A method for clustering observation targets based on single-satellite continuous observation constraints

Abstract

This invention relates to a clustering method for observation targets based on single-satellite continuous observation constraints, comprising the following steps: at each moment, determining whether the observation target is physically potentially visible relative to the satellite using off-axis angle constraints and detection distance constraints; when the observation target is physically potentially visible relative to the satellite, using direction vector similarity analysis to extract the spatial nearest neighbor targets of the observation target, constructing a set of satellite line-of-sight candidate directions pointing towards the geometric center of the observation target group; selecting the optimal satellite line-of-sight candidate direction that covers the most observation targets from the set of satellite line-of-sight candidate directions, and obtaining the optimal coverage target set under the optimal satellite line-of-sight candidate direction; and taking the intersection of the optimal coverage target sets at all moments within the sliding window, selecting the intersection target group that is always located in the optimal field of view corresponding to the optimal satellite line-of-sight candidate direction within the sliding window as the continuous observation target set.

Description

Technical Field

[0001] This invention relates to the field of satellite observation technology, and in particular to a clustering method for observation targets based on single-satellite continuous observation constraints. Background Technology

[0002] With the continuous development of multi-constellation and distributed remote sensing systems, the demand for space surveillance and target observation missions is increasing, and the number of targets that can be observed at the same time is increasing significantly. Existing observation planning methods are mostly based on single-moment visibility judgments, lacking modeling of the "continuous observation" process, resulting in unreasonable allocation of satellite resources, redundant output results, and difficulty in supporting subsequent scheduling optimization.

[0003] Furthermore, traditional methods typically focus on observation planning for a single target, failing to adequately consider the continuous observation needs under multi-target clustering, thus limiting the effectiveness of continuous space situation monitoring and stable tracking. Additionally, the approach of making independent decisions on a satellite-by-satellite basis often suffers from the following problems: Repeated observations of the same target by multiple satellites result in a waste of resources; A single satellite faces multiple pointing requirements at the same time, which violates the single-pointing limitation of payload / attitude; The lack of a unified model for sensor geometric constraints renders observation plans unenforceable. The observation efficiency is low if the target is only located in the center of the satellite's field of view. Summary of the Invention

[0004] To address at least some of the problems mentioned above in the prior art, this invention provides a clustering method for observation targets based on single-star continuous observation constraints, comprising the following steps: Calculate the direction vector of the observed target relative to the satellite and the direction vector of the satellite pointing towards the Earth's center. and the relative distance between the observed target and the satellite and using direction vectors and Calculate the off-axis angle, and at each moment use the off-axis angle constraint and the detection range constraint to determine whether the observed target is physically potentially visible relative to the satellite; When determining whether the observed target is physically potentially visible relative to the satellite, direction vector similarity analysis is used to extract the target's characteristics. A spatially nearby target, by... We calculate and normalize the weighted average of the direction vectors of each spatial neighboring target relative to the satellite, and construct a candidate satellite line-of-sight pointing set pointing towards the geometric center of the observed target group. The optimal satellite line-of-sight candidate direction is selected from the set of candidate directions to cover the most observed targets, and the optimal target set covered by the optimal satellite line-of-sight candidate direction is obtained; and The intersection of the optimal coverage target sets at all times within the sliding window is used to select the intersection target group that is always located in the optimal field of view corresponding to the optimal satellite line of sight candidate within the sliding window as the continuous observation target set.

[0005] Furthermore, when the off-axis angle is within a set range and the relative distance is less than or equal to the farthest detection distance, the observed target is determined to be potentially visible relative to the satellite.

[0006] Furthermore, the set of candidate satellite line-of-sight directions is calculated: , Where v represents the original composite vector of the satellite line-of-sight candidate pointing direction. Indicates the observation target Compared to satellites directional vector, Indicates the observation target Space neighbor targets 1, 2, ..., k relative to the satellite The direction vector.

