Method, device and equipment for analyzing target magnitude characteristics of GEO satellites

Abstract

The present application relates to a method, device and equipment for analyzing target magnitude characteristics of GEO satellites, and relates to the technical field of target magnitude characteristics analysis. The method includes: setting the position coordinate parameters of the GEO satellite and the observation station, and obtaining the historical photometric data of the GEO satellite obtained by the observation station within a specified time; constructing the SD coordinate system through coordinate conversion, and obtaining the solar longitude and solar latitude in the SD coordinate system; using the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis, performing photometric feature representation of the historical photometric data of the GEO satellite, and using a diffusion kernel density estimation algorithm based on adaptive bandwidth to analyze and process the photometric features, and obtaining the target magnitude distribution around each data point in the photometric feature representation. The present method can be used to perform photometric feature representation and target magnitude characteristic analysis on massive GEO satellite historical photometric data.

Description

Technical Field

[0001] The present application relates to the technical field of target magnitude characteristic analysis, and in particular to a target magnitude characteristic analysis method, device and equipment for GEO satellites. Background Art

[0002] Ground-based optical observations have always been a hot topic in space situational awareness research. The geostationary orbit (GEO) is an area of ​​great significance. The data obtained by ground-based telescope observations are generally divided into two types, photometric curve data and visible light images. However, due to the long distance between GEO satellites and the ground (about 36,000 kilometers), it is difficult for ground-based telescopes to obtain optical images of GEO satellites, and usually only photometric data can be obtained. With the continuous improvement of space target monitoring systems, the photometric data of GEO satellites that can be obtained by ground-based telescopes is increasing. There is an urgent need for a new way to analyze the massive photometric data of GEO satellites for photometric feature representation, and to obtain effective feature information from the photometric feature representation for target magnitude characteristic analysis. Summary of the invention

[0003] Based on this, it is necessary to provide a target magnitude characteristic analysis method, device and equipment for GEO satellites to address the above technical problems.

[0004] A method for analyzing target magnitude characteristics of a GEO satellite, the method comprising:

[0005] Set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within the specified time;

[0006] Through coordinate transformation, a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station is constructed, and the solar longitude and solar latitude in the SD coordinate system are obtained;

[0007] The photometric characteristics of the historical photometric data of the GEO satellite are represented with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis. The photometric characteristics are analyzed and processed using the diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric characteristic representation.

[0008] In one embodiment, a SD coordinate system with a GEO satellite as the coordinate origin and pointed to by the GEO satellite at the measuring station is constructed by coordinate transformation, including:

[0009] Construct a VVLH coordinate system with the GEO satellite as the coordinate origin, expressed as XYZ;

[0010] According to the relative position relationship between the GEO satellite and the measuring station, XYZ is rotated clockwise around the Z axis by the first Euler angle to obtain the transition coordinate system, which is expressed as X'Y'Z';

[0011] Rotate X'Y'Z' around the Y' axis clockwise by the second Euler angle to obtain the SD coordinate system pointed by the GEO satellite to the measuring station, expressed as X"Y"Z"; where both the transition coordinate system and the SD coordinate system take the GEO satellite as the coordinate origin.

[0012] In one embodiment, the coordinate transformation matrix M between the VVLH coordinate system and the SD coordinate system is expressed as

[0013]

[0014]

[0015]

[0016] in, and They represent the first and second Euler angles respectively. represents the second Euler angle rotation clockwise, Represents a clockwise rotation of the first Euler angle.

