Method, device and equipment for detecting space debris based on probability density distribution

Abstract

The present invention proposes a method, device, equipment and computer medium for detecting space debris based on probability density distribution. The detection method includes the following steps: using the Monte Carlo method to simulate and define the spatial state of space debris; detecting and eliminating outliers based on Mahalanobis distance; updating the relative path of the tracking spacecraft; aiming at the target in the camera field of view to determine the observability condition of the target relative to the tracking spacecraft; determining the target detection strategy and simulating it; selecting the optimal non-cooperative target detection strategy suitable for the research scenario for the detection of space debris. Based on the state probability density distribution, the present invention proposes three target detection strategies for the detection strategy of non-cooperative targets, considering the relative position relationship and attitude relationship between the tracking spacecraft and the target: the same-orbit, high-orbit and low-orbit tracking detection strategies, showing good engineering application potential, and laying the foundation for the navigation control of the active approach phase of the tracking spacecraft.

Description

Technical Field

[0001] The present invention relates to the technical field of space debris detection, and in particular to a method, device and equipment for detecting space debris based on probability density distribution. Background Art

[0002] With the increase of human space activities, the number of failed spacecraft is increasing, and they have become one of the main sources of space debris. During the long-term detention of failed spacecraft, it is difficult for the ground to intervene in their behavior. The collision between them will lead to a sharp increase in space debris, which poses a serious threat to the life and safety of spacecraft. Therefore, in order to manage and reduce space debris, space debris cleaning is required, usually by tracking spacecraft (removing satellites) to capture debris, move them out of useful orbits, and clear or clean them up. In this process, tracking spacecraft equipped with special equipment need to approach the debris target.

[0003] Among the detection methods known to the inventors, such as the two-line orbital element (TLE / SGP4) method, although it can preliminarily predict the orbital information of non-cooperative (i.e., non-responsive or out-of-control) targets, there is a certain accuracy error for unpowered space debris, because unpowered debris is more susceptible to environmental effects. In addition, the observation equipment (camera) on the tracking spacecraft has a limited field of view. Before performing active relative approach, it is necessary to ensure that the spacecraft can maneuver toward the correct target debris. Therefore, it is necessary to detect non-cooperative targets through cameras in complex space environments, which requires more optimized detection methods and strategies. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for detecting space debris based on probability density distribution, which solves the problem of more accurate detection of non-cooperative targets by cameras in complex space environments.

[0005] In order to achieve the above purpose, this application adopts the following technical solutions:

[0006] In a first aspect, a method for detecting space debris based on probability density distribution is provided, comprising the following steps:

[0007] A Monte Carlo method is used to simulate and define the spatial state of space debris to obtain first space debris state data;

[0008] Performing outlier detection and elimination on the first space debris state data according to the Mahalanobis distance to obtain second space debris state data;

[0009] Update the relative path of the tracking spacecraft;

[0010] aiming of a target in the camera field of view to determine the observability condition of the target relative to the tracking spacecraft;

[0011] Determine and simulate a target detection strategy based on the observability condition;

[0012] According to the target detection strategy and simulation results, the optimal non-cooperative target detection strategy suitable for the research scenario is selected for the detection of space debris.

[0013] In a first possible implementation manner of the first aspect, the using of the Monte Carlo method to simulate and define the spatial state of the space debris specifically includes:

[0014] The Monte Carlo method is used to simulate and establish a spatial model that reflects the distribution and dynamic characteristics of space debris in the actual environment, which is achieved through the following formula:

[0015] x i =N(μ,Σ)

[0016] In the formula, x i Represents the state quantity of the sample point, N represents the normal distribution, μ represents the center point, and Σ represents the covariance matrix.