[0007] Furthermore, a greedy strategy is adopted, at each discrete timestamp, from the set of candidate satellite line-of-sight directions... The candidate satellite line-of-sight pointing that covers the most targets is selected as the current local optimum, including: Combined with the sensor's field of view half angle Perform conical filtering on each candidate satellite line of sight: calculate the number of observation targets that can be covered by the candidate satellite line of sight; The optimal satellite line-of-sight candidate direction with the largest number of covered targets is selected as the current local optimum, and the target group covered by the optimal satellite line-of-sight candidate direction is obtained as the optimal coverage target set.

[0008] Furthermore, it is defined as a satellite line-of-sight candidate pointing The following is the set of covered targets : , Where G represents the total set of objectives. Indicate target At any moment For satellite Geometric visibility, Indicates the observation target The angle between the direction vector of satellite s and the direction vector c of the satellite's line of sight; Select the optimal set of observation targets that covers the largest number of targets. : .

[0009] Furthermore, it also includes: acquiring time-series location data of satellites and multiple observation targets, including: Unify the satellite ephemeris and the trajectory of the observed target into the same geocentric inertial coordinate system; The trajectory data of the observed target is processed by time-series interpolation to obtain continuous trajectories with second-level intervals.

[0010] Furthermore, it also includes: outputting the right ascension and declination tables of satellite lines of sight at each time moment, merging tables of target set invariant intervals, histograms of duration distribution of continuous observation windows for single satellites, Gantt charts of continuous coverage for single satellites, evolution diagrams of global coverage for single satellites, and heatmaps of time-satellite pairs for single satellites. It also takes the union of continuous observation segments to generate a summary table of continuous segments.

[0011] Furthermore, if time and If the intersection of the target groups is exactly the same, then the timestamps are merged into a continuous interval, and the target set remains unchanged interval merge table is output.

[0012] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps according to the method described above.

[0013] This invention provides a computer system, comprising: A processor, configured to execute machine-executable instructions; and A memory that stores machine-executable instructions that, when executed by a processor, perform the steps of the method described above.

[0014] The present invention has at least the following beneficial effects: The method of the present invention solves the problems of the prior art, which only considers instantaneous visibility, lacks modeling of continuous observation constraints, and has redundant output results.

[0015] The method of this invention first determines the potential visibility of the observed target relative to the satellite by using off-axis angle constraints and relative distance constraints. Then, it selects the optimal satellite line-of-sight candidate pointing that covers the most observed targets by using the half-angle of the field of view. By comprehensively considering geometric constraints, it achieves accurate modeling of constellation observation tasks.

[0016] The method of this invention supports continuous observation and determination of a single satellite. This method solves the resource waste and physical conflicts caused by single-moment visibility criteria through mathematical modeling: First, it utilizes sliding window intersection operations to ensure that the intersection target group is always within the satellite's optimal continuous field of view within a preset time. Simultaneously, it ensures that each satellite corresponds to only one specific observation axis at any given time through optimal pointing candidate generation, thereby avoiding payload attitude conflicts. Finally, by merging observation results across time periods, it significantly reduces the search space complexity of constellation-level mission planning, thereby improving the rationality of multi-satellite monitoring and mission scheduling.

[0017] This invention uses direction vector similarity analysis to extract the observed target. of By taking a number of spatially nearest targets and normalizing the weighted average of their direction vectors, several candidate lines of sight pointing to the geometric center of this group of targets can be quickly synthesized, effectively reducing the complexity of the search space and achieving efficient computation in large-scale constellations.

[0018] The present invention outputs a table of right ascension and declination of satellite lines of sight at each time point, a table of merged intervals of invariant target sets, and a table of summary of continuous segments, which makes the expression concise and facilitates task planning and subsequent scheduling optimization.

[0019] Considering the limited computing resources, in order to make efficient use of computing resources, this invention uses a greedy strategy to quickly select the candidate satellite line of sight that covers the most targets as the current local optimum at each moment. Attached Figure Description

[0020] To further illustrate the above and other advantages and features of the various embodiments of the present invention, a more specific description of the embodiments of the invention will be presented with reference to the accompanying drawings. It is to be understood that these drawings depict only typical embodiments of the invention and are therefore not intended to limit its scope. In the drawings, identical or corresponding parts will be indicated by identical or similar reference numerals for clarity.

[0021] Figure 1 A flowchart of an observation target clustering method based on single-star continuous observation constraints according to an embodiment of the present invention is shown.

