Track determination method and device, storage medium and electronic equipment

Abstract

The invention discloses an orbit determination method and device, a storage medium and electronic equipment, and relates to the technical field of spaceflight. The method comprises the following steps: generating an initial orbit parameter population according to double-row orbit data in a preset time period; performing fitness evaluation on each individual in the initial orbit parameter population to obtain fitness corresponding to each individual; on the basis of the fitness, high-quality individuals needing to be reserved in the initial orbit parameter population are determined, and a first new individual and a second new individual are generated in combination with a preset simplex tuning algorithm and a preset genetic algorithm so as to update the initial orbit parameter population; continuously performing iterative updating on the new orbit parameter population until a preset condition is met, and outputting a final iterative target orbit parameter population; and determining orbit determination parameters according to the target orbit parameter population. According to the method, the problem of local optimal solution of a simplex tuning method and the problem of excessive dependence on initial simplex can be solved, so that the accuracy and the stability of orbit determination can be improved.

Description

Technical Field

[0001] The present application relates to the field of aerospace technology, and in particular to an orbit determination method, device, storage medium and electronic equipment. Background Art

[0002] TLE (Two-Line Element) data is an important basic data for satellite positioning, navigation, communication, missile warning and other applications. How to accurately determine the orbit based on TLE data is of great significance for applications such as satellite orbit research, remote sensing, and geophysical measurement.

[0003] At present, the simplex tuning method is usually used to determine orbital parameters for orbit determination. However, the local optimization nature of the simplex tuning method easily causes the orbital parameters to fall into the local optimal solution, making it impossible to find the global optimal solution, affecting the accuracy of orbit determination. At the same time, the effect of the simplex tuning method is too dependent on the construction of the initial simplex. The selection of the initial simplex has a great influence on the quality of the final solution. If the initial simplex is not selected properly, it is likely to converge to a local optimal solution of poor quality, so it is difficult to ensure the accuracy and stability of orbit determination. Summary of the invention

[0004] In view of this, the present application provides a trajectory determination method, device, storage medium and electronic device, the main purpose of which is to solve the problem of local optimal solution of the simplex tuning method and the problem of over-reliance on the initial simplex, thereby improving the accuracy and stability of trajectory determination.

[0005] According to a first aspect of the present application, a trajectory determination method is provided, the method comprising:

[0006] Obtaining dual-row orbit data for a preset period of time and the epoch time of orbit determination;

[0007] generating an initial orbit parameter population according to the double-row orbit data of the preset time period;

[0008] Performing fitness evaluation on each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual;

[0009] Based on the fitness, determine the high-quality individuals that need to be retained in the initial orbit parameter population, and generate a first new individual and a second new individual respectively by combining a preset simplex tuning algorithm and a preset genetic algorithm;

[0010] According to the retained high-quality individuals, the first new individuals, and the second new individuals, the initial orbit parameter population is updated to obtain a new orbit parameter population;

[0011] Continue to iteratively update the new orbit parameter population until a preset condition is met, and then output a target orbit parameter population of the final iteration;

[0012] The orbit determination parameters corresponding to the epoch time are determined according to the target orbit parameter population.

[0013] According to a second aspect of the present application, a track determination device is provided, the device comprising:

[0014] An acquisition unit, used to acquire dual-row orbit data of a preset period and an epoch time of orbit determination;

[0015] A first generating unit, configured to generate an initial orbit parameter population according to the double-row orbit data of the preset time period;

[0016] An evaluation unit, used to evaluate the fitness of each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual;

[0017] A second generating unit is used to determine the high-quality individuals that need to be retained in the initial orbit parameter population based on the fitness, and generate a first new individual and a second new individual respectively by combining a preset simplex tuning algorithm and a preset genetic algorithm;

[0018] an updating unit, configured to update the initial orbit parameter population according to the retained high-quality individuals, the first new individuals, and the second new individuals to obtain a new orbit parameter population;

[0019] An output unit, used to continue iteratively updating the new orbit parameter population until a preset condition is met, and output a target orbit parameter population of the final iteration;

[0020] A determination unit is used to determine the orbit determination parameters corresponding to the epoch time according to the target orbit parameter population.

[0021] According to a third aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned orbit determination method is implemented.

[0022] According to a fourth aspect of the present application, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned orbit determination method when executing the program.

[0023] By means of the above technical scheme, the present application provides a method for determining an orbit, which, compared with the prior art, determines the fitness of each individual in the initial orbit parameter population, and determines the high-quality individuals that need to be retained in the initial orbit parameter population based on the fitness, and combines the preset simplex tuning algorithm and the preset genetic algorithm to generate the first new individual and the second new individual respectively, so that the orbit parameter population can be continuously updated and iterated, thereby determining the orbit determination parameters at the epoch time. Since the present application combines the global search capability of the genetic algorithm and the local optimization capability of the simplex tuning algorithm in the process of updating and iterating the orbit parameter population, it can solve the problem that the orbit parameters are easily trapped in the optimal layout solution, thereby improving the accuracy of orbit determination. At the same time, when using the simplex tuning algorithm, the present application uses the retained high-quality individuals as the initial simplex, so that the local optimal trap problem caused by improper selection of the initial simplex can be avoided, thereby improving the accuracy and stability of orbit determination.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 A schematic diagram of a process flow of a track determination method provided in an embodiment of the present application is shown;