[0017] Based on any one of the foregoing possible implementation manners of the first aspect, in a second possible implementation manner of the first aspect, performing outlier detection on the first space debris state data according to the Mahalanobis distance specifically includes:

[0018] The Mahalanobis distance Δ is used for discrimination using the following formula:

[0019] Δ 2 =(x-μ) T Σ -1 (x-μ)

[0020] In the formula, considering the correlation of each dimension, Δ represents the distance of the state quantity x relative to the center point μ; based on this, Δ defines an ellipsoid with equal probability from the center point, and its value is:

[0021] P(Δ<q α )≥α,

[0022] Where α is the ratio of the given fraction of the total number of individuals located in the ellipsoid to the total number.

[0023] Based on any of the foregoing possible implementations of the first aspect, in a third possible implementation of the first aspect, updating the relative path of the tracking spacecraft specifically includes the following steps:

[0024] Establish the attitude dynamics model of the tracking spacecraft;

[0025] Generate the relative position of the target according to the Monte Carlo method;

[0026] State extrapolation and coordinate conversion: using a high-precision extrapolation model, the relative position is converted from a relative coordinate system to an inertial coordinate system;

[0027] Camera coordinate system conversion: convert the direction vector of the target relative to the tracking spacecraft into the reference system of the camera.

[0028] Based on the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, establishing an attitude dynamics model of the tracking spacecraft specifically includes:

[0029] The quaternion equation is used to describe the attitude dynamics model of the tracking spacecraft, and its initial attitude quaternion and angular velocity are defined as q JC and w JC , the corresponding kinetic model is:

[0030]

[0031] The generating the relative position of the target according to the Monte Carlo method specifically includes:

[0032] Given an estimated target position speed The random initial state generated by the Monte Carlo method is:

[0033]

[0034]

[0035] In the formula, record is the state offset corrected in the relative coordinate system.

[0036] Based on the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, aiming at a target in the camera field of view and determining an observability condition of the target relative to the tracking spacecraft specifically includes the following steps:

[0037] Given the relative position Δr from the tracking spacecraft to the target i C (t), detecting whether the relative position is within the field of view of the tracking spacecraft camera;

[0038] When the camera aperture is fully open, the field of view is β, and the minimum and maximum distances for target detection are d min and d max , the cumulative target recognition time required is Δt r, for each time update, the target observability is checked by the following judgment method:

[0039] When x i ≥0, the target is located at the tracking spacecraft x C Axis front;

[0040] When min ≤r i ≤d max , the target is located at a range of distances relative to the tracking spacecraft;

[0041] when The target is located relative to the tracking spacecraft y C Axis field of view;

[0042] when The target is located relative to the tracking spacecraft z C Axis field of view;

[0043] Where xi, yi and zi are Δr i C (t) is the triaxial component, ri is Δr i C The 2-norm of (t).

[0044] Based on any of the foregoing possible implementations of the first aspect, in a sixth possible implementation of the first aspect, the target detection strategy includes: a same-orbit tracking detection strategy, a high-orbit tracking detection strategy, and a low-orbit tracking detection strategy;

[0045] The co-track tracking detection strategy is defined as the tracking spacecraft and the target spacecraft are located on the same orbit at different true anomalies, and the tracking spacecraft is located behind the target spacecraft, that is, the relative position vector Δr in the velocity direction in the relative coordinate system established with the target mass center is v ≤0;

[0046] The high-orbit tracking detection strategy is defined as the tracking spacecraft operating in an orbit higher than the orbit of the target spacecraft, that is, the relative position vector Δr in the radial vector direction in the relative coordinate system established with the target as the center R ≥0;

[0047] The low-orbit tracking detection strategy is to track the target spacecraft in front of the spacecraft on a lower orbit.

[0048] In a second aspect, a space debris detection device based on probability density distribution is provided, comprising:

[0049] A data acquisition module, used for simulating and defining the space state of space debris by using the Monte Carlo method to obtain first space debris state data;

[0050] A data processing module, configured to detect and remove outliers on the first space debris state data according to the Mahalanobis distance, so as to obtain second space debris state data;

[0051] The path update module is used to update the relative path of the tracking spacecraft;

[0052] A target aiming module, used for aiming a target in the camera field of view, and determining an observability condition of the target relative to the tracking spacecraft;

[0053] A target detection strategy and simulation module, used to determine the target detection strategy and simulate it according to the observability condition;

[0054] The optimal non-cooperative target detection strategy determination module is used to select the optimal non-cooperative target detection strategy suitable for the research scenario according to the target detection strategy and simulation results, for the detection of space debris.

