A GNSS time service anomaly monitoring method and device based on dynamic graph

Abstract

The application relates to a GNSS time service abnormality monitoring method and device based on a dynamic graph, which is applied to the field of GNSS time service monitoring, and can fully utilize the advantages of multi-station redundant information of a cooperative time service system and improve the performance of a test statistic, in view of the low feature dimension and insensitivity to slight abnormalities of clock difference solution of a single station. Through processing and joint testing of receiver data of the whole network at the system level, the limitation of the terminal level monitoring method can be broken through, the perception ability to slight abnormalities is improved, and the whole judgment and decision of the system are assisted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of GNSS time service anomaly detection, in particular to a GNSS time service anomaly monitoring method and device based on a dynamic graph. BACKGROUND

[0002] The global navigation satellite system (GNSS) has the characteristics of all-day, all-weather, low cost and high precision, and is the main source of time information for major infrastructure. GNSS receivers can obtain high-precision satellite navigation time references by receiving satellite navigation signals, aligning local clocks to the time references, and enabling distributed systems to work together.

[0003] GNSS signals have low landing power, are vulnerable to attacks and tampering, and are easily affected by the electromagnetic environment. For example, ionospheric fluctuations such as electromagnetic storms and multipath effects such as non-line-of-sight transmission can degrade or even fail time synchronization accuracy; deliberate time synchronization attacks such as time service deception can gradually offset the calculated clock difference, causing actual synchronization to fail. This GNSS deception has been revealed in many fields and is commonly referred to as a time synchronization attack. When GNSS signals are abnormal, communication systems cannot function properly, affecting people's production and life. Therefore, monitoring technology for time synchronization is of great significance to improving the robustness of mobile communication services.

[0004] Currently, there are rich research results for time synchronization monitoring. The T-RAIM algorithm is an effective time information monitoring algorithm that uses position information as prior information to construct pseudo-range residuals, and through the test of pseudo-range residuals, it can exclude abnormal time service stars and improve the reliability of clock differences. However, this method only uses pseudo-range for testing, and PPP solution mainly uses carrier phase observations, so this method has certain limitations; constructing a test statistic based on clock difference solves the limitations of pseudo-range-based monitoring, but only uses single-station clock solution, which has the disadvantage of low feature dimension and insensitivity to small abnormalities. Existing monitoring algorithms are designed and run separately at the terminal level, which limits the sensitivity of the algorithm. Single-point applications cannot meet the future application scenarios of high precision and high security, and interconnection has become a future trend. SUMMARY

[0005] Therefore, it is necessary to provide a GNSS time service anomaly monitoring method and device based on a dynamic graph to solve the above technical problems.

[0006] A GNSS time service anomaly monitoring method based on a dynamic graph, the method comprising:

[0007] According to the GNSS satellite signals received by the receiver, the position and clock difference of the base station are calculated;

[0008] According to the position and clock difference of the base station, a dynamic graph anomaly model is constructed; the dynamic graph anomaly model comprises nodes representing the base stations, edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base station, and a clock difference time variation set of the clock difference between the edges connecting two nodes;

[0009] According to the clock difference time variation set, a test statistic is constructed;

[0010] For the dynamic graph anomaly model, a decision matrix is constructed, and a final decision result is obtained according to the test statistic corresponding to each element in the decision matrix.

[0011] In one of the embodiments, the method further comprises: obtaining pseudo-range and carrier phase observation values according to the GNSS satellite signals received by the receiver; obtaining high-precision satellite orbits and clock differences through a network to construct a PPP observation equation; and estimating the position and clock difference of the base station by using an EKF method.

[0012] In one of the embodiments, the method further comprises: constructing a dynamic graph anomaly model according to the position and clock difference of the base station, wherein the dynamic graph anomaly model is:

[0013] ;

[0014] wherein, a set of nodes representing the base stations, a set of edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base station, a clock difference time variation set of the clock difference between the edges connecting two nodes, and t represents time.

[0015] In one of the embodiments, the method further comprises: designing a whitening filter for the clock difference sequence in the clock difference time variation set to filter the clock difference sequence; wherein the regular equation of the whitening filter is:

[0016] ;

[0017] wherein, a correlation coefficient, a filter parameter, a prediction error power.

[0018] In one of the embodiments, the method further comprises: obtaining a prediction error sequence according to the clock difference time variation set, wherein the prediction error sequence is:

[0019] ;

[0020] wherein, a prediction error noise, and satisfies a normal distribution , denotes the mean, denotes the variance;

[0021] The test statistic is constructed using the prediction error noise as:

[0022] ;

[0023] The test statistic is subject to:

[0024] ;

[0025] According to the test statistic, a decision threshold is obtained .

