CN113568426B - Satellite cluster collaborative planning method based on multiple satellites and multiple loads - Google Patents
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Abstract
The invention provides a satellite cluster collaborative planning method based on multiple satellites and multiple loads, which is used for analyzing capacity matching relation between various resources and tasks and space-time logic constraint relation of a dynamic task constraint network according to task requirements of different imaging modes of a sensor, establishing an efficient collaborative task allocation and coordination mechanism, meeting the requirements of timeliness, economy and flexibility, constructing a hierarchical distributed type various satellite resource joint task planning technology and realizing comprehensive management and control of resources and joint task planning. Aiming at the characteristics and requirements of users on tasks with different time periods, a reasonable rule and a look-ahead mechanism are utilized to formulate related scheduling strategies, models and methods, so that quick response to dynamic environments and requirements is realized, and collaborative task planning of a microsatellite cluster is completed.
Description
Technical Field
The invention relates to a satellite cluster collaborative planning method based on multiple satellites and multiple loads, and belongs to the technical field of aerospace science.
Background
Today, global observation satellites are actively launched and deployed in various countries of the world, and the number of satellites is greatly increased, but the ever-increasing global remote sensing data requirement is still not met, so that the maximum acquisition of an effective satellite band becomes a necessary trend of technical development. At present, most of satellite remote sensing observation demands in China require a satellite operation management unit to manually coordinate the satellite observation angles by using sufficient manpower, or a simple scheduling algorithm is adopted to a certain extent, and along with the increase of the number of satellites, the manual coordination mode is replaced by an intelligent collaborative planning method. The conventional intelligent collaborative planning method is to establish a simulation scene, load satellite orbit parameters, satellite load parameters and other constraint conditions, simulate a satellite transit scene, and call satellites in a satellite resource library to cover a target area for multiple times according to the maximum coverage rate or the minimum observation angle until shooting tasks are completed.
The existing intelligent collaborative planning method can complete all coverage tasks of a target area, greatly utilizes satellite transit time according to the principle of 'transit instant shooting', but fails to reasonably set sensor shooting tasks according to the characteristics of earth rotation and near polar solar synchronous satellite orbits, so that the overlapping degree of shooting strip areas is high, and the acquisition rate of space resources is low. From the perspective of comprehensive management of space resources, based on the requirements of timeliness, economy and flexibility, a hierarchical distributed type various satellite resource combined task plan is constructed, and the development of satellite cluster collaborative planning of multiple satellites and multiple loads is a necessary trend.
Disclosure of Invention
The invention aims to overcome the defects, and provides a satellite cluster collaborative planning method based on multi-satellite multi-load, namely a five-step optimization planning model, which is used for analyzing the capacity matching relation between various resources and tasks and the space-time logic constraint relation of a dynamic task constraint network according to the task demands of different imaging modes of a sensor, establishing an efficient collaborative task allocation and coordination mechanism, meeting the demands of timeliness, economy and flexibility, constructing a hierarchical distributed type various satellite resource joint task planning technology and realizing comprehensive management and control of resources and joint task planning. Aiming at the characteristics and requirements of users on tasks with different time periods, a reasonable rule and a look-ahead mechanism are utilized to formulate related scheduling strategies, models and methods, so that quick response to dynamic environments and requirements is realized, and collaborative task planning of a microsatellite cluster is completed.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the embodiment comprises the following steps: the method comprises the steps of firstly, determining a resolution requirement according to the ground object type of a target area, and dividing the target area into a plurality of subareas; step two, effective strip information can be obtained according to satellite transit information and sensor breadth, and effective strips are ordered according to shooting starting time of the effective strips and sub-region priority to obtain a first effective strip sequence; substituting the solar altitude angle and other data sources such as real-time prediction cloud pictures, deleting the strips which do not meet the constraint conditions such as illumination, cloud quantity and the like through constraint condition thresholds such as the illumination condition, the cloud quantity and the like which are set by people, so that the relative sequence of the reserved effective strips is kept unchanged, and a second effective strip sequence is obtained. And step four, establishing a target area acquisition model according to the second effective strip sequence. In this step, for the rotatable sensor, n effective strips are selected from the second effective strip sequence in the process of establishing the target area acquisition model, specifically, the method is to select the ordered strips for a new time from the angle of maximized coverage of the remaining non-acquired areas, delete the area covered by the new strips from the attention area library after each acquisition, continuously update the attention area library, and form a new attention area for next acquisition (fig. 3); and fifthly, arranging corresponding high-resolution cameras to shoot according to different ground object requirements in the high-resolution requirement area, and carrying out joint filling shooting on the rest area by using middle-low resolution cameras, wherein the synthetic aperture radar sensors are cooperated in parallel to finish collaborative planning of the satellite cluster.
