Deep space exploration transfer section starlight angular distance information optimization and autonomous navigation method and device

Abstract

The embodiment of the invention provides a deep space exploration transfer section starlight angular distance information optimization and autonomous navigation method and device, the method is adopted by a detector with an optical sensor, and the method comprises the following steps: collecting a deep space image through the optical sensor; the deep space image comprises a plurality of fixed stars and at least one planet; according to the deep space image and the ephemeris, selecting the planet closest to the detector from the at least one planet as a target planet; constructing an optical sensor measurement coordinate system for the collected deep space image, and determining a fixed star vector of each fixed star in the plurality of fixed stars and a planet vector of the target planet in the optical sensor measurement coordinate system; determining a target fixed star combination in all fixed star combinations according to the fixed star vectors of all fixed stars; and according to the fixed star vectors of the three fixed stars in the target fixed star combination and the planet vector of the target planet, determining optimal deep space exploration transfer section starlight angular distance information.

Description

Technical Field

[0001] The present invention relates to the field of deep space detection by detectors, and in particular to a method and device for optimizing starlight angular distance information and autonomous navigation in a deep space detection transfer phase. Background Art

[0002] Autonomous navigation technology has received widespread attention in deep space exploration missions such as small body sampling return and planetary landing. These missions require the probe to use its own sensors to determine orbital parameters, thereby avoiding time delays in the navigation process. At present, the docking and attachment process of the probe is the main application scenario of autonomous navigation. A major feature of the deep space exploration transfer stage is the large spatial scale and long target distance, which poses a severe challenge to the effective range of the navigation sensor. X-pulsar signal receivers are ideal information sources for measuring information on distant targets. However, according to the latest research, the signal error of X-pulsars will seriously reduce the navigation accuracy, and the signal receiver is large in size and heavy in weight, which is not suitable for deep space probes to carry.

[0003] Optical autonomous navigation methods have been used in the transfer phase of several deep space exploration missions, including the Deep Space 1 and M-ARGO asteroid exploration missions. In these missions, the probes estimate the orbital state by planning asteroid observation sequences. However, due to the limited accuracy of the asteroid ephemeris, the final navigation accuracy is not high. Under good observation conditions, the autonomous navigation accuracy of the Deep Space 1 and M-ARGO probes in the transfer phase is only 150 kilometers and 1000 kilometers respectively.

[0004] In the process of implementing the present invention, the applicant discovered that there are at least the following problems in the prior art:

[0005] The existing autonomous navigation methods for deep space exploration have problems such as low navigation accuracy and large amount of navigation parameter calculation. Summary of the invention

[0006] The embodiments of the present invention provide a method and device for optimizing starlight angular distance information in a deep space exploration transfer phase and autonomous navigation, so as to solve the problems of low navigation accuracy and large amount of navigation parameter calculation in the existing deep space exploration autonomous navigation methods.

[0007] To achieve the above-mentioned purpose, on the one hand, an embodiment of the present invention provides a method for optimizing starlight angular distance information in a deep space exploration transfer phase, which is adopted by a detector having an optical sensor, and the method comprises:

[0008] Acquiring a deep-space image by an optical sensor; the deep-space image includes a plurality of stars and at least one planet; the number of the plurality of stars is greater than or equal to 3;

[0009] Selecting a planet closest to the detector from the at least one planet as a target planet according to the deep space image and the ephemeris;

[0010] For the acquired deep space image, an optical sensor measurement coordinate system is constructed, in which a star vector sum of each of the plurality of stars and a planet vector of the target planet are determined; the optical sensor measurement coordinate system coincides with a body coordinate system of the detector;

[0011] According to the star vectors of all stars, a target star combination is determined in all star combinations; wherein each star combination is composed of any three stars from all stars, and the sum of the inner products of the star vectors of the three stars in the target star combination is the minimum value in all star combinations;

[0012] According to the star vectors of the three stars in the target star combination and the planet vector of the target planet, the optimal starlight angular distance information of the deep space exploration transfer stage is determined.

[0013] In a second aspect, an embodiment of the present invention provides an autonomous navigation method based on the optimization of starlight angular distance information in a deep space exploration transfer phase, comprising:

[0014] Using any of the above-mentioned methods for optimizing the starlight angular distance information of the transfer phase of deep space exploration, the star vectors of three stars in the target star combination, the planet vector of a target planet and the starlight angular distance information corresponding to the three stars in the target star combination are determined;

[0015] Obtain the solar light pressure perturbation and planetary gravity perturbation data through orbital vibration model;

[0016] The starlight angular distance, solar light pressure perturbation and planetary gravitational perturbation data of each star are input into the unscented Kalman filter algorithm to determine the operating status of the detector.

[0017] In a third aspect, an embodiment of the present invention provides a device for optimizing starlight angular distance information in a deep space exploration transfer phase, which is adopted by a detector having an optical sensor, and the device comprises:

[0018] An image acquisition unit, configured to acquire a deep-space image through an optical sensor; the deep-space image includes a plurality of stars and at least one planet; the number of the plurality of stars is greater than or equal to 3;

[0019] a target planet determination unit, configured to select a planet closest to the detector from the at least one planet as a target planet according to the deep space image and the ephemeris;

[0020] a vector determination unit, configured to construct an optical sensor measurement coordinate system for the acquired deep space image, and determine the star vector sum of each of the plurality of stars and the planet vector of the target planet in the optical sensor measurement coordinate system; the optical sensor measurement coordinate system coincides with the body coordinate system of the detector;

[0021] A target star combination determining unit is used to determine a target star combination among all star combinations according to the star vectors of all stars; wherein each star combination is composed of any three stars among all stars, and the sum of the inner products of the star vectors of the three stars in the target star combination is the minimum value among all star combinations;

[0022] The starlight angular distance determination unit is used to determine the optimal starlight angular distance information of the deep space exploration transfer stage according to the star vectors of the three stars in the target star combination and the planet vector of the target planet.

[0023] In a fourth aspect, an embodiment of the present invention provides an autonomous navigation device based on the optimization of starlight angular distance information in a deep space exploration transfer phase, comprising:

[0024] A device for optimizing the starlight angular distance information of a deep space exploration transfer phase using any of the above-mentioned methods for optimizing the starlight angular distance information of a deep space exploration transfer phase, so as to determine the star vectors of three stars in a target star combination, the planet vector of a target planet, and the starlight angular distance information corresponding to the three stars in the target star combination;

[0025] Orbital vibration model unit, used to obtain solar light pressure perturbation and planetary gravity perturbation data;

[0026] The operating state determination unit is used to input the starlight angular distance, solar light pressure perturbation and planetary gravitational perturbation data of each star into the unscented Kalman filter algorithm to determine the operating state of the detector; the operating state includes: the three-directional position vector and velocity vector of the detector.

[0027] The above technical solution has the following beneficial effects: when obtaining the starlight angular distance used to determine the motion state of the detector, the embodiment of the present invention limits the number of stars providing starlight angular distance information to 3, and provides a basis for determining the target star combination, which significantly reduces the demand for detector computing resources when determining the optimal starlight angular distance and improves computing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 This is a flow chart of a method for optimizing starlight angular distance information in a deep space exploration transfer phase according to one embodiment of the present invention;

[0030] Figure 2 This is a flow chart of an autonomous navigation method based on the optimization of starlight angular distance information in the deep space exploration transfer phase, one of the embodiments of the present invention;

[0031] Figure 3 This is a schematic diagram of a device for optimizing the starlight angular distance information in a deep space exploration transfer phase according to one embodiment of the present invention;

[0032] Figure 4 This is an architecture diagram of an autonomous navigation device based on the optimization of starlight angular distance information in a deep space exploration transfer phase according to one embodiment of the present invention;

[0033] Figure 5 It is a schematic diagram of an autonomous navigation scheme for deep space exploration transfer segment based on starlight angular distance according to one embodiment of the present invention;

[0034] Figure 6 It is a schematic diagram of the target measurement principle of the optical sensor measurement coordinate system according to one embodiment of the present invention;

[0035] Figure 7 This is a schematic diagram of the principle of measuring the starlight angular distance according to one embodiment of the present invention;