[0026] In one embodiment, it further comprises: constructing a decision matrix for the dynamic graph anomaly model, the decision formula of the decision matrix being:

[0027] ;

[0028] The scores of all nodes are calculated using the elements in the decision matrix as:

[0029] ;

[0030] Among them, the decision information is determined according to the score as:

[0031] ;

[0032] When the decision value is 1, it means that the current information is not available, and when the decision value is 0, it means that the current information is available.

[0033] In one embodiment, it further comprises: when the number of abnormal decisions in a row of the decision matrix exceeds half, it is considered that the decision node information is not reliable; otherwise, it is considered to be reliable.

[0034] A GNSS timing anomaly monitoring device based on a dynamic graph, the device comprising:

[0035] A solving module for solving the position and clock difference of the base station according to the GNSS satellite signals received by the receiver;

[0036] A dynamic graph construction module for constructing a dynamic graph anomaly model according to the position and clock difference of the base station; the dynamic graph anomaly model comprises nodes representing the base stations, edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base stations, and a clock difference time variation set of the clock difference between the two nodes connected by the edges;

[0037] a statistical quantity calculation module, configured to construct a test statistical quantity according to the clock difference time variation set;

[0038] a decision module, configured to construct a decision matrix for the dynamic graph anomaly model, and obtain a final decision result according to the test statistical quantity corresponding to each element in the decision matrix.

[0039] A computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0040] obtain the position and clock difference of the base station according to the GNSS satellite signals received by the receiver;

[0041] construct a dynamic graph anomaly model according to the position and clock difference of the base station; the dynamic graph anomaly model comprises nodes representing the base stations, edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base station, and a clock difference time variation set of the clock difference between the edges connecting two nodes;

[0042] construct a test statistical quantity according to the clock difference time variation set;

[0043] construct a decision matrix for the dynamic graph anomaly model, and obtain a final decision result according to the test statistical quantity corresponding to each element in the decision matrix.

[0044] A computer readable storage medium, which stores a computer program, and the computer program implements the following steps when executed by a processor:

[0045] obtain the position and clock difference of the base station according to the GNSS satellite signals received by the receiver;

[0046] construct a dynamic graph anomaly model according to the position and clock difference of the base station; the dynamic graph anomaly model comprises nodes representing the base stations, edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base station, and a clock difference time variation set of the clock difference between the edges connecting two nodes;

[0047] construct a test statistical quantity according to the clock difference time variation set;

[0048] construct a decision matrix for the dynamic graph anomaly model, and obtain a final decision result according to the test statistical quantity corresponding to each element in the decision matrix.

[0049] The GNSS timing anomaly monitoring method and device based on a dynamic graph have the advantages of low feature dimension, insensitivity to small anomalies, full use of the advantages of multi-station redundant information of a cooperative timing system, and improved performance of the test statistic. Through processing and joint testing of the receiver data of the entire network at the system level, the limitations of the terminal layer monitoring method can be broken through, the perception ability of small anomalies can be improved, and overall judgment and decision-making of the system can be assisted. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 FIG. 1 is a flowchart of a GNSS timing anomaly monitoring method based on a dynamic graph according to an embodiment;

[0051] Figure 2 FIG. 2 is a flowchart of the overall working process of a GNSS timing anomaly monitoring method based on a dynamic graph according to an embodiment;

[0052] Figure 3 FIG. 3 is a node situation decision diagram of a GNSS timing anomaly monitoring method based on a dynamic graph according to an embodiment;

[0053] Figure 4 FIG. 4 is a basic diagram of data sampling according to an embodiment;

[0054] Figure 5 FIG. 5 is a diagram of the relationship between the joint test statistic and the detection threshold of stations according to an embodiment;

[0055] Figure 6 FIG. 6 is a structural block diagram of a GNSS timing anomaly monitoring device based on a dynamic graph according to an embodiment;

[0056] Figure 7 FIG. 7 is an internal structure diagram of a computer device according to an embodiment. DETAILED DESCRIPTION

[0057] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0058] In one embodiment, as shown in FIG. 1, a GNSS timing anomaly monitoring method based on a dynamic graph is provided, including the following steps: Figure 1

[0059] Step 102: The position and clock difference of the base station are calculated according to the GNSS satellite signals received by the receiver.

[0060] Step 104: A dynamic graph anomaly model is constructed according to the position and clock difference of the base station.

[0061] ​The dynamic graph anomaly model comprises nodes representing base stations, edges representing communication connection relationships of the base stations, a position time variation set of positions of the base stations, and a clock difference time variation set of clock differences between two nodes connected by an edge.

[0062] In step 106, a test statistic is constructed according to the clock difference time variation set.

[0063] In step 108, a decision matrix is constructed for the dynamic graph anomaly model, and a final decision result is obtained according to test statistics corresponding to each element in the decision matrix.

