Longitudinal multiple determination method, device and equipment for earth coverage period calculation

Abstract

The invention provides a longitudinal multiple determination method, device and equipment for earth coverage period calculation, and relates to the technical field of spaceflight, and the method comprises the steps: carrying out the orbit recursion based on satellite parameters, so as to form the target longitude and latitude sequence data of a satellite under each regression orbit; carrying out sea-land identification processing on the target longitude and latitude sequence data to obtain a target imaging segmental arc time sequence diagram; and determining the number of regression cycles required for completing all imageable arc segments under each regression orbit in the target imaging arc segment time sequence diagram by taking the imaging duration of each orbit of the load as a constraint, and using the number of regression cycles as a longitudinal multiple for calculating the earth coverage cycle of the satellite. According to the method, the coverage period calculation requirement under the requirement of only land imaging or only ocean imaging can be met, the fitness of ground coverage period calculation is improved, and the more refined analysis requirement in actual work is met.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and in particular to a method, device and equipment for determining a longitudinal multiple for calculating a ground coverage period. Background Art

[0002] In the mission development phase of remote sensing satellites, indicators such as the satellite's ground coverage period are required to evaluate the satellite's orbital effectiveness. The industry can use general orbit simulation software for coverage analysis. However, due to the limitations of simulation software evaluation, a simple evaluation method is more operational. This method uses orbit regression parameters, payload swath width, and payload working time per orbit as input to calculate and output the coverage period, that is, the number of days required for the satellite to cover the entire theoretical global visible range at least once. The algorithm calculates the coverage period according to the following formula:

[0003] T cov =T·N al ·[N cr / (1-K)]; where the coverage period is T cov , N al is the vertical multiple, N cr The symbol [·] indicates rounding up.

[0004] Among the parameters required for this algorithm, the vertical multiple N al Calculate according to the following formula:

[0005] N al =0.5·P / t perrev ; where P is the orbital period, t perrev is the working time of the payload on each track, which are all known quantities.

[0006] Although this algorithm has good adaptability to global and regional coverage of specified dimensional ranges, it cannot meet the more refined analysis needs in actual work. Summary of the invention

[0007] In view of this, the purpose of the present invention is to provide a method, device and equipment for determining the longitudinal multiple of the ground coverage period calculation, which can adapt to the coverage period calculation needs under the requirements of "land imaging only" or "ocean imaging only", improve the adaptability of the ground coverage period calculation, and meet the more refined analysis needs in actual work.

[0008] In a first aspect, an embodiment of the present invention provides a method for determining a longitudinal multiple for ground coverage period calculation, including:

[0009] Obtain satellite parameters corresponding to the satellite and the imaging duration of each orbit of the payload;

[0010] Perform orbit recursion on the satellite based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit;

[0011] Performing sea and land identification processing on the target longitude and latitude sequence data of the satellite under each regression orbit to obtain a target imaging arc segment timing diagram, wherein the target imaging arc segment timing diagram includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram;

[0012] Taking the imaging duration of each orbit of the payload as a constraint, the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc timing diagram is determined, and the number of regression cycles is used as the longitudinal multiple, which is used to calculate the satellite's ground coverage period.

[0013] In one embodiment, orbit recursion is performed on the satellite based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit, including:

[0014] Based on the satellite parameters, the satellite orbit is recursively calculated to form the initial longitude and latitude sequence data corresponding to the satellite;

[0015] The initial longitude and latitude sequence data are processed in sections to obtain the intermediate longitude and latitude sequence data of the satellite in each regression orbit;

[0016] Based on the ascending and descending orbit patterns corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit are screened out from the intermediate longitude and latitude sequence data.

[0017] In one implementation, based on the ascending and descending orbit mode corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit is screened out from the intermediate longitude and latitude sequence data, including:

[0018] Determine the latitude argument segment based on the ascending and descending orbit pattern corresponding to the satellite;

[0019] According to the latitude argument segment, the target longitude and latitude sequence data of the satellite in each regression orbit are filtered out from the intermediate longitude and latitude sequence data.

[0020] In one embodiment, sea and land identification processing is performed on the target latitude and longitude sequence data of the satellite in each regression orbit to obtain a target imaging arc segment time sequence diagram, including:

[0021] For each data point in the target longitude and latitude sequence data of the satellite under each regression orbit, identifying whether the data point is located on land or sea;

[0022] A land imaging arc segment timing diagram is generated based on data points located on land, or an ocean imaging arc segment timing diagram is generated based on data points located on the ocean; wherein the land imaging arc segment timing diagram is used to describe the corresponding longitude and latitude sequence when the satellite passes over the land in each regression orbit, and the ocean imaging arc segment timing diagram is used to describe the corresponding longitude and latitude sequence when the satellite passes over the ocean in each regression orbit.