The specific scheme is as follows:
a satellite cluster collaborative planning method based on multi-star and multi-load, namely a five-step optimization planning model, comprises the following steps:
(1) Decomposing the target area into a plurality of subareas according to the ground object category and the task demand;
(2) Sequencing all the effective strips according to the shooting start time of the effective strips and the priority of the subareas to obtain a first effective strip sequence;
(3) Deleting the effective strip which does not meet the constraint condition of the image requirement in the first effective strip sequence to obtain a second effective strip sequence;
(4) Establishing a target area acquisition model according to the second effective strip sequence; the target area acquisition model maximizes coverage of the target area and minimizes one or a combination of more of acquisition times, acquisition costs, or final acquisition time;
(5) And (3) setting different loads according to the ground object types and task demands of each subarea, and executing a shooting plan according to the target area acquisition model obtained in the step (4).
Further, the effective strip is a part, overlapping with the target area, of the strip acquired by any satellite in the shooting time interval; the shooting time interval is within a task time range.
Further, the image requirement constraint condition in the step (3) includes illumination, cloud cover, shooting time, coverage or side sway angle.
Further, in the step (4), the target area acquisition model minimizes acquisition cost; the target area acquisition model comprises a fixed sensor target area acquisition model;
the establishment of the fixed sensor target area acquisition model comprises the following steps:
(411) Defining that the second valid band sequence comprises n valid bands, each valid band being denoted b i I is more than or equal to 1 and less than or equal to n; will b i Between the target area and each effective strip b i All intersections between are divided into m sub-regions of interest, each of which is denoted SSR j ，1≤j≤m；
(412) Introducing constraint conditions of a target area acquisition model;
(413) A fixed sensor target area acquisition model is built according to steps (411) and (412).
Further, in step (412), the constraint includes:
the shooting times of the same satellite at the same moment are not more than 1 time;
through the covering depth d corresponding to each sub-interest area j J is more than or equal to 1 and less than or equal to m, and the shooting times of each sub interest area are restrained; the depth of coverage d j D, setting according to task requirements j ≥0。
Further, the fixed sensor target area acquisition model is as follows:
wherein c i To obtain b i Acquisition cost of c i >0；x i As binary variable, x i =1 represents acquisition b i ，x i =0 represents not taking b i ；q ij Representing the effective band b i SSR whether covering a region of interest j ，q ij =0 represents the effective band b i SSR without covering sub-interest area j ，q ij =1 represents the effective band b i Covering SSR of interest region j 。
Further, in the step (4), the target area acquisition model minimizes acquisition cost; the target area acquisition model comprises a rotatable sensor target area acquisition model;
the establishment of the rotatable sensor target area acquisition model comprises the following steps:
(421) Selecting n active bands from the second active band sequence, each active band being denoted b i I is more than or equal to 1 and less than or equal to n; will b i Between the target area and each effective strip b i All intersections between are divided into m sub-regions of interest, each of which is denoted SSR j ，1≤j≤m；
(422) Introducing constraint conditions of a target area acquisition model;
(423) A rotatable sensor target zone acquisition model is built according to steps (421) and (422).
Further, in the step (421), the method for selecting n valid bands from the second valid band sequence includes: selecting a new time from the angle of maximum coverage of the remaining non-collected areas, after the collection of each selected effective strip is completed, deleting the area covered by the effective strip from the non-collected areas, continuously updating the concerned area library, and forming a new concerned area to collect the next time until the whole coverage of the target area is realized; the region of interest library is a set of target regions of each shooting task.
