Method for associating satellite on-orbit anomalies with space environment based on density distribution drift

Abstract

The application provides a satellite in-orbit anomaly and space environment correlation method based on density distribution drift, comprising the following steps: S1: determining a typical repetitive anomaly of an in-orbit spacecraft, on the basis of which, it can be considered that the event has consistent environmental correlation characteristics, so that it can be statistically analyzed together and further analyzed; S2: using the occurrence of the abnormal event as a condition, the space environment data is divided into two parts, and the probability density distribution of the two parts is counted; S3: according to the difference between the probability density distribution of the two parts in S2, the influence of the space environment is formed to form an explainable correlation characteristic, and the correlation analysis is completed. The method is suitable for analyzing the in-orbit repetitive anomaly phenomenon, and the possible correlation characteristics between the change characteristics of the space environment and a kind of repetitive in-orbit anomaly phenomenon are analyzed by using the distribution characteristic construction method in statistics.

Description

Technical Field

[0001] This invention relates to the fields of space environment risk early warning and spacecraft on-orbit operation management, and particularly to a method for correlating satellite on-orbit anomalies with the space environment based on density distribution drift. Background Technology

[0002] Satellites in orbit generate numerous anomalies. Analyzing their correlation with the space environment can provide support and reference for identifying the causes of ground-based positioning anomalies and determining whether they are due to space environmental influences. This method is applicable to the analysis of repetitive anomalies in orbit. It utilizes statistical methods and the changing characteristics of the space environment to conduct data feature engineering analysis, obtaining the possible correlation characteristics between a certain type of repetitive anomaly and the environment.

[0003] Based on current technology, the correlation between on-orbit anomalies and the space environment is mainly based on the following methods:

[0004] 1. Simply count whether any anomalies occur within the time period of a space environment event (i.e., solar proton events, geomagnetic storms, geomagnetic substorms, etc.).

[0005] 2. By conducting Pearson correlation coefficient analysis on various space environment detection factors and typical spacecraft telemetry parameters, the correlation coefficient between the two is obtained and used for judgment.

[0006] The above methods have the following problems in analyzing the correlation between anomalies and the space environment:

[0007] 1. Simple statistical methods are prone to misjudgment. In practical applications, many anomalies that are suspected to be caused by the environment cannot be matched with space environment events.

[0008] 2. Regarding the method of calculating the correlation coefficient, since the time resolution of space environment data and on-orbit telemetry data are basically out of sync, and the spatial location of space environment detection data and corresponding telemetry data are also basically inconsistent, the results obtained from the correlation coefficient analysis are usually weak in significance and have low effectiveness. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a method for correlating satellite on-orbit anomalies with the space environment based on density distribution drift. This method is applicable to the analysis of repetitive on-orbit anomalies and utilizes statistical methods to construct distribution characteristics to analyze the possible correlation between the changing characteristics of the space environment and a class of repetitive on-orbit anomalies.

[0010] To achieve the above objectives, the present invention adopts the following solution:

[0011] This invention provides a method for correlating satellite on-orbit anomalies with the space environment based on density distribution drift, comprising the following steps:

[0012] S1: Identify typical repetitive anomalies of spacecraft in orbit. Based on this, it can be assumed that such events have consistent environmental correlation characteristics, so that they can be statistically analyzed together and further analyzed.

[0013] S2: Using the occurrence or non-occurrence of abnormal events as a condition, the spatial environment data are divided into two parts, and the probability density distribution of the two parts is statistically analyzed.

[0014] S3: Based on the impact of the difference in probability density distribution between the two in S2 on each spatial environment, interpretable correlation features are formed, and the correlation analysis is completed.

[0015] Further, step S1 includes the following steps:

[0016] S1-1: Select a repetitive anomaly under a certain orbital profile and obtain its anomaly time {t}. A};

[0017] S1-2: Prepare a space environment dataset E(t), including typical and common space environment measured data and ground monitoring data. The range of t should cover at least the earliest to the latest time when the anomaly occurred in step S1-1, and should be no less than 1 year.

[0018] Furthermore, in step S1-1, the repetitive anomalies include anomalies of the same type of single machine and the same phenomenon, or multiple similar anomalies of single machine and the same phenomenon; in step S1-2, the types of the space environment dataset at least cover factors such as geomagnetic disturbances, high-energy particles, and solar activity.