[0023] In one embodiment, the imaging duration of each trajectory of the payload is used as a constraint to determine the number of regression cycles required to complete all imageable arcs under each regression trajectory in the target imaging arc timing diagram, including:

[0024] For each regression orbit in the target imaging arc segment, based on the imaging duration of each orbit of the payload and the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression cycle, the imageable arc segment corresponding to the satellite under the regression orbit in the current regression cycle is determined from the imageable arc segments not covered under the regression orbit in the target imaging arc segment timing diagram;

[0025] Mark the imageable arc segment corresponding to the satellite under the regression orbit in the current regression period as the covered imageable arc segment, so as to update the imageable arc segment not covered under the regression orbit in the target imaging arc segment timing diagram;

[0026] Take the next regression cycle as the new current regression cycle, and continue to determine the imageable arc segment corresponding to the satellite under the regression orbit in the new current regression cycle from the updated uncovered imageable arc segment, until each imageable arc segment under each regression orbit in the target imaging arc segment timing diagram is covered, and the number of regression cycles is obtained.

[0027] In one embodiment, based on the imaging duration of each orbit of the payload and the imageable arc segment corresponding to the satellite in the regression orbit in the previous regression period, the imageable arc segment corresponding to the satellite in the regression orbit in the current regression period is determined from the imageable arc segment not covered in the target imaging arc segment timing diagram, including:

[0028] Based on the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression period, the cumulative starting point corresponding to the current regression period is determined from the imageable arc segment under the regression orbit that is not covered in the target imaging arc segment timing diagram;

[0029] Starting from the cumulative starting point, the cumulative duration of the imageable arc segments that are not covered under the regression track in the target imaging arc segment timing diagram is counted until the imaging duration of each track of the payload is reached, so as to obtain the cumulative end point corresponding to the current regression cycle;

[0030] The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in the regression orbit during the current regression period.

[0031] In a second aspect, an embodiment of the present invention further provides a device for determining a longitudinal multiple for ground coverage period calculation, comprising:

[0032] The data acquisition module is used to obtain the satellite parameters corresponding to the satellite and the imaging duration of each track of the payload;

[0033] An orbit recursion module is used to perform orbit recursion on the satellite based on satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit;

[0034] The sea and land identification module is used to perform sea and land identification processing on the target longitude and latitude sequence data of the satellite in each regression orbit to obtain a target imaging arc segment timing diagram, which includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram;

[0035] The longitudinal multiple determination module is used to determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc timing diagram, taking the imaging time of each payload track as a constraint, and taking the number of regression cycles as the longitudinal multiple, which is used to calculate the satellite's ground coverage period.

[0036] In one embodiment, the track recursion module is specifically used to:

[0037] Based on the satellite parameters, the satellite orbit is recursively calculated to form the initial longitude and latitude sequence data corresponding to the satellite;

[0038] The initial longitude and latitude sequence data are processed in sections to obtain the intermediate longitude and latitude sequence data of the satellite in each regression orbit;

[0039] Based on the ascending and descending orbit patterns corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit are screened out from the intermediate longitude and latitude sequence data.

[0040] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0041] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0042] The embodiment of the present invention provides a method, device and equipment for determining a longitudinal multiple for calculating a ground coverage period. After obtaining satellite parameters corresponding to the satellite and the imaging time per orbit of the payload, the satellite is first orbitally recursively calculated based on the satellite parameters to form target longitude and latitude sequence data of the satellite under each regression orbit; then, sea and land identification processing is performed on the target longitude and latitude sequence data of the satellite under each regression orbit to obtain a target imaging arc segment timing diagram, which includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram; finally, the imaging time per orbit of the payload is used as a constraint to determine the number of regression cycles required for all imageable arc segments under each regression orbit in the target imaging arc segment timing diagram to be completed, and the number of regression cycles is used as the longitudinal multiple, which is used for calculating the ground coverage period of the satellite. The above method forms the target longitude and latitude sequence data of the satellite in each regression orbit through orbit recursion, and then performs land and sea identification processing on the target longitude and latitude sequence data to generate a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram respectively, and on this basis, the longitudinal multiples adapted to the land or ocean are determined with the imaging time of each track of the payload as a constraint. Compared with the traditional technology of calculating the longitudinal multiple with a simple formula, the embodiment of the present invention can distinguish between land and ocean, so as to adapt to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improve the adaptability of the ground coverage period calculation, and meet the more refined analysis requirements in actual work.