Further, the rotatable sensor target area acquisition model is as follows:
wherein c i To obtain b i Acquisition cost of c i >0；x k i As binary variable, x k i =1 represents acquisition b i ，x k i =0 represents not taking b i ；q k ij Representing the effective band b i SSR whether covering a region of interest j ，q k ij =0 represents b acquired at the kth rotational position i Can not cover SSR of sub-interest region j ，q k ij B acquired at kth rotational position =1 i Can cover SSR of interest zone j The method comprises the steps of carrying out a first treatment on the surface of the K represents the rotation position of the sensor, K is more than or equal to 1 and less than or equal to K, and K is at most K possible rotation positions of the rotatable sensor.
Further, the task requirements in the steps (1) and (5) include a task priority requirement and a sensor requirement.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the satellite cluster collaborative planning method based on multi-star and multi-load, a five-step optimization planning model is established, coverage of a target area is maximized, one or more of acquisition times, acquisition cost and final acquisition time are minimized according to ground feature characteristics of the target area and actual shooting task requirements, effective coverage is increased by using sensors to the maximum extent, and an efficient collaborative task allocation and coordination mechanism is successfully established; according to the invention, by using a heuristic algorithm, the maximum coverage rate and the full coverage shortest time are comprehensively considered, satellites with different loads are called in a satellite resource library, a nearly most suitable satellite shooting plan scheme is quickly found, the satellite transit time is greatly utilized, and space resources are more quickly and effectively acquired;
(2) In the satellite cluster collaborative planning method based on multi-star and multi-load, two complex situations of fixed angle and rotatable angle of the sensor are considered in the process of establishing the five-step optimization planning model, and the method corresponds to a real scene and has wide application range;
(3) According to the satellite cluster collaborative planning method based on multi-star and multi-load, the strip planning problem is converted into the calculation geometric problem, so that the calculation process is simplified, and the calculation efficiency is improved.
Drawings
FIG. 1 is a prior art acquisition strip automatically generated using an existing "maximum coverage" algorithm;
FIG. 2 is a collection strip generated using the multi-star multi-load based satellite cluster collaborative planning method of the present invention;
FIG. 3 is a schematic diagram of a method for updating a region of interest library for a fourth step of selecting an effective strip in a five-step optimization planning model;
FIG. 4 is a schematic diagram of the process of establishing a fixed sensor target area acquisition model according to the present invention;
FIG. 5 is a schematic diagram of the division of sub-interest areas according to the present invention;
FIG. 6 is an acquisition strip automatically generated using the existing "maximum coverage" algorithm in example 1;
FIG. 7 is a rotatable sensor acquisition strip generated using the five-step optimization planning model of the present invention in example 1;
fig. 8 is a schematic diagram of rotatable sensor observation simulation established for the five-step optimization planning model of the present invention in the Hainan province in example 1.
Detailed Description
The features and advantages of the present invention will become more apparent and clear from the following detailed description of the invention.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
A large number of user tasks exist in a planning period, the tasks are divided into different priorities according to the urgency degree of the observation tasks, the diversity of the observation targets is added, the multi-star tasks are cooperatively planned into complex tasks, multiple observation possibilities exist between satellite resources and the observation tasks, the satellite resources and the observation tasks are limited by multiple constraints, and different tasks can collide in part of the observation time, so that the optimal selection decision problem of the multi-star observation strips is very important. Taking all satellite loads, orbits, shooting plans and target areas into consideration, developing a heuristic algorithm, establishing a task allocation and management mechanism, establishing a collaborative task planning model and realizing optimization and management of tasks.
The optimization planning model will be executed in five steps. Dividing the ground object type of a target area; the second step searches for all solutions; thirdly, removing part of the strips according to constraint conditions; fourth, an algorithm matrix is established aiming at the strips meeting all constraint conditions, and further screening is carried out to remove redundancy; and fifthly, setting different loads according to the type of the target ground object to execute a shooting plan. The satellite clusters can combine the optical image with the radar image, combine the high-resolution image with the medium-low-resolution image, shorten the complete coverage period, and obtain the full coverage period according to the target requirement, only provide the region meeting the coverage requirement after optimizing, delete the strips which do not help the final coverage, increase the shooting efficiency, and reduce the storage redundancy.