[0017] In one embodiment, a diffusion kernel density estimation algorithm based on adaptive bandwidth is used to analyze and process the photometric feature to obtain the target magnitude distribution around each data point in the photometric feature representation, including:

[0018] The data points in the photometric feature representation are sampled to obtain sample data, and the standardized sample data x is obtained by one-dimensionalizing and linearly normalizing the sample data. s ;

[0019] The standardized sample data is evenly divided into blocks according to the sample number n, and the Gaussian kernel function is used to calculate the minimum difference and maximum difference between the sample data in the block in turn, and used as the bandwidth h and interval length H in the diffusion kernel density estimation respectively. The diffusion kernel density estimation value of any data point in the sample data in the block is calculated based on the bandwidth h and the Gaussian kernel function, which is expressed as

[0020]

[0021] Among them, v represents any data point, v~u n is the data point v relative to the number of sample data points u in the block n The state of x s is the standardized sample data, k h () represents the estimated value of diffusion kernel density when the bandwidth h tends to infinity;

[0022] The shape of the Gaussian kernel function is adjusted by the interval length H, and then the bandwidth h in the diffusion kernel density estimation is optimized. The diffusion kernel density estimation value of each data point under different bandwidths h and interval lengths H is calculated. In order to realize the adaptive adjustment of bandwidth, the bandwidth h and interval length H at each data point are selected by the local integrated mean square error, and the bandwidth with the minimum local integrated root mean square error is obtained as the optimal bandwidth.

[0023] By introducing the local bandwidth optimization parameter λ L represents the optimal bandwidth h * The relationship between the interval length H and the local optimal bandwidth is obtained by continuously optimizing the interval length H locally. Expressed as

[0024]

[0025] in, To make x s and the corresponding local optimal bandwidth The weight function of the mean square error concentration is Optimize bandwidth locally The mean of To optimize the parameter λ in the local bandwidth L Adjust the interval length under the local bandwidth optimization parameter

[0026] Locally Optimize Bandwidth Perform inverse normalization to obtain the adaptive bandwidth that matches the sample data, and calculate the diffusion kernel density estimate of the sample data under the adaptive bandwidth to obtain the probability distribution F(x; h) of the sample data, which is expressed as

[0027]

[0028]

[0029] Where x = x1,…,x n is the sample data, x i represents the i-th sample, a k It is a variable representation, used to separately describe the situations that need to be classified, k is an integer value from 1 to n-1, and e represents an exponential constant;

[0030] The probability distribution of the sample data is mapped to the photometric feature representation, and the target magnitude value is extrapolated using the Monte Carlo simulation algorithm to obtain the target magnitude distribution around each data point in the photometric feature representation.

[0031] In one of the embodiments, the method further includes: constructing a database to store the photometric features extracted from the photometric feature representation, and in the database, using a software robot to automatically classify the photometric features according to preset labels; wherein the preset labels include station location, satellite shape, satellite surface material and modular components.

[0032] In one of the embodiments, the memory of the database includes RAM, ROM and flash.

[0033] A target magnitude characteristic analysis device for a GEO satellite, the device comprising:

[0034] The data acquisition module is used to set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within a specified time;

[0035] A coordinate conversion module is used to construct a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station through coordinate conversion, and obtain the solar longitude and solar latitude in the SD coordinate system;

[0036] The feature representation module is used to represent the photometric features of the historical photometric data of the GEO satellite with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis, and to analyze and process the photometric features using a diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric feature representation.

[0037] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within the specified time;

[0039] Through coordinate transformation, a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station is constructed, and the solar longitude and solar latitude in the SD coordinate system are obtained;

[0040] The photometric characteristics of the historical photometric data of the GEO satellite are represented with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis. The photometric characteristics are analyzed and processed using the diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric characteristic representation.

[0041] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0042] Set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within the specified time;

[0043] Through coordinate transformation, a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station is constructed, and the solar longitude and solar latitude in the SD coordinate system are obtained;

[0044] The photometric characteristics of the historical photometric data of the GEO satellite are represented with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis. The photometric characteristics are analyzed and processed using the diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric characteristic representation.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. This application constructs a coordinate system (SD coordinate system) pointing from the GEO satellite to the measuring station through coordinate transformation. The SD coordinate system is convenient for expressing the relative position and geometric relationship between "sun-satellite-measurement station". The photometric characteristics of the historical photometric data of the GEO satellite are represented based on the SD coordinate system, which can more directly reflect the translation invariance of the GEO satellite and the changes in the satellite's attitude and motion. The translation invariance means that the photometric characteristics of the satellite will not change with the relative motion of the in-orbit satellite relative to the measuring station.

