Train positioning system, integrity monitoring method, equipment and medium

Abstract

The invention relates to the technical field of rail transit, in particular to a train positioning system, an integrity monitoring method, equipment and a medium. According to the invention, the GNSS / INS / NHC combined model is constructed based on the extended Kalman filter EKF, and accurate detection of a plurality of fault satellites is realized by calculating the test statistics based on the filtering information. A corresponding state observation matrix is established through the NHC model, and the integrity performance of the system is remarkably improved under the condition that no satellite signal exists. In order to evaluate the performance of the method, the test statistics of a plurality of fault satellites and the integrity performance of the system when the GNSS signal is interrupted are calculated, and the positioning and integrity performance of the system under the GNSS / INS combination and the GNSS / INS / NHC combination are compared. Experimental results show that the accuracy rate of the method in multi-satellite fault detection reaches 100%, and compared with integrity monitoring of GNSS / INS combination, the integrity performance of the system is remarkably improved.

Description

Technical Field

[0001] The present disclosure relates to the field of rail transit technology, and in particular to a train positioning system, an integrity monitoring method, a device and a medium. Background Art

[0002] With the widespread application of global navigation satellite system (GNSS) technology in railway train positioning systems, the performance and safety of GNSS-based train positioning have received increasing attention in recent years. However, due to the complexity of the train operating environment, the existing GNSS-based integrity monitoring methods cannot meet the specific requirements of railway safety operations. In order to further improve the positioning performance and safety performance of trains, GNSS is usually combined with an inertial navigation system (INS), and the extended Kalman filter method is used to estimate the position information of the system. In addition, integrity monitoring methods for GNSS / INS combined navigation systems are also common. However, when the GNSS signal is interrupted for a long time, the positioning accuracy and safety performance of the GNSS / INS combination drop sharply, and the accurate detection of multiple faulty satellites has always been a difficulty in the GNSS / INS combination integrity monitoring method.

[0003] In summary, a technical solution is urgently needed for GNSS / INS combination integrity monitoring. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a train positioning system, an integrity monitoring method, a device and a medium.

[0005] In a first aspect, a train positioning system includes: a system error state model and an observation state model; The system error state model is used to describe the state of the system changing over time, including: position, speed and attitude; based on the system error state model, the state at the next moment is predicted; The observation state model is used to provide observation information on the system state based on the observation data of GNSS and INS; based on the real-time observation data of the observation state model, the system error state model predicts the state correction state estimate at the next moment to obtain the train positioning.

[0006] Furthermore, the discrete time error state model of the system error state model is expressed as follows:

[0007] in, represents the system state vector at time k; and Respectively and The transformation matrix; and Respectively and The process noise matrix; is the system process noise vector; The expression is as follows:

[0008] in, Represent the position error vector, velocity error vector, attitude error vector, angular velocity error vector and acceleration error vector in turn; and are the GNSS clock error and clock drift respectively.

[0009] Furthermore, the observation state model includes: a global navigation satellite system GNSS observation model and a non-holonomic constrained NHC observation model.

[0010] Furthermore, the global navigation satellite system GNSS observation model is expressed as follows:

[0011] in, represents the measurement matrix of GNSS; represents the GNSS observation noise vector; and are the pseudorange vector predicted by INS and the pseudorange vector observed by GNSS respectively; and are the pseudorange rate vector predicted by INS and the pseudorange rate vector observed by GNSS respectively.

[0012] Furthermore, the nonholonomic constrained NHC observation model is expressed as follows:

[0013] in, represents the observation matrix of NHC; represents the observation noise vector of NHC; and They represent the speed predicted by INS and the actual speed vector of the train in the carrier b coordinate system respectively; and Respectively represent the INS predicted speed values ​​corresponding to the lateral and vertical directions of the train.

[0014] In a second aspect, a train positioning satellite fault integrity monitoring method is based on the above-mentioned train positioning system; Calculate the test statistic by using the innovation vector in the extended Kalman filter (EKF) algorithm , and use it to detect GNSS satellite failures; by comparing the test statistic with the test threshold, it is determined whether the current satellite is faulty. It is expressed as follows:

[0015] in, represents the i-th value in the GNSS observation update; represents the GNSS observation vector at time k; yes The predicted value of represents the covariance of the i-th value in the GNSS observation update; is the system state covariance matrix; yes The predicted value of yes The covariance matrix of .