The traditional observation method is to maximize the coverage of a single acquisition, irrespective of previous acquisitions, resulting in a low observation efficiency, repeated acquisition of the same area, and in particular in the case of high repetition period orbits, the same strips tend to be chosen. As shown in fig. 1, for example, in italy, by using the existing "maximum coverage" algorithm to automatically generate acquisition strips, each acquisition plan is independent of the others, so the output overlaps with the previous data, resulting in a series of overlapping and redundant acquisitions, and many portions of the target area are not completely covered. Through the selection of the optimization algorithm, all the stripes are traversed, and the solution of fig. 2 can be realized after redundancy is eliminated.
How to delete redundant strips meeting constraint conditions at the same time, and maximizing effective coverage by using sensors is a difficult technical bottleneck to break through in a five-step optimization planning mode. Researchers make much effort on how to select an excellent algorithm to solve the redundancy problem, and starting from a simple model, a heuristic algorithm thought is applied to gradually construct a complex model meeting the requirements.
In order to solve the stripe problem, the following concepts are defined:
r is the region of interest, i.e. the area of the earth's surface that needs to be covered, i.e. the target area. The shape of R does not make any assumption.
T is the time frame of the planning problem, i.e. the task time frame. Let T be the initial time T 0 And a final time T f Given interval [ T ] 0 ，T f ]。
S is a set of satellites considered in the planning problem. It is assumed that the position of each satellite in S is known and that the simulation of the scene and the acquisition of the sensor strip can be performed for each instant in T.
For each satellite S e S, ps is a set of S possible sensor angular positions.
At a given satellite S e S, the sensor position p e Ps and the shooting time interval t 0 ，t 1 ]In the case of (2), then a (s, p, t 0 ，t 1 ) Defined as being at t by satellite s 0 ，t 1 ]During the period ofThe sensor angle position p.
A is the set of all the acquirable stripe sets; represented as a set of satellite positions and time ranges T for all acquirable stripes,
coverage target region overlap region set SR: intersection of set a with region R, the set of all of their subsets corresponding to the target region;
all valid stripe sets a': a set of overlapping portions of each stripe in the set a and the target region R;
an active set of stripes B that satisfies the image demand constraint: the method comprises the steps that an effective band set B meeting the image demand constraint condition is obtained after all effective band sets A' are screened by the image demand constraint condition, each effective band is obtained through one-time acquisition, and each effective band in the B is expressed as In fixed sensor mode, set +.> Rotation sensor mode, set-> t iq And t ir Representing the moment when the sensor starts scanning R and the moment when it leaves R in the ith acquisition, respectively, i.e. the active band b i A shooting start time and a shooting end time of (a); n is the actual acquisition of the sensor in the fixed sensor mode or in the rotatable sensor modeThat is, a set of bands used to build a target region acquisition model; specifically, in the fixed sensor mode, it is assumed that N is common in the set B 1 A valid stripe, typically n=n 1 The method comprises the steps of carrying out a first treatment on the surface of the In the rotatable sensor mode, N is assumed to be common in set B 2 The effective strips, typically N < N 2 The method for selecting n effective strips from the set B is to select the ordered strips from the angle of maximum coverage of the rest non-collected areas, delete the area covered by the strips from the non-collected area library after each strip collection, form a new non-collected area library, collect the next time, and continuously update and iterate until all the coverage of the areas is completed.
Each effective strip b i The corresponding acquisition cost is defined as c i ，c i >0. It should be noted that where i=1, … …, n denotes the ith acquisition, but the satellites in the different acquisitions may be the same, i.e. the first acquisition b 1 (s 1 ,p 1 ,t 1q ,t 1r ) And second acquisition b 2 (s 2 ,p 2 ,t 2q ,t 2r ) Although satellites are denoted as s respectively 1 Sum s 2 But in practice may be the same satellite; although the number of effective strips actually collected by the sensor in the fixed sensor mode or the rotatable sensor mode, that is, the number of strips used for establishing the target area collection model is recorded as n, the number of effective strips actually collected by the sensor in the fixed sensor mode or the rotatable sensor mode is different in actual situations;
sub-region of interest SSR j : applying each effective strip b i Sub-regions of interest with R and all intersections between them divided into R, denoted as { SSR } 1 ，...，SSR j ，…，SSR m }；
Aggregation for active stripes There are several constraints as follows:
(1) In any moment, the satellite cannot make two shots at the same time, namely, the two shots cannot be performed in the same satellite and the same time period, namely:
(2) Depth of coverage d j : if the sub-region of interest SSR of R j In particular, it is recommended to record more than once. Parameter d j Is a non-negative integer that allows these "high regions of interest" to be acquired multiple times. It also allows for inclusion of d j Regions of sub-interest of=0, which means that they are "not of interest", so acquisition is not required at all.