[0055] According to a third aspect, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to the first aspect when executed by the processor.

[0056] According to a fourth aspect, a computer storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method according to the first aspect are implemented.

[0057] The space debris detection method of the present invention is based on the state probability density distribution, the detection strategy for non-cooperative targets, and the relative position relationship and attitude relationship between the tracking spacecraft and the target. Three target detection strategies are proposed: the same-orbit tracking detection strategy, the high-orbit tracking detection strategy, and the low-orbit tracking detection strategy. The Monte Carlo research method was used for experimental analysis, which showed the advantages of the low-orbit tracking detection strategy in terms of target recognition rate, etc., showing good engineering application potential, and laying the foundation for the navigation control of the active approach phase of the tracking spacecraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic flow chart of a method for detecting space debris based on probability density distribution provided in an embodiment of the present application;

[0059] Figure 2 A schematic flow chart of another method for detecting space debris based on probability density distribution provided in an embodiment of the present application;

[0060] Figure 3 A schematic flow chart of another method for detecting space debris based on probability density distribution according to an embodiment of the present application;

[0061] Figure 4 A schematic diagram of the tracking spacecraft model and the body coordinate system definition provided in the embodiment of the present application;

[0062] Figure 5 A schematic structural block diagram of a space debris detection device based on probability density distribution provided in an embodiment of the present application;

[0063] Figure 6 A schematic structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of the present application are clearly described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present application.

[0065] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0066] The description of the method flow in the specification of the present application and the steps of the flowchart in the drawings of the present specification do not have to be strictly executed according to the step numbers, and the method steps can be executed in a different order. Moreover, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0067] In conjunction with the accompanying drawings and preferred embodiments, a method, device, equipment and storage medium for detecting space debris based on probability density distribution provided by the embodiments of the present application are described in detail below.

[0068] As human space activities gradually increase, there are already thousands of spacecraft in orbit, and it is estimated that more than 100 spacecraft will exceed their service life every year within 10 years. In the space debris mission, the two-row orbital element (TLE / SGP4) method is used to determine and predict the initial orbit of non-cooperative targets in space. Due to factors such as model uncertainty and measurement errors, the positioning of non-cooperative targets by tracking spacecraft in space cannot be an accurate point, but a probability distribution around a certain point in space.

[0069] Therefore, the present application provides a method for detecting space debris based on probability density distribution, and proposes maneuvering strategies under different initial relative scenarios, so as to provide reference and optimization for the initial state of the active relative approach segment, so as to meet the maneuvering strategies of tracking spacecraft under different initial scenarios.

[0070] See also Figure 1-2 , Figure 1 , Figure 2 FIG. 1 is a schematic flow chart of a method for detecting space debris based on probability density distribution according to an embodiment of the present application, such as Figure 1-2 As shown, the method of the embodiment of the present application includes the following steps:

[0071] Step 101: using a Monte Carlo simulation method to define the state space of space debris and obtain first space debris state data.

[0072] Monte Carlo simulation method is used to generate a large number of random samples to cover various possible states of space debris (including position, velocity, attitude, etc.). Monte Carlo method is used to simulate and establish a comprehensive state space model to reflect the distribution and dynamic characteristics of space debris in the actual environment:

[0073] x i =N(μ,Σ)

[0074] In the formula, x i Represents the state quantity of the sample point, N represents the normal distribution, μ represents the center point, and Σ represents the covariance matrix.

[0075] Step 102: perform outlier detection on the first space debris state data and remove outliers according to the Mahalanobis distance to obtain second space debris state data.