[0064] In the GNSS time service anomaly monitoring method based on the dynamic graph, the clock difference solution for a single station has a low feature dimension and is not sensitive to small anomalies. The method can fully utilize the advantages of the multi-station redundancy information of the cooperative time service system and improve the performance of the test statistic. By processing and jointly testing the receiver data of the entire network at the system level, the limitations of the terminal layer monitoring method can be overcome, the perception ability of small anomalies can be improved, and the overall judgment and decision of the system can be assisted.

[0065] In one of the embodiments, pseudo-range and carrier phase observation values are obtained according to GNSS satellite signals received by a receiver; high-precision satellite orbits and clock differences are obtained through a network to construct a PPP observation equation; and the position and clock difference of the base station are estimated by using an EKF method.

[0066] In one of the embodiments, a dynamic graph anomaly model is constructed according to the position and clock difference of the base station as follows:

[0067] ;

[0068] wherein, a set of nodes representing base stations is denoted as V, a set of edges representing communication connection relationships of the base stations is denoted as E, a position time variation set of positions of the base stations is denoted as X, a clock difference time variation set of clock differences between two nodes connected by an edge is denoted as Y, and t represents time.

[0069] In one of the embodiments, a whitening filter is designed for the clock difference sequence in the clock difference time variation set to filter the clock difference sequence; wherein the normal equation of the whitening filter is as follows:

[0070] ;

[0071] wherein, a correlation coefficient is denoted as r, a filter parameter is denoted as w, and a prediction error power is denoted as Q.

[0072] In addition, according to the clock difference time change set, a prediction error sequence is obtained as:

[0073] ;

[0074] wherein, represents a prediction error noise, and satisfies a normal distribution , represents a mean, represents a variance; a test statistic is constructed using the prediction error noise as:

[0075] ;

[0076] The test statistic is subject to a chi-square distribution with a degree of freedom of N under a hypothesis condition.

[0077] ;

[0078] According to the test statistic, a decision threshold is obtained as . Under a hypothesis condition, no abnormal calculation data, the test statistic is subject to a chi-square distribution with a degree of freedom of N. Under a hypothesis condition, abnormal data occurs, the test statistic is subject to a chi-square distribution with a degree of freedom of N and a non-central parameter of . Since the characteristics of abnormal data are quite different, there is no fixed theoretical expression. According to the Neyman-Pearson criterion, the decision threshold .

[0079] In one embodiment, for a dynamic graph anomaly model, a decision matrix is constructed, and a decision formula of the decision matrix is:

[0080] ;

[0081] The score of all nodes is calculated using the elements in the decision matrix as:

[0082] ;

[0083] wherein, the decision information is determined according to the score as:

[0084] ;

[0085] When the decision value is 1, it indicates that the current information is unavailable, and when the decision value is 0, it indicates that the current information is available.

[0086] In addition, when the number of abnormal decisions in a row of the decision matrix exceeds half, it is considered that the decision node information is not reliable; otherwise, it is considered to be reliable.

[0087] As shown in Figure 2 , first, the position and clock error of the station are estimated in real time using GNSS observation data and high-precision orbit clock error data stream, and the inter-station clock error is used to construct the test statistic; then the overall situation of all stations is judged, and finally the alarm information of the station to be monitored is output. As shown in Figure 3 , it is a schematic diagram for overall situation decision, and the pairwise decision of the inter-station joint test statistic can construct a decision matrix. When the number of rows corresponding to a station exceeds half, it is considered that the station information is not reliable. As shown in Figure 4 , Figure 5 , the test statistic and node score of a station in the system relative to other stations are shown. The horizontal coordinate unit of Figure 4 is time, and the vertical coordinate is the test statistic. Figure 5 The horizontal coordinate unit of Figure 4 is time, and the vertical coordinate is the node score. As can be seen from , a single test statistic often presents a large number of false alarms or false alarms, and the joint test statistic can balance this problem and solve the shortcomings of a single test statistic.

[0088] It should be understood that although each step in the flowchart of Figure 1 is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise stated in this article, the execution of these steps has no strict order restriction, and these steps can be executed in other order. Moreover, Figure 1 At least part of the steps in may include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed with at least part of other steps or other steps. Sub-steps or stages of the stage are executed in rotation or alternation.

[0089] Figure 6 In one embodiment, as shown in , a dynamic graph-based GNSS timing anomaly monitoring device is provided, comprising a solving module 602, a dynamic graph construction module 604, a statistic calculation module 606 and a decision module 608, wherein:

[0090] The solving module 602 is used to solve the position and clock error of the base station according to the GNSS satellite signal received by the receiver.

[0091] The dynamic graph construction module 604 is configured to construct a dynamic graph anomaly model according to the positions of the base stations and the clock differences. The dynamic graph anomaly model includes nodes representing the base stations, edges representing the communication connection relationships of the base stations, a position time variation set of the positions of the base stations, and a clock difference time variation set of the clock differences between the edges connecting two nodes.