[0027] Figure 2 A schematic diagram of a process for generating an initial orbit parameter population provided in an embodiment of the present application is shown;

[0028] Figure 3 A schematic diagram of a process for evaluating individual fitness provided in an embodiment of the present application is shown;

[0029] Figure 4 A schematic diagram of the process of retaining high-quality individuals and generating new individuals provided in an embodiment of the present application is shown;

[0030] Figure 5 A schematic diagram of population update provided by an embodiment of the present application is shown;

[0031] Figure 6 A schematic diagram of the overall process of track determination provided by an embodiment of the present application is shown;

[0032] Figure 7A schematic structural diagram of a track determination device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0034] The local optimization property of the simplex tuning method can easily cause the orbit parameters to fall into the local optimal solution. At the same time, the effect of the simplex tuning method is too dependent on the construction of the initial simplex, which may lead to the final convergence to a local optimal solution of poor quality. Therefore, it is difficult to ensure the accuracy and stability of orbit determination.

[0035] In order to solve the above problems, an embodiment of the present invention provides a track determination method, such as Figure 1 As shown, the method includes:

[0036] Step 10: Obtain the double-row orbit data for a preset period of time and the epoch time of orbit determination.

[0037] Among them, the epoch time is the time when the orbit determination is expected, which can be selected according to actual business needs. The epoch time is any moment in the preset time period, and the intercept length of the preset time period can be set according to actual business needs. The two-line orbit data is TLE data, which contains orbit parameters. The orbit parameters specifically include: BSTAR, orbit inclination, right ascension of ascending node, orbit eccentricity, perigee argument, mean anomaly, mean motion and other seven parameters. Among them, BSTAR is a parameter that describes the attenuation of satellite orbit air resistance.

[0038] The embodiment of the present invention is mainly applicable to the scenario of determining the satellite orbit. The execution subject of the embodiment of the present invention is a device or equipment capable of determining the satellite orbit.

[0039] Specifically, the dual-row orbit data of a preset time period, i.e., TLE data, can be obtained from specific websites (such as the Celestrak website provided by the U.S. Air Force, the Spacetrack website provided by the European Space Agency, etc.). Then, the epoch time at which orbit determination is required is determined, and the epoch time is the starting point of orbit propagation. Since the dual-row orbit data may contain noise or outdated data, in order to improve the data quality, it is necessary to pre-process the dual-row orbit data corresponding to each moment in the preset time period, so that the pre-processed dual-row orbit data corresponding to each moment in the preset time period can be used as the input data for subsequent orbit determination. The pre-processing process usually includes removing erroneous data, standardizing the data format, and filtering data.

[0040] The embodiment of the present invention can ensure the accuracy of subsequent process data by cleaning the dual-row track data. In addition, the embodiment of the present invention can ensure the accuracy and effectiveness of the dual-row track data by cleaning the data and determining the epoch time, thereby providing reliable input for subsequent track determination and dissemination.

[0041] Step 20: Generate an initial orbit parameter population according to the double-row orbit data of the preset time period.

[0042] For the embodiment of the present invention, although the two-line orbit data (TLE data) is convenient and fast, it is relatively simple and contains only limited information, so there are certain uncertainties and errors. When performing satellite navigation positioning calculations, the orbit parameters in the TLE data cannot be directly used as the orbit determination parameters at the epoch time, and they need to be corrected and processed. In order to determine the accurate orbit parameters at the epoch time, that is, the orbit determination parameters, the embodiment of the present invention first generates an initial orbit parameter population, that is, the parent orbit parameter population, based on the two-line orbit data (TLE data). For the generation process of the initial orbit parameter population, as shown in FIG. Figure 2 As shown, including:

[0043] Step 21: Determine orbit parameter constraints based on the double-row orbit data at the epoch time and the double-row orbit data at each target time close to the epoch time in the preset time period.

[0044] Among them, the orbital parameter constraints are the orbital parameter constraints at the epoch time.

[0045] For the embodiment of the present invention, in order to determine the orbit parameter constraint conditions, step 21 specifically includes: determining the orbit parameters of the epoch moment and the orbit parameters of the target moments according to the double-row orbit data of the epoch moment and the double-row orbit data of each target moment, and calculating the orbit parameter difference between any two adjacent moments in the epoch moment and each target moment; determining the positive difference and the negative difference in the orbit parameter difference between any two adjacent moments; determining the maximum positive difference in the positive difference and the maximum absolute value in the negative difference, and calculating the average value of the positive difference and the average absolute value of the negative difference; if the number of positive differences is a preset threshold, determining the upper limit of the orbit parameter to be the sum of the orbit parameter at the epoch moment and the maximum positive difference, and the lower limit of the orbit parameter to be the orbit parameter of the previous target moment corresponding to the epoch moment; if the negative difference is If the number is a preset threshold, the lower limit of the orbital parameter is determined to be the difference between the orbital parameter at the epoch and the maximum absolute value, and the upper limit of the orbital parameter is the orbital parameter at the previous target moment; if the number of positive differences or the number of negative differences is less than the preset threshold, the difference between the orbital parameter at the epoch and the orbital parameter at the previous target moment is calculated; if the difference is a positive number, the upper limit of the orbital parameter is determined to be the sum of the orbital parameter at the epoch and the average value of the positive differences, and the lower limit of the orbital parameter is the orbital parameter at the previous target moment; if the difference is a negative number, the lower limit of the orbital parameter is determined to be the difference between the orbital parameter at the epoch and the average absolute value of the negative differences, and the upper limit of the orbital parameter is the orbital parameter at the previous target moment; according to the upper limit and the lower limit of the orbital parameter, the orbital parameter constraint condition is determined.