[0036] Figure 8 is a schematic diagram of the positional relationship among a probe, a planet and the sun according to one embodiment of the present invention;

[0037] Fig. 9 It is a schematic diagram of a tetrahedron composed of three star vectors in a measurement coordinate system according to one embodiment of the present invention;

[0038] Fig.10 This is a schematic diagram of the autonomous navigation process of the deep space exploration transfer phase according to one embodiment of the present invention;

[0039] Fig.11 This is a diagram of the apparent magnitude of the Earth;

[0040] Fig.12 This is a diagram of the apparent magnitude of Mars;

[0041] Fig.13 This is a diagram of Jupiter's apparent magnitude;

[0042] Fig.14 is a schematic diagram of observability scales under different star combinations according to one embodiment of the present invention;

[0043] Fig.15 This is a schematic diagram of the change in the observability scale under the combination of star 1 and star 3 according to one embodiment of the present invention;

[0044] Fig.16 This is a schematic diagram of the change in observability scale for the combination of star 1 and star 2 according to one embodiment of the present invention;

[0045] Fig.17 This is a schematic diagram of the change in observability scale for the combination of star 2 and star 3 according to one embodiment of the present invention;

[0046] Fig.18 It is a schematic diagram of the iterative process of a simulated annealing particle swarm algorithm according to one embodiment of the present invention;

[0047] Fig.19 is a schematic diagram of state estimation errors of different star combinations according to one embodiment of the present invention;

[0048] Fig. 20 It is a schematic diagram of the error in estimating the state of the optimal star combination according to one embodiment of the present invention;

[0049] Fig.21 is a schematic diagram of the estimation error of the random star combination state according to one embodiment of the present invention;

[0050] Fig. 22 This is a schematic diagram of the variation of the random star combination state estimation error with the measurement error according to one embodiment of the present invention;

[0051] Fig.23 This is a schematic diagram of the variation of the estimation error of the optimal star combination state with the measurement error according to one embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] The inventors consider that the time delay and unknown faults caused by the transfer segment navigation method based on the ground station in the prior art will seriously affect the navigation accuracy of the transfer segment of the future long-distance solar system planetary exploration mission, and reduce the success rate of the subsequent exploration mission to capture, approach and fly over celestial bodies. Therefore, the inventors believe that in order to improve the reliability of deep space exploration missions, it is necessary to provide an autonomous navigation method for the transfer segment of deep space exploration. The inventors found that optical sensors are ideal information sources for measuring information of distant targets. Unlike X-pulsar signal receivers, optical sensors can be used to observe a variety of space targets, including stars, planets and planetary satellites, and have long range, low energy consumption and simple structure, which is in line with the trend of miniaturization of future spacecraft. In addition, the development of universal optical sensors also facilitates the acquisition of large-scale space measurement information. Therefore, optical autonomous navigation is the most suitable navigation method for the transfer segment of deep space exploration. In order to overcome the problem of low precision, the starlight angle distance (Starlight Angle Distances, SAD) composed of planetary vectors and starlight vectors has gradually become effective observation information. Their ephemeris accuracy is higher in the case of long-term observation and cataloging by ground observation stations. The inventors found that due to the limited computing and storage resources on the satellite, it is difficult for the detector to process all optical image information simultaneously on orbit. Therefore, it is necessary to select the most appropriate combination of planetary vectors and starlight vectors to obtain starlight angular distance information, and then realize the autonomous navigation scheme with the best accuracy. In order to determine the optimal measurement information combination, the current method usually obtains the optimal measurement information through traversal method based on observability analysis. However, due to the large number of observable planets and stars, and the navigation system model is a typical nonlinear model, the navigation system consumes a lot of computing and storage resources when performing observability analysis and traversing measurement information, and it cannot be realized autonomously on the satellite. Therefore, the inventors believe that it is necessary to construct a simple and efficient observability index and simplify the measurement information optimization selection algorithm, so as to select the least optical measurement information while reducing the computational burden on the satellite, and realize autonomous navigation of the deep space exploration transfer segment.

[0054] In a first aspect, an embodiment of the present invention provides a method for optimizing starlight angular distance information in a deep space exploration transfer phase, which is adopted by a detector having an optical sensor, and the method comprises:

[0055] Step S11, collecting a deep-space image by an optical sensor; the deep-space image includes a plurality of stars and at least one planet; the number of the plurality of stars is greater than or equal to 3;

[0056] Step S12, selecting a planet closest to the detector from the at least one planet as a target planet according to the deep space image and the ephemeris;

[0057] Step S13, constructing an optical sensor measurement coordinate system for the acquired deep space image, determining the star vector sum of each of the plurality of stars and the planet vector of the target planet in the optical sensor measurement coordinate system; the optical sensor measurement coordinate system coincides with the body coordinate system of the detector;

[0058] Step S14, determining a target star combination among all star combinations based on the star vectors of all stars; wherein each star combination is composed of any three stars among all stars, and the sum of the inner products of the star vectors of the three stars in the target star combination is the minimum value among all star combinations;

[0059] Step S15, determining the optimal starlight angular distance information of the deep space exploration transfer stage according to the star vectors of the three stars in the target star combination and the planet vector of the target planet.

[0060] The embodiments of the present invention have the following technical effects: when obtaining the starlight angular distance used to determine the motion state of the detector, the embodiments of the present invention limits the number of stars providing starlight angular distance information to 3, and provides a basis for determining the target star combination, which significantly reduces the demand for detector computing resources when determining the optimal starlight angular distance and improves computing efficiency.

[0061] Furthermore, constructing an optical sensor measurement coordinate system for the acquired deep space image, and determining the star vector sum of each star in the plurality of stars and the planet vector of the target planet in the optical sensor measurement coordinate system, comprises:

[0062] The optical sensor measurement coordinate system coincides with the detector's body coordinate system; the origin of the detector's body coordinate system is fixed at the detector's centroid, the detector's body coordinate system's X-axis points to the front of the detector, the detector's body coordinate system's Y-axis points to the right of the detector's body coordinate system's X-axis and is perpendicular to the detector's body coordinate system's X-axis, the detector's body coordinate system's Z-axis points to the detector's bottom and is perpendicular to the detector's XY plane, and satisfies the right-hand rule;

[0063] For each star and planet, the following formulas are applied to determine the star vector of the star and the planet vector of the planet:

[0064]

[0065] Where f represents the focal length of the optical sensor, (m x ,m y ) represents the coordinates of the target on the imaging plane of the optical sensor, and the target includes each star or planet.

[0066] Further, determining a target star combination from among all star combinations according to the star vectors of all stars includes:

[0067] Initialize the particle swarm, which includes m particles ζ i ; where i = 1, 2, ..., m, each particle in the particle group represents a potential solution set containing the initial position and velocity, and each particle corresponds to a star combination, i.e., ζ i =(x i ,v i ), where x i ={x i1 ,x i2 ,x i3}, x i1 、x i2 、x i3 Number the three stars in the particle respectively;

[0068] For each particle ζ in the particle swarm i , calculate each particle ζ i The fitness function o(ζ i )=|s1s2|+s1s3|+|s2s3|, s1, s2, s3 are the star vectors of the three stars in the star combination corresponding to the particle, denoted as f(ζ i )=o(ζ i ), record each particle ζ i Position P id 、Fitness f(P id )=o(P id ), compare all m particles ζ i The fitness of the particle ζ corresponding to the minimum fitness is selected pd Position as the best position P pd , the corresponding fitness is the global optimal fitness f(P pd );

[0069] According to the global optimal fitness f(ζ pd ), at this time pd =P pd , the initial temperature of the annealing algorithm is calculated to be T0 = f(ζ pd ) / ln5;

[0070] Remember F SA (ζ id ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T0, each particle ζ id Position P id The annealing algorithm fitness is calculated as follows:

[0071]

[0072] Where exp() represents the natural exponential function, ζ id represents the current particle, ζ pd Indicates the current particle position;

[0073] Apply the idea of ​​roulette wheel selection algorithm to select the optimal position P of the individual id Select one to replace the global optimal position P pd ;

[0074] P pd Replace with P rd , substitute into the particle swarm formula, and update the speed of each particle as follows:

[0075]