[0022] Figure 2 A histogram showing the duration distribution of a single-satellite continuous observation window according to an embodiment of the present invention is illustrated.

[0023] Figure 3 A single-satellite continuous coverage Gantt chart is shown according to an embodiment of the present invention.

[0024] Figure 4 A single-star global coverage evolution curve is shown according to an embodiment of the present invention.

[0025] Figure 5 A single-satellite time-satellite pair heatmap is shown according to an embodiment of the present invention. Detailed Implementation

[0026] It should be noted that the components in the accompanying drawings may be shown exaggerated for illustrative purposes and may not be to scale.

[0027] In this invention, the various embodiments are merely intended to illustrate the solutions of the invention and should not be construed as limiting.

[0028] In this invention, unless otherwise specified, the quantifiers “a” and “one” do not exclude scenarios involving multiple elements.

[0029] It should also be noted that, in the embodiments of the present invention, only a portion of the parts or components may be shown for clarity and simplicity. However, those skilled in the art will understand that, under the teachings of the present invention, the required parts or components can be added as needed for specific scenarios.

[0030] It should also be noted that within the scope of this invention, the terms "same", "equal", and "equal to" do not mean that the two values ​​are absolutely equal, but allow for a certain reasonable error. In other words, the terms also cover "substantially the same", "substantially equal", and "substantially equal to".

[0031] It should also be noted that in the description of this invention, the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not explicitly or implicitly suggest that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0032] Furthermore, the embodiments of the present invention describe the process steps in a specific order. However, this is only for the convenience of distinguishing each step, and is not a limitation on the order of each step. In different embodiments of the present invention, the order of each step can be adjusted according to the process.

[0033] Figure 1 A flowchart of an observation target clustering method based on single-star continuous observation constraints according to an embodiment of the present invention is shown.

[0034] A clustering method for observation targets based on single-satellite continuous observation constraints includes the following steps: Step 1: Obtain the time-series location data of the satellite and multiple observation targets.

[0035] Satellite time-series location data Temporal location data of the observed target. .

[0036] In practical engineering, to ensure calculation accuracy, it is necessary to unify satellite ephemeris (such as two-line orbital elements TLE or precise ephemeris) and the observed target trajectory into the same geocentric inertial coordinate system (such as the J2000 coordinate system). Simultaneously, considering the high-speed motion of the satellite, the data processing algorithm supports high-precision time-series interpolation processing for low-frequency observed target trajectory data (data points every few minutes or tens of minutes), encrypting the originally sparse data into continuous trajectories with second-level intervals to meet the requirements of discrete time points at the second level. The analysis requirements.

[0037] Step 2, calculate the observed target Compared to satellites Direction vector The direction vector of the satellite pointing towards the Earth's center and observation targets and satellite relative distance and using direction vectors and Calculate the off-axis angle The off-axis angle constraint and the detection range constraint are used to determine the observation target. Is it relative to a satellite? Physically potentially visible.

[0038] When off-axis angle Within the set range and relative distance When the detection distance is less than or equal to the farthest detection distance, the target is determined. Compared to satellites Potentially visible.

[0039] Step 2 is a step for determining instantaneous visibility based on multiple geometric and physical constraints. This step serves as a coarse screening mechanism, rigorously establishing the physical working boundary model of the sensor.

[0040] , in For the observation target at time t Location, For the satellite at time t The location.

[0041] The direction vector of the satellite pointing towards the Earth's center: .

[0042] Observation target and satellite Relative distance: .

[0043] Off-axis angle .

[0044] The off-axis angle must satisfy the upper and lower limits of the off-axis angle ( (This can effectively avoid Earth's shading or background stray light); Observation target and satellite The relative distance needs to satisfy the maximum detection range constraint. (To ensure signal-to-noise ratio and detection probability). Only targets that simultaneously satisfy the above geometric constraints are considered physically "potentially visible" at the current moment.