[0046] Among them, the preset threshold is equal to the logarithm of adjacent moments between the epoch moment and each target moment. For example, there are 5 target moments, and the epoch moment and the 5 target moments before it can constitute 5 adjacent moment pairs, so the preset threshold can be determined to be 5.

[0047] For example, five target moments before the epoch moment and immediately adjacent to the epoch moment are selected from each moment in the preset time period, and then the orbital parameters of the epoch moment and the orbital parameters of the five target moments are determined based on the TLE data of the epoch moment and the TLE data of the five target moments, and the orbital parameter differences between any two adjacent moments in the five target moments and the epoch moment are calculated, and there are five orbital parameter differences in total. Then, the positive and negative differences of the five orbital parameter differences are determined, and the maximum positive difference of the positive differences and the maximum absolute value of the negative differences are determined, and the average value of the positive differences and the average absolute value of the negative differences are calculated. If the number of positive differences is 5, it means that the orbital parameters will increase over a period of time. Therefore, the upper limit of the orbital parameters is determined to be the sum of the orbital parameters at the epoch time and the maximum positive difference, and the lower limit of the orbital parameters is the orbital parameters at the previous moment corresponding to the epoch time; if the number of negative differences is 5, it means that the orbital parameters will decrease over a period of time. Therefore, the lower limit of the orbital parameters is determined to be the difference between the orbital parameters at the epoch time and the maximum absolute value, and the upper limit of the orbital parameters is the orbital parameters at the previous target moment; if the number of positive differences or the number of negative differences is less than 5, it means that the orbital parameters have both increased and decreased over a period of time. At this time, the difference between the orbital parameters at the epoch time and the orbital parameters at the previous target moment is further calculated. If the difference is a positive number, the upper limit of the orbital parameters is determined to be the sum of the orbital parameters at the epoch time and the average value of the positive differences, and the lower limit of the orbital parameters is the orbital parameters at the previous target moment; if the difference is a negative number, the lower limit of the orbital parameters is determined to be the difference between the orbital parameters at the epoch time and the average absolute value of the negative differences, and the upper limit of the orbital parameters is the orbital parameters at the previous target moment. After the upper and lower limits of the orbital parameters are determined, the orbital parameter constraints at the epoch time can be determined.

[0048] Since the orbital parameters specifically include seven parameters, namely BSTAR, orbital inclination, right ascension of ascending node, orbital eccentricity, argument of perigee, mean anomaly, and mean motion, it is necessary to determine the positive difference, negative difference, the maximum positive difference among the positive differences, and the maximum absolute value among the negative differences for each parameter in the above manner, and calculate the average value of the positive difference and the average absolute value of the negative difference, so as to determine the upper and lower limits of the parameter corresponding to the parameter, and then obtain the constraint conditions of each parameter.

[0049] Step 22: Generate an initial orbit parameter population according to the orbit parameter constraints.

[0050] Among them, the initial orbit parameter population is the parent orbit parameter population, and each individual in the parent orbit parameter population is randomly generated based on the orbit parameter constraints, and the number of individuals can be set according to actual business needs.

[0051] For the embodiment of the present invention, when generating the initial orbit parameter population according to the orbit parameter constraints, each initial orbit parameter is first randomly generated according to the orbit parameter constraints, and then each initial orbit parameter is determined as each individual in the initial orbit parameter population.

[0052] Specifically, for the BSTAR at the epoch time, orbital inclination, right ascension of the ascending node, orbital eccentricity, argument of perigee, mean anomaly, and each parameter in the mean motion, a value within the constraints of each parameter is randomly generated according to the constraints of each parameter. After the values ​​of each parameter are generated, they are combined into an individual, and the above process is repeated to generate a total of P individuals, thus forming the initial orbital parameter population, i.e., the parent orbital parameter population.

[0053] The P individuals generated in the embodiment of the present invention can provide sufficient solution space for subsequent optimization. Each individual represents a set of parameters, and these individuals are evolved through the optimization algorithm in the subsequent steps.

[0054] Step 30: Perform fitness evaluation on each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual.

[0055] In the embodiment of the present invention, after the initial orbit parameter population (parent orbit parameter population) is determined, the fitness of each individual in the population is evaluated. Figure 3 As shown, including:

[0056] Step 31: Determine the orbit parameters at each moment in the preset time period according to the double-row orbit data of the preset time period.