[0076] Among them, k represents the number of iterations, c1 and c2 represent learning factors, usually 2, r1 and r2 represent two different random numbers between 0 and 1, ω represents the weight, represents the speed of the individual optimal particle at the k+1th iteration, represents the speed of the individual optimal particle at the kth iteration, Table particle ζ i The initial position of represents the optimal position of the individual at the kth iteration;

[0077] Then calculate the fitness of each particle and update the optimal position P of each particle id and the optimal position of the population P pd ;

[0078] Perform annealing operation, the input is the current temperature, and the output is the initial temperature T of the next iteration:

[0079] T=δ×T0 (4)

[0080] Among them, T0 represents the current temperature, δ∈(0,1) represents the annealing factor;

[0081] Determine whether the preset termination condition is met, if the preset termination condition is met, stop searching and output the return value, if the preset termination condition is not met, go to the memory F SA (ζ id ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T0, each particle ζ id Position P id The steps of calculating the fitness of the annealing algorithm;

[0082] The preset termination condition is the judgment condition formula that satisfies the observability metric to reach the minimum value:

[0083] min(|s1s2|+|s1s3|+|s2s3|)

[0084] starccos(s i sj ) ≤F0 i,j=1,2,3,i≠j (5)

[0085] Among them, s1, s2, and s3 are the star vectors of the three stars in the star combination corresponding to the particle; F0 represents the field angle of the optical sensor;

[0086] The return value includes a target star combination;

[0087] The roulette wheel selection algorithm is used to select operators, including:

[0088] Generate a random number pBet between 0 and 1;

[0089] According to the annealing algorithm fitness f SA (P id ), calculate the cumulative probability

[0090] According to the cumulative probability, the individual optimal position P of the rth particle that meets the preset optimization conditions is selected. rd Instead of the global optimal position P pd ; The preset selection condition is: comfit(r-1) <pBet<comfit(r)。(7)

[0091] Further, according to the star vectors of the three stars in the target star combination and the planet vector of the target planet, the optimal starlight angular distance information of the deep space exploration transfer phase is determined, including:

[0092] Determine the starlight angular distance of the i-th star according to the starlight angular distance measurement model of the i-th star:

[0093] A i,k =arccos(s0s i,k )+v i,k (8)

[0094] Among them, s i,k , i = 1, 2, 3 represents the star vector of the i-th star in the target star combination, k represents the time, s i,k =(cosα i,k cosδ i,k ,cosα i,k sinδ i,k ,sinα i,k ) T , α i,k and δ i,krepresent the right ascension and declination of the ith star in the target star combination, A i,k , i = 1, 2, 3 represents the starlight angular distance composed of the starlight vector of the i-th star in the target star combination and the planet vector of the target planet; s0 represents the direction vector between the planet and the probe, v i,k Represents the measurement noise, which obeys the Gaussian distribution and satisfies

[0095] According to n s The measurement model composed of the starlight angular distance information determines the starlight angular distances corresponding to the three stars in the target star combination:

[0096]

[0097] Among them, E(v k )=O, O represents the zero matrix, z k represents the measurement information vector, Indicates n s Starlight vectors, n s Indicates the number of starlight angular distances, v k represents the measurement noise, h(x k ) represents n s A vector composed of starlight vectors R k The covariance matrix of the measurement noise is represented by the measurement matrix H. k is h(x k )About x k The partial derivative matrix of is expressed as:

[0098]

[0099] Among them, H i,k The specific expression is:

[0100]

[0101] in,

[0102]

[0103]

[0104] Among them, (x, y, z) represents the position vector of the probe, (x0, y0, z0) represents the position vector of the planet, and δ i represents the declination of the ith star, α i represents the right ascension of the ith star.

[0105] Second, as Figure 2As shown, an embodiment of the present invention provides an autonomous navigation method based on the optimization of starlight angular distance information in a deep space exploration transfer phase, comprising:

[0106] Step S21, using any of the above-mentioned methods for optimizing the starlight angular distance information of the transfer phase of deep space exploration, to determine the star vectors of three stars in the target star combination, the planet vector of a target planet, and the starlight angular distance information corresponding to the three stars in the target star combination;

[0107] Step S22, obtaining solar light pressure perturbation and planetary gravity perturbation data through an orbital vibration model;

[0108] Step S23, inputting the starlight angular distance, solar light pressure perturbation and planetary gravitational perturbation data of each star into the unscented Kalman filter algorithm to determine the operating status of the detector.

[0109] Thirdly, Figure 3 As shown, an embodiment of the present invention provides a device for optimizing starlight angular distance information in a deep space exploration transfer phase, which is adopted by a detector having an optical sensor, and the device comprises:

[0110] An image acquisition unit 300 is used to acquire a deep-space image through an optical sensor; the deep-space image includes a plurality of stars and at least one planet; the number of the plurality of stars is greater than or equal to 3;

[0111] A target planet determination unit 301 is used to select a planet closest to the detector from the at least one planet as a target planet according to the deep space image and the ephemeris;

[0112] A vector determination unit 302 is used to construct an optical sensor measurement coordinate system for the acquired deep space image, and determine the star vector sum of each star in the plurality of stars and the planet vector of the target planet in the optical sensor measurement coordinate system; the optical sensor measurement coordinate system coincides with the body coordinate system of the detector;

[0113] The target star combination determining unit 303 is used to determine the target star combination among all star combinations according to the star vectors of all stars; wherein each star combination is composed of any three stars among all stars, and the sum of the inner products of the star vectors of the three stars in the target star combination is the minimum value among all star combinations;

[0114] The starlight angular distance determination unit 304 is used to determine the optimal starlight angular distance information of the deep space exploration transfer stage according to the star vectors of the three stars in the target star combination and the planet vector of the target planet.

[0115] Further, the vector determination unit 302 includes:

[0116] The optical sensor measurement coordinate system coincides with the detector's body coordinate system; the origin of the detector's body coordinate system is fixed at the detector's centroid, the detector's body coordinate system's X-axis points to the front of the detector, the detector's body coordinate system's Y-axis points to the right of the detector's body coordinate system's X-axis and is perpendicular to the detector's body coordinate system's X-axis, the detector's body coordinate system's Z-axis points to the detector's bottom and is perpendicular to the detector's XY plane, and satisfies the right-hand rule;

[0117] The vector determination module is used to apply the following formula (1) to each star and planet to determine the star vector of the star and the planet vector of the planet.

[0118] Furthermore, the target star combination determination unit 303 includes:

[0119] Particle swarm initialization module, used to initialize the particle swarm, the particle swarm includes m particles ζ i ; where i = 1, 2, ..., m, each particle in the particle group represents a potential solution set containing the initial position and velocity, and each particle corresponds to a star combination, i.e., ζ i =(x i ,v i ), where x i ={x i1 ,x i2 ,x i3}, x i1 、x i2 、x i3 Number the three stars in the particle respectively;

[0120] The particle fitness determination module is used to determine the fitness of each particle in the particle swarm. i , calculate each particle ζ i The fitness function o(ζ i )=|s1s2|+|s1s3|+|s2s3|, s1, s2, s3 are the star vectors of the three stars in the star combination corresponding to the particle, denoted as f(ζ i )=o(ζ i ), record each particle ζ i Position P id , fitness f(P id )=o(P id ), compare all m particles ζ i The fitness of the particle ζ corresponding to the minimum fitness is selected pd Position as the best position P pd , the corresponding fitness is the global optimal fitness f(P pd );

[0121] The annealing initial temperature determination module is used to determine the global optimal fitness f(ζ pd ), at this time pd =P pd , the initial temperature of the annealing algorithm is calculated to be T0 = f(ζ pd ) / ln5;

[0122] Annealing fitness determination module, used to record F SA (ζ id ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T0, each particle ζ id Position P id The fitness calculation of the annealing algorithm is as shown in formula (2);

[0123] The global optimal update module is used to apply the idea of ​​the roulette wheel selection algorithm to update the optimal position P of the individual id Select one to replace the global optimal position P pd ;

[0124] Particle velocity update module, used to update P pd Replace with P rd , substitute into the particle swarm formula, and update the speed of each particle as shown in formula (3):

[0125] The particle position update module is used to recalculate the fitness of each particle and update the optimal position P of each particle. id and the optimal position of the population P pd ;

[0126] Annealing module, performs annealing operation, the input is the current temperature, and the output is the initial temperature T of the next iteration. The specific formula is formula (4);

[0127] A termination judgment module, used to judge whether a preset termination condition is met, and if so, stop searching and output a return value; if not, trigger the annealing fitness determination module;

[0128] The preset termination condition is to satisfy the judgment condition formula (5) that the observability metric reaches the minimum value;

[0129] The return value includes a target star combination;

[0130] The roulette wheel selection algorithm is used to select operators, including:

[0131] Generate a random number pBet between 0 and 1;

[0132] According to the annealing algorithm fitness f SA (P id ), and the cumulative probability is calculated by formula (6);

[0133] According to the cumulative probability, the individual optimal position P of the rth particle that meets the preset optimization conditions is selected. rd Instead of the global optimal position P pd ; The preset optimization condition is formula (7).