[0047] 2. This application considers that the historical photometric data required for photometric feature representation is in years, and the historical photometric data has the defects of uneven distribution, random sparseness and density, and has a large noise impact. By using the diffusion kernel density estimation algorithm based on adaptive bandwidth to analyze and process the photometric features, the target magnitude distribution around each data point in the photometric feature representation is obtained, so that the photometric feature representation can better represent the detailed features of the satellite surface shape, components, etc.

[0048] 3. This application constructs a database to store and automatically classify the photometric features extracted from the photometric feature representation. The database storage method is convenient for query search and embedding of algorithm interfaces. It can adjust the observation task requirements in time according to the decision and update the database in real time, which is conducive to space monitoring and early warning. In addition, in terms of label classification, automatic classification is performed through software robots, which realizes process automation and more efficiently describes and classifies the photometric characteristics of GEO satellites. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a flow chart of a target magnitude characteristic analysis method for a GEO satellite in one embodiment;

[0050] Figure 2 A schematic diagram of a coordinate conversion process of converting a VVLH coordinate system into a SD coordinate system in one embodiment;

[0051] Figure 3 is a schematic diagram of the sun's position in the SD coordinate system in one embodiment;

[0052] Figure 4 FIG. 1 is a schematic diagram of a 3D model of Astra-2E in one embodiment; wherein: Figure 4 (a) is the front view of the 3D model of Astra-2E. Figure 4 (b) is the rear view of the 3D model of Astra-2E;

[0053] Figure 5 is a photometric characteristic heat map of Astra-2E in one embodiment; wherein, Figure 5 (a) is a heat map of the photometric characteristics of Astra-2E based on the observation results of station 1. Figure 5 (b) is a heat map of the photometric characteristics of Astra-2E based on the observation results of Station 2.

[0054] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] In one embodiment, taking the Astra-2E satellite as an example, a target magnitude characteristic analysis method for a GEO satellite is provided. Figure 1 As shown, the following steps are included:

[0057] Step S1, setting the position coordinate parameters of the GEO satellite and the observation station, and obtaining the historical photometric data of the GEO satellite obtained by the observation station within a specified time.

[0058] Specifically, the sub-satellite point of the Astra-2E satellite was set at (0°N, 130°E), and a measurement station was built at (16°N, 118°E).

[0059] In order to obtain a complete representation of the photometric characteristics, observations are required at multiple points at night (covering the solar equatorial phase) and throughout the year (covering the solar declination phase), so that they can be better used to judge the status of a single GEO satellite and the associated change trends of a group of spacecraft to complete specific tasks.

[0060] Step S2, constructing a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station through coordinate conversion, and obtaining the solar longitude and solar latitude in the SD coordinate system.

[0061] Specifically, in order to accurately describe the photometric characteristics of the satellite, this application constructs a coordinate system from the GEO satellite to the measuring station through coordinate transformation. The specific steps include:

[0062] Construct a VVLH coordinate system with the GEO satellite as the coordinate origin, expressed as XYZ; according to the relative position relationship between the GEO satellite and the measuring station, rotate XYZ clockwise around the Z axis by the first Euler angle to obtain a transitional coordinate system, expressed as X'Y'Z'; rotate X'Y'Z' clockwise around the Y' axis by the second Euler angle to obtain the SD coordinate system, expressed as X"Y"Z"; wherein both the transitional coordinate system and the SD coordinate system use the GEO satellite as the coordinate origin. The coordinate conversion process from the VVLH coordinate system to the SD coordinate system is as follows: Figure 2 shown.