[0076] After using the Monte Carlo simulation method to generate preliminary first space debris state data, the state data generated by the simulation is analyzed to identify outliers that deviate from the normal state distribution, and the outliers are eliminated to obtain second space debris state data, which is the processed state data.

[0077] Specifically, for multivariate, correlated normal distribution, Mahalanobis distance (MD) Δ is used for discrimination using the following formula:

[0078] Δ 2 =(x-μ) T Σ -1 (x-μ)

[0079] In the formula, considering the correlation of each dimension, Δ represents the distance of the state quantity x relative to the center point μ. Therefore, Δ defines an ellipsoid with equal probability from the center point, and its value is:

[0080] P(Δ<q α )≥α

[0081] Where α is the ratio of the given fraction of the total number of individuals located in the ellipsoid to the total number.

[0082] Step 103, update the relative path of the tracking spacecraft.

[0083] The Monte Carlo method and high-precision extrapolation model are used to update the relative path of the spacecraft, and the camera positioning and direction vector conversion technologies are combined to realize the visibility judgment of the target. The direction vector of the target satellite relative to the tracking spacecraft is converted to obtain the direction in the camera reference system.

[0084] See also Figure 3 , update the relative path of the tracking spacecraft, specifically including the following steps:

[0085] Step 103a, establishing an attitude dynamics model of the tracking spacecraft.

[0086] The attitude of the spacecraft is expressed using quaternions, and the initial attitude quaternions and angular velocities are set to specific values. Based on these initial conditions, the dynamic model uses quaternion equations to describe the attitude changes of the spacecraft.

[0087] For example, the attitude dynamics model of the tracking spacecraft is given by the quaternion equation, and its initial attitude quaternion and angular velocity are defined as q JC and w JC , the corresponding kinetic model is:

[0088]

[0089] Step 103b: Generate the target relative position according to the Monte Carlo method.

[0090] According to the second space debris state data, the relative position of the target is randomly generated by the Monte Carlo method, that is, given the estimated target position speed The random initial state generated by the Monte Carlo method is:

[0091]

[0092]

[0093] In the formula, record is the state offset corrected in the relative coordinate system.

[0094] Step 103c, state extrapolation and coordinate conversion, uses a high-precision extrapolation model to convert the relative position from the relative coordinate system to the inertial coordinate system.

[0095] Considering that the state extrapolation is performed in an inertial frame, the relative coordinate system centered on H is converted to an inertial frame centered on Jr0. The correction state is calculated in the J reference frame as Δr i T and Perform a reference frame transformation where: is the transformation matrix from the relative coordinate system to the inertial system.

[0096] Step 103d, camera coordinate system conversion, converts the direction vector of the target relative to the tracking spacecraft into the reference system of the camera.

[0097] Considering that the camera on the tracking spacecraft is always fixed on the satellite body without relative motion, the transformation matrix of the camera relative to the spacecraft system and the orbital coordinate system is R(q JC (t)), the direction vector of the target relative to the tracking spacecraft is converted to the reference frame of the camera:

[0098] Δr i C (t) = R(q JC (t)){Jr i (t)-Jr C (t)}

[0099] Thus, the relative position Δr from the tracking spacecraft to the target can be given i C (t), by detecting whether the relative position is within the field of view (FOV) of the tracking spacecraft camera, the visibility of the target relative to the tracking spacecraft can be determined.

[0100] Step 104, aiming at the target in the camera field of view, determines the observability condition of the target relative to the tracking spacecraft.

[0101] Assume that the camera is fixed at the x coordinate system of the tracking spacecraft CIn order to analyze the observability of the target, the aperture needs to be fully opened, the field of view is denoted as β, and the minimum and maximum distances for target detection are denoted as d min and d max , the cumulative target recognition time required is Δt r For each time update, the target observability is checked by the following judgment method, where x i , yi and zi are Δr i C (t) is the triaxial component, ri is Δr i C The 2-norm of (t). The specific judgment is as follows:

[0102] When x i ≥0, the target is located at the tracking spacecraft x C Axis front;

[0103] When min ≤r i ≤d max , the target is located at a range of distances relative to the tracking spacecraft;

[0104] when The target is located relative to the tracking spacecraft y C Axis field of view;

[0105] when The target is located relative to the tracking spacecraft z C Axis field of view.