[0045] Step 3, when observing the target Is it relative to a satellite? When physically potentially visible, direction vector similarity analysis is used to extract the observed target. of A spatially nearby target, by... A spatially nearby target relative to the satellite The direction vectors are weighted, averaged, and normalized to obtain a set of candidate satellite line-of-sight pointing targets pointing towards the geometric center of the observed target group. Direction vector similarity analysis analyzes the similarity of multiple different targets relative to satellites. Direction vector and observed target Compared to satellites The similarity between the direction vectors is used to filter targets with a similarity greater than a threshold, thus selecting those spatially similar to the observed target. Multiple adjacent targets.

[0046] Calculate the candidate pointing set of satellite lines of sight: , Where v represents the original composite vector of the satellite line-of-sight candidate pointing direction. Indicates the observation target Space neighbor targets 1, 2, ..., k relative to the satellite The direction vector.

[0047] Traditional full-space attitude search involves extremely large computational costs. This invention uses direction vector similarity analysis to reduce the infinite continuous attitude search space into a finite set of high-quality discrete candidates.

[0048] Step 4: Use a greedy strategy to select the optimal satellite line of sight candidate direction from the set of satellite line of sight candidate directions, which covers the most observation targets, and obtain the optimal set of covered targets under the optimal satellite line of sight candidate direction.

[0049] Defined as a candidate direction of a satellite line of sight The following is the set of covered targets : , Where G represents the total set of objectives. Indicates the observation target At any moment For satellite Geometric visibility, Indicates the observation target The angle between the direction vector of satellite s and the direction vector c of the satellite's line of sight; Select the optimal set of observation targets that covers the largest number of targets. : .

[0050] Specifically, a greedy strategy is adopted, which involves selecting candidate satellite line-of-sight pointers at each discrete timestamp. The candidate satellite line-of-sight pointing that covers the most targets is selected as the current local optimum, including: Combined with the sensor's field of view half angle Perform conical filtering on each satellite line-of-sight candidate pointing direction: calculate the number of observation targets that can be covered under the satellite line-of-sight candidate pointing direction; The optimal satellite line-of-sight candidate pointing direction, which covers the largest number of targets, is selected as the satellite line-of-sight pointing direction (i.e., the local optimum) for that timestamp. The group of targets covered by the optimal satellite line-of-sight candidate pointing direction is the optimal coverage target set.

[0051] Step 5, set the length of the sliding window to be... Seconds, take the intersection of the optimal coverage target sets at all times within the sliding window, and filter out... Within seconds, the target group that is consistently located at the intersection of the optimal field of view and the optimal satellite line of sight candidate. As a set of continuously observed targets.

[0052] Optimal set of observation targets for a single satellite On the timeline Take the intersection directly and filter the target group of the intersection: .

[0053] Because both satellite S and the observed target are moving at high speed, at time... The target group that can be seen, The exact time may change as the star flies out of the field of view. The logic behind continuous observation of a single star is: to search for... Throughout the duration, the target group never left the intersection of the optimal field of view. As long as the intersection is not empty ( This indicates that satellite s is in this... By maintaining a stable attitude evolution over a long period, continuous and uninterrupted observation of the intersecting target group can be completed. This avoids the need to plan fragmented observation schemes with flickering.

[0054] Step 6: Output the right ascension and declination table of satellite lines of sight at each time point, the merged table of target set invariant intervals, the histogram of duration distribution of continuous observation windows for single satellites, the Gantt chart of continuous coverage for single satellites, the evolution chart of global coverage for single satellites, and the heat map of time-satellite pairs for single satellites. Take the union of continuous observation segments to generate a summary table of continuous segments.

[0055] Because the time step is very short (e.g., on the order of seconds), the algorithm outputs a large amount of highly repetitive data. This step introduces a target set invariant interval merging algorithm, i.e., if at time... and Clustering results If they are exactly the same, the timestamps will be merged into a single continuous interval. (that is, [ This greatly reduces the volume of output data, transforming fragmented line-of-sight calculation results into a "task Gantt chart" format that can be directly invoked by the business layer, significantly reducing the parsing and scheduling complexity of the subsequent task planning platform.

[0056] The union of consecutive observation segments means that when multiple consecutive observation segments are temporally adjacent, they are merged into a longer time period, and the observation targets covered by each segment are merged (duplicated) to obtain the total number of observation targets covered in the merged time period.