[0057] For an embodiment of the present invention, after obtaining the TLE data of a preset time period, the orbital parameters at each moment in the preset time period, namely, BSTAR, orbital inclination, right ascension of ascending node, orbital eccentricity, argument of perigee, mean anomaly, and average motion, can be obtained based on the TLE data.

[0058] For example, the preset time period includes 10 moments in total, and the orbital parameters of these 10 moments can be determined based on the TLE data of these 10 moments.

[0059] Step 32: Input the initial orbital parameters corresponding to each individual and the orbital parameters at each moment in the preset time period into a simplified perturbation theory model for calculation to obtain the position vector and velocity vector corresponding to each individual and the position vector and velocity vector corresponding to each moment.

[0060] Among them, the simplified perturbation theory model is the SGP4 model, the input data of the SGP4 model is the orbital parameters, and the output data is the position vector and the velocity vector.

[0061] For the embodiment of the present invention, after the initial orbital parameters corresponding to each individual and the orbital parameters at each moment in the preset time period are known, the SGP4 model can be used to calculate the position vector and velocity vector corresponding to each individual, as well as the position vector corresponding to each moment, so as to perform fitness evaluation based on the above position vector and velocity vector.

[0062] Step 33: Determine the fitness corresponding to each individual based on the position vector and velocity vector corresponding to each individual, and the position vector and velocity vector corresponding to each moment.

[0063] For the embodiment of the present invention, when calculating the fitness corresponding to each individual, for any one of the individuals, based on the position vector and velocity vector corresponding to the any one individual, and the position vector and velocity vector corresponding to each moment, the parameter errors at each moment are calculated respectively; the parameter errors at each moment are added to obtain the total error; and the fitness corresponding to the any one individual is determined according to the total error. The specific formula is as follows:

[0064]

[0065] Among them, ε i represents the parameter error at the i-th moment, Represents the position vector and velocity vector at the i-th moment, represents the position vector and velocity vector of the jth individual, N represents the number of moments in the preset period, M j Represents the total error, that is, the objective function. The smaller the total error, the higher the individual's fitness. The higher the fitness, the better the individual.

[0066] For example, there are 10 moments in total. For a certain individual, it is necessary to calculate the parameter error corresponding to each of the 10 moments based on the position vector and velocity vector of the individual and the position vector and velocity vector of these 10 moments, and finally add up the total error. Based on the total error, the fitness of the individual can be determined.

[0067] Step 40: Based on the fitness, determine the high-quality individuals that need to be retained in the initial orbit parameter population, and generate a first new individual and a second new individual respectively by combining a preset simplex tuning algorithm and a preset genetic algorithm.

[0068] Among them, the sum of the number of high-quality individuals, the number of first new individuals, and the number of second new individuals is equal to the number of individuals in the parent trajectory parameter population.

[0069] For the embodiment of the present invention, after determining the fitness of each individual in the initial orbit parameter population (parent orbit parameter population), it is necessary to screen out high-quality individuals that need to be retained based on the fitness, and at the same time, combine the preset simplex tuning algorithm and the preset genetic algorithm to generate the first new individual and the second new individual respectively, so as to realize the update of the initial orbit parameter population. For this process, as shown in Figure 4 As shown, including:

[0070] Step 41: Screen out a preset proportion of individuals from the individuals according to the fitness, and determine high-quality individuals that need to be retained from the preset proportion of individuals.

[0071] The preset ratio may be set according to actual business requirements, and as a preferred embodiment, it may be set to 20%.

[0072] For the embodiment of the present invention, the individuals can be sorted from high to low according to their corresponding fitness, and then the individuals with a preset proportion of rankings are screened out, and then the high-quality individuals to be retained are screened out from the individuals with the preset proportion according to their rankings.

[0073] For example, the number of individuals in the initial orbital parameter population is P. All individuals are sorted from high to low according to fitness, and the top S individuals accounting for 20% are determined based on the sorting results. Then, the top N individuals are selected from the top S individuals based on fitness ranking, and these N individuals are regarded as high-quality individuals that need to be retained.

[0074] Step 42: Based on the high-quality individuals, a first new individual is generated using a preset simplex optimization algorithm.

[0075] For the embodiment of the present invention, when generating the first new individuals, the number of the first new individuals is determined according to the number of individuals in the preset ratio and the number of the high-quality individuals, and the high-quality individuals are used as the initial simplex to generate the number of first new individuals in sequence.

[0076] Specifically, the top N individuals among the S individuals are retained, and SN first new individuals are generated. When generating new individuals, the preset simplex tuning algorithm is used, and the retained N high-quality individuals are used as the initial simplex to generate the N+1th individual to the Sth individual in sequence. When generating, the initial simplex is adjusted and changed to obtain the N+1th individual. Similarly, the simplex is adjusted to obtain the N+2th individual. Then, the fitness of the N+1th individual and the fitness of the N+2th individual are calculated using the above fitness evaluation formula, and the fitness of the N+2th individual is compared with that of the N+1th individual. If the fitness of the N+2th individual is higher than that of the N+1th individual, the N+2th individual is retained and the simplex transformation of the N+3th individual is continued. If the fitness of the N+2th individual is lower than or equal to that of the N+1th individual, the initial simplex is adjusted and changed. If the initial simplex is changed 100 times and still cannot generate a new individual with a fitness higher than that of the N+1th individual, the original individual is retained and the simplex transformation of the N+2th individual is continued. Repeat the above process until SN first new individuals are generated.