[0134] Furthermore, the starlight angular distance determination unit 304 is configured as follows:

[0135] By formula (8), the starlight angular distance of the i-th star is determined according to the starlight angular distance measurement model of the i-th star;

[0136] Through formula (9) to formula (17), according to n s The measurement model composed of the starlight angular distance information determines the starlight angular distances corresponding to the three stars in the target star combination.

[0137] Fourthly, Figure 4 As shown, an embodiment of the present invention provides an autonomous navigation device based on the optimization of starlight angular distance information in the deep space exploration transfer phase, comprising:

[0138] A deep space exploration transfer phase starlight angular distance information optimization device 400 using any of the deep space exploration transfer phase starlight angular distance information optimization methods as described above, so as to determine the star vectors of three stars in the target star combination, the planet vector of a target planet and the starlight angular distance information corresponding to the three stars in the target star combination;

[0139] The orbital vibration model unit 401 is used to obtain the solar light pressure perturbation and planetary gravity perturbation data;

[0140] The operating state determination unit 402 is used to input the starlight angular distance, solar light pressure perturbation and planetary gravitational perturbation data of each star into the unscented Kalman filter algorithm to determine the operating state of the detector; the operating state includes: the three-directional position vector and velocity vector of the detector.

[0141] The above technical solution of the embodiment of the present invention is described in detail below in conjunction with specific application examples. For technical details not introduced during the implementation process, please refer to the relevant description in the previous text.

[0142] The transfer phase of deep space exploration plays a vital role in planetary exploration missions. Optical sensors are ideal information sources for autonomous navigation in the transfer phase of deep space exploration. In order to achieve autonomous navigation with the best accuracy under the condition of severely limited onboard resources, starlight angular distance is selected as the navigation information source. The minimum number of stars required for the navigation system is determined by observability matrix analysis. Then, combined with the starlight angular distance navigation principle, an observability scalar calculation metric is proposed to overcome the shortcomings of traditional observability analysis methods with large computational complexity. This metric can be used to analyze the impact of different starlight angular distances on navigation accuracy. On this basis, the optimal navigation method is combined with the optimal starlight angular distance selection algorithm to improve the autonomous navigation performance of the transfer phase of deep space exploration.

[0143] 1. Autonomous navigation scheme for deep space exploration transfer phase based on starlight angular distance

[0144] 1.1 Transfer segment autonomous navigation system framework

[0145] Autonomous navigation scheme for deep space exploration transfer phase based on starlight angular distance information Figure 5 As shown. Figure 5 In the navigation system state model, the main components are the gravitational perturbations of the sun and planets and the solar light pressure perturbation model. The measurement model is mainly based on the principle of measuring the starlight angular distance information, and is calculated from the starlight vector and planet vector measured by the optical sensor. Due to the large number of observable planets and stars, the navigation system needs to combine the perturbation model and the measurement model to analyze the observability, and select the optimal measurement information as the navigation measurement information source based on the analysis results. Finally, based on the measurement information and the system state model, the detector position state estimation result is obtained through the Unscented Kalman Filter (UKF) algorithm.

[0146] 1.2 Navigation system model

[0147] The following is an explanation of the navigation system related coordinate system definitions.

[0148] The solar inertial coordinate system is an inertial coordinate system centered on the sun and fixed relative to the X-axis along the direction of the intersection of the ecliptic and the solar equatorial plane. The Z-axis points to the vertical direction of the solar equator, and the Y-axis, the X-axis, and the Z-axis form a right-handed coordinate system. In the embodiment of the present invention, the solar inertial coordinate system is selected as the navigation coordinate system.

[0149] The origin of the body coordinate system is fixed at the center of mass of the spacecraft. The X-axis points to the front of the spacecraft. The Y-axis points to the right of the X-axis and is perpendicular to the X-axis. The Z-axis points to the bottom of the spacecraft, is perpendicular to the XY plane, and satisfies the right-hand rule.

[0150] The embodiment of the present invention assumes that the optical sensor measurement coordinate system coincides with the body coordinate system. The target measurement principle is as follows: Figure 6The target direction vector is expressed as formula (1).

[0151] 1.2.1 System State Model

[0152] The system state model is constructed based on the on-orbit dynamics model of the probe, including the gravitational perturbations of the sun and planets and the solar light pressure perturbations. The dynamics model is expressed as formula (18):

[0153]

[0154] in (x,y,z) and represents the position and velocity of the probe in the navigation coordinates, t represents time, δ(t) represents process noise (used to describe the unmodeled dynamics model), represents the derivative of x with respect to time t, f(x,t) is expressed as:

[0155]

[0156] where u s represents the solar gravitational constant, N=8 represents the number of major planets in the solar system, μ i represents the gravitational constant of the ith planet, (x i ,y i ,z i ) represents the position of the ith planet in the navigation coordinate system, (x si ,y si ,z si ) represents the relative position of the probe and the i-th planet, represents the distance between the sun and the probe, represents the distance between the sun and the i-th planet, represents the distance between the probe and the i-th planet, η represents the shadow coefficient, P SR represents the solar light pressure perturbation in 1 light year, C R represents the surface reflection coefficient of the detector, A R represents the cross-sectional area of ​​the spacecraft perpendicular to the direction of sunlight, m ​​represents the mass of the probe, AU 2 represents 1 astronomical unit squared. Further, the discrete form of f(x, t) is expressed as formula (20):

[0157]

[0158] Where T represents the time interval. The discrete form of the system state model is expressed as:

[0159] x k =f(x k-1 )+w k(twenty one)

[0160] Among them, w k Represents the discrete form of state noise δ(t), obeys Gaussian distribution, and satisfies E(w k )=0,

[0161] 1.2.2 Measurement Model

[0162] The navigation system measurement model is mainly constructed based on the principle of measuring starlight angular distance information. Since planets move slowly and are larger in size than planetary satellites, it is relatively easy for optical sensors to acquire and process images. Therefore, in order to improve the accuracy of starlight angular distance measurement, the center of mass direction vector of each of the eight planets in the solar system is selected as the planetary vector of starlight angular distance. The measurement principle is as follows Figure 7 As shown. Figure 7 In the equation, t and r represent the position vectors of the planet and the probe in the navigation coordinate system, respectively. s represents the relative position vector of the planet and the probe in the navigation coordinate system. i , i = 1, 2, 3 represents the starlight vector of the i-th star, specifically expressed as: s i =(cosα i cosδ i ,cosα i sinδ i ,sinα i ) T , where α i and δ i A represents the right ascension and declination of the ith star. i , i = 1, 2, 3 represents the starlight angular distance composed of the i-th starlight vector and the planet vector. The i-th starlight angular distance measurement model is expressed as formula (8).

[0163] n s The measurement model composed of the starlight angular distance information is expressed as formula (9) to formula (17).

[0164] The direction information of stars can be obtained through the ephemeris. However, due to the huge number of stars, it is necessary to determine which stars or stars are selected as measurement information based on the star magnitude and observability analysis results. Since the position information of nearby objects is more accurate and the optical image information obtained is more precise, the planet closest to the detector is used as the planetary vector measurement information source of the starlight angular distance.

[0165] 2. Observability Analysis

[0166] Observability is the key to obtaining high-precision navigation results. Based on the observability analysis results, the available information at different times can be determined, and the performance of the autonomous navigation system at different times can be estimated. Therefore, the observability analysis process is a key step in selecting the optimal combination of measurement information and improving navigation performance.