[0063] According to the first Euler angle and the second Euler angle in the above coordinate transformation process, the coordinate transformation matrix M between the VVLH coordinate system and the SD coordinate system is obtained, which is expressed as

[0064]

[0065]

[0066]

[0067] in, and They represent the first and second Euler angles respectively. represents the second Euler angle rotation clockwise, Indicates the clockwise rotation of the first Euler angle. Further, according to the coordinate transformation matrix M, the coordinate transformation formula from the VVLH coordinate system to the SD coordinate system can be obtained, which is expressed as

[0068]

[0069] Where (x”, y”, z”) T Represents a vector in the SD coordinate system, (x, y, z) T represents a vector in the VVLH coordinate system, and the superscript T represents matrix transpose.

[0070] It can be understood that the essence of coordinate transformation is to multiply the coordinates in the original coordinate system by several Euler rotation matrices to obtain the coordinates in the new coordinate system. In spatial coordinate transformation, Euler angles are a way to describe the posture of an object in space. These Euler angles are usually calculated based on the direction cosine matrix (also called rotation matrix) of the object, rather than being set manually. The direction cosine matrix describes the rotation relationship of one coordinate system relative to another coordinate system, so that the posture of the object can be obtained through a series of rotations. Usually, the direction cosine matrix can be calculated by expressing the rotation matrix as the product of a series of rotations. For Euler angles, common rotation sequences include rotation sequences around fixed axes, such as roll-pitch-yaw (roll-pitch-yaw) sequence, or rotation sequences around variable axes, such as yaw-pitch-roll (yaw-pitch-roll) sequence. Assuming that there are three Euler angles, namely α, β and γ (usually corresponding to roll, pitch and yaw), the first Euler angle and the second Euler angle in this application correspond to the roll angle and pitch angle, respectively, which determines the order of the axes around which the rotation is carried out during the coordinate transformation. Among them, the basic rotation matrix corresponding to the three Euler angles is as follows:

[0071]

[0072]

[0073]

[0074] Furthermore, the sun position in the SD coordinate system can be calculated based on the coordinate transformation matrix M, and by considering that the sun longitude and latitude in the SD coordinate system change periodically over time, the sun position in the SD coordinate system is decomposed into the sun longitude J s and solar latitude I s Among them, the solar longitude J s and solar latitude I s It can be understood as the azimuth and altitude angle of the sun in the SD coordinate system.

[0075] The sun position in the SD coordinate system is Figure 3 Compared with the traditional VVLH coordinate system, the SD coordinate system links the relative position relationship between the satellite and the station. Therefore, the sun position in the SD coordinate system can represent the observation geometry relationship between the sun, satellite and station.

[0076] Step S3, taking the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis, the photometric feature representation of the historical photometric data of the GEO satellite is performed, and the photometric feature is analyzed and processed by a diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric feature representation.

[0077] Specifically, taking the Astra-2E satellite as an example, the process of representing the photometric characteristics of historical photometric data is as follows:

[0078] like Figure 4 (a) and Figure 4 As shown in (b), Astra-2E is a communication satellite in GEO, which consists of 5 transponders, a pair of solar panels, a body with gold film, and some aluminum rods. Both the transponder and the solar sail are mirror-reflecting components with obvious photometric characteristics.

[0079] The observational geometric relationship between the sun, satellite and station is represented according to the sun longitude and sun latitude in the SD coordinate system, and the photometric characteristics of the historical photometric data of the GEO satellite are represented with the sun longitude in the SD coordinate system as the horizontal axis and the sun latitude in the SD coordinate system as the vertical axis. Specifically, when representing the photometric characteristics, the historical photometric data can be used as a heat map in the form of a heat map as the vertical axis to obtain a photometric characteristic heat map of the Astra-2E satellite as a photometric characteristic representation. The photometric characteristic heat map of the Astra-2E satellite drawn based on the observation results of the positions of station 1 and station 2 is shown in the figure below: Figure 5 (a) and Figure 5 (b). Figure 5 (a) and Figure 5 (b) It can be seen that the main contents of the photometric features that can be extracted from the photometric feature heat map of the Astra-2E satellite include:

[0080] (a) Luminosity data is presented in the form of a heat map, differentiated by different colors. The closer the color is to red, the stronger the luminosity.