[0077] In the process of simplex change, reflection, expansion, contraction and folding operations can be used to adjust the vertices of the simplex so that it gradually converges to the local optimal solution. Among them, for the contraction operation, if the reflection operation fails to improve the optimal solution, the simplex is contracted as a whole; for the reflection operation, the worst vertex is reflected based on the center of the simplex set to obtain a new point; for the folding operation, if the contraction operation fails, the worst vertex is flipped to explore a new solution space; for the expansion operation, if the reflection point is better than the current optimal point, the reflection point is further expanded.

[0078] When using the simplex tuning algorithm, the embodiment of the present invention adopts the retained high-quality individuals as the initial simplex, thereby avoiding the local optimal trap problem caused by improper selection of the initial simplex, thereby improving the accuracy and stability of orbit determination.

[0079] Step 43: Based on the individuals other than the individuals in the preset proportion among the individuals, a preset genetic algorithm is used to generate a second new individual.

[0080] In the embodiment of the invention, for the remaining PS individuals, a preset genetic algorithm is used to generate a second new individual, and the number of the second new individuals is also PS, such as Figure 5 shown.

[0081] For example, a preset genetic algorithm is used to perform selection, crossover and mutation operations on the remaining 80% of individuals. Specifically, individuals are selected by using methods such as roulette selection or tournament selection. After selection, any two individuals are subjected to single-point crossover or multi-point crossover to generate offspring. In order to maintain the diversity of the population and avoid falling into a local optimal solution, it is also necessary to perform mutation operations on the newly generated individuals. Mutation usually randomly changes certain parameters of the individual, and the mutation rate can be set between 1% and 5%. Through the above operations of genetic evolution, the population can gradually adapt to the fitness requirements of the objective function.

[0082] The embodiment of the present invention determines the best combination of P individuals and SN first new individuals through experiments, ensuring the calculation efficiency and accuracy of the algorithm. The experiment verifies the best effect of using the best individual and simplex tuning method for the first 20% of individuals and the genetic algorithm for selection, crossover and mutation operations for the remaining 80% of individuals.

[0083] The embodiment of the present invention optimizes the operation steps of the genetic algorithm, performs a global search for individuals through the processes of selection, crossover, mutation, etc., and performs a local search through the simplex algorithm, thereby ensuring that individuals with high fitness gradually approach the optimal solution after multiple rounds of iterations, effectively avoiding the premature phenomenon in the genetic algorithm.

[0084] Step 50: Update the initial orbit parameter population according to the retained high-quality individuals, the first new individuals and the second new individuals to obtain a new orbit parameter population.

[0085] For the embodiment of the present invention, after determining the retained high-quality individuals and generating the first new individual and the second new individual by combining the preset simplex tuning algorithm and the preset genetic algorithm, the initial orbital parameter population is updated according to the high-quality individuals, the first new individual and the second new individual to obtain a new orbital parameter population, that is, a child orbital parameter population corresponding to the parent orbital parameter population.

[0086] When generating new individuals, the embodiment of the present invention combines the global search capability of the genetic algorithm and the local optimization capability of the simplex tuning algorithm, so that the problem that the orbit parameters are easily trapped in the layout optimal solution can be solved, thereby improving the orbit determination accuracy. In addition, the hybrid algorithm design of the embodiment of the present invention significantly improves the operation rate compared to the traditional simplex tuning algorithm. This improvement of the embodiment of the present invention is particularly suitable for processing large-scale TLE data and can obtain high-precision orbit determination parameters in a relatively short time.

[0087] Step 60: Continue to iteratively update the new orbit parameter population until a preset condition is met, and then output the target orbit parameter population of the final iteration.

[0088] The preset condition may specifically be a fitness stability condition or a maximum iteration number condition.

[0089] For the embodiment of the present invention, after determining the new orbit parameter population, the above process is repeated, that is, the fitness of each individual in the new orbit parameter population is continued to be calculated, so as to screen out high-quality individuals that need to be retained, and generate new individuals in combination with the preset simplex tuning algorithm and the preset genetic algorithm, so as to continue to iterate and update the new orbit parameter population until the fitness of all individuals in the population does not change much in consecutive generations, indicating that convergence has occurred at this time, the iteration is stopped, and the target orbit parameter population of the final iteration is output, or a maximum number of iterations is set. When the maximum number of iterations is reached, the iteration is stopped and the target orbit parameter population is output.

[0090] Step 70: Determine the orbit determination parameters corresponding to the epoch time according to the target orbit parameter population.

[0091] For the embodiment of the present invention, after the target orbit parameter population of the final iteration is output, the orbit determination parameters corresponding to the epoch time are determined according to the target orbit parameter population. Regarding the specific method for determining the orbit determination parameters, the embodiment of the present invention specifically provides two implementation methods.

[0092] For the first implementation, the method includes: calculating the average orbital parameter of each target individual according to the orbital parameter corresponding to each target individual in the target orbital parameter population; and determining the average orbital parameter as the orbit determination parameter corresponding to the target moment.