[0019] Further, step S2 includes the following steps:

[0020] S2-1: Separate the information that matches the anomalous time from the spatial environment dataset E(t) to form two sets {t}. A}、{t B}∈{t}, where {t B} represents the time when no anomalies occurred;

[0021] S2-2: For {t A}、{t B Data equalization can be achieved through separate sampling to obtain {t} A '}、{t B '}∈{t}, and {t A '}∈{t A}、{t B '}∈{t B};

[0022] S2-3: For the spatial environment E = {E1, E2, E3, ...}, generate the set of environmental factors E for each anomaly. A ={E1(t),E2(t),E3(t),…},t∈{t A '}, and the set of environmental factors E under non-abnormal conditions. B ={E1(t),E2(t),E3(t),…},t∈{t B '}.

[0023] Furthermore, step S3 includes the following steps:

[0024] S3-1: Calculate the difference in probability density distribution;

[0025] S3-2: Calculate the comparison value of the probability density distribution difference based on the existing data;

[0026] S3-3: Compare the results of step S3-1 with those of step S3-2 to complete the correlation analysis.

[0027] Further, step S3-1 includes the following steps:

[0028] S3-1-1: Calculate E A and E B The probability distribution function F corresponding to each environmental factor E1, E2, E3, ... etc. i (E)=P(E≥E i ), i = 1, 2, 3, ..., and their corresponding probability density distribution function f. i (E)=d F i (E) / dE, i=1,2,3,…, for f i (E) can be characterized according to the Poisson distribution or other suitable distribution forms, thus obtaining f i The corresponding feature parameter μ i ;

[0029] S3-1-2: To E A and E B After calculating the probability density distribution, the distribution characteristic coefficients M of both are obtained. A ={μ A1 ,μ A2 ,μ A3 ,…},M B ={μ B1 ,μ B2 ,μ B3 ..., and calculate its abnormal environment probability drift vector D = M. A -M B ={d1,d2,d3,…}.

[0030] Furthermore, step S3-2 specifically includes:

[0031] Using the known spatial environment anomaly data H = {H1, H2, ...}, the anomaly environment probability drift matrix is ​​formed using the steps included in step S2.

[0032] Furthermore, the known space environment anomaly data refers to historical anomaly data for which relatively clear conclusions have been drawn through ground fault reproduction or analysis.

[0033] Furthermore, step S3-3 specifically includes:

[0034] For the anomaly vector D to be analyzed, the probability drift matrix of historically known anomaly environments is compared with that of the anomaly vector D. Perform matching analysis on each vector in the matrix, and calculate D and D'. Hi The similarity between vectors can be calculated using methods such as the F-norm, and one or more of the closest vectors D can be selected. N Therefore, the anomaly D to be analyzed may be attributed to D. N Corresponding environmental anomalies were identified, and a correlation analysis was performed.

[0035] The beneficial effects of this invention are:

[0036] This invention proposes a method for correlating satellite on-orbit anomalies with the space environment based on density distribution drift. This method can analyze and attribute on-orbit anomalies encountered by mid-to-high-orbit satellites that are suspected to be caused by the space environment. It utilizes the probability distribution characteristics of the space environment at historical anomaly moments to obtain the potential environmental impacts of currently analyzed repeatable anomalies. This method can also be extended to perform similar analyses on newly occurring anomaly space environment profiles and attribute their potential environmental impacts. Attached Figure Description

[0037] Figure 1 A schematic diagram of the overall method in an embodiment of the present invention. Detailed Implementation

[0038] To make the technical solutions and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be fully described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0040] Traditional analyses of the correlation between the space environment and spacecraft on-orbit anomalies employ Pearson correlation coefficients, calculated by comparing the temporal detection results of various telemetry parameters causing the anomalies with temporal data on the space environment. This correlation coefficient is then directly used as a standard parameter for correlation. While this method achieves a certain level of correlation, the definition of correlation coefficient differs from that of correlation. It indicates the degree of consistent change between two parameters. In most cases, the influence of the space environment on spacecraft anomalies does not fall into this category, but rather primarily manifests in the following two ways:

[0041] 1) The first type is cumulative. Spacecraft are continuously affected by certain environmental factors and accumulate their effects, causing the rate of change of key parameters characterizing the impact rather than the spacecraft itself changing with the space environment. Only when the space environment changes drastically can the correlation coefficient increase to a certain extent.