[0043] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic flow chart of a method for determining a longitudinal multiple for ground coverage period calculation provided by an embodiment of the present invention;

[0047] Figure 2 A longitudinal multiple N provided in an embodiment of the present invention al=6 simulation demonstration diagram;

[0048] Figure 3 A schematic diagram of land and ocean identification results provided by an embodiment of the present invention;

[0049] Figure 4 An imaging arc segment timing diagram provided by an embodiment of the present invention;

[0050] Figure 5 A timing diagram of imageable arc segments in a first regression cycle provided by an embodiment of the present invention;

[0051] Figure 6 A timing diagram of imageable arc segments in a second regression cycle provided by an embodiment of the present invention;

[0052] Figure 7 A timing diagram of imageable arc segments in a third regression cycle provided by an embodiment of the present invention;

[0053] Figure 8 A timing diagram of an imageable arc segment under a fourth regression cycle provided by an embodiment of the present invention;

[0054] Fig. 9 A schematic diagram of the structure of a device for determining a longitudinal multiple for ground coverage period calculation provided by an embodiment of the present invention;

[0055] Fig.10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are 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.

[0057] At present, the traditional algorithm has good adaptability for global and regional coverage within a specified latitude range. However, under some more detailed requirements, it is necessary to specifically calculate the parameters required by the algorithm. Based on this, the present invention provides a method, device and equipment for determining the longitudinal multiple of the ground coverage period calculation, which can adapt to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improve the adaptability of the ground coverage period calculation, and meet the more refined analysis requirements in actual work.

[0058] To facilitate understanding of this embodiment, firstly, a method for determining a longitudinal multiple for ground coverage period calculation disclosed in an embodiment of the present invention is described in detail, see Figure 1 The flowchart of a method for determining a longitudinal multiple for ground coverage period calculation is shown, and the method mainly includes the following steps S102 to S108:

[0059] Step S102, obtaining satellite parameters corresponding to the satellite and the imaging duration of each orbit of the payload.

[0060] The satellite parameters include orbital epochs, epoch orbital parameters, orbital return days, and the current orbit ascending and descending mode of the satellite (orbit ascending mode or orbit descending mode). The satellite may be a SAR (Synthetic Aperture Radar) satellite.

[0061] Step S104, performing orbit recursion on the satellite based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit.

[0062] In one example, based on the epoch orbital parameters, the satellite orbit can be recursively calculated according to the forecast duration to form the initial longitude and latitude sequence data corresponding to the satellite, and the initial longitude and latitude sequence data can be segmented by orbit and screened according to the ascending and descending orbit mode to obtain the target longitude and latitude sequence data of the satellite in each regression orbit. The segmentation process is used to divide the initial longitude and latitude sequence data into data subsets corresponding to each regression orbit, and the data screening process is used to screen out the data subsets that meet the ascending and descending orbit mode of the satellite, such as screening out the data subsets in the ascending orbit mode or the data subsets in the descending orbit mode.

[0063] Step S106, performing sea and land identification processing on the target longitude and latitude sequence data of the satellite in each regression orbit to obtain a target imaging arc segment time sequence diagram.

[0064] Among them, the target imaging arc segment timing diagram includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram, and the target imaging arc segment timing diagram is composed of imageable arc segments. The land imaging arc segment timing diagram is used to describe the corresponding longitude and latitude sequence when the satellite passes over the land in each regression orbit, and the ocean imaging arc segment timing diagram is used to describe the corresponding longitude and latitude sequence when the satellite passes over the ocean in each regression orbit. In one example, the target longitude and latitude sequence data of the satellite in each regression orbit are processed in a batch processing manner to identify each data point in the target longitude and latitude sequence data located on land or ocean, so as to form a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram.

[0065] Step S108, taking the imaging time of each orbit of the payload as a constraint, determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc timing diagram, and use the number of regression cycles as the longitudinal multiple, which is used to calculate the satellite's ground coverage period.

[0066] In one example, taking the land imaging arc segment timing diagram as an example, based on the land imaging arc segment timing diagram, the imageable arc segment within the first regression cycle is examined. Specifically, the portion of the specified cumulative duration is cumulatively intercepted in each row (i.e., each track) of data as the imageable arc segment of the track under the imaging duration constraint of each track of the payload; repeat until all segments within a regression cycle are covered, and the number of regression cycles experienced at this time is taken as the longitudinal multiple, which can meet the coverage cycle calculation requirements under the "land imaging only" requirement. Similarly, the same processing method can be used, with the imaging duration of each track of the payload as a constraint, to obtain another longitudinal multiple based on the ocean imaging arc segment timing diagram, which can meet the coverage cycle calculation requirements under the "ocean imaging only" requirement.

[0067] The method for determining the longitudinal multiple for calculating the ground coverage period provided by the embodiment of the present invention forms the target longitude and latitude sequence data of the satellite in each regression orbit through orbit recursion, and then performs sea and land identification processing on the target longitude and latitude sequence data to generate a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram respectively, and on this basis, the longitudinal multiple adapted to the land or ocean is determined with the imaging time length of each track of the payload as a constraint. Compared with the traditional technology of calculating the longitudinal multiple with a simple formula, the embodiment of the present invention can distinguish between land and ocean, so as to adapt to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improve the adaptability of the ground coverage period calculation, and meet the more refined analysis requirements in actual work.