[0081] (b) The extreme points in the heat map indicate the presence of specular reflection on the satellite;

[0082] (c) The satellite will have a mirror reflection near the point (0,0), which is manifested as Figure 5 (a) and Figure 5 (b) shows a dark point near the (0,0) point in the photometric characteristic heat map, but the dark point is not fully displayed due to the occlusion of the earth. When the solar declination and right ascension are close to zero, the earth will cast a shadow on the target.

[0083] (d) When the solar phase angle increases, the color of the corresponding area in the thermal map gradually tends to blue, indicating that the larger the phase angle, the lower the brightness of the satellite;

[0084] (e) Figure 5The six local extreme points around (0,0) in the photometric characteristic heat map shown in (a) are associated with the four transponders on the front of the satellite in the 3D model of Astra-2E, due to the mirror reflections they produce. Two of the points are darker in color because the corresponding mirror reflections of the two transponders are superimposed.

[0085] Furthermore, since the historical photometric data required for the photometric feature heat map is in years, the data distribution is uneven, the sparsity and density are very random, and there is a large noise impact. Therefore, this application further uses a diffusion kernel density estimation algorithm based on adaptive bandwidth to analyze and process the photometric features, obtain the target star magnitude distribution around each data point in the photometric feature representation, so that the photometric heat map can better characterize the satellite surface shape, components and other detailed features. The specific steps include:

[0086] First, the data points in the photometric feature representation are sampled to obtain sample data, and the sample data is one-dimensionalized and linearly normalized to obtain the standardized sample data x s .

[0087] Then, the standardized sample data is evenly divided into blocks according to the sample number n, and the Gaussian kernel function is used to calculate the minimum difference and maximum difference between the sample data in the block in turn, and used as the bandwidth h and interval length H in the diffusion kernel density estimation respectively. The diffusion kernel density estimation value of any data point in the sample data in the block is calculated based on the bandwidth h and the Gaussian kernel function, which is expressed as

[0088]

[0089] Among them, v represents any data point, v~u n is the data point v relative to the number of sample data points u in the block n The state of x s is the standardized sample data, k h () represents the estimated value of the diffusion kernel density when the bandwidth h tends to infinity.

[0090] The shape of the Gaussian kernel function is then adjusted by the interval length H, and the bandwidth h in the diffusion kernel density estimation is optimized. The diffusion kernel density estimation value of each data point under different bandwidths h and interval lengths H is calculated. In order to achieve adaptive bandwidth adjustment, the bandwidth h and interval length H at each data point are selected by the local integrated mean square error, and the bandwidth with the minimum local integrated root mean square error is obtained as the optimal bandwidth.

[0091] Then, by introducing the local bandwidth optimization parameter λ L represents the optimal bandwidth h *The relationship between the interval length H and the local optimal bandwidth is obtained by continuously optimizing the interval length H locally. Expressed as

[0092]

[0093] in, To make x s and the corresponding local optimal bandwidth The weight function of the mean square error concentration is Optimize bandwidth locally The mean of To optimize the parameter λ in the local bandwidth L Adjust the interval length under the local bandwidth optimization parameter

[0094] Locally Optimize Bandwidth Perform inverse normalization to obtain the adaptive bandwidth that matches the sample data, and calculate the diffusion kernel density estimate of the sample data under the adaptive bandwidth to obtain the probability distribution F(x; h) of the sample data, which is expressed as

[0095]

[0096]

[0097] Where x = x1,…,x n is the sample data, x i represents the i-th sample, a k It is a variable representation used to separately describe the situations that need to be classified. k is an integer ranging from 1 to n-1, and e represents an exponential constant.

[0098] Finally, the probability distribution of the sample data is mapped to the photometric feature representation, and the target magnitude value is extrapolated using the Monte Carlo simulation algorithm to obtain the target magnitude distribution around each data point in the photometric feature representation.