[0057] The method of this invention can comprehensively consider coverage performance, continuous observation stability, and constellation coordination capabilities, providing more reliable support and decision-making basis for space situational awareness and continuous observation tasks.

[0058] Figure 2 A histogram showing the duration distribution of a single-satellite continuous observation window according to an embodiment of the present invention is illustrated. Figure 3 A single-satellite continuous coverage Gantt chart is shown according to an embodiment of the present invention. Figure 4 A single-star global coverage evolution curve is shown according to an embodiment of the present invention. Figure 5 A single-satellite time-satellite pair heatmap is shown according to an embodiment of the present invention.

[0059] The method of the present invention will be verified by simulation below. Simulation scenario description: Field of view of satellite sensors Off-axis limit Off-axis lower limit: Maximum detection range =5000km The value of k in the equation is 8. Walker constellation configuration: Walker(72 / 6 / 0) configuration.

[0060] Seed satellite parameters: semi-major axis: 6878km, eccentricity: 0.001, inclination: 60°, right ascension of ascending node: 0°, argument of perigee: 0°, true perigee: 0°.

[0061] A total of 44 observation targets, with a window duration of: =30s.

[0062] The histogram of single-satellite continuous observation window duration distribution output by the simulation is as follows: Figure 2 As shown, it statistically analyzes the duration of all visible windows of a single satellite over a period of time and displays the frequency of occurrence in different duration intervals. Horizontal axis: Continuous observation window duration (unit: minutes), representing the length of time a satellite can continuously cover the target during each satellite transit; Vertical axis: Frequency.

[0063] This histogram is primarily used to assess the single-speech observation capability of a single satellite. If the histogram is concentrated over a short duration (e.g., 1 minute), it indicates that the satellite mainly "passes over" targets and is suitable for transient signal detection; if the distribution is concentrated over a longer duration, it indicates that the satellite can provide a longer continuous data acquisition time.

[0064] The simulation output of the single-star continuous coverage Gantt chart is as follows: Figure 3 As shown, this is a time-series chart. It uses time as the horizontal axis and horizontal bars (Gantt bars) to visually represent when a satellite is visible to a target. It is often combined with orbital prediction data. The horizontal axis of the Gantt chart represents time (e.g., UTC time). The vertical axis represents the satellite number. Each rectangle represents a "visible time window." The starting point of the rectangle is the arrival time (AOS, i.e., the start of signal acquisition) and the ending point is the departure time (LOS, i.e., the loss of signal). The elevation angle may be distinguished by color within the rectangle.

[0065] A single-satellite continuous coverage Gantt chart can be used for mission planning and conflict analysis. Planners can visually see how many transit opportunities there are per day and how long the intervals are, thus determining whether the mission's time constraints (such as the need for continuous observation or observation during specific time periods) are met.

[0066] The simulation output of the single-star global coverage evolution curve is as follows: Figure 4As shown, the horizontal axis represents time (UTC time), and the vertical axis represents frequency. The blue curve (Active pairs) represents the number of active satellite pairs that meet the continuous observation conditions at that moment. The blue curve reflects the total number of instantaneous observation connections of the system. The orange curve (Unique targets) represents the number of unique targets covered by all active satellite pairs at that moment (after unioning and deduplication). The orange curve reflects the instantaneous coverage breadth of the satellite constellation (how many different targets can be seen).

[0067] The simulation output of the single-satellite time-satellite pair heatmap is as follows: Figure 5 As shown, the left vertical axis represents the satellite number, the color represents the number of observed targets, and the horizontal axis represents time (divided into 1-minute segments).

[0068] While some embodiments of the present invention have been described in this application, those skilled in the art will understand that these embodiments are merely illustrative. Numerous variations, alternatives, and improvements will arise in those skilled in the art under the teachings of this invention without departing from its scope. The appended claims are intended to define the scope of the invention and thereby cover methods and structures within the scope of the claims themselves and their equivalents.