[0068] In one implementation, within the framework of calculating the SAR satellite coverage period using a simple formula, the present invention provides a set of longitudinal multiples N al The method is a refined design method. This process can identify land and ocean, improve the adaptability of coverage period calculation, and thus meet the requirements of "land coverage only" or "ocean coverage only". This method inputs the satellite initial orbit parameters, orbit epoch, orbit return days, and imaging duration constraints per orbit, calculates and outputs the longitudinal multiple as a necessary calculation parameter for the coverage period. Different from the above 0.5·P / t perrev This method can distinguish between land and ocean and can meet the requirement of covering only the land part of the world.

[0069] For example, in view of the existing SAR satellite coverage period concise calculation formula, the embodiment of the present invention provides a refined calculation method for distinguishing the longitudinal multiples of the ground / ocean, which improves the adaptability of the coverage period calculation. The embodiment of the present invention calculates the longitudinal coefficient N in the following concise formula: al :

[0070] T cov =T·N al ·[N cr / (1-K)];

[0071] Where, T cov is the coverage period, T is the regression period, N cr The symbol [·] indicates rounding up.

[0072] See also Figure 2 A longitudinal multiple N shown al =6. This simulation does not distinguish between ocean and land. In the embodiment of the present invention, considering the coverage requirements of "land imaging only" or "ocean imaging only", different N values ​​can be obtained under the same load working time. al , and there must be N al ≤6, thereby improving the calculation accuracy of the SAR satellite's ground coverage period and reducing the number of times the SAR satellite is turned on.

[0073] For ease of understanding, an embodiment of the present invention provides a specific implementation of a method for determining a longitudinal multiple for ground coverage period calculation.

[0074] For the aforementioned step S102, the calculation conditions obtained include information such as orbital epochs, epoch orbital parameters, orbital regression days, and payload imaging duration constraints per orbit.

[0075] For the aforementioned step S104, the embodiment of the present invention provides an implementation method for performing orbit recursion on the satellite based on satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit, including the following steps 1.1 to 1.3:

[0076] Step 1.1, perform orbit recursion on the satellite based on the satellite parameters to form initial longitude and latitude sequence data corresponding to the satellite.

[0077] First, based on the epoch orbital elements, orbit recursion can be performed according to the forecast duration to generate the time series data of the satellite's position and latitude argument in the inertial coordinate system (hereinafter referred to as the "inertial system"). Among them, the inertial system is J2000; the input form of the satellite's current orbit is the orbital elements; the position sequence is expressed in rectangular coordinates.

[0078] Optionally, the orbit recursion may be performed according to a standard J2 or J4 recursion method, which will not be described in detail in the embodiment of the present invention.

[0079] Then, for the above time series data, the inertial position sequence of the satellite is converted to the earth-fixed coordinate system (hereinafter referred to as the "earth-fixed system"), and then converted to the expression form of longitude and latitude to form the initial longitude and latitude sequence data. Among them, the earth-fixed system is WGS84. The coordinate conversion from the inertial system to the earth-fixed system adopts the standard algorithm defined by the IAU1980 specification, and the embodiment of the present invention will not be repeated here.

[0080] In the specific implementation, for any fixed rectangular coordinate point [x, y, z] T , the steps to convert to longitude, latitude and height [L, B, H] are as follows:

[0081] (1) Constant setting:

[0082] The major radius of the earth (equatorial radius): a = 6378.137 km;

[0083] The short radius of the earth (polar radius): b = 6356.7523 km;

[0084] First eccentricity:

[0085] (2) Calculation of longitude:

[0086] longitude:

[0087] (3) Calculation of geocentric latitude:

[0088] Geocentric Latitude:

[0089] (4) Iterative initial value calculation of geographic latitude:

[0090] Initial value of the radius of curvature of the yoke: N0 = a;

[0091] Initial height:

[0092] Initial value of geographic latitude:

[0093] (5) Iterative calculation of geographic latitude:

[0094] For the i-th iteration cycle, the calculation is as follows using the result of the i-1th round.

[0095]

[0096] Repeat (5) until the residual |B i -B i-1 |<1×10-8 rad, you can get the initial longitude and latitude sequence data corresponding to the satellite.

[0097] Step 1.2, segmenting the initial longitude and latitude sequence data to obtain the intermediate longitude and latitude sequence data of the satellite in each regression orbit;

[0098] Step 1.3, based on the ascending and descending orbit mode corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit is screened out from the intermediate longitude and latitude sequence data. In one embodiment, the latitude argument segment is first determined based on the ascending and descending orbit mode corresponding to the satellite; then, according to the latitude argument segment, the target longitude and latitude sequence data of the satellite in each regression orbit is screened out from the intermediate longitude and latitude sequence data.

[0099] For example, for the latitude argument and its corresponding initial longitude and latitude sequence data, the data is segmented by track, and a data subset is intercepted according to the specified latitude argument segment. For example, for a regression track with 179 regression circles, it is divided into 179 segments and stored separately, and a subset that meets the latitude argument segment is found in each segment, that is, the target longitude and latitude sequence data.