[0106] Step 105, determining and simulating a target detection strategy based on the observability condition of the target relative to the tracking spacecraft.

[0107] Considering the different spatial positions of the tracking spacecraft and the non-cooperative target and the loading of the tracking spacecraft attitude, three detection strategies in different modes are proposed and studied by Monte Carlo simulation method. The detection strategies include:

[0108] Step 105a, co-track tracking detection mode. The co-track tracking mode is defined as the tracking spacecraft and the target spacecraft are located on the same orbit at different true anomalies, and the tracking spacecraft is located behind the target spacecraft, that is, the relative position vector Δr in the velocity direction in the relative coordinate system established with the target mass center is v≤0. Take different relative distances between the tracking spacecraft and the target spacecraft for Monte Carlo simulation. Combined with the above steps 101-104, simulate the random Monte Carlo state of the target spacecraft, eliminate outliers according to the boundary value, and analyze the visibility and average observation time of the target as the distance between the tracking spacecraft and the target approaches and the field of view continues to increase. The following conclusions are drawn: Under the same-track tracking strategy, the demand for attitude control of the spacecraft is small, and the tracking observation time for non-cooperative targets is long. If the tracking spacecraft camera has a large field of view, or the uncertainty of the target position is small, this target detection strategy can be used to detect the target.

[0109] Step 105b, high orbit tracking mode. The high orbit tracking mode is defined as the tracking spacecraft is located in an orbit higher than the orbit of the target spacecraft, that is, the relative position vector Δr in the radial vector direction in the relative coordinate system established with the target as the center R ≥0. Monte Carlo simulation is performed on different scenarios of the tracking spacecraft and the target spacecraft, including: the tracking spacecraft always points to the relative coordinate system x H Negative axis, tracking spacecraft around y H Rotate -45° and -90° respectively. Combined with the above steps 101-104, simulate the random Monte Carlo state of the target spacecraft, remove outliers according to the boundary value, and analyze the visibility and average observation time of the target as the distance between the tracking spacecraft and the target increases and the field of view continues to increase. The following conclusions are drawn: Compared with the same-orbit tracking strategy, the advantage of the high-orbit tracking strategy is that the tracking spacecraft moves relative to the target, thereby increasing the field of view of the camera swing and improving the probability of observing the target.

[0110] Step 105c, low orbit tracking mode. The low orbit tracking strategy is that the tracking spacecraft attempts to track the target spacecraft in front of the spacecraft in a lower orbit. Since spacecraft in low orbit generally have a higher tracking velocity, one advantage of this strategy is that once it finds the target, it can maneuver and approach the target with less fuel ΔV. Take the scenario where the tracking spacecraft is below the orbit of the target spacecraft for Monte Carlo simulation. Combined with the above steps 101-104, simulate the random Monte Carlo state of the target spacecraft, eliminate outliers according to the boundary value, and analyze the visibility and average observation time of the target as the tracking spacecraft approaches the target and the field of view continues to increase. The following conclusions are drawn: The low orbit tracking strategy has the highest probability of detecting the target, but the average observation time is low. When equipped with a camera with a larger field of view, the detection capability of the target can be further increased.

[0111] Exemplarily, the simulation process of the three detection strategies in the embodiments of the present application is further described below.