[0016] In a third aspect, a protection level-based train positioning integrity monitoring method is based on the above-mentioned train positioning system; Horizontal Protection Level HPL and Global Navigation Satellite System GNSS Measurement Matrix , measurement error And λ, expressed as follows:

[0017] in, The characteristic slope The maximum value in is expressed as follows:

[0018] in, and Respectively represent matrices The first row, i-th column element and the second row, i-th column element of ; Representation Matrix The element in the i-th row and i-th column of ; represents the unit matrix; the value of λ and the missed detection rate Related, expressed as follows:

[0019] in, Indicates the inspection threshold corresponding to each satellite; represents the chi-square distribution density function of the variable x; Represents basic mathematical integral expressions; When the set horizontal alarm limit HAL, horizontal protection limit HPL and set horizontal position error HPE meet the following formula, the system is in a no-alarm state:

[0020] When HPL>HAL, the train positioning unit issues an alarm to the driver within the alarm time threshold TTA, indicating that the current position output is unavailable.

[0021] In a fourth aspect, a train positioning method includes: a train positioning system based on the above; Train positioning is performed using system error state model and observation state model; The system error state model is used to describe the state of the system changing over time, including: position, speed and attitude; based on the system error state model, the state at the next moment is predicted; The observation state model is used to provide observation information on the system state based on the observation data of GNSS and INS; based on the real-time observation data of the observation state model, the system error state model predicts the state correction state estimate at the next moment to obtain the train positioning.

[0022] In a fifth aspect, an electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; a memory storing a computer program; The processor is used to implement the above-mentioned method for monitoring the integrity of a train positioning satellite failure, or the above-mentioned method for monitoring the integrity of a train positioning based on a protection level, or the above-mentioned method for train positioning when executing a computer program stored in the memory.

[0023] In a sixth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for monitoring the integrity of a train positioning satellite failure, or the above-mentioned method for monitoring the integrity of a train positioning based on a protection level, or the above-mentioned method for train positioning.

[0024] The present disclosure at least includes the following beneficial effects: The present invention discloses a train positioning unit integrity monitoring method based on a GNSS / INS / NHC combination, which deeply evaluates and analyzes the positioning performance, satellite fault detection performance and integrity performance of the system.

[0025] The disclosed NHC-assisted GNSS / INS integration can effectively improve the train positioning performance in satellite-blocked environments; the NHC-assisted GNSS / INS integration can improve the correct detection rate of multiple faulty satellites; in satellite signal-blocked environments, the NHC-assisted GNSS / INS combination can reduce system positioning errors while improving the integrity of train positioning.

[0026] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures indicated in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a schematic diagram of the integrity monitoring architecture of an embodiment of the present disclosure; Figure 2 This is a schematic diagram of the train positioning principle of the embodiment of the present disclosure; Figure 3 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0030] GNSS can not only provide high-precision location information for trains, but also issue an alarm in time when the system fails or the positioning error exceeds the alarm threshold, that is, to achieve GNSS integrity monitoring. GNSS integrity monitoring is divided into system level and user level. Compared with the user level, system level integrity monitoring requires the use of external systems or infrastructure, while user level integrity monitoring does not require these external supports, so it has received widespread attention. Among the user-level integrity monitoring methods, the typical representative is receiver autonomous integrity monitoring (RAIM). RAIM mainly consists of two parts: fault detection (FD) and protection level (PL) calculation. Some studies focus on one of the tasks, while others focus on both tasks at the same time. The purpose of fault detection is to timely identify and detect potential or existing faults in the GNSS system to ensure the normal operation of the system and the accuracy of the data. The protection level mainly quantifies the reliability of positioning information, improves data quality, and protects system safety by comparing with the alarm limit (AL).

[0031] GNSS determines the position information by receiving satellite signals, which has high accuracy but is susceptible to environmental interference. INS, on the other hand, is based on Newton's laws of mechanics and uses sensors such as accelerometers and gyroscopes to infer the position, which can continuously provide navigation information, but there are errors that accumulate over time. EKF is a filtering algorithm suitable for nonlinear systems. Its principle is to perform a first-order Taylor series expansion of the nonlinear function at the filter value, ignore the higher-order terms, obtain a local linearization model, and then apply Kalman filtering for estimation. In GNSS / INS integrated navigation, EKF fuses the observation data of GNSS and INS, and achieves the optimal estimation of the train's motion state through two stages of prediction and update. Through the fusion of EKF, the GNSS / INS integrated navigation system can suppress the error divergence of INS in an environment where GNSS signals are unreliable, and improve the accuracy and stability of the navigation system. This integrated navigation and positioning principle has broad application prospects in train navigation, aviation, navigation and other fields.