[0100] In a specific implementation, the subset is extracted by segmentation according to the track and latitude argument segment, and the find() function of MATLAB is used to implement it. Specifically, the data sequence number of the interval that meets the specified latitude argument u is found. The pseudo code is as follows:

[0101] ind=find(u∈[u min ,u max ]);

[0102] Among them, taking the regression orbit with 179 regression circles as an example, ind is a cell array of size 179, specifically, recorded as ind{1}, ind{2}, … ind{179}, storing the data sequence numbers of 179 orbits; the latitude argument u corresponding to the data sequence numbers in all 179 cell arrays satisfies the latitude argument segment [u min ,u max ].

[0103] Furthermore, for a specified latitude argument interval [u min ,u max ], follow the following principles: In the coverage period calculation framework applicable to the embodiment of the present invention, the calculation of the longitudinal multiple only considers the ascending orbit or the descending orbit. The present invention can select the ascending orbit mode or the descending orbit mode. In the ascending orbit mode, the latitude argument segment is [-90°, +90°]; in the descending orbit mode, the latitude argument segment is [+90°, +270°].

[0104] For the aforementioned step S106, an embodiment of the present invention provides a specific implementation method for performing sea and land identification processing on the target longitude and latitude sequence data of the satellite under each regression orbit to obtain a target imaging arc segment timing diagram, including: for each data point in the target longitude and latitude sequence data of the satellite under each regression orbit, identifying whether the data point is located on land or in the ocean; generating a land imaging arc segment timing diagram based on the data points located on land, or generating an ocean imaging arc segment timing diagram based on the data points located on the ocean.

[0105] The target longitude and latitude sequence data under each regression track are processed in batch mode to identify whether the longitude and latitude are on land or ocean. The same operation is performed on the target longitude and latitude sequence data under each regression track to obtain the target imaging arc segment time sequence diagram.

[0106] For example, for each of the 179 tracks of data, land and sea identification is performed in turn. Batch processing is performed on the data points at each moment in the longitude and latitude sequence in the track to identify whether the longitude and latitude are on land or sea. Taking the coverage requirement of "land imaging only" as an example, the following is obtained: Figure 3 The time periods corresponding to the data subsets identified as land are sorted out, and the above 179 tracks are processed in the same way to form the following Figure 4 A timing diagram of an imaging arc segment is shown, wherein the black line segment represents the longitude and latitude of the land to be visited, that is, the imageable arc segment that is not covered.

[0107] Optionally, the ocean / land coordinate database is provided by Matlab's coast file. Based on this, sea and land identification is a simulation method based on the secondary development of the coast database of "Matrix Laboratory" (MATLAB), which uses Matlab's powerful matrix processing capabilities to generate geographic coordinate finite element grids and combines satellite longitude and latitude sequences for batch logic judgment.

[0108] For the aforementioned step S108, the embodiment of the present invention provides a specific implementation method for determining the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc segment timing diagram, taking the imaging time length of each trajectory of the payload as a constraint, including the following steps 2.1 to 2.3:

[0109] Step 2.1, for each regression track in the target imaging arc segment, based on the imaging duration of each track of the payload and the imageable arc segment corresponding to the satellite under the regression track in the previous regression cycle, determine the imageable arc segment corresponding to the satellite under the regression orbit in the current regression cycle from the imageable arc segments not covered under the regression track in the target imaging arc segment timing diagram. In practical applications, when determining the imageable arc segments covered in the current regression cycle, the imageable arc segments under each regression track are processed in parallel, that is, in each regression cycle, the imageable arc segments covered by each regression track are determined.

[0110] In specific implementation, it includes the following (a) to (c):

[0111] (a) Based on the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression period, the cumulative starting point corresponding to the current regression period is determined from the imageable arc segment under the regression orbit that is not covered in the target imaging arc segment timing diagram.

[0112] In one example, for any imageable arc segment under a regression track, if the current regression period is the first regression period, the starting point of the first imageable arc segment under the regression track is directly used as the cumulative starting point corresponding to the first regression period.

[0113] In another example, for any imageable arc segment under a regression orbit, if the current regression period is a subsequent regression period, the next data point corresponding to the end point of the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression period is used as the cumulative starting point corresponding to the current regression period.

[0114] (b) Starting from the cumulative starting point, the cumulative duration of the imageable arc segments that are not covered under the regression track in the target imaging arc segment timing diagram is counted until the imaging duration of each track of the payload is reached to obtain the cumulative end point corresponding to the current regression cycle.

[0115] In one example, the horizontal coordinate of the target imaging arc segment timing diagram is the latitude argument, and the vertical coordinate is the track number. The latitude argument is related to time, so cumulative duration statistics can be performed based on the latitude argument. When the duration reaches the pre-input payload imaging duration per track, the cumulative duration statistics can be stopped, and the data point where the cumulative duration statistics are stopped is used as the cumulative end point.