[0092] The statistical quantity calculation module 606 is configured to construct a test statistic according to the clock difference time variation set.

[0093] The decision module 608 is configured to construct a decision matrix for the dynamic graph anomaly model, and obtain a final decision result according to the test statistics corresponding to each element in the decision matrix.

[0094] The specific limitations of the GNSS time service anomaly monitoring device based on a dynamic graph can refer to the limitations of the GNSS time service anomaly monitoring method based on a dynamic graph described above, and will not be described here. Each module in the GNSS time service anomaly monitoring device based on a dynamic graph can be realized by software, hardware, and combinations thereof, in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to each module by the processor.

[0095] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in FIG. 8. Figure 7 The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. The processor of the computer device is configured 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, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store base station position and clock difference data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a GNSS time service anomaly monitoring method based on a dynamic graph.

[0096] Those skilled in the art can understand that Figure 7 the structure shown in FIG. 8 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0097] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method in the above embodiments when executing the computer program.

[0098] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the method in the above embodiments.

[0099] A person of ordinary skill in the art can understand that all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing related hardware, 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 above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present 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 but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM) and the like.

[0100] Any combination of the technical features of the above embodiments can be made, and in order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0101] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A dynamic graph based GNSS timing anomaly monitoring method, characterized in that, The method comprises: solving the position and clock error of the base station according to the GNSS satellite signals received by the receiver; constructing a dynamic graph anomaly model according to the position and clock error of the base station; the dynamic graph anomaly model comprises nodes representing the base stations, edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base stations, and a clock error time variation set of the clock error between the two nodes connected by the edges; constructing a test statistic according to the clock error time variation set; constructing a decision matrix for the dynamic graph anomaly model, and obtaining a final decision result according to the test statistic corresponding to each element in the decision matrix.

2. The method of claim 1, wherein, The method for solving the position and clock error of the base station according to the GNSS satellite signals received by the receiver comprises: obtaining pseudo-range and carrier phase observation values according to the GNSS satellite signals received by the receiver; obtaining high-precision satellite orbits and clock errors through a network to construct a PPP observation equation; estimating the position and clock error of the base station by using an EKF method.

3. The method of claim 1, wherein, The method for constructing a dynamic graph anomaly model according to the position and clock error of the base station comprises: The method for constructing a dynamic graph anomaly model according to the position and clock error of the base station is as follows: ； wherein, denotes a set of nodes representing base stations, denotes a set of edges representing base station communication connections, denotes a set of position time variations representing base station positions, denotes a set of clock difference time variations representing clock differences between two nodes connected by an edge, t denotes time.

4. The method of claim 3, wherein, Before constructing a test statistic according to the clock error time variation set, the method further comprises: designing a whitening filter for the clock error sequence in the clock error time variation set to filter the clock error sequence; wherein the normal equation of the whitening filter is as follows: ； wherein denotes a correlation coefficient, denotes a filter parameter, is the prediction error power.

5. The method of claim 4, wherein, The method for constructing a test statistic according to the clock error time variation set comprises: obtaining a prediction error sequence according to the clock error time variation set is as follows: ； wherein, denotes the prediction error noise and satisfies a normal distribution , denotes the mean, denotes the variance; constructing a test statistic by using the prediction error noise is as follows: ； The test statistic is subject to a hypothesis condition as follows: ； Based on the test statistics, a decision threshold is obtained .

6. The method of claim 5, wherein, The method for constructing a decision matrix for the dynamic graph anomaly model and obtaining a final decision result according to the test statistic corresponding to each element in the decision matrix comprises: A decision matrix is constructed for the dynamic graph anomaly model The decision formula of the decision matrix is: ； calculating the scores of all nodes by using the elements in the decision matrix is as follows: ； wherein the score is determined according to the decision information is determined as: ； When the decision value is 1, it indicates that the current information is not available, and when the decision value is 0, it indicates that the current information is available.

7. The method of claim 6, wherein, The method further comprises: when the number of abnormal decisions in a row of the decision matrix exceeds half, it is considered that the decision node information is not reliable; otherwise, it is considered that the decision node information is reliable. 8.A device for GNSS timing anomaly monitoring based on dynamic graph, characterized in that, The device comprises: a solving module configured to solve the position and clock error of the base station according to the GNSS satellite signals received by the receiver; a dynamic graph construction module configured to construct a dynamic graph anomaly model according to the position and clock error of the base station; the dynamic graph anomaly model comprises nodes representing the base stations, edges representing the communication connection relationship of the base stations, a position time variation set of the position of the base stations, and a clock error time variation set of the clock error between the two nodes connected by the edges; a statistic calculation module configured to construct a test statistic according to the clock error time variation set; a decision module configured to construct a decision matrix for the dynamic graph anomaly model, and obtain a final decision result according to the test statistic corresponding to each element in the decision matrix.

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