[0032] The nonholonomic constraints (NHC) of trains refer to the constraints on the motion state of the train based on certain assumptions during the running of the train to improve the accuracy and reliability of the navigation system. Specifically, NHC assumes that the train does not skid, drift or bounce during the running process, that is, the lateral and vertical velocities of the train are theoretically zero. This assumption allows the construction of virtual observations in the navigation system to constrain the motion of the train. In practical applications, NHC is usually used in combination with GNSS and INS to suppress the error divergence of the navigation system when the GNSS signal is interrupted or interfered. By introducing NHC, the accuracy and stability of the integrated navigation system can be significantly improved, especially in an environment where the GNSS signal is unreliable.

[0033] like Figure 1 As shown, the present disclosure provides a train positioning system, including: a system error state model and an observation state model; The system error state model is used to describe the state of the system changing over time, including: position, speed and attitude; based on the system error state model, the state at the next moment is predicted; The observation state model is used to provide observation information on the system state based on the observation data of GNSS and INS; based on the real-time observation data of the observation state model, the system error state model predicts the state correction state estimate at the next moment to obtain the train positioning.

[0034] The specific implementation is as follows: The integrity monitoring structure of the GNSS / INS / NHC integrated navigation system is as follows: Figure 1 The train positioning principle of GNSS / INS / NHC combination is as follows Figure 2As shown. The system mainly includes six modules: GNSS measurement model, IMU measurement unit, inertial navigation system, extended Kalman filter, non-holonomic constraint model and fault detection and protection level. Among them, the inertial navigation system (INS) uses the original inertial measurement unit (IMU) data (including acceleration and angular velocity) to generate navigation solutions, including position, velocity and attitude (P, V and A). In the GNSS measurement model, the difference between the GNSS pseudorange and pseudorange rate measurements and the pseudorange and pseudorange rate predicted by INS is used as the measurement vector. Subsequently, the non-holonomic constraint (NHC) model uses the difference between the train restricted speed and the INS estimated speed as the observation vector to update the state of the extended Kalman filter. The fault detection and protection level uses the filter innovation vector and the state observation matrix to realize satellite fault detection and system integrity performance evaluation.

[0035] EKF is usually used for data processing of GNSS / INS combination, which includes system error state model and observation state model. The system error state model is used to describe the state of the system that changes over time, such as position, velocity, and attitude, and how these states are affected by various errors. The observation state model provides observation information on the system state based on the observation data of GNSS and INS. These two models are interrelated in EKF. The system model is used to predict the state at the next moment, while the observation model is used to correct the state estimate based on the current observation data. Subsequently, by iterating these two models, high-precision processing of GNSS / INS combined navigation data can be achieved. Its discrete time error state model can be expressed as:

[0036] in, represents the system state vector at time k; and Respectively and The transformation matrix; and Respectively and The process noise matrix; is the system process noise vector. It can be expressed as follows:

[0037] in, Position error vector, velocity error vector, attitude error vector, angular velocity error vector and acceleration error vector; and They are the GNSS clock error and clock drift respectively.

[0038] The observation model of the system is divided into two parts: the global navigation satellite system (GNSS) observation model and the non-holonomic constraint (NHC) observation model. Among them, the GNSS observation model is expressed as:

[0039] in, represents the measurement matrix of GNSS; represents the GNSS observation noise vector; and They are the pseudorange vector predicted by INS and the pseudorange vector observed by GNSS; and are the pseudorange rate vector predicted by INS and the pseudorange rate vector observed by GNSS respectively.

[0040] The NHC observation model is expressed as follows:

[0041] in, represents the observation matrix of NHC; represents the observation noise vector of NHC; and They represent the speed predicted by INS and the actual speed vector of the train in the carrier (b) coordinate system respectively; and They represent the INS predicted speed values ​​corresponding to the lateral and vertical directions of the train respectively. The main function of the NHC observation model is that when the GNSS fails or the satellite signal is blocked, the NHC observation model can still constrain the system state and its covariance matrix to a certain extent. , minimize the system’s position error (PE), and lay the foundation for the subsequent evaluation of the system’s integrity performance (the relationship between HAL (horizontal alarm limit), HPL (horizontal protection limit) and HPE (horizontal position error)).

[0042] The present invention constructs a GNSS / INS / NHC combination model based on the extended Kalman filter EKF, and realizes accurate detection of multiple faulty satellites by calculating the test statistic based on the filter innovation. The NHC model establishes the corresponding state observation matrix, which significantly improves the integrity performance of the system in the absence of satellite signals. In order to evaluate the performance of this method, the test statistics of multiple faulty satellites and the integrity performance of the system when the GNSS signal is interrupted are calculated, and the positioning and integrity performance of the system under the GNSS / INS combination and the GNSS / INS / NHC combination are compared. Experimental results show that this method achieves 100% accuracy in multi-satellite fault detection, and significantly improves the integrity performance of the system compared with the integrity monitoring of the GNSS / INS combination.