[0116] (c) The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in the regression orbit in the current regression period.

[0117] Step 2.2, mark the imageable arc segment corresponding to the satellite in the current regression period under the regression orbit as the covered imageable arc segment, so as to update the imageable arc segment not covered under the regression orbit in the target imaging arc segment timing diagram. By marking the imageable arc segment corresponding to the satellite in the current regression period under the regression orbit as the covered imageable arc segment, repeated counting of the same imageable arc segment can be avoided.

[0118] Step 2.3, take the next regression cycle as the new current regression cycle, and continue to determine the imageable arc segments corresponding to the satellite in the regression orbit in the new current regression cycle from the updated uncovered imageable arc segments, until each imageable arc segment under each regression orbit in the target imaging arc segment timing diagram is covered, and the number of regression cycles is obtained. In the specific implementation, repeat the above steps 2.1 to 2.2, and stop the iteration when each imageable arc segment under each regression orbit in the target imaging arc segment timing diagram is covered, and take the number of regression cycles that have passed at this time as the longitudinal multiple.

[0119] For ease of understanding, the present invention provides an application example for determining the longitudinal multiple. First, based on the above-mentioned imageable arc segment timing diagram, the imageable arc segment in the first regression cycle is examined. Specifically, a portion of the specified cumulative time length is accumulated in each row (i.e., each track) of data to be used as the imaging time length constraint t perrev (Take 8 minutes as an example) The imaging time period of the current track. Take the ascending track mode as an example, see Figure 5 The time series diagram of imageable arc segments in the first regression cycle is shown, where the black line segments represent the longitude and latitude of the land to be visited, that is, the imageable arc segments that are not covered; the dark gray line segments represent the longitude and latitude visited in this round, that is, the imageable arc segments covered in the current regression cycle; the light gray line segments represent the longitude and latitude visited, that is, the imageable arc segments that have been covered. The imageable segments in this regression cycle are marked in dark gray, indicating that the imaging segments can be covered in this regression cycle.

[0120] For subsequent regression cycles, the same processing is used. Arcs that have been marked as imageable in the previous regression cycle are not counted repeatedly. This is repeated until all segments in a regression cycle have been covered (marked with light colors). Figure 6 The timing diagram of the imageable arc segment in the second regression cycle is shown. Figure 7 The timing diagram of the imageable arc segment under the third regression cycle is shown. Figure 8 The timing diagram of the imageable arc segment under the fourth regression cycle is shown. At this time, the number of regression cycles experienced is the longitudinal multiple, that is, the longitudinal multiple is 4.

[0121] In the embodiment of the present invention, the cumulative duration statistics can be performed in each imageable arc segment in each track, and the imageable arc segments of the current regression cycle and the current track are counted when the imaging duration constraint of each track expires. The imageable arc segments that have been marked as imageable in the historical regression cycle are not repeatedly counted. This is different from the method of using the entire continuous arc segment to divide the working duration constraint of each track in the concise algorithm to meet the requirements of detailed design.

[0122] In summary, the embodiment of the present invention includes a calculation condition input link, a track recursion link, a coordinate conversion link, a track data processing link, a land and sea identification link, and a longitudinal multiple calculation link, so as to calculate and output the longitudinal multiple in the coverage period concise algorithm. The embodiment of the present invention enables the coverage period concise algorithm to distinguish between land and sea through simulation calculation of the longitudinal coefficient, and can adapt to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only". The embodiment of the present invention improves the adaptability of the concise algorithm and meets the needs of more refined analysis in actual work.

[0123] Based on the above-mentioned embodiment, the embodiment of the present invention provides a device for determining the longitudinal multiple for ground coverage period calculation, see Fig. 9 The structure diagram of a device for determining a longitudinal multiple for calculating a ground coverage period is shown, and the device mainly includes the following parts:

[0124] The data acquisition module 902 is used to obtain satellite parameters corresponding to the satellite and the imaging duration of each track of the payload;

[0125] An orbit recursion module 904 is used to perform orbit recursion on the satellite based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit;

[0126] The sea and land identification module 906 is used to perform sea and land identification processing on the target longitude and latitude sequence data of the satellite in each regression orbit to obtain a target imaging arc segment timing diagram, which includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram;

[0127] The longitudinal multiple determination module 908 is used to determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc timing diagram, taking the imaging time of each orbit of the payload as a constraint, and taking the number of regression cycles as the longitudinal multiple. The longitudinal multiple is used to calculate the satellite's ground coverage period.