By the above definition, the optimization mode can be defined as: finding the best choice to allow acquisitionMake->If the depth parameter d is covered by j =0 SSR certain sub-regions of interest j Marked as not of interest, the optimization mode is defined as:
in a sense, the best choice is to apply a certain function F ({ b) 1 ，...，b n }) minimize. F may have different definitions depending on the target, for example:
F＝n m (minimum number of acquisitions), or(lowest shooting cost) or f=max t ir ，i＝1，...，n t (shortest acquisition time) any one or more of the above.
Thus, the target may be a threshold with a minimum final time/maximum budget, etc. while being able to cover a maximum target area.
Model one: fixed sensor
Satellite cluster with fixed sensor, active stripe b i Is the set of bands that remain after intersection of set a of complete satellite bands with target region R and removal of non-compliance. The planning objective is to select the appropriate acquisition strip within the task time T so as to cover the target region R at the lowest cost (or minimum acquisition number, or minimum final time).
In accordance with the foregoing description of the invention,in the second effective stripe sequence, the effective stripe set used for establishing the target area acquisition model is n, and at this time, n refers to the effective stripe number n which can be actually acquired by the fixed sensor, because the fixed sensor acquisition stripe is a sub-satellite point stripe, the acquisition number n is a fixed value, and corresponds to n acquisitions which are increased according to the moment when the sensor starts to scan R n Is the last acquisition, b n+1 Only after the task time frame. B as input data i The time at which the scan R starts and the time at which it leaves R are respectively t iq And t ir . In order to make the model meaningful, we need to assume +.>t iq 0 or more, and t nr ≤T f . As shown in fig. 4, the target region R is a region represented by a rectangle, and is exemplified by 4 acquisitions, i.e., n=4, { b 1 ，b 2 ，b 3 ，b 4 And is a set of stripes ordered by the moment the sensor starts scanning R.
The target region R is acquired 4 times { b 1 ，b 2 ，b 3 ，b 4 Cover, b 3 Is superfluous and should not occur in the best solution.
The conventional algorithm is to first select b 1 As it covers a specific area in the target area R. It will then iteratively select acquisitionsb 2 ，b 3 ，b 4 As they all cover a new one of the target areas R. Once the entire target region R is covered, the algorithm stops. With this procedure four acquisitions have to be made to cover the whole region of interest. Note that this example is merely introduced to illustrate that any particular non-optimal method may provide a viable solution (if sufficient acquisition is available), but may not result in an optimal solution.
We express the problem as a mathematical programming problem, let x i Is a binary variable (i.e., it can only take values 0 and 1), by x i =1 or x i =0 indicates whether acquisition b is to be used i Each effective strip b i The corresponding acquisition cost is defined as c i ，c i >0. The above-described planning problem can be expressed as
SSR of sub-region of interest if there is one R j Without being covered by any available acquisition, the constraints of the model (1) described above will never be satisfied. To be able to calculate the solution of (1), we model this problem as an integer linear programming problem. Applying each effective strip b i Sub-regions of interest with R and all intersections between them divided into R, denoted as { SSR } 1 ，...，SSR j ，…，SSR m From this part of the sub-region of interest, a matrix Q can be obtained, if the sub-region of interest SSR is j Is effectively banded b i Overlay, then its matrix element q ij And the value of (2) is 1, otherwise, 0. With this new matrix, equation (1) can be expressed as:
with this expression, we include, among the constraints of model (2), the coverage depth constraint forcing each sub-region of interest SSR j Is at least d j Covered by a different active stripe.