[0167] 2.1 Observability Matrix

[0168] The observability matrix can be used to determine the minimum number of star angular distance measurement data required to achieve autonomous navigation. The observability matrix is ​​defined as:

[0169]

[0170] in n represents the system state dimension. k When the rank of is n, the system satisfies the observability condition.

[0171] When there is only one starlight angular distance information, the observability matrix (22) is obtained after the simplest row transformation:

[0172]

[0173] Obviously, the rank of the observability matrix is ​​5, which is not full rank.

[0174] When there are two starlight angular distance information, the observability matrix (22) is obtained after the simplest row transformation:

[0175]

[0176] in

[0177]

[0178] Obviously, the rank of the observability matrix is ​​5, which is not full rank.

[0179] When there are three starlight angular distance information, the observability matrix (22) is obtained after the simplest row transformation:

[0180]

[0181] Obviously, the observability matrix is ​​full rank at this time, and the system meets the observability condition. The three starlight angular distances are composed of three starlight vectors and one planet vector. Therefore, when there are at least 3 stars and 1 planet, the probe can achieve autonomous navigation.

[0182] 2.2 Observability Metrics

[0183] The observability matrix rank provides the minimum number of stars required for autonomous navigation. In order to select the optimal star combination, it is necessary to quantify the observability of the autonomous navigation system, that is, the observability metric. Figure 7 , the positional relationship between the probe, the planet and the sun is as follows Figure 8 As shown. Where t = k t t0,r=k r r0,s=k s s0. k t , k r and k s Represent the modulus lengths of t, r and s respectively. t, k t and t0 are obtained through the planetary ephemeris. θ represents the angle between vectors t and s. φ is the complementary angle between vectors s and r. s0 represents the direction vector from the planet to the probe in the navigation coordinate system, expressed as:

[0184]

[0185] in Represents the rotation matrix from the measurement coordinate system to the navigation coordinate system, represents the rotation matrix from the detector body coordinate system to the navigation coordinate system, represents the rotation matrix from the measurement coordinate system to the body coordinate system, which is assumed to be the unit matrix in the embodiment of the invention. I0 represents the direction vector from the planet to the probe in the measurement coordinate system, which can be directly obtained by the measurement sensor. Therefore, r0 is expressed as:

[0186]

[0187] further:

[0188] θ=arccos(t0s0) (29)

[0189] φ=arccos(-r0s) (30)

[0190] According to the sine theorem, the position of the detector in the navigation coordinate system is expressed as:

[0191]

[0192] According to formula (31), the accuracy of r0 depends on the rotation matrix and vector 10. Since 10 depends on the nearest planet, the quantized rotation matrix The determination accuracy is very important for evaluating navigation accuracy. Represents the attitude matrix determined by a set of three star vectors. According to the above analysis, the starlight angular distance autonomous navigation can be equivalently regarded as determining the position of the detector in the navigation coordinate system by the attitude determination results of the planet and starlight vector with known azimuth information. According to the above analysis, the accuracy of starlight angular distance autonomous navigation can be equivalently expressed by the accuracy of starlight attitude determination.

[0193] Theorem 1: The determination accuracy is expressed as:

[0194]

[0195] in represent The determination error, represent Determine the error covariance.

[0196] Proof: Let I1, I2 and I3 represent the starlight vector in the measurement coordinate system, then:

[0197]

[0198] in v represents the measurement noise, which is equal to the measurement noise in formula (9). According to the least squares method, The estimated result is expressed as:

[0199]

[0200] The estimation error is expressed as:

[0201]

[0202] but,

[0203] Theorem 2. When the three star light vectors are perpendicular to each other, the attitude matrix Make sure the accuracy is optimized.

[0204] Proof: According to Theorem 1, The determination accuracy is converted into:

[0205]

[0206] in() * represents the adjoint matrix.

[0207] In formula (36), R is the optical sensor design parameter. Therefore, when When the best accuracy is achieved, that is reaches the minimum value, at which point det(L T L) reaches its maximum value and tr[(L T L)* ] reaches the minimum value.

[0208] On the one hand, for det(L T L), such as Fig. 9 As shown, its size is actually equal to the volume of the tetrahedron formed by the three star vectors. This is because the volume of the tetrahedron is calculated as:

[0209]

[0210] The volume of a tetrahedron can also be calculated using the following formula:

[0211]

[0212] in α, β, γ represent the angles between two star vectors, ||l1||, ||l2||, ||l3|| represent the modulus lengths of the three star vectors, all equal to 1. Obviously, when V s When the maximum value is reached, there is:

[0213] sinp=sin(p-α)=sin(p-β)=sin(p-γ) (39)

[0214] Then we have:

[0215]

[0216] On the other hand, tr[(L T L) * ]=λ1+λ2+λ3, where λ1, λ2, λ3 represent matrices (L T L) * In addition, When tr[(L T L) * ]=λ1+λ2+λ3 reaches the minimum value, λ1=λ2=λ3. When , the star vector matrix L is an orthogonal matrix. Then λ1=λ2=λ3=1. This means that when When det(L T L) reaches its maximum value and tr[(L T L) * ] reaches the minimum value.

[0217] In summary, when the three star light vectors are perpendicular to each other, the attitude matrix Make sure the accuracy is optimized.

[0218] According to Theorem 1 and Theorem 2, the closer the inner product of the star vector is to 0, the higher the state estimation accuracy. When the sum of the inner products between the three stars is equal to 0, the state estimation accuracy is optimal. Therefore, the observable metric of the starlight angular distance autonomous navigation system is expressed as:

[0219] o=|s1s2|+|s1s3|+|s2s3| (41)

[0220] Therefore, the optimal stellar combination can be optimized and selected based on the observability metric.

[0221] 3. Optimal navigation solution based on optimization algorithm

[0222] According to the above observability analysis, the observability metric o can be used to evaluate the accuracy of autonomous navigation. The minimum value of o will be used as the optimization target for selecting the optimal star combination, thereby optimizing the accuracy of autonomous navigation. In this section, a navigation star combination selection scheme based on observability metric is given, and a simulated annealing particle swarm algorithm is used to select the optimal star combination.

[0223] 3.1 Starlight Angular Distance Optimization Model Based on Observability Metrics

[0224] Measure the star vector l in the coordinate system i (i=1,2,3) can be obtained by processing the image information obtained by the optical sensor. The observability metric o can be obtained by calculating the inner product of the star vectors. Since the number of starlight vectors is 3, the calculation process of o greatly reduces the computational complexity compared to the traditional observability evaluation method involving complex matrix calculation. Therefore, the optimal star combination can be obtained when o is minimized. The specific optimization model is expressed as formula (5) as the constraint condition.

[0225] 3.2 Simulated annealing particle swarm optimization algorithm

[0226] The simulated annealing algorithm is a global optimization algorithm based on the metal annealing mechanism. When the computing resources are large enough and the computing time is long enough, the algorithm can converge to the global optimal solution by probability. However, the limited processing resources of the detector limit the effectiveness of the algorithm. The particle swarm algorithm has good stability in optimal solution search and global convergence, but its search domain is small and it is easy to fall into the local optimal solution.

[0227] Considering the advantages and disadvantages of the two algorithms, the optimal navigation scheme combines simulated annealing and particle swarm optimization to optimize the combination of star measurement information. The specific optimization selection process is the optimal star combination optimization scheme based on simulated annealing particle swarm optimization.

[0228] Parameters that need to be initialized: weight parameter ω, learning factors c1 and c2, annealing rate δ, particle swarm evolution generation M, number of particles m; Output result: When the observability measure o reaches the minimum value, the global optimal position of the particle is output.

[0229] The specific process is as follows:

[0230] Step 1: Initialize particle swarm ζ i , i = 1, 2, ..., m, where each particle represents a potential solution set (i.e., a star combination) containing the initial position and velocity;

[0231] Step 2: Calculate ζ for each particle i The fitness function o(ζ i ), denoted as f(ζ i ), record the position P of each particle (the i-th particle) id , the global optimal position P pd 、Fitness f(P id ) and the global optimal fitness f(P pd ).