[0112] In the simulation verification, the tracking satellite CleanSpace One is taken as an example for simulation. Figure 4 , Figure 4 To track the spacecraft model and the coordinate system of the body, combine Figure 4 The satellite attribute parameters are as follows: the volume is 34×34×34cm, the mass is 35kg, the area of ​​the satellite perpendicular to the velocity vector is about 0.342m2, and the camera is always fixed on the tracking satellite body to achieve x tracking along the body. C The camera field of view α is 32°×32°, the minimum and maximum distances for target detection are dmin and dmax, which are 0km and 5km respectively, Δt r =5s. The non-cooperative target space debris is in the same orbit as the tracking spacecraft, and the covariance matrix of its state is defined as: Σ = diag (0.4 2 , 0.7 2 , 0.0 2 , 0.7 2 , 0.5 2 , 0.1 2 ), where the position standard deviation is in km and the velocity standard deviation is in m / s.

[0113] The outliers of the target are eliminated. For a given score Σ, in order to make the generated random target within the 99.99% distribution range, the critical Mahalanobis distance ΔC = q99.9% = 4.74 is determined. Assuming that the component of any direction of the randomly generated sample state quantity exceeds the maximum range, it is an outlier, that is, Δ ≥ ΔC, then the absolute value in this direction can be calculated separately:

[0114] Δr R =2.2kmΔr V =4.7kmΔr H =1.4km

[0115] Δv R =5.8m / sΔv V =2.6m / sΔv H =3.8m / s

[0116] Co-track tracking detection strategy and simulation verification: Monte Carlo simulation is performed with the relative distance between the tracking spacecraft and the target spacecraft being 2.5km, 3km and 3.5km respectively, and the tracking spacecraft is assumed to always point in the direction of the target. Monte Carlo simulation is performed on the target state, and the number of samples is taken as 1000; outliers are eliminated from the random Monte Carlo state of the simulated target spacecraft, and the simulation results show that as the distance between the tracking spacecraft and the target approaches, the static field of view FOV cannot observe the state of all random samples at the same time, and only about 78% to 85% of the total samples can be observed. As the distance between the tracking spacecraft and the target increases, the field of view continues to increase, and the number of target samples that can be located and observed by the tracking spacecraft will also increase accordingly. The average tracking observation time of random samples entering the field of view of the tracking spacecraft under different relative initial positions is 606s (Δr V =2.5km)、677s(Δr V =3.0km) and 663s(Δr V =3.5km). The following conclusions are drawn: Under the same-track tracking strategy, the demand for satellite attitude control is small, and the tracking observation time for non-cooperative targets is long. If the tracking satellite camera has a large field of view or the uncertainty of the target position is small, this target detection strategy can be used to detect the target.

[0117] High orbit tracking detection strategy and simulation verification: Monte Carlo simulation is performed on different scenarios of the tracking spacecraft and the target spacecraft, including: the tracking spacecraft always points to the relative coordinate system x H Negative axis, tracking satellite around y H The target state is simulated by Monte Carlo, with a sample size of 1000. The random Monte Carlo state of the simulated target spacecraft is eliminated for outliers, and the visibility and average observation time of the target in different scenarios are analyzed. The simulation results show that: the tracking satellite orbits y H When the rotation angle changes from 0° to -45° and -90°, the observation rate of the sample points of the target increases from 92.79% to 93.19% and 93.59%, but the average observation time of each sample decreases from 326s to 169s and 113s. Therefore, the following conclusions are drawn: Compared with the same-track tracking strategy, the advantage of the high-orbit tracking strategy is that it tracks the movement of the satellite relative to the target, thereby increasing the field of view of the camera swing and improving the probability of observing the target.

[0118] Low-orbit tracking detection strategy and simulation verification. Take the scenario where the tracking spacecraft is below the orbit of the target spacecraft. Monte Carlo simulation is performed. During the simulation, the camera is at the nadir and y H The axis direction is swung, and Δt is taken s=20 swings in 6000s. Monte Carlo simulation is performed on the target state, and the number of samples is 1000. The random Monte Carlo state of the simulated target spacecraft is eliminated for outliers, and the visibility and average observation time of the target in different scenes with the tracking spacecraft are analyzed. The simulation results show that the initial position of the target random sample point and the visibility in the camera field of view have an observation rate of up to 97.6%, and the average observation time is 410s. The following conclusions are drawn: the low-orbit tracking strategy has the highest probability of detecting the target, but the average observation time is low. When equipped with a camera with a larger field of view, the detection capability of the target can be further increased.