[0093] For the second implementation, the method includes: determining the target individual with the highest fitness from among the target individuals according to the fitness corresponding to each target individual in the target orbit parameter population; and determining the orbit parameter corresponding to the target individual with the highest fitness as the predetermined parameter at the target moment. The overall process of orbit determination is as follows: Figure 6 shown.

[0094] An orbit determination method provided by an embodiment of the present invention determines the fitness of each individual in the initial orbit parameter population, and determines the high-quality individuals that need to be retained in the initial orbit parameter population based on the fitness, and combines a preset simplex tuning algorithm and a preset genetic algorithm to generate a first new individual and a second new individual respectively, so that the orbit parameter population can be continuously updated and iterated, thereby determining the orbit determination parameters at the epoch time. Since the embodiment of the present invention combines the global search capability of the genetic algorithm and the local optimization capability of the simplex tuning algorithm in the process of updating and iterating the orbit parameter population, it can solve the problem that the orbit parameters are easily trapped in the layout optimal solution, thereby improving the accuracy of orbit determination. At the same time, when using the simplex tuning algorithm, the embodiment of the present invention uses the retained high-quality individuals as the initial simplex, so that the local optimal trap problem caused by improper selection of the initial simplex can be avoided, thereby improving the accuracy and stability of orbit determination.

[0095] Further, as Figure 1-4 The specific implementation of the method shown in the embodiment provides a track determination device, such as Figure 7 As shown, the device includes: an acquisition unit 101, a first generation unit 102, an evaluation unit 103, a second generation unit 104, an update unit 105, an output unit 106 and a determination unit 107.

[0096] The acquisition unit 101 may be used to acquire dual-row orbit data of a preset period of time and an epoch time of orbit determination.

[0097] The first generating unit 102 may be configured to generate an initial orbit parameter population according to the two-row orbit data of the preset time period.

[0098] The evaluation unit 103 may be used to perform fitness evaluation on each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual.

[0099] The second generating unit 104 may be used to determine the high-quality individuals that need to be retained in the initial orbit parameter population based on the fitness, and generate a first new individual and a second new individual respectively in combination with a preset simplex tuning algorithm and a preset genetic algorithm.

[0100] The updating unit 105 may be configured to update the initial orbit parameter population according to the retained high-quality individuals, the first new individuals, and the second new individuals to obtain a new orbit parameter population.

[0101] The output unit 106 may be used to continue iteratively updating the new orbit parameter population until a preset condition is met, and then output a target orbit parameter population of the final iteration.

[0102] The determination unit 107 may be configured to determine the orbit determination parameters corresponding to the epoch time according to the target orbit parameter population.

[0103] In some embodiments, the first generating unit 102 includes: a first determining module and a first generating module.

[0104] The first determination module may be configured to determine orbital parameter constraints based on the double-row orbital data at the epoch time and the double-row orbital data at each target time close to the epoch time in the preset time period.

[0105] The first generating module may be used to generate an initial orbital parameter population according to the orbital parameter constraints.

[0106] In some embodiments, the first determination module can be specifically used to determine the orbital parameters of the epoch moment and the orbital parameters of each target moment based on the double-row orbital data of the epoch moment and the double-row orbital data of each target moment, and calculate the orbital parameter difference between any two adjacent moments of the epoch moment and each target moment; determine the positive difference and the negative difference in the orbital parameter difference between any two adjacent moments; determine the maximum positive difference in the positive difference and the maximum absolute value in the negative difference, and calculate the average value of the positive difference and the average absolute value of the negative difference; if the number of positive difference values ​​is a preset threshold, determine the upper limit of the orbital parameter to be the sum of the orbital parameter at the epoch moment and the maximum positive difference, and the lower limit of the orbital parameter to be the orbital parameter of the previous target moment corresponding to the epoch moment; if the number of negative difference values ​​is a preset threshold Set a threshold, then determine the lower limit of the orbital parameter as the difference between the orbital parameter at the epoch and the maximum absolute value, and the upper limit of the orbital parameter as the orbital parameter at the previous target moment; if the number of positive differences or the number of negative differences is less than the preset threshold, calculate the difference between the orbital parameter at the epoch and the orbital parameter at the previous target moment; if the difference is a positive number, determine the upper limit of the orbital parameter as the sum of the orbital parameter at the epoch and the average value of the positive differences, and the lower limit of the orbital parameter is the orbital parameter at the previous target moment; if the difference is a negative number, determine the lower limit of the orbital parameter as the difference between the orbital parameter at the epoch and the average absolute value of the negative differences, and the upper limit of the orbital parameter is the orbital parameter at the previous target moment; determine the orbital parameter constraint condition according to the orbital parameter upper limit and the orbital parameter lower limit.

[0107] The first generating module may be specifically configured to randomly generate each initial orbit parameter according to the orbit parameter constraint condition; and determine each initial orbit parameter as each individual in the initial orbit parameter population.

[0108] In some embodiments, the evaluation unit 103 includes: a calculation module and a second determination module.

[0109] The second determination module may be configured to determine the orbit parameters at each moment in the preset time period according to the double-row orbit data in the preset time period.