[0232] Step 3: According to the global optimal fitness f(ζ pd ) calculates the initial temperature f(P pd )>0

[0233] Step 4: Remember F SA (ζ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T, each P id The fitness of the annealing algorithm is calculated as shown in formula (2).

[0234] Step 5: Apply the idea of ​​roulette wheel selection algorithm to select the optimal position P of the individual id Select one to replace the global optimal position P pd , denoted as P rd Among them: the roulette selection method is a kind of selection operator, also known as the proportional selection method, and its basic idea is: to select individuals with good fitness in the population by probability, and the higher the fitness of each individual, the higher the probability of being selected. In the embodiment of the present invention, the algorithm idea is as follows:

[0235] pBet = rand(), (pBet is a random number between 0 and 1)

[0236] According to formula (6), according to the annealing algorithm fitness f SA (P id ) Calculate the cumulative probability;

[0237] According to the cumulative probability, select the individual optimal position P of the rth particle that meets the conditions rdInstead of the global optimal position P pd The condition is formula (7);

[0238] Step 6: P pd Replace with P rd , substitute into particle swarm formula (3) and update the velocity of each particle;

[0239] Step 7: Calculate the fitness of each particle and update the optimal position P of each particle. id and the optimal position of the population P pd

[0240] Step 8: Perform annealing operation according to formula (4);

[0241] Step 9: Determine whether the termination condition set in advance is met. If it is met, stop searching and output the return value. If it is not met, go to step 4.

[0242] Output: Output the results, output the global optimal solution of the particle swarm that meets the problem constraints.

[0243] According to the above steps, a particle group, that is, a star combination, which makes the observability metric optimal is obtained.

[0244] Based on the optimization of the above measurement information, the autonomous navigation scheme for the deep space exploration transfer phase based on the optimization of starlight angular distance information is obtained as follows: Fig.10 shown.

[0245] 4. Simulation Verification

[0246] The performance analysis of the autonomous optical navigation method based on starlight angular distance will be given. The scenario is selected as the transfer phase of the Jupiter exploration mission, and the simulation parameters are shown in Table 1.

[0247] project Numeric unit Initial Position (5.31,13.76,-0.01)*1010 m Initial Speed (-14.04,35.86,0.42)*103 m / s Simulation time 1.8 Year Initial position error (100010001000) m Initial speed error (101010) m / s Sensor sensitivity threshold 6.5 Magnitude Optical sensor resolution 4096*4096 Pixel Optical sensor focal length 170 Millimeters Measurement noise 0.1 Pixel Sampling period 12 Hour

[0248] Table 1 Simulation conditions

[0249] 4.1 Simulation scenario description

[0250] In the simulation, the star ephemeris is obtained from the Yibagu star catalog. Considering that the starlight angular distance information is obtained from three stars and one central planet, the number of visible stars needs to be determined according to different central stars. From the perspective of the entire detection process of the probe, the Earth, Mars and Jupiter are the three nearest central planets observable by the probe. Moreover, the large scale of the three planets makes their image information and center of mass information easy to obtain and process. According to the introduction of the starlight angular distance information in the previous section, the planet closest to the probe is used as the central planet of the starlight angular distance information. The three stars are selected according to the optimization algorithm to achieve the optimization of navigation accuracy.

[0251] After building the simulation scene, the visibility of the planets and stars is determined by the sensitivity threshold of the optical sensor and the apparent magnitude of the target. The apparent magnitudes of the three planets are as follows: Fig.11 , Fig.12 and Fig.13 shown.

[0252] according to Fig.11 , Fig.12 and Fig.13 As shown, the central planet may be different at different times. Note that at any time, there are far more than 3 stars that can be observed. Therefore, the starlight angular distance measurement information can be optimized and selected according to the observability metric and simulated annealing particle swarm algorithm to achieve orbit determination.

[0253] 4.2 Comparison and analysis of starlight angular distance information optimization selection

[0254] In order to demonstrate the effectiveness of the method proposed in the embodiment of the invention, a Monte Carlo simulation is performed in the above-mentioned scenario.

[0255] The observability of different star combinations is as follows Fig.14 As shown in the figure. In the simulation, according to the sensitivity threshold of the optical sensor and the Yatani star catalog, 3596 stars can be observed. The coordinate axes in the figure represent different stars. The observability of different three-star combinations is reflected by the color of the blocks in the figure. When the color is closer to blue, the observable metric is smaller and the navigation accuracy is higher.

[0256] according to Fig.14 , in order to better represent the changes in the observability of the system under different star combinations, Fig.15 , Fig.16 and Fig.17 Three images are given. In the three images, it is assumed that two of the three stars are fixed, and the number of each fixed star has been given in the figure. Fig.15 The star 1 is numbered 798 and the star 3 is numbered 1841. Fig.16 Among them are Star 1 No. 798 and Star 2 No. 2803. Fig.17 The three stars are numbered 1841 and the two stars are numbered 2803. The image shows how the observability of the system changes with the third star when the two stars are fixed.

[0257] according to Fig.14 , Fig.15 , Fig.16 and Fig.17 It can be seen that the system's observation capability changes very complexly with the star combination, and it is difficult to obtain the optimal star combination through analytical relationships. For detectors with severely limited on-board resources, it is difficult to select the optimal star combination through traversal methods to achieve the optimal system observability.

[0258] In order to deal with the above problems, the simulated annealing particle swarm algorithm is used to select the optimal star combination. The iterative process of the algorithm is as follows Fig.18 shown.

[0259] Based on the selection results of the optimization algorithm, the spacecraft obtains the position state estimation results under the optimal star combination and random star combination conditions. The simulation was performed 1000 times, and the spacecraft position state estimation error is as follows Fig.19 shown.

[0260] The estimated errors for different star combinations are as follows Fig. 20 and Fig.21 As shown, some curves are enlarged in the figure. The root mean square error results of the detector position estimation results are shown in Table 2.

[0261] X direction Y direction Z direction Total error Optimal star combination 750.46 2564.93 3174.31 4149.49 Random Star Combination 8690.96 13023.4 2153.27 15804.38

[0262] Table 2 Root mean square error of detector position estimation

[0263] according to Fig. 20 , Fig.21 ,The results in Table 2 show that the navigation accuracy of the optimal star combination is higher than that of the random star combination in most of the time. These results prove the effectiveness of this method.

[0264] 4.3 Effect of measurement noise

[0265] Measurement noise often affects navigation accuracy based on the optical image processing accuracy. The embodiment of the present invention gives the state estimation result under the condition of measurement noise changing from 0.1 pixel to 1.0 pixel. Fig. 22 and Fig.23 As shown in Figure 1. 1000 simulations were performed under each measurement noise value and the average value of the state estimation error was obtained. Fig. 22 It is obvious that the accuracy of state estimation varies nonlinearly with the measurement noise, and the accuracy under the optimal star combination is higher than that under the random star combination. In addition, it is obvious that the accuracy of state estimation decreases as the measurement noise increases. This is because the starlight angular distance is composed of the "probe-planet" vector and the star vector, and the navigation accuracy mainly depends on the extraction accuracy of the above two vectors. If the navigation accuracy is required to be higher, the accuracy of the image processing algorithm needs to be higher, which will also lead to higher requirements for the complexity and efficiency of the image processing algorithm. Therefore, it is necessary to balance navigation accuracy and computational efficiency in actual engineering practice.

[0266] It is obvious that the position estimation accuracy is affected differently by noise changes in different directions. This is related to the orbital state of the detector. Fig. 22 and Fig.23The position of the planet in the solar system indirectly provides distance information for the accuracy of the detector state estimation. The relative position information of the planet and the detector is mainly reflected in the position estimation result of the detector. The angle between the vector in different directions and the coordinate system has different results for the position estimation in different directions. Therefore, if you want to improve the state estimation accuracy of the detector in a certain direction, you need to select the appropriate star to be observed during the design of the detector orbit.