[0119] Step 106, according to the target detection strategy and the simulation results, select the optimal non-cooperative target detection strategy suitable for the research scenario for the detection of space debris.

[0120] The present invention proposes a detection method for space debris based on state probability density distribution, a detection strategy for non-cooperative targets, and three target detection strategies: co-orbit tracking detection strategy, high-orbit tracking detection strategy, and low-orbit tracking detection strategy, considering the relative position relationship and attitude relationship between the tracking spacecraft and the target. The Monte Carlo research method was used for experimental analysis, which showed the advantages of the low-orbit tracking detection strategy in terms of target recognition rate, etc., showing good engineering application potential, and laying the foundation for navigation control of the active approach phase of the tracking satellite.

[0121] See also Figure 5 , corresponding to the method for detecting space debris based on probability density distribution in the embodiment of the present application, the embodiment of the present application provides a device for detecting space debris based on probability density distribution, the detection device comprising:

[0122] A data acquisition module 1001 is used to simulate and define the spatial state of space debris using a Monte Carlo method to obtain first space debris state data;

[0123] A data processing module 1002 is used to detect and remove outliers on the first space debris state data according to the Mahalanobis distance to obtain second space debris state data;

[0124] A path updating module 1003 is used to update the relative path of the tracking spacecraft;

[0125] A target aiming module 1004 is used for aiming the target in the camera field of view and determining the observability condition of the target relative to the tracking spacecraft;

[0126] A target detection strategy and simulation module 1005 is used to determine and simulate a target detection strategy according to the observability condition;

[0127] The optimal non-cooperative target detection strategy determination module 1006 is used to select the optimal non-cooperative target detection strategy suitable for the research scenario according to the target detection strategy and simulation results, for the detection of space debris.

[0128] See also Figure 6 , corresponding to the method for detecting space debris based on probability density distribution in an embodiment of the present application, an embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each process of the embodiment of the method for detecting space debris based on probability density distribution in the above-mentioned embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0129] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0130] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0131] Corresponding to the method for detecting space debris based on probability density distribution in an embodiment of the present application, an embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the embodiment of the method for detecting space debris based on probability density distribution in the above-mentioned embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0132] The processor is the processor in the electronic device described in the sixth embodiment of the present application. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0133] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0135] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for detecting space debris based on probability density distribution, characterized in that: The steps include: A Monte Carlo method is used to simulate and define the spatial state of space debris to obtain first space debris state data; Performing outlier detection and elimination on the first space debris state data according to the Mahalanobis distance to obtain second space debris state data; Update the relative path of the tracking spacecraft; aiming of a target in the camera field of view to determine the observability condition of the target relative to the tracking spacecraft; Determine and simulate a target detection strategy based on the observability condition; According to the target detection strategy and simulation results, the optimal non-cooperative target detection strategy suitable for the research scenario is selected for the detection of space debris.

2. The method for detecting space debris based on probability density distribution according to claim 1, characterized in that: The Monte Carlo method is used to simulate and define the spatial state of space debris, specifically including: The Monte Carlo method is used to simulate and establish a spatial model that reflects the distribution and dynamic characteristics of space debris in the actual environment, which is achieved through the following formula: x i =N(μ,Σ) In the formula, x i Represents the state quantity of the sample point, N represents the normal distribution, μ represents the center point, and Σ represents the covariance matrix.

3. The method for detecting space debris based on probability density distribution according to claim 1, characterized in that: The performing outlier detection on the first space debris state data according to the Mahalanobis distance specifically includes: The Mahalanobis distance Δ is used for discrimination using the following formula: D 2 =(x-μ) T S -1 (x-μ) In the formula, considering the correlation of each dimension, Δ represents the distance of the state quantity x relative to the center point μ; based on this, Δ defines an ellipsoid with equal probability from the center point, and its value is: P(Δ<q α )≥α, Where α is the ratio of the given fraction of the total number of individuals located in the ellipsoid to the total number.