[0099] It can be understood that since the kernel density estimation does not take into account the range of the sample data, it will produce boundary deviations when estimating the distribution. Therefore, this application proposes a diffusion kernel density estimation algorithm, and combines it with the Monte Carlo simulation algorithm to extrapolate the target magnitude, so that the data coverage is stronger, and the problem of unrecognizable features caused by data missing and noise is compensated, so that the photometric heat map can better characterize the detailed features of the satellite surface shape, components, etc.

[0100] It can be understood that in the process of photometric feature representation, the size of the bandwidth determines the smoothness of the kernel density estimation function, and the smaller the bandwidth, the smoother it is. A smaller bandwidth can cause more high-value or low-value areas to appear in the density distribution results, which is suitable for revealing the local characteristics of the density distribution, while a larger bandwidth can make the hot spot area more obvious at the global scale. The adaptive bandwidth function can adaptively adjust the bandwidth according to the density of the data points, and obtain the optimal bandwidth and the optimal interval of the adaptive network kernel density estimation parameters by following the value and position changes of the sample points.

[0101] Furthermore, the method also includes: constructing a database to store the photometric features extracted from the photometric feature representation, and in the database, using a software robot to automatically classify the photometric features according to preset labels; wherein the preset labels include station location, satellite shape, satellite surface material and modular components.

[0102] Among them, the database memory exists in various forms, including but not limited to RAM (random access memory), ROM (read-only memory) and flash (flash memory), etc. There are both random storage and removable and permanent storage, and the media reading forms are various.

[0103] By classifying the photometric features, it is easy to query and classify the photometric features in the database according to the satellite name, station location, modular component type and material, etc., so as to facilitate the subsequent calling and embedding of state estimation, trajectory prediction and other algorithms. Among them, the preset labels for photometric feature classification include:

[0104] (1) Location of the station: different names in different provinces;

[0105] (2) Satellite shape: sphere, cylinder, cone, cuboid, regular hexahedron, etc.;

[0106] (3) Satellite surface materials: silver and gold polyimide films (PI films), solar panel materials, white paint, tertiary gallium arsenide (GaAs), aluminum alloy, carbon fiber, etc.;

[0107] (4) Modular components: transponders, solar panels, optical solar reflectors, composite aluminum skin honeycomb panels, antennas, etc.

[0108] By drawing the photometric characteristic heat map of GEO satellites as above, photometric characteristic representation is realized, and a database is constructed to store and automatically classify the photometric characteristics extracted from the photometric characteristic heat map, which can assist space situational awareness decision-making and specifically realize the following functions:

[0109] (1) Real-time stability estimation: When the stability of a single observed object fluctuates from the assumed mode, the change trend of the object within a given time period is automatically determined. All observed objects in the photometric characteristic heat map database are covered, and continuous automatic analysis and monitoring are carried out to timely discover the randomness of the photometric characteristic heat map set of the observed object.

[0110] (2) Predicted direction, movement, and posture: Once the stability of an object changes or its characteristics no longer conform to the historical statistical data of the object, an abnormal risk warning will be automatically activated.

[0111] (3) Determine material composition and analysis: Utilize multiple optical bands to determine the potential material composition of space objects and support correlation analysis between components. This can be done either continuously or in real time on demand.

[0112] (4) Anomaly detection: The photometric characteristic heat map is the basis for detecting state changes. It ensures the stability of the attitude of each spacecraft during its mission cycle. If an abnormal state such as tumbling occurs, the photometric characteristic heat map will produce a more obvious change.

[0113] (5) Trend analysis based on years: After observing and collecting historical photometric data throughout the year, a complete heat map is obtained through this method, and the state can be further compared with it based on the time series to estimate the state change. Generally speaking, the greater the amount of ground-based optical observation information collected in the photometric characteristic heat map database, the more accurate the photometric characteristic heat map drawn is, and the higher the confidence level is.

[0114] (6) Collision warning: If the photometric characteristic heat maps of both spacecraft become unstable, there is a possibility of collision resulting in the generation of debris. As an auxiliary means of space situational awareness, observation data can be collected through specific sensors to detect whether a collision will occur. When a collision generates debris, detecting and estimating the surface material of the damaged satellite helps predict how the debris will propagate and the degree of detectable changes in the future.