[0128] The longitudinal multiple determination device for ground coverage period calculation provided by the embodiment of the present invention forms the target longitude and latitude sequence data of the satellite in each regression orbit through orbit recursion, and then performs sea and land identification processing on the target longitude and latitude sequence data to generate a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram respectively, and on this basis, the longitudinal multiple adapted to the land or ocean is determined with the imaging time length of each track of the payload as a constraint. Compared with the traditional technology of calculating the longitudinal multiple with a simple formula, the embodiment of the present invention can distinguish between land and ocean, so as to adapt to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improve the adaptability of ground coverage period calculation, and meet the more refined analysis requirements in actual work.

[0129] In one implementation, the track recursion module 904 is specifically used to:

[0130] Based on the satellite parameters, the satellite orbit is recursively calculated to form the initial longitude and latitude sequence data corresponding to the satellite;

[0131] The initial longitude and latitude sequence data are processed in sections to obtain the intermediate longitude and latitude sequence data of the satellite in each regression orbit;

[0132] Based on the ascending and descending orbit patterns corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit are screened out from the intermediate longitude and latitude sequence data.

[0133] In one implementation, the track recursion module 904 is specifically used to:

[0134] Determine the latitude argument segment based on the ascending and descending orbit pattern corresponding to the satellite;

[0135] According to the latitude argument segment, the target longitude and latitude sequence data of the satellite in each regression orbit are filtered out from the intermediate longitude and latitude sequence data.

[0136] In one implementation, the sea-land identification module 906 is specifically used to:

[0137] For each data point in the target longitude and latitude sequence data of the satellite under each regression orbit, identifying whether the data point is located on land or sea;

[0138] A land imaging arc segment timing diagram is generated based on data points located on land, or an ocean imaging arc segment timing diagram is generated based on data points located on the ocean; wherein the land imaging arc segment timing diagram is used to describe the corresponding longitude and latitude sequence when the satellite passes over the land in each regression orbit, and the ocean imaging arc segment timing diagram is used to describe the corresponding longitude and latitude sequence when the satellite passes over the ocean in each regression orbit.

[0139] In one implementation, the vertical multiple determination module 908 is specifically configured to:

[0140] For each regression orbit in the target imaging arc segment, based on the imaging duration of each orbit of the payload and the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression cycle, the imageable arc segment corresponding to the satellite under the regression orbit in the current regression cycle is determined from the imageable arc segments not covered under the regression orbit in the target imaging arc segment timing diagram;

[0141] Mark the imageable arc segment corresponding to the satellite under the regression orbit in the current regression period as the covered imageable arc segment, so as to update the imageable arc segment not covered under the regression orbit in the target imaging arc segment timing diagram;

[0142] Take the next regression cycle as the new current regression cycle, and continue to determine the imageable arc segment corresponding to the satellite under the regression orbit in the new current regression cycle from the updated uncovered imageable arc segment, until each imageable arc segment under each regression orbit in the target imaging arc segment timing diagram is covered, and the number of regression cycles is obtained.

[0143] In one implementation, the vertical multiple determination module 908 is specifically configured to:

[0144] Based on the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression period, the cumulative starting point corresponding to the current regression period is determined from the imageable arc segment under the regression orbit that is not covered in the target imaging arc segment timing diagram;

[0145] Starting from the cumulative starting point, the cumulative duration of the imageable arc segments that are not covered under the regression track in the target imaging arc segment timing diagram is counted until the imaging duration of each track of the payload is reached, so as to obtain the cumulative end point corresponding to the current regression cycle;

[0146] The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in the regression orbit during the current regression period.

[0147] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0148] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0149] Fig.10A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 10, a memory 11, a bus 12 and a communication interface 13, wherein the processor 10, the communication interface 13 and the memory 11 are connected via the bus 12; the processor 10 is used to execute an executable module stored in the memory 11, such as a computer program.

[0150] The memory 11 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 13 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0151] The bus 12 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.10 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0152] Among them, the memory 11 is used to store programs, and the processor 10 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 10 or implemented by the processor 10.

[0153] The processor 10 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 10. The above processor 10 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 11, and the processor 10 reads the information in the memory 11 and completes the steps of the above method in combination with its hardware.

[0154] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0156] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for determining a longitudinal multiple for ground coverage period calculation, characterized in that: include: Obtain satellite parameters corresponding to the satellite and the imaging duration of each orbit of the payload; Performing orbit recursion based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit; Performing land and sea identification processing on the target latitude and longitude sequence data of the satellite under each regression orbit to obtain a target imaging arc segment timing diagram, wherein the target imaging arc segment timing diagram includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram; Taking the imaging duration of each orbit of the payload as a constraint, determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc timing diagram, and use the number of regression cycles as the longitudinal multiple, which is used to calculate the satellite's ground coverage period.

2. The method for determining the longitudinal multiple for ground coverage period calculation according to claim 1, characterized in that: Performing orbit recursion on the satellite based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit, including: Performing orbit recursion on the satellite based on the satellite parameters to form initial longitude and latitude sequence data corresponding to the satellite; The initial longitude and latitude sequence data is processed in sections to obtain intermediate longitude and latitude sequence data of the satellite in each regression orbit; Based on the ascending and descending orbit mode corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit is filtered out from the intermediate longitude and latitude sequence data.