[0110] The calculation module can be used to input the initial orbital parameters corresponding to each individual and the orbital parameters at each moment in the preset time period into a simplified perturbation theory model for calculation, so as to obtain the position vector and velocity vector corresponding to each individual and the position vector and velocity vector corresponding to each moment.

[0111] The second determination module may also be used to determine the fitness corresponding to each individual based on the position vector and velocity vector corresponding to each individual, and the position vector and velocity vector corresponding to each moment.

[0112] In some embodiments, the second determination module can be specifically used to calculate the parameter error at each moment for any one of the individuals based on the position vector and velocity vector corresponding to the any one individual, and the position vector and velocity vector corresponding to each moment; add the parameter errors at each moment to obtain a total error; and determine the fitness corresponding to the any one individual based on the total error.

[0113] In some embodiments, the second generating unit 104 includes: a third determining module and a second generating module.

[0114] The third determination module may be used to screen out a preset proportion of individuals from the individuals according to the fitness, and determine high-quality individuals that need to be retained from the preset proportion of individuals.

[0115] The second generation module can be used to generate a first new individual based on the high-quality individual using a preset simplex optimization algorithm.

[0116] The second generating module may also be configured to generate a second new individual using a preset genetic algorithm based on other individuals among the individuals except the individuals in the preset proportion.

[0117] In some embodiments, the second generation module can be specifically used to determine the number of the first new individuals based on the number of individuals in the preset ratio and the number of the high-quality individuals; use the high-quality individuals as initial simplexes to sequentially generate the number of first new individuals.

[0118] In some embodiments, the determination unit 107 can be specifically used to calculate the average value of the orbital parameters of each target individual according to the orbital parameters corresponding to each target individual in the target orbital parameter population; determine the average value of the orbital parameters as the orbit determination parameters corresponding to the target moment; or determine the target individual with the highest fitness from the target individuals according to the fitness corresponding to each target individual in the target orbital parameter population; and determine the orbital parameters corresponding to the target individual with the highest fitness as the orbit determination parameters at the target moment.

[0119] It should be noted that for other corresponding descriptions of the functional units involved in the track determination device provided in the embodiment of the present invention, reference can be made to Figure 1-4 The corresponding description in will not be repeated here.

[0120] Based on the above Figure 1-4 The method shown in the embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned Figure 1-4 The orbit determination method shown.

[0121] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0122] Based on the above Figure 1-4 The method shown, and Figure 7 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, which can be a personal computer, a tablet computer, a server, or other network equipment, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1-4 The orbit determination method shown.

[0123] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0124] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0125] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0127] The embodiment of the present invention determines the fitness of each individual in the initial orbit parameter population, and determines the high-quality individuals that need to be retained in the initial orbit parameter population based on the fitness, and combines the preset simplex tuning algorithm and the preset genetic algorithm to generate the first new individual and the second new individual respectively, so as to continuously update and iterate the orbit parameter population, thereby determining the orbit determination parameters at the epoch time. Since the embodiment of the present invention combines the global search capability of the genetic algorithm and the local optimization capability of the simplex tuning algorithm in the process of updating and iterating the orbit parameter population, it can solve the problem that the orbit parameters are easily trapped in the layout optimal solution, thereby improving the accuracy of orbit determination. At the same time, when using the simplex tuning algorithm, the embodiment of the present invention uses the retained high-quality individuals as the initial simplex, so that the local optimal trap problem caused by improper selection of the initial simplex can be avoided, thereby improving the accuracy and stability of orbit determination.

[0128] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0129] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for determining a track, characterized in that: include: Obtaining dual-row orbit data for a preset period of time and the epoch time of orbit determination; generating an initial orbit parameter population according to the double-row orbit data of the preset time period; Performing fitness evaluation on each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual; Based on the fitness, determine the high-quality individuals that need to be retained in the initial orbit parameter population, and generate a first new individual and a second new individual respectively by combining a preset simplex tuning algorithm and a preset genetic algorithm; According to the retained high-quality individuals, the first new individuals, and the second new individuals, the initial orbit parameter population is updated to obtain a new orbit parameter population; Continue to iteratively update the new orbit parameter population until a preset condition is met, and then output a target orbit parameter population of the final iteration; The orbit determination parameters corresponding to the epoch time are determined according to the target orbit parameter population.

2. The method according to claim 1, characterized in that The generating an initial orbit parameter population according to the double-row orbit data of the preset time period comprises: Determine orbit parameter constraints according to the double-row orbit data at the epoch time and the double-row orbit data at each target time close to the epoch time in the preset time period; generating an initial orbital parameter population according to the orbital parameter constraints; and / or The step of performing fitness evaluation on each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual includes: Determining the orbital parameters at each moment in the preset period according to the double-row orbital data of the preset period; Inputting the initial orbital parameters corresponding to each individual and the orbital parameters at each moment in the preset time period into a simplified perturbation theory model for calculation, respectively, to obtain the position vector and velocity vector corresponding to each individual, respectively, and the position vector and velocity vector corresponding to each moment; Based on the position vectors and velocity vectors corresponding to the individuals, and the position vectors and velocity vectors corresponding to the respective moments, the fitness corresponding to the individuals is determined.