The new model (2) is now applied to the example of fig. 4, indicating that some acquisitions may not be needed. We assume each valid stripe b i The corresponding acquisition cost is defined as c i Are all equal, so, depending on the linearity of the problem, we can set the cost of 4 acquisitions to c i =1. In this example, 4 effective stripes subdivide the region of interest R into 18 sub-regions of interest, as shown in fig. 5.
Matrix Q T Expressed as:
matrix Q T The first row indicates the sub-region of interest SSR 1 Is only effectively banded b 1 Overlay, third row represents b 3 Covering sub-region of interest SSR 3 ，SSR 4 ，SSR 8 ，SSR 13 ，SSR 16 And SSR (simple sequence repeat) 17 . Through computer intersection operation, settingSolution of model (2) using the present example data is x 3 ＝0，x 1 ＝x 2 ＝x 4 =1, which means that b 3 Is a redundant stripe, and does not need to be photographed.
Model two: rotatable sensor
Now it is assumed that the satellite has a rotatable sensor, i.e. the sensor angle can be varied within a certain range, which allows a better acquisition of the target area. The rotatable sensor has K possible discrete modes (typically k.ltoreq.256) added to the fixed sensor and only one can be selected per imaging, thus the matrix Q T Becomes a three-dimensional matrix if b can be acquired at the kth position i To shoot sub-region of interest SSR j Q is k ij And 1, otherwise, the value is 0. The model (2) becomes:
the first set of constraints in the model (3) provides that there must be at least one set of valid strips covering the sub-regions of interest, ensuring that the sub-regions of interest can be covered entirely. The second set of constraints prevents satellites from acquiring multiple sets of valid bands at the same location.
This process provides a viable solution Sol to our problems j In complex models such as rotatable sensors, the Cost of the solution is involved j The Cost is calculated according to the specific conditions of the satellite cluster, wherein the Cost comprises a plurality of limiting factors such as time, frequency limitation, residual electric quantity and the like required by rotation of the sensor j So that Sol j *Cost j Minimum, this is the best possible solution.
Through the analysis of the two models, the strip planning problem is converted into a calculation geometric problem, namely, a matrix Q is solved T And therefore, the optimal effective band can be determined and selected, and the redundancy problem is solved.
And dividing different ground object types and task demands according to the five-step optimization planning model, and completing the satellite cluster combined task planning task after defining the sensor mode and shooting the strip.
Example 1
Step (1): the target area is divided into several major categories such as optical high-resolution demand areas (such as town areas and the like), optical medium-low-resolution demand areas (such as unmanned mountain areas, bare lands and the like), synthetic aperture radar satellite demand areas (such as cloudy islands, oceans, ports and the like), other demand areas and the like. And decomposing the region AOI file according to the satellite image requirement of the target region.
Step (2): according to the satellite orbit parameter theory, dynamically acquiring the latest orbit long half axis, orbit inclination angle, ascending intersection point right ascent and other satellite parameters of a satellite cluster, and setting satellite cluster orbit information. Setting the condition, the sensor mode and the sensor parameters of each satellite carrying sensor. After the construction of the scene is completed, the spatial corresponding relation between the target area and the satellite is displayed in the scene in real time. And predicting the photographable strips according to the satellite transit information in the simulated scene, and sequencing the effective strips according to the regional priority and the shooting start time of the effective strips.
Step (3): substituting the solar altitude angle and the data of the predicted cloud picture, setting constraint conditions such as illumination, cloud cover, shooting time, coverage, side swing angle and the like of remote sensing satellite image requirements, deleting effective strips which do not meet the threshold values of all conditions, reserving the effective strips which meet the threshold values of all conditions, and reserving the effective strips according to the sequence of the step (2) to obtain an effective strip set which meets the constraint conditions;
step (4): and adopting an optimization algorithm to simulate a plurality of effective strips in an effective strip set meeting constraint conditions into a plurality of rows of matrixes, converting the matrixes into unit matrixes, and finishing the solving problem, thereby deleting redundant strips simultaneously meeting all constraint conditions from the plurality of effective strips. When the sensor is rotatable, the sequenced strips are required to be selected for the first time from the angle of maximized coverage of the remaining non-acquisition areas, after each strip acquisition is completed, the area covered by the strip is deleted from the non-acquisition area library, a new non-acquisition area library is formed, the next acquisition is carried out, the iteration is continuously updated until the complete coverage of the area is completed, and a complete target area acquisition model is established.