[0115] Through the above-mentioned space situational awareness decision-making, timely feedback can be provided to adjust the observation and monitoring task requirements, and the satellite's sub-satellite point coordinates and the station's geographical location can be continuously input and updated in the stored database. According to the requirements of the observation task, the relevant data of the satellite and the station can be queried, combined, and used from the database, and the photometric feature heat map can be redrawn to realize the photometric feature representation.

[0116] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0117] In one embodiment, a target magnitude characteristic analysis device for a GEO satellite is provided, comprising:

[0118] The data acquisition module is used to set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within a specified time;

[0119] A coordinate conversion module is used to construct a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station through coordinate conversion, and obtain the solar longitude and solar latitude in the SD coordinate system;

[0120] The feature representation module is used to represent the photometric features of the historical photometric data of the GEO satellite with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis, and to analyze and process the photometric features using a diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric feature representation.

[0121] For the specific definition of the target magnitude characteristics analysis device for GEO satellites, please refer to the definition of the target magnitude characteristics analysis method for GEO satellites above, which will not be repeated here. Each module in the above-mentioned target magnitude characteristics analysis device for GEO satellites can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0122] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a target magnitude characteristic analysis method for GEO satellites is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0123] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0125] Set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within the specified time;

[0126] Through coordinate transformation, a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station is constructed, and the solar longitude and solar latitude in the SD coordinate system are obtained;

[0127] The photometric characteristics of the historical photometric data of the GEO satellite are represented with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis. The photometric characteristics are analyzed and processed using the diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric characteristic representation.

[0128] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0129] Set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within the specified time;

[0130] Through coordinate transformation, a SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station is constructed, and the solar longitude and solar latitude in the SD coordinate system are obtained;

[0131] The photometric characteristics of the historical photometric data of the GEO satellite are represented with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis. The photometric characteristics are analyzed and processed using the diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric characteristic representation.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0133] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for analyzing the magnitude characteristics of a target for a GEO satellite, characterized in that: The method comprises: Set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within the specified time; The SD coordinate system with the GEO satellite as the coordinate origin and pointed to the measuring station by the GEO satellite is constructed by coordinate conversion, and the solar longitude and solar latitude in the SD coordinate system are obtained, specifically including: constructing a VVLH coordinate system with the GEO satellite as the coordinate origin, expressed as XYZ; according to the relative position relationship between the GEO satellite and the measuring station, rotating the XYZ clockwise around the Z axis by a first Euler angle to obtain a transition coordinate system, expressed as X'Y'Z'; rotating the X'Y'Z' clockwise around the Y' axis by a second Euler angle to obtain a SD coordinate system pointed to the measuring station by the GEO satellite, expressed as X"Y"Z"; wherein the transition coordinate system and the SD coordinate system both use the GEO satellite as the coordinate origin; the coordinate conversion matrix between the VVLH coordinate system and the SD coordinate system is expressed as: in, and They represent the first and second Euler angles respectively. represents the second Euler angle rotation clockwise, Indicates the first Euler angle rotation clockwise; The photometric characteristics of the historical photometric data of the GEO satellite are represented with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis. The photometric characteristics are analyzed and processed using the diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric characteristic representation.