4. The method for detecting space debris based on probability density distribution according to any one of claims 1 to 3, characterized in that: Update the relative path of the tracking spacecraft, including the following steps: Establish the attitude dynamics model of the tracking spacecraft; Generate the relative position of the target according to the Monte Carlo method; State extrapolation and coordinate conversion: using a high-precision extrapolation model, the relative position is converted from a relative coordinate system to an inertial coordinate system; Camera coordinate system conversion: convert the direction vector of the target relative to the tracking spacecraft into the reference system of the camera.

5. The method for detecting space debris based on probability density distribution according to claim 4, characterized in that: The establishment of the attitude dynamics model of the tracking spacecraft specifically includes: The quaternion equation is used to describe the attitude dynamics model of the tracking spacecraft, and its initial attitude quaternion and angular velocity are defined as q JC and w JC , the corresponding kinetic model is: The generating the relative position of the target according to the Monte Carlo method specifically includes: Given an estimated target position speed The random initial state generated by the Monte Carlo method is: In the formula, record is the state offset corrected in the relative coordinate system.

6. The method for detecting space debris based on probability density distribution according to any one of claims 1 to 3, characterized in that: Aiming the target in the camera field of view and determining the observability condition of the target relative to the tracking spacecraft specifically includes the following steps: Given the relative position Δr from the tracking spacecraft to the target i C (t), detecting whether the relative position is within the field of view of the tracking spacecraft camera; When the camera aperture is fully open, the field of view is β, and the minimum and maximum distances for target detection are d min and d max , the cumulative target recognition time required is Δt r , for each time update, the target observability is checked by the following judgment method: When x i ≥0, the target is located at the tracking spacecraft x C Axis front; When min ≤r i ≤d max , the target is located at a range of distances relative to the tracking spacecraft; when The target is located relative to the tracking spacecraft y C Axis field of view; when The target is located relative to the tracking spacecraft z C Axis field of view; Where xi, yi and zi are Δr i C (t) is the triaxial component, ri is Δr i C The 2-norm of (t).

7. The method for detecting space debris based on probability density distribution according to claim 1, characterized in that: The target detection strategies include: same-orbit tracking detection strategy, high-orbit tracking detection strategy and low-orbit tracking detection strategy; The co-track tracking detection strategy is defined as the tracking spacecraft and the target spacecraft are located on the same orbit at different true anomalies, and the tracking spacecraft is located behind the target spacecraft, that is, the relative position vector Δr in the velocity direction in the relative coordinate system established with the target mass center is v ≤0; The high-orbit tracking detection strategy is defined as the tracking spacecraft operating in an orbit higher than the orbit of the target spacecraft, that is, the relative position vector Δr in the radial vector direction in the relative coordinate system established with the target as the center R ≥0; The low-orbit tracking detection strategy is to track the target spacecraft in front of the spacecraft on a lower orbit.

8. A space debris detection device based on probability density distribution, characterized in that: include: A data acquisition module, used for simulating and defining the space state of space debris by using the Monte Carlo method to obtain first space debris state data; A data processing module, configured to detect and remove outliers on the first space debris state data according to the Mahalanobis distance, so as to obtain second space debris state data; The path update module is used to update the relative path of the tracking spacecraft; A target aiming module, used for aiming a target in the camera field of view, and determining an observability condition of the target relative to the tracking spacecraft; A target detection strategy and simulation module, used to determine the target detection strategy and simulate it according to the observability condition; The optimal non-cooperative target detection strategy determination module is used to select the optimal non-cooperative target detection strategy suitable for the research scenario according to the target detection strategy and simulation results, for the detection of space debris.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for detecting space debris based on probability density distribution as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for detecting space debris based on probability density distribution as claimed in any one of claims 1 to 7.

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