[0267] 6. Conclusion

[0268] The embodiment of the present invention provides an autonomous navigation method for the transfer phase of deep space exploration based on the optimization of starlight angular distance information. Due to limited computing resources, it is difficult to select the optimal measurement information from all observable stars. For autonomous navigation systems with strong nonlinearity, the calculation process of the traditional measurement information optimization selection method based on observability analysis is very complicated. In order to overcome this problem, the embodiment of the present invention proposes an observability measurement scale based on scalar calculation in combination with the principle of starlight angular distance positioning. Based on this observability metric, a navigation scheme that uses a simulated annealing particle algorithm to optimize the selection of the optimal measurement information is proposed. The simulation process verifies the effectiveness of the above method, and the impact of measurement noise on navigation accuracy is also analyzed based on the simulation results.

[0269] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0270] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0271] The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0272] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including". In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0273] Those skilled in the art may also understand that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention may be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly demonstrate the interchangeability of hardware and software, the various illustrative components, units, and steps described above have generally described their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present invention.

[0274] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing starlight angular distance information in the transfer phase of deep space exploration, characterized in that: Employed by a detector having an optical sensor, the method comprises: Acquiring a deep-space image by an optical sensor; the deep-space image includes a plurality of stars and at least one planet; the number of the plurality of stars is greater than or equal to 3; Selecting a planet closest to the detector from the at least one planet as a target planet according to the deep space image and the ephemeris; For the acquired deep space image, an optical sensor measurement coordinate system is constructed, in which a star vector sum of each of the plurality of stars and a planet vector of the target planet are determined; the optical sensor measurement coordinate system coincides with a body coordinate system of the detector; According to the star vectors of all stars, a target star combination is determined in all star combinations; wherein each star combination is composed of any three stars from all stars, and the sum of the inner products of the star vectors of the three stars in the target star combination is the minimum value in all star combinations; According to the star vectors of the three stars in the target star combination and the planet vector of the target planet, the optimal starlight angular distance information of the deep space exploration transfer stage is determined.

2. The method for optimizing the starlight angular distance information in the deep space exploration transfer phase according to claim 1, characterized in that: The method of constructing an optical sensor measurement coordinate system for the acquired deep space image, and determining the star vector sum of each star in the plurality of stars and the planet vector of the target planet in the optical sensor measurement coordinate system, comprises: The optical sensor measurement coordinate system coincides with the detector's body coordinate system; the origin of the detector's body coordinate system is fixed at the detector's centroid, the detector's body coordinate system's X-axis points to the front of the detector, the detector's body coordinate system's Y-axis points to the right of the detector's body coordinate system's X-axis and is perpendicular to the detector's body coordinate system's X-axis, the detector's body coordinate system's Z-axis points to the detector's bottom and is perpendicular to the detector's XY plane, and satisfies the right-hand rule; For each star and planet, the following formulas are applied to determine the star vector of the star and the planet vector of the planet: Where f represents the focal length of the optical sensor, (m x ,m y ) represents the coordinates of the target on the imaging plane of the optical sensor, and the target includes each star or planet.

3. The method for optimizing the starlight angular distance information in the deep space exploration transfer phase according to claim 1, characterized in that: The step of determining a target star combination from among all star combinations based on the star vectors of all stars includes: Initialize the particle swarm, which includes m particles ζ i ; where i = 1, 2, ..., m, each particle in the particle group represents a potential solution set containing the initial position and velocity, and each particle corresponds to a star combination, i.e., ζ i =(x i ,v i ), where x i ={x i1 ,x i2 ,x i3 }, x i1 、x i2 、x i3 Number the three stars in the particle respectively; For each particle ζ in the particle swarm i , calculate each particle ζ i The fitness function o(ζ i )=|s1s2|+|s1s3|+|s2s3|, s1, s2, s3 are the star vectors of the three stars in the star combination corresponding to the particle, denoted as f(ζ i )=o(ζ i ), record each particle ζ i Position P id 、Fitness f(P id )=o(P id ), compare all m particles ζ i The fitness of the particle ζ corresponding to the minimum fitness is selected pd Position as the best position P pd , the corresponding fitness is the global optimal fitness f(P pd ); According to the global optimal fitness f(ζ pd ), at this time pd =P pd , the initial temperature of the annealing algorithm is calculated to be T0 = f(ζ pd ) / ln5; Remember F SA (ζ id ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T0, each particle ζ id Position P id The annealing algorithm fitness is calculated as follows: Where exp() represents the natural exponential function, ζ id represents the current particle, ζ pd Indicates the current particle position; Apply the idea of ​​roulette wheel selection algorithm to select the optimal position P of the individual id Select one to replace the global optimal position P pd ; P pd Replace with P rd , substitute into the particle swarm formula, and update the speed of each particle as follows: Among them, k represents the number of iterations, c1 and c2 represent learning factors, r1 and r2 represent two different random numbers between 0 and 1, and ω represents the weight. represents the speed of the individual optimal particle at the k+1th iteration, represents the speed of the individual optimal particle at the kth iteration, Table particle ζ i The initial position of represents the optimal position of the individual at the kth iteration; Then calculate the fitness of each particle and update the optimal position P of each particle id and the optimal position of the population P pd ; Perform annealing operation, the input is the current temperature, and the output is the initial temperature T of the next iteration: T=δ×T0, where T0 represents the current temperature and δ∈(0,1) represents the annealing factor; Determine whether the preset termination condition is met, if the preset termination condition is met, stop searching and output the return value, if the preset termination condition is not met, go to the memory F SA (ζ id ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T0, each particle ζ id Position P id The steps of calculating the fitness of the annealing algorithm; The preset termination condition is the judgment condition formula that satisfies the observability metric to reach the minimum value: min(|s1s2|+|s1s3|+|s2s3|) s.t.arccos(s i s j )≤F0i,j=1,2,3,i≠j Among them, s1, s2, and s3 are the star vectors of the three stars in the star combination corresponding to the particle; F0 represents the field angle of the optical sensor; The return value includes a target star combination; The roulette wheel selection algorithm is used to select operators, including: Generate a random number pBet between 0 and 1; According to the annealing algorithm fitness f SA (P id ), calculate the cumulative probability According to the cumulative probability, the individual optimal position P of the rth particle that meets the preset optimization conditions is selected. rd Instead of the global optimal position P pd ; The preset selection condition is: comfit(r-1) <pBet<comfit(r)。 4. The method for optimizing the starlight angular distance information in the deep space exploration transfer phase according to claim 1, characterized in that: According to the star vectors of the three stars in the target star combination and the planet vector of the target planet, the optimal starlight angular distance information of the deep space exploration transfer phase is determined, including: Determine the starlight angular distance of the i-th star according to the starlight angular distance measurement model of the i-th star: A i,k =arccos(s0s i,k )+v i,k , where s i,k , i = 1, 2, 3 represents the star vector of the i-th star in the target star combination, k represents the time, s i,k =(cosα i,k cosδ i,k ,cosα i,k sinδ i,k ,sinα i ,k) T , α i,k and δ i,k represent the right ascension and declination of the ith star in the target star combination, A i,k , i = 1, 2, 3 represents the starlight angular distance composed of the starlight vector of the i-th star in the target star combination and the planet vector of the target planet; s0 represents the direction vector between the planet and the probe, v i,k represents the measurement noise, which obeys Gaussian distribution and satisfies E(v i,k )=0, According to n s The measurement model composed of the starlight angular distance information determines the starlight angular distances corresponding to the three stars in the target star combination: Among them, E(v k )=O, O represents the zero matrix, z k represents the measurement information vector, Indicates n s Starlight vector, m s Indicates the number of starlight angular distances, v k represents the measurement noise, h(x k ) represents n s A vector composed of starlight vectors R k The covariance matrix of the measurement noise is represented by the measurement matrix H. k is h(x k )About x k The partial derivative matrix of is expressed as: Among them, H i,k The specific expression is: in, Among them, (x, y, z) represents the position vector of the probe, (x0, y0, z0) represents the position vector of the planet, and δ i represents the declination of the ith star, α i represents the right ascension of the ith star.

5. An autonomous navigation method based on the optimization of starlight angular distance information in the deep space exploration transfer phase, characterized in that: include: Using any one of the starlight angular distance information optimization methods for the deep space exploration transfer phase as described in claims 1 to 4, the star vectors of three stars in the target star combination, the planet vector of a target planet and the starlight angular distance information corresponding to the three stars in the target star combination are determined; Obtain the solar light pressure perturbation and planetary gravity perturbation data through orbital vibration model; The starlight angular distance, solar light pressure perturbation and planetary gravitational perturbation data of each star are input into the unscented Kalman filter algorithm to determine the operating status of the detector.