2. The target magnitude characteristic analysis method for GEO satellites according to claim 1, characterized in that: The diffusion kernel density estimation algorithm based on adaptive bandwidth is used to analyze and process the photometric characteristics to obtain the target magnitude distribution around each data point in the photometric characteristic representation, including: The data points in the photometric feature representation are sampled to obtain sample data, and the sample data is one-dimensionalized and linearly normalized to obtain standardized sample data x s ; The standardized sample data is evenly divided into blocks according to the sample number n, and the Gaussian kernel function is used to calculate the minimum difference and the maximum difference between the sample data in the block in turn, and they are used as the bandwidth h and the interval length H in the diffusion kernel density estimation respectively, and the diffusion kernel density estimation value of any data point in the sample data in the block is calculated according to the bandwidth h and the Gaussian kernel function, which is expressed as Among them, v represents any data point, v~u n is the data point v relative to the number of sample data points u in the block n The state of x s is the standardized sample data, k h () represents the estimated value of diffusion kernel density when the bandwidth h tends to infinity; The shape of the Gaussian kernel function is adjusted by the interval length H, and then the bandwidth h in the diffusion kernel density estimation is optimized. The diffusion kernel density estimation value of each data point under different bandwidths h and interval lengths H is calculated. In order to realize the adaptive adjustment of bandwidth, the bandwidth h and interval length H at each data point are selected by the local integrated mean square error, and the bandwidth with the minimum local integrated root mean square error is obtained as the optimal bandwidth. By introducing the local bandwidth optimization parameter λ L represents the optimal bandwidth h * The relationship between the interval length H and the local optimal bandwidth is obtained by continuously optimizing the interval length H locally. Expressed as in, To make x s and the corresponding local optimal bandwidth The weight function of the mean square error concentration is Optimize bandwidth locally The mean of To optimize the parameter λ in the local bandwidth L Adjust the interval length under the local bandwidth optimization parameter The local optimal bandwidth Perform inverse normalization to obtain an adaptive bandwidth that matches the sample data, and calculate the diffusion kernel density estimate of the sample data under the adaptive bandwidth to obtain the probability distribution of the sample data, which is expressed as Where x = x1,…,x n is the sample data, x i represents the i-th sample, a k is a variable, k is an integer ranging from 1 to n-1; The probability distribution of the sample data is mapped to the photometric feature representation, and the target magnitude value is extrapolated in combination with the Monte Carlo simulation algorithm to obtain the target magnitude distribution around each data point in the photometric feature representation.

3. The target magnitude characteristic analysis method for GEO satellites according to claim 2, characterized in that: The method also includes: constructing a database to store the photometric features extracted from the photometric feature representation, and in the database, using a software robot to automatically classify the photometric features according to preset labels; wherein the preset labels include station locations, satellite shapes, satellite surface materials, and modular components.

4. The target magnitude characteristic analysis method for GEO satellites according to claim 3, characterized in that: The memory of the database includes RAM, ROM and flash.

5. A target magnitude characteristic analysis device for GEO satellites, characterized in that: The device comprises: The data acquisition module is used to set the position coordinate parameters of the GEO satellite and the station, and obtain the historical photometric data of the GEO satellite obtained by the station observation within a specified time; The coordinate conversion module is used to construct an SD coordinate system with the GEO satellite as the coordinate origin and the GEO satellite pointing to the measuring station through coordinate conversion, and obtain the solar longitude and solar latitude in the SD coordinate system, specifically including: constructing a VVLH coordinate system with the GEO satellite as the coordinate origin, expressed as XYZ; according to the relative position relationship between the GEO satellite and the measuring station, rotating the XYZ clockwise around the Z axis by a first Euler angle to obtain a transition coordinate system, expressed as X'Y'Z'; rotating the X'Y'Z' clockwise around the Y' axis by a second Euler angle to obtain a SD coordinate system pointed to the measuring station by the GEO satellite, expressed as X"Y"Z"; wherein the transition coordinate system and the SD coordinate system both have the GEO satellite as the coordinate origin; the coordinate conversion matrix between the VVLH coordinate system and the SD coordinate system is expressed as: in, and They represent the first and second Euler angles respectively. represents the second Euler angle rotation clockwise, Indicates the first Euler angle rotation clockwise; The feature representation module is used to represent the photometric features of the historical photometric data of the GEO satellite with the solar longitude in the SD coordinate system as the horizontal axis and the solar latitude in the SD coordinate system as the vertical axis, and to analyze and process the photometric features using a diffusion kernel density estimation algorithm based on adaptive bandwidth to obtain the target magnitude distribution around each data point in the photometric feature representation.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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