Step (5): according to the optical high-resolution requirement area, the optical medium-low resolution requirement area, the synthetic aperture radar satellite image requirement area and other requirement areas, the requirement decomposition is carried out, different types of sensors are arranged to simultaneously and independently complete the strip acquisition task, when the optical high-resolution sensor can not complete shooting on time, the medium-low resolution optical sensor or the synthetic aperture radar satellite sensor is used for carrying out seam filling shooting, and the effective coverage is increased by using the sensor to the maximum extent.
Through the five-step optimization planning model, the optimization is repeated, various sensors are effectively matched, the efficient coverage of a target area is realized, and the collaborative planning task of the satellite cluster is completed together.
Fig. 6 is a census double star acquisition strip of 11 months 1-11 months 14 days automatically generated by using the existing "maximum coverage" algorithm, with a sensor maximum yaw angle of 42.3 °, each acquisition plan being independent of the other plans, thus producing a series of redundant and overlapping acquisitions. Table 1 collects the strip parameters for the existing "maximum coverage" algorithm.
Table 1 "maximum coverage" algorithm to collect stripe parameters
Fig. 7 shows a five-step optimization planning model of the invention, which automatically generates the 2020-11-14 census double-star acquisition strips after eliminating the redundant strips in fig. 6, because each acquisition plan has prospective performance, the best strip is selected after multiple simulation iterations, and the method has the advantage of high coverage compared with the existing maximum coverage rate algorithm.
Table 2 optimization planning model acquisition of stripe parameters
And 5, establishing a five-step optimization planning model for Hainan province on 11 months 1 to 14 months 2020 to perform observation simulation. Wherein, the sea-mouth city, the three-city, the eastern city and the 4-city district of Jones county in Hainan province are divided according to the AOI in the first step, the optical high-precision remote sensing image is determined, the rest areas are covered by the optical medium-low-resolution remote sensing image, and the simulation result is shown in figure 8. The small-width strips in the figure are observation strips for checking 1/2/3/4 of the satellite, the maximum yaw angle of the sensor is 42.3 degrees, and the strip numbers are 1-4, 9, 11 and 15-17 respectively; the large-breadth strip is an observation strip of a census 1/2 star, the maximum lateral swing angle of the sensor is 42.3 degrees, and the strip numbers are 5-8, 10, 12-14 and 18-23 respectively; after the imaging area is less than 0.1% of the strips through threshold control screening, 9 strips are formed in total by the detailed investigation series satellites, 14 strips are formed in total by the general investigation series satellites, 23 strips are formed in total, and the sequence list according to shooting time is shown in table 3:
TABLE 3 five-step optimization planning model parameters for Hainan province
The invention has been described in detail in connection with the specific embodiments and exemplary examples thereof, but such description is not to be construed as limiting the invention. It will be understood by those skilled in the art that various equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its embodiments without departing from the spirit and scope of the present invention, and these fall within the scope of the present invention. The scope of the invention is defined by the appended claims.
What is not described in detail in the present specification is a well known technology to those skilled in the art.
Claims (10)
1. The satellite cluster collaborative planning method based on multi-satellite multi-load is characterized by comprising the following steps of:
(1) Decomposing the target area into a plurality of subareas according to the ground object category and the task demand;
(2) Sequencing all the effective strips according to the shooting start time of the effective strips and the priority of the subareas to obtain a first effective strip sequence;
(3) Deleting the effective strip which does not meet the constraint condition of the image requirement in the first effective strip sequence to obtain a second effective strip sequence;
(4) Establishing a target area acquisition model according to the second effective strip sequence; the target area acquisition model maximizes coverage of the target area and minimizes one or a combination of more of acquisition times, acquisition costs, or final acquisition time;
(5) Setting different loads according to the ground object category and task demand of each subarea, and executing a shooting plan according to the target area acquisition model obtained in the step (4);
the target zone acquisition model includes a fixed sensor target zone acquisition model or a rotatable sensor target zone acquisition model.