Claims

1. A clustering method for observation targets based on single-star continuous observation constraints, characterized in that, Includes the following steps: Calculate the direction vector of the observed target relative to the satellite and the direction vector of the satellite pointing towards the Earth's center. and the relative distance between the observed target and the satellite and using direction vectors and Calculate the off-axis angle, and at each moment use the off-axis angle constraint and the detection range constraint to determine whether the observed target is physically potentially visible relative to the satellite; When determining whether the observed target is physically potentially visible relative to the satellite, direction vector similarity analysis is used to extract the target's characteristics. A spatially nearby target, by... We calculate and normalize the weighted average of the direction vectors of each spatial neighboring target relative to the satellite, and construct a candidate satellite line-of-sight pointing set pointing towards the geometric center of the observed target group. The optimal satellite line of sight candidate direction is selected from the set of satellite line of sight candidate directions to cover the most observed targets, and the optimal target set under the optimal satellite line of sight candidate direction is obtained; as well as The intersection of the optimal coverage target sets at all times within the sliding window is used to select the intersection target group that is always located in the optimal field of view corresponding to the optimal satellite line of sight candidate within the sliding window as the continuous observation target set.

2. The observation target clustering method based on single-star continuous observation constraints according to claim 1, characterized in that, When the off-axis angle is within the set range and the relative distance is less than or equal to the farthest detection distance, the observed target is determined to be potentially visible relative to the satellite.

3. The observation target clustering method based on single-star continuous observation constraints according to claim 1, characterized in that, Calculate the candidate pointing set of satellite lines of sight: ， Where v represents the original composite vector of the satellite line-of-sight candidate pointing direction. Indicates the observation target Compared to satellites directional vector, Indicates the observation target Space neighbor targets 1, 2, ..., k relative to the satellite The direction vector.

4. The observation target clustering method based on single-star continuous observation constraints according to claim 3, characterized in that, A greedy strategy is adopted, and at each discrete timestamp, the candidate points of view from the satellite line of sight are selected. The candidate satellite line-of-sight pointing that covers the most targets is selected as the current local optimum, including: Combined with the sensor's field of view half angle Perform conical filtering on each candidate satellite line of sight: calculate the number of observation targets that can be covered by the candidate satellite line of sight; Select the optimal satellite line-of-sight candidate pointing direction that covers the most targets, and obtain the target group covered by the optimal satellite line-of-sight candidate pointing direction as the optimal coverage target set.

5. The observation target clustering method based on single-star continuous observation constraints according to claim 4, characterized in that, Defined in a satellite line-of-sight candidate pointing The following is the set of covered targets : ， Where G represents the total set of objectives. Indicates the observation target At any moment For satellite Geometric visibility, Indicates the observation target The angle between the direction vector of satellite s and the direction vector c of the satellite's line of sight; Select the optimal coverage target set with the largest number of elements. The corresponding optimal satellite line-of-sight candidate direction is obtained: 。 6. The observation target clustering method based on single-star continuous observation constraints according to claim 1, characterized in that, Also includes: Acquire temporal location data of satellites and multiple observed targets, including: Unify the satellite ephemeris and the trajectory of the observed target into the same geocentric inertial coordinate system; The trajectory data of the observed target is processed by time-series interpolation to obtain continuous trajectories with second-level intervals.

7. The observation target clustering method based on single-star continuous observation constraints according to claim 1, characterized in that, Also includes: Output the right ascension and declination table of satellite lines of sight at each time point, the merged table of invariant intervals of the target set, the histogram of duration distribution of continuous observation windows for a single satellite, the Gantt chart of continuous coverage for a single satellite, the evolution chart of global coverage for a single satellite, and the heat map of time-satellite pairs for a single satellite. Take the union of continuous observation segments to generate a summary table of continuous segments.

8. The observation target clustering method based on single-star continuous observation constraints according to claim 7, characterized in that, If time and If the intersection of the target groups is exactly the same, then the timestamps are merged into a continuous interval, and the target set remains unchanged interval merge table is output.

9. A computer-readable storage medium having a computer program stored thereon, the computer program performing the steps of the method according to any one of claims 1-8 when executed by a processor.

10. A computer system, comprising: A processor, which is configured to execute machine-executable instructions; as well as A memory having stored machine-executable instructions that, when executed by a processor, perform the steps of the method according to any one of claims 1-8.

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