3. The method for determining the longitudinal multiple for ground coverage period calculation according to claim 2, characterized in that: Based on the ascending and descending orbit mode corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit is screened out from the intermediate longitude and latitude sequence data, including: Determining a latitude argument segment based on an ascending and descending orbit mode corresponding to the satellite; According to the latitude argument segment, the target longitude and latitude sequence data of the satellite in each regression orbit is filtered out from the intermediate longitude and latitude sequence data.

4. The method for determining the longitudinal multiple for ground coverage period calculation according to claim 1, characterized in that: Performing sea and land identification processing on the target latitude and longitude sequence data of the satellite in each regression orbit to obtain a target imaging arc segment time sequence diagram, including: For each data point in the target longitude and latitude sequence data of the satellite under each regression orbit, identifying whether the data point is located on land or sea; A land imaging arc segment timing diagram is generated based on data points located on land, or an ocean imaging arc segment timing diagram is generated based on data points located on the ocean; wherein the land imaging arc segment timing diagram is used to describe the longitude and latitude sequence corresponding to when the satellite passes over the land under each of the regression orbits, and the ocean imaging arc segment timing diagram is used to describe the longitude and latitude sequence corresponding to when the satellite passes over the ocean under each of the regression orbits.

5. The method for determining the longitudinal multiple for ground coverage period calculation according to claim 1, characterized in that: Taking the imaging time of each track of the payload as a constraint, determining the number of regression cycles required to complete all imageable arcs under each regression track in the target imaging arc segment timing diagram, including: For each of the regression orbits in the target imaging arc segment, based on the imaging duration of each orbit of the payload and the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression cycle, determine the imageable arc segment corresponding to the satellite under the regression orbit in the current regression cycle from the imageable arc segments not covered under the regression orbit in the target imaging arc segment timing diagram; Marking the imageable arc segment corresponding to the satellite under the regression orbit in the current regression period as a covered imageable arc segment, so as to update the imageable arc segment not covered under the regression orbit in the target imaging arc segment timing diagram; Take the next regression cycle as the new current regression cycle, and continue to determine the imageable arc segment corresponding to the satellite under the regression orbit in the new current regression cycle from the updated uncovered imageable arc segments, until each imageable arc segment under each regression orbit in the target imaging arc segment timing diagram is covered, and the number of regression cycles is obtained.

6. The method for determining the longitudinal multiple for ground coverage period calculation according to claim 5, characterized in that: Based on the imaging duration of each orbit of the payload and the imageable arc segment corresponding to the satellite in the regression orbit in the previous regression cycle, the imageable arc segment corresponding to the satellite in the regression orbit in the current regression cycle is determined from the imageable arc segments not covered in the target imaging arc segment timing diagram, including: Based on the imageable arc segment corresponding to the satellite under the regression orbit in the previous regression period, determine the cumulative starting point corresponding to the current regression period from the imageable arc segment not covered under the regression orbit in the target imaging arc segment timing diagram; Starting from the cumulative starting point, the cumulative duration of the imageable arc segments not covered under the regression track in the target imaging arc segment timing diagram is counted until the imaging duration of each track of the payload is reached, so as to obtain the cumulative end point corresponding to the current regression cycle; The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in the regression orbit in the current regression period.

7. A device for determining a longitudinal multiple for calculating a ground coverage period, characterized in that: include: The data acquisition module is used to obtain the satellite parameters corresponding to the satellite and the imaging duration of each track of the payload; An orbit recursion module, used for performing orbit recursion on the satellite based on the satellite parameters to form target longitude and latitude sequence data of the satellite in each regression orbit; A sea and land identification module is used to perform sea and land identification processing on the target longitude and latitude sequence data of the satellite under each regression orbit to obtain a target imaging arc segment timing diagram, wherein the target imaging arc segment timing diagram includes a land imaging arc segment timing diagram or an ocean imaging arc segment timing diagram; The longitudinal multiple determination module is used to determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc segment timing diagram, taking the imaging time of each orbit of the payload as a constraint, and taking the number of regression cycles as the longitudinal multiple, and the longitudinal multiple is used for calculating the ground coverage period of the satellite.

8. The device for determining the longitudinal multiple for ground coverage period calculation according to claim 7, characterized in that: The track recursion module is specifically used for: Performing orbit recursion on the satellite based on the satellite parameters to form initial longitude and latitude sequence data corresponding to the satellite; The initial longitude and latitude sequence data is processed in sections to obtain intermediate longitude and latitude sequence data of the satellite in each regression orbit; Based on the ascending and descending orbit mode corresponding to the satellite, the target longitude and latitude sequence data of the satellite in each regression orbit is filtered out from the intermediate longitude and latitude sequence data.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 6.

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