6. A device for optimizing starlight angular distance information in the transfer phase of deep space exploration, characterized in that: Employed by a detector having an optical sensor, the device comprises: An image acquisition unit, configured to acquire a deep-space image through an optical sensor; the deep-space image includes a plurality of stars and at least one planet; the number of the plurality of stars is greater than or equal to 3; a target planet determination unit, configured to select a planet closest to the detector from the at least one planet as a target planet according to the deep space image and the ephemeris; a vector determination unit, configured to construct an optical sensor measurement coordinate system for the acquired deep space image, and determine the star vector sum of each of the plurality of stars and the planet vector of the target planet in the optical sensor measurement coordinate system; the optical sensor measurement coordinate system coincides with the body coordinate system of the detector; A target star combination determining unit is used to determine a target star combination among all star combinations according to the star vectors of all stars; wherein each star combination is composed of any three stars among all stars, and the sum of the inner products of the star vectors of the three stars in the target star combination is the minimum value among all star combinations; The starlight angular distance determination unit is used to determine the optimal starlight angular distance information of the deep space exploration transfer stage according to the star vectors of the three stars in the target star combination and the planet vector of the target planet.

7. The device for optimizing the starlight angular distance information in the deep space exploration transfer phase as claimed in claim 6, characterized in that: The vector determination unit comprises: The optical sensor measurement coordinate system coincides with the detector's body coordinate system; the origin of the detector's body coordinate system is fixed at the detector's centroid, the detector's body coordinate system's X-axis points to the front of the detector, the detector's body coordinate system's Y-axis points to the right of the detector's body coordinate system's X-axis and is perpendicular to the detector's body coordinate system's X-axis, the detector's body coordinate system's Z-axis points to the detector's bottom and is perpendicular to the detector's XY plane, and satisfies the right-hand rule; The vector determination module is used to apply the following formulas to each star and planet to determine the star vector of the star and the planet vector of the planet: Where f represents the focal length of the optical sensor, (m x ,m y ) represents the coordinates of the target on the imaging plane of the optical sensor, and the target includes each star or planet.

8. The device for optimizing the starlight angular distance information in the deep space exploration transfer phase as claimed in claim 6, characterized in that: The target star combination determination unit comprises: Particle swarm initialization module, used to initialize the particle swarm, the particle swarm includes m particles ζ i ; where i = 1, 2, ..., m, each particle in the particle group represents a potential solution set containing the initial position and velocity, and each particle corresponds to a star combination, i.e., ζ i =(x i ,v i ), where x i ={x i1 ,x i2 ,x i3 }, x i1 、x i2 、x i3 Number the three stars in the particle respectively; The particle fitness determination module is used to determine the fitness of each particle in the particle swarm. i , calculate each particle ζ i The fitness function o(ζ i )=|s1s2|+|s1s3|+|s2s3|, s1, s2, s3 are the star vectors of the three stars in the star combination corresponding to the particle, denoted as f(ζ i )=o(ζ i ), record each particle ζ i Position P id 、Fitness f(P id )=o(P id ), compare all m particles ζ i The fitness of the particle ζ corresponding to the minimum fitness is selected pd Position as the best position P pd , the corresponding fitness is the global optimal fitness f(P pd 0; The annealing initial temperature determination module is used to determine the global optimal fitness f(ζ pd ), at this time pd =P pd , the initial temperature of the annealing algorithm is calculated to be T0 = f(ζ pd ) / ln5; Annealing fitness determination module, used to record F SA (ζ id ) function is the function for calculating the fitness of the annealing algorithm. At the current temperature T0, each particle ζ id Position P id The annealing algorithm fitness is calculated as follows: Where exp() represents the natural exponential function, ζ id represents the current particle, ζ pd Indicates the current particle position; The global optimal update module is used to apply the idea of ​​the roulette wheel selection algorithm to update the optimal position P of the individual id Select one to replace the global optimal position P pd ; Particle velocity update module, used to update P pd Replace with P rd , substitute into the particle swarm formula, and update the speed of each particle as follows: Among them, k represents the number of iterations, c1 and c2 represent learning factors, r1 and r2 represent two different random numbers between 0 and 1, and ω represents the weight. represents the speed of the individual optimal particle at the k+1th iteration, represents the speed of the individual optimal particle at the kth iteration, Table particle ζ i The initial position of represents the optimal position of the individual at the kth iteration; The particle position update module is used to recalculate the fitness of each particle and update the optimal position P of each particle. id and the optimal position of the population P pd ; Annealing module performs annealing operation, the input is the current temperature, and the output is the initial temperature T of the next iteration: T=δ×T0, where T0 represents the current temperature and δ∈(0,1) represents the annealing factor; A termination judgment module, used to judge whether a preset termination condition is met, and if so, stop searching and output a return value; if not, trigger the annealing fitness determination module; The preset termination condition is the judgment condition formula that satisfies the observability metric to reach the minimum value: min(|s1s2|+|s1s3|+|s2s3|) s.t.arccos(s i s j )≤F0i,j=1,2,3,i≠j Among them, s1, s2, and s3 are the star vectors of the three stars in the star combination corresponding to the particle; F0 represents the field angle of the optical sensor; The return value includes a target star combination; The roulette wheel selection algorithm is used to select operators, including: Generate a random number pBet between 0 and 1; According to the annealing algorithm fitness f SA (P id ), calculate the cumulative probability According to the cumulative probability, the individual optimal position P of the rth particle that meets the preset optimization conditions is selected. rd Instead of the global optimal position P pd ; The preset selection condition is: comfit(r-1) <pBet<comfit(r)。 9. The device for optimizing the starlight angular distance information in the deep space exploration transfer phase as claimed in claim 6, characterized in that: The starlight angle determination unit is configured as follows: Determine the starlight angular distance of the i-th star according to the starlight angular distance measurement model of the i-th star: A i,k =arccos(s0s i,k )+v i,k , where s i,k , i = 1, 2, 3 represents the star vector of the i-th star in the target star combination, k represents the time, s i,k =(cosα i,k cosδ i,k ,cosα i,k sinδ i,k ,sinα i,k ) T , α i,k and δ i,k represent the right ascension and declination of the ith star in the target star combination, A i,k , i = 1, 2, 3 represents the starlight angular distance composed of the starlight vector of the i-th star in the target star combination and the planet vector of the target planet; s0 represents the direction vector between the planet and the probe, v i,k represents the measurement noise, which obeys Gaussian distribution and satisfies E(v i,k )=0, According to n s The measurement model composed of the starlight angular distance information determines the starlight angular distances corresponding to the three stars in the target star combination: Among them, E(v k )=O, O represents the zero matrix, z k represents the measurement information vector, Indicates n s Starlight vectors, n s Indicates the number of starlight angular distances, v k represents the measurement noise, h(x k ) represents n s A vector composed of starlight vectors R k Represents the covariance matrix of the measurement noise. Measurement matrix H k is h(x k )About x k The partial derivative matrix of is expressed as: Among them, H i,k The specific expression is: in, Among them, (x, y, z) represents the position vector of the probe, (x0, y0, z0) represents the position vector of the planet, and δ i represents the declination of the ith star, α i represents the right ascension of the ith star.

10. An autonomous navigation device based on the optimization of starlight angular distance information in the deep space exploration transfer phase, characterized in that: include: A device for optimizing starlight angular distance information in a deep space exploration transfer phase using any one of the methods for optimizing starlight angular distance information in a deep space exploration transfer phase as described in claims 1 to 4, so as to determine star vectors of three stars in a target star combination, a planet vector of a target planet, and starlight angular distance information corresponding to the three stars in the target star combination; Orbital vibration model unit, used to obtain solar light pressure perturbation and planetary gravity perturbation data; An operation state determination unit is used to input the starlight angular distance, solar light pressure perturbation and planetary gravitational perturbation data of each star into the unscented Kalman filter algorithm to determine the operation state of the detector; The operating state includes: three-directional position vectors and a velocity vector of the detector.

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