]

[0056] Among them, a long is the semi-major axis of the long-period regression orbit. long Around the Earth R long Circle, in one regression cycle, the equator is divided into R long The calculation formula of arc length δ for each segment is:

[0057]

[0058] The calculation formula for global leak-free coverage overlap rate is as follows.

[0059]

[0060] Calculation method of fuel consumption for switching between long-period and short-period orbits

[0061] The fuel consumption of orbit switching is expressed as the speed increment of orbit switching. Δa represents the change in the semi-major axis of the long-short period regression orbit.

[0062] Δa=a long -a short (10)

[0063] a represents the semi-major axis of the satellite's current orbit (either a long-period regression orbit or a short-period regression orbit). v is the satellite's velocity in the current orbit, expressed as:

[0064]

[0065] Since the correction efficiency of radial velocity pulse is half of that of track velocity pulse, the track velocity pulse Δv is usually used. T Under the premise that both the long and short period regression 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 out of orbital plane: Normal phase velocity pulse Δv N The orbital inclination can be changed 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 revisit cycle

[0075] The regression days of the short-period regression orbit are used as the evaluation index, so the function is established as follows:

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

[0077] Among them, N short is the number of days for the short-period orbital regression.

[0078] As an implementation method 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 regression orbit by using a parameter adaptive genetic algorithm, such as Figure 3 The specific steps are as follows:

[0079] Step 1: Parameter initialization. Given the initial population size N P , crossover probability P C , mutation probability P D , natural selection ratio m, semi-major axis value range [a min a max ], the range of load incident angle [α1α2], the maximum regression period in days N max .

[0080] Step 2: Initial population creation: Generate the most primitive population based on the given upper and lower bounds of the decision variables.

[0081] Step 3: Based on the analytical design method of the sun-synchronous regression orbit, the decision variables are modified and the objective function is calculated. long0 ,a short0 , it is necessary to satisfy the constraint of sun-synchronous regression, so the following correction is made.

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

[0083]

[0084] However, the parameter regression characteristics cannot be guaranteed. In the non-spherical gravity field perturbation, J2 perturbation plays a dominant role. Therefore, the satellite orbit period T is calculated by the following formula long ,T short .

[0085]

[0086] Since the orbit is a sun-synchronous regression orbit, the node day D N =86400s. The regression parameter Q is calculated by formula (21) and (22): long ,Q short .

[0087]

[0088] The number of long- and short-period regression orbits R is obtained by formulas (23) and (24): long ,R short And the number of days of the return period N long ,N short .

[0089]

[0090] Based on the obtained R long ,R short , N long ,N short By analytical method, the closest regression orbit parameter a is obtained long ,a short ,i long ,i short , based on this parameter, calculate the value of the objective function η(x), f(x), and p(x).

[0091]

[0092]

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

[0094] The calculation method of the fitness function is as follows: the initial fitness of each individual in the population is 0, and 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. In this way, the fitness function of each individual is obtained.

[0095] Step 5: Get offspring through crossover and mutation operations. The parent generation is selected through the roulette method based on the fitness function, and the offspring 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, determine whether the number of feasible solutions m on the Pareto front exceeds 1 / 3 of the population size. If it exceeds this range, then 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 view of 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. On 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 view of the problem that the time required for strict regression orbit design in the optimization process is long, which affects the iterative optimization speed, the embodiment of the present invention obtains the value of the optimization target based on the sun-synchronous regression orbit analytical design method, without the 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 view of the problem that it is easy to fall into the local optimal solution in the multi-objective optimization process, the parameter adaptive correction is introduced in the genetic algorithm part. According to the changing trend of the Pareto frontier, the population quantity 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] Embodiment 2:

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

[0101] Building modules for obtaining integrated optimization models of multi-mission remote sensing satellite regression orbits;

[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 constraints, orbit height constraints, maximum revisit period constraints, global leak-free coverage constraints; the optimization objective is Among them, η(x) is the long-period global leak-free coverage overlap rate, f(x) is the orbit switching fuel consumption rate increment, and p(x) is the short-period revisit time.

[0104] As an implementation method 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 regression orbit through a parameter adaptive genetic algorithm.

[0105] As an implementation method of an embodiment of the present invention, the optimization module uses a parameter adaptive genetic algorithm to perform multi-objective optimization on the long-period global leak-free coverage overlap rate, track switching fuel consumption and short-period revisit time based on constraints.

[0106] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for integrated design of multi-mission remote sensing satellite orbits, characterized in that: include: Step S1, obtaining a multi-task remote sensing satellite regression orbit integrated optimization model; Step S2: performing multi-objective optimization on the integrated optimization model of the multi-task remote sensing satellite regression orbit.

2. The multi-mission remote sensing satellite orbit integrated design method according to claim 1, characterized in that: In step S1, the integrated optimization model of multi-task remote sensing satellite regression orbit includes: setting decision variables, constraints and optimization objectives; wherein the constraints include: sun-synchronous regression constraint, orbit height constraint, maximum revisit period constraint, global leak-free coverage constraint; the optimization objective is Among them, η(x) is the long-period global leak-free coverage overlap rate, f(x) is the orbit switching fuel consumption rate increment, and p(x) is the short-period revisit time.

3. The multi-mission remote sensing satellite orbit integrated design method as claimed in claim 2, characterized in that: In step S2, a multi-objective optimization is performed on the integrated optimization model of the multi-task remote sensing satellite regression orbit by using a parameter adaptive genetic algorithm.

4. The multi-mission remote sensing satellite orbit integrated design method as claimed in claim 3, characterized in that: In step S2, a parameter adaptive genetic algorithm is used to perform multi-objective optimization on the long-period global leak-free coverage overlap rate, orbit switching fuel consumption and short-period revisit time based on constraint conditions.

5. A multi-mission remote sensing satellite orbit integrated design device, characterized in that: include: Building modules for obtaining integrated optimization models of multi-mission remote sensing satellite regression orbits; The optimization module is used to perform multi-objective optimization on the integrated optimization model of the multi-mission remote sensing satellite regression orbit.

6. The multi-mission remote sensing satellite orbit integrated design device according to claim 5, characterized in that: The integrated optimization model of the mission remote sensing satellite return orbit includes: setting decision variables, constraints and optimization objectives; the constraints include: sun-synchronous return constraint, orbit height constraint, maximum revisit period constraint, global leak-free coverage constraint; the optimization objective is Among them, η(x) is the long-period global leak-free coverage overlap rate, f(x) is the orbit switching fuel consumption rate increment, and p(x) is the short-period revisit time.

7. The multi-mission remote sensing satellite orbit integrated design device according to claim 6, characterized in that: The optimization module performs multi-objective optimization on the integrated optimization model of the multi-mission remote sensing satellite regression orbit through parameter adaptive genetic algorithm.

8. The multi-mission remote sensing satellite orbit integrated design device according to claim 7, characterized in that: The optimization module uses a parameter-adaptive genetic algorithm to perform multi-objective optimization on the long-period global leak-free coverage overlap rate, orbit switching fuel consumption, and short-period revisit time based on constraints.

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