3. The method according to claim 2, characterized in that Determining the orbit parameter constraint condition according to the double-row orbit data at the epoch time and the double-row orbit data at each target time close to the epoch time in the preset time period includes: Determine the orbital parameters at the epoch time and the orbital parameters at the target time according to the double-row orbital data at the epoch time and the double-row orbital data at the target time, and calculate the orbital parameter difference between any two adjacent moments of the epoch time and the target time; Determine the positive difference and the negative difference in the orbit parameter difference between any two adjacent moments; Determine the maximum positive difference among the positive differences and the maximum absolute value among the negative differences, and calculate the average value of the positive differences and the average absolute value of the negative differences; If the number of positive differences is a preset threshold, the upper limit of the orbital parameter is determined to be the sum of the orbital parameter at the epoch time and the maximum positive difference, and the lower limit of the orbital parameter is the orbital parameter at the previous target time corresponding to the epoch time; If the number of the negative difference values ​​is a preset threshold, the lower limit of the orbital parameter is determined to be the difference between the orbital parameter at the epoch time and the maximum absolute value, and the upper limit of the orbital parameter is the orbital parameter at the previous target time; If the number of the positive difference values ​​or the number of the negative difference values ​​is less than a preset threshold, calculating the difference between the orbital parameter at the epoch time and the orbital parameter at the previous target time; If the difference is a positive number, the upper limit of the orbital parameter is determined to be the sum of the orbital parameter at the epoch time and the average value of the positive difference, and the lower limit of the orbital parameter is the orbital parameter at the previous target time; If the difference is a negative number, the lower limit of the orbital parameter is determined to be the difference between the orbital parameter at the epoch time and the average absolute value of the negative difference, and the upper limit of the orbital parameter is the orbital parameter at the previous target time; Determining the orbital parameter constraint condition according to the orbital parameter upper limit and the orbital parameter lower limit; and / or The step of generating an initial orbital parameter population according to the orbital parameter constraint condition comprises: According to the orbital parameter constraints, each initial orbital parameter is randomly generated; The respective initial orbit parameters are determined as respective individuals in the initial orbit parameter population.

4. The method according to claim 2, characterized in that: The determining the fitness corresponding to each individual based on the position vector and the velocity vector respectively corresponding to each individual, and the position vector and the velocity vector corresponding to each moment, comprises: For any one of the individuals, based on the position vector and velocity vector corresponding to the any one of the individuals, and the position vector and velocity vector corresponding to each moment, respectively calculate the parameter error at each moment; Adding the parameter errors at each moment to obtain a total error; The fitness corresponding to any one of the individuals is determined according to the total error.

5. The method according to any one of claims 1 to 4, characterized in that: The method of determining the high-quality individuals to be retained in the initial orbit parameter population based on the fitness, and generating the first new individual and the second new individual respectively by combining the preset simplex tuning algorithm and the preset genetic algorithm, comprises: According to the fitness, a preset proportion of individuals are screened out from the individuals, and high-quality individuals that need to be retained from the preset proportion of individuals are determined; Based on the high-quality individuals, a first new individual is generated using a preset simplex optimization algorithm; Based on the other individuals among the individuals except the individuals in the preset proportion, a second new individual is generated by using a preset genetic algorithm.

6. The method according to claim 5, characterized in that The step of generating a first new individual based on the high-quality individual by using a preset simplex optimization algorithm comprises: Determining the number of the first new individuals according to the number of the individuals in the preset proportion and the number of the high-quality individuals; The high-quality individuals are used as initial simplexes to sequentially generate the number of first new individuals.

7. The method according to claim 1, characterized in that Determining the orbit determination parameters corresponding to the epoch time according to the target orbit parameter population includes: Calculating the average value of the orbital parameters of each target individual in the target orbital parameter population according to the orbital parameters corresponding to each target individual in the target orbital parameter population; Determine the average value of the orbital parameters as the orbit determination parameter corresponding to the target time; or According to the fitness corresponding to each target individual in the target trajectory parameter population, determining a target individual with the highest fitness from among the target individuals; The orbital parameters corresponding to the target individual with the highest fitness are determined as the regulation parameters at the target time.

8. A track determination device, characterized in that: include: An acquisition unit, used to acquire dual-row orbit data of a preset period and an epoch time of orbit determination; A first generating unit, configured to generate an initial orbit parameter population according to the double-row orbit data of the preset time period; An evaluation unit, used to evaluate the fitness of each individual in the initial orbit parameter population to obtain the fitness corresponding to each individual; A second generating unit is used to determine the high-quality individuals that need to be retained in the initial orbit parameter population based on the fitness, and generate a first new individual and a second new individual respectively by combining a preset simplex tuning algorithm and a preset genetic algorithm; an updating unit, configured to update the initial orbit parameter population according to the retained high-quality individuals, the first new individuals, and the second new individuals to obtain a new orbit parameter population; An output unit, used to continue iteratively updating the new orbit parameter population until a preset condition is met, and output a target orbit parameter population of the final iteration; A determination unit is used to determine the orbit determination parameters corresponding to the epoch time according to the target orbit parameter population.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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