[0042] 2) The second type is probability-based. Spacecraft are affected by certain environmental factors and have a certain probability of sudden anomalies. This includes a certain probability threshold that triggers the anomaly. Below this threshold, the probability approaches 0. Above this threshold, the probability increases with the enhancement of the environment, but it is still a random event.

[0043] Therefore, from the perspective of the impact mechanism, it can be seen that simply using the correlation coefficient as an assessment method cannot characterize the actual process of the impact on the space environment.

[0044] This invention introduces a method for correlating satellite on-orbit anomalies with the space environment based on density distribution drift. By utilizing the differences in probability distribution characteristics of the space environment under conditions of anomaly occurrence and non-occurrence, environmental factors that may cause anomalies are identified and analyzed. Finally, the method combines the possible effect mechanisms of environmental factors to make inferences and derive the corresponding correlation characteristics.

[0045] The invention will now be further described with reference to the accompanying drawings. Figure 1 :

[0046] (1) Select a repetitive anomaly under a certain track profile (which can be anomalies of the same type and the same phenomenon, or multiple anomalies of similar type and the same phenomenon), and obtain its anomaly time {t}. A};

[0047] (2) Prepare a space environment dataset E(t), including typical common space environment measured data and ground monitoring data, covering at least: geomagnetic disturbances, high-energy particles, solar activity and other factors, where the range of t is at least from the earliest time to the latest time of the anomaly in (1), and not less than 1 year;

[0048] (3) Separate the information in the space environment dataset that matches the abnormal time to form two sets {t}. A}、{t B}∈{t}, where {t B} represents the time when no anomalies occurred;

[0049] For {t A}、{t B Data equalization can be achieved through separate sampling to obtain {t} A '}、{t B '}∈{t}, and {t A '}∈{t A}、{t B '}∈{t B Separate sampling is one method of achieving equilibrium.

[0050] The sampling process can employ oversampling or undersampling, and can be performed with or without replacement. Furthermore, the effectiveness of the sampling can be ensured by comparing the distribution characteristics of the data before and after sampling.

[0051] (4) For the spatial environment E = {E1, E2, E3, ...}, generate the environmental factor sets E for abnormal and non-abnormal conditions respectively. A ={E1(t),E2(t),E3(t),…},t∈{t A '},as well as

[0052] E B ={E1(t),E2(t),E3(t),…},t∈{t B '};

[0053] (5) Calculate E A and E B The probability distribution function F corresponding to each environmental factor E1, E2, E3, ... etc. i (E)=P(E≥E i ), i = 1, 2, 3, ..., and their corresponding probability density distribution function f. i (E)=d F i (E) / dE, i=1,2,3,…, for f i (E) can be characterized according to the Poisson distribution or other suitable distribution forms, thus obtaining f i The corresponding feature parameter μ i ;

[0054] (6) For E A and E B After calculating the probability density distribution, the distribution characteristic coefficients M of both are obtained. A ={μ A1 ,μA2 ,μ A3 ,…},M B ={μ B1 ,μ B2 ,μ B3 ..., and calculate its abnormal environment probability drift vector D = M. A -M B ={d1,d2,d3,…};

[0055] (7) Using the known space environment anomaly data H={H1,H2,…} (through ground fault reproduction or analysis of historical anomaly data with relatively clear conclusions), the anomaly environment probability drift matrix is ​​formed by following steps (3) to (7).

[0056] (8) For the anomaly vector D to be analyzed in (1), compare it with the historical known anomaly environment probability drift matrix. Perform matching analysis on each vector in the matrix, and calculate D and D'. Hi The similarity between vectors can be calculated using methods such as the F-norm, and one or more of the closest vectors D can be selected. N Therefore, the anomaly D to be analyzed may be attributed to D. N The corresponding environmental anomaly.

[0057] In the description of this specification, references to terms such as "an embodiment" and "example" refer to specific features, structures, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms are not necessarily intended to refer to corresponding embodiments or examples in a suitable manner.