2. The method for collaborative planning of a cluster of satellites based on multiple satellites and multiple loads according to claim 1, wherein the effective band is a portion of any satellite overlapping a target area in a band acquired at a shooting time interval; the shooting time interval is within a task time range.
3. The method for collaborative planning of satellite clusters based on multi-star and multi-load according to claim 1, wherein the image demand constraints in step (3) include illumination, cloud cover, shooting time, coverage, or roll angle.
4. The method according to claim 1, wherein in the step (4), the target area acquisition model minimizes acquisition cost; the target area acquisition model comprises a fixed sensor target area acquisition model;
the establishment of the fixed sensor target area acquisition model comprises the following steps:
(411) Defining that the second valid band sequence comprises n valid bands, each valid band being denoted b i I is more than or equal to 1 and less than or equal to n; will b i Between the target area and each effective strip b i All intersections between are divided into m sub-regions of interest, each of which is denoted SSR j ，1≤j≤m；
(412) Introducing constraint conditions of a target area acquisition model;
(413) A fixed sensor target area acquisition model is built according to steps (411) and (412).
5. The method of collaborative planning for a satellite cluster based on multiple satellites and multiple loads according to claim 4, wherein in step (412), the constraints include:
the shooting times of the same satellite at the same moment are not more than 1 time;
through the covering depth d corresponding to each sub-interest area j， J is more than or equal to 1 and less than or equal to m, and the shooting times of each sub interest area are restrained; the depth of coverage d j D, setting according to task requirements j ≥0。
6. The satellite cluster collaborative planning method based on multi-star and multi-load according to claim 5, wherein a fixed sensor target area acquisition model is as follows:
wherein c i To obtain b i Acquisition cost of c i >0；x i As binary variable, x i =1 represents acquisition b i ，x i =0 represents not taking b i ；q ij Representing the effective band b i SSR whether covering a region of interest j ，q ij =0 represents the effective band b i SSR without covering sub-interest area j ，q ij =1 represents the effective band b i Covering SSR of interest region j 。
7. The method according to claim 1, wherein in the step (4), the target area acquisition model minimizes acquisition cost; the target area acquisition model comprises a rotatable sensor target area acquisition model;
the establishment of the rotatable sensor target area acquisition model comprises the following steps:
(421) Selecting n active bands from the second active band sequence, each active band being denoted b i I is more than or equal to 1 and less than or equal to n; will b i Between the target area and each effective strip b i All intersections between are divided into m sub-regions of interest, each of which is denoted SSR j ，1≤j≤m；
(422) Introducing constraint conditions of a target area acquisition model;
(423) A rotatable sensor target zone acquisition model is built according to steps (421) and (422).
8. The method for collaborative planning of satellite clusters based on multi-satellite and multi-load according to claim 7, wherein in the step (421), the method for selecting n valid stripes from the second valid stripe sequence is as follows: selecting a new time from the angle of maximum coverage of the remaining non-collected areas, after the collection of each selected effective strip is completed, deleting the area covered by the effective strip from the non-collected areas, continuously updating the concerned area library, and forming a new concerned area to collect the next time until the whole coverage of the target area is realized; the region of interest library is a set of target regions of each shooting task.
9. The satellite cluster collaborative planning method based on multi-star and multi-load according to claim 8, wherein the rotatable sensor target area acquisition model is:
wherein c i To obtain b i Acquisition cost of c i >0；x k i As binary variable, x k i =1 represents acquisition b i ，x k i =0 represents not taking b i ；q k ij Representing the effective band b i SSR whether covering a region of interest j ，q k ij =0 represents b acquired at the kth rotational position i Can not cover SSR of sub-interest region j ，q k ij B acquired at kth rotational position =1 i Can cover SSR of interest zone j The method comprises the steps of carrying out a first treatment on the surface of the K represents the rotation position of the sensor, K is more than or equal to 1 and less than or equal to K, and K is at most K possible rotation positions of the rotatable sensor.
10. The method of claim 1, wherein the task demands in steps (1) and (5) include task priority demands and sensor demands.
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