[0058] It must be pointed out that the above description of the embodiments is not intended to limit the invention but only to help understand the core idea of ​​the invention. For those skilled in the art, any improvements to the invention and equivalent alternatives made to the invention without departing from the principle of the invention are also within the scope of protection of the claims of the invention.

Claims

1. A method for correlating satellite on-orbit anomalies with the space environment based on density distribution drift, characterized in that, Includes the following steps: S1: Identify typical repetitive anomalies of spacecraft in orbit. Based on these anomalies, assume they have consistent environmental correlation characteristics, and then perform statistical analysis on them together. S2: Using the occurrence or non-occurrence of abnormal events as a condition, the spatial environment data are divided into two parts, and the probability density distribution of the two parts is statistically analyzed. S3: Based on the impact of the difference in probability density distribution between the two in S2 on each spatial environment, form interpretable correlation characteristics and complete the correlation analysis; Step S3 includes the following steps: S3-1: Calculate the difference in probability density distribution; S3-2: Calculate the comparison value of the probability density distribution difference based on the existing data; S3-3: Compare the results of step S3-1 with those of step S3-2 to complete the correlation analysis; Step S3-1 includes the following steps: S3-1-1: Calculate E A and E B The probability distribution function F corresponding to each environmental factor E1, E2, E3, ... i (E)=P(E≥E i ), i=1,2,3,…, and their corresponding probability density distribution functions. f i (E)=d F i (E) / dE, i=1,2,3,…, right f i (E) can be characterized according to the Poisson distribution or other suitable distribution forms, thus obtaining f i The corresponding feature parameter μ i ; S3-1-2: To E A and E B After calculating the probability density distribution, the distribution characteristic coefficients M of both are obtained. A ={μ A1 ,μ A2 ,μ A3 ,…},M B ={μ B1 , μ B2 , μ B3 , …}, and calculate its abnormal environment probability drift vector D=M A -M B ={d1,d2,d3,…}; Step S3-3 is as follows: The anomaly vector D to be analyzed, and the probability drift matrix of historically known anomaly environments. Perform matching analysis on each vector in the matrix, and calculate D and D'. Hi The similarity between them is calculated using the F-norm, and one or more of the closest vectors D are selected. N Therefore, the anomaly D to be analyzed is attributed to D. N Corresponding environmental anomalies were identified, and a correlation analysis was performed.

2. The method according to claim 1, characterized in that, Step S1 includes the following steps: S1-1: Select a repetitive anomaly under a certain orbital profile and obtain its anomaly time {t}. A }; S1-2: Prepare a space environment dataset E(t), including typical and common space environment measured data and ground monitoring data. The range of t should cover at least the earliest to the latest time when the anomaly occurred in step S1-1, and should be no less than 1 year.

3. The method according to claim 2, characterized in that, In step S1-1, the repetitive anomalies include anomalies of the same type of single machine and the same phenomenon, or anomalies of multiple single machines and the same phenomenon; in step S1-2, the types of the space environment dataset at least cover: geomagnetic disturbances, high-energy particles, and solar activity factors.

4. The method according to claim 3, characterized in that, Step S2 includes the following steps: S2-1: Separate the information that matches the anomalous time from the spatial environment dataset E(t) to form two sets {t}. A }、{t B }∈{t}, where {t B } represents the time when no anomalies occurred; S2-2: For {t A }、{t B Data equalization can be achieved through separate sampling to obtain {t} A '}、{t B '}∈{t}, and {t A '}∈{t A }、{t B '}∈{t B }; S2-3: For the spatial environment E={E1,E2,E3,…}, generate the set of environmental factors E for abnormal situations. A ={E1(t),E2(t),E3(t),…},t∈{t A '}, and the set of environmental factors E under non-abnormal conditions. B ={E1(t),E2(t),E3(t),…},t∈{t B '}.

5. The method according to claim 1, characterized in that, Step S3-2 is as follows: Using the known spatial environment anomaly data H={H1,H2,…}, the anomaly environment probability drift matrix is ​​formed using the steps included in step S2. =[D H1 D H2 ,…].

6. The method according to claim 5, characterized in that, The known space environment anomaly data refers to historical anomaly data for which relatively clear conclusions have been drawn through ground fault reproduction or analysis.

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