[0043] A train positioning satellite fault integrity monitoring method based on the above-mentioned train positioning system; Calculate the test statistic by using the innovation vector in the extended Kalman filter (EKF) algorithm , and use it to detect GNSS satellite failures; by comparing the test statistic with the test threshold, it is determined whether the current satellite is faulty. It is expressed as follows:

[0044] in, represents the i-th value in the GNSS observation update; represents the GNSS observation vector at time k; yes The predicted value of represents the covariance of the i-th value in the GNSS observation update; is the system state covariance matrix; yes The predicted value of yes The covariance matrix of .

[0045] The specific implementation is as follows: Since the accumulated error of the inertial navigation system (INS) can be corrected by the global navigation satellite system (GNSS), and the probability of failure of the inertial measurement unit (IMU) is very small, this paper only analyzes the impact of GNSS error on the train positioning unit. The test statistic ( ), and use it to detect GNSS failures. By comparing the test statistic with the test threshold, it is possible to determine whether the current satellite is faulty. It can be expressed as:

[0046] in, represents the i-th value in the GNSS observation update; represents the GNSS observation vector at time k; yes The predicted value of represents the covariance of the i-th value in the GNSS observation update; is the system state covariance matrix; yes The predicted value of yes The covariance matrix of .

[0047] A protection level-based train positioning integrity monitoring method, based on the above-mentioned train positioning system; Horizontal Protection Level HPL and Global Navigation Satellite System GNSS Measurement Matrix , measurement error And λ, expressed as follows:

[0048] in, The characteristic slope The maximum value in is expressed as follows:

[0049] in, and Respectively represent matrices The first row, i-th column element and the second row, i-th column element of ; Representation Matrix The element in the i-th row and i-th column of ; represents the unit matrix; the value of λ and the missed detection rate Related, expressed as follows:

[0050] in, Indicates the inspection threshold corresponding to each satellite; represents the chi-square distribution density function of the variable x; Represents basic mathematical integral expressions; When the set horizontal alarm limit HAL, horizontal protection limit HPL and set horizontal position error HPE meet the following formula, the system is in a no-alarm state:

[0051] When HPL>HAL, the train positioning unit issues an alarm to the driver within the alarm time threshold TTA, indicating that the current position output is unavailable.

[0052] The specific implementation is as follows: Since trains are more concerned about horizontal positioning performance and integrity performance, this paper only considers the horizontal protection level (HPL) of the system. The horizontal protection level (HPL) is usually related to the global navigation satellite system (GNSS) measurement matrix. , measurement error And λ, the calculation formula is:

[0053] in, The characteristic slope The maximum value in , which can be expressed as follows:

[0054] in, and Respectively represent matrices The first row, i-th column element and the second row, i-th column element of ; Representation Matrix The element in the i-th row and i-th column of ; represents the unit matrix. The value of λ and the missed detection rate The setting is related to , which can be expressed as follows:

[0055] in, Indicates the inspection threshold corresponding to each satellite; represents the chi-square distribution density function of the variable x; Represents basic mathematical integral expression.

[0056] HAL (horizontal alarm limit), HPL (horizontal protection limit) and HPE (horizontal position error) are the state identification parameters of the train positioning unit, which ensure the safety of the train to a certain extent. When the system is in a non-alarm state, the relationship between HPL, HPE and HAL can be expressed as follows:

[0057] When the horizontal protection error (HPE) exceeds the horizontal alarm limit (HAL), the calculated horizontal protection limit (HPL) should also be greater than HAL. At this time, HPL>HAL will occur, and the train positioning unit should issue an alarm to the driver within the alarm time threshold (TTA) to indicate that the current position output is unavailable. Here, TTA represents the shortest time interval required to issue an alarm to the control center when there is an integrity risk to the positioning unit. The rules for setting TTA are similar to those for setting HAL and depend on the demand for location services in specific operating scenarios.

[0058] The fault detection statistics and protection level can be regarded as two independent modules, one for detecting satellite faults and the other for evaluating the position error of the system. The matrices in formula (5) and formula (7) are the same as those in formula (1).

[0059] A train positioning method, comprising: a train positioning system based on the above; Train positioning is performed using system error state model and observation state model; The system error state model is used to describe the state of the system changing over time, including: position, speed and attitude; based on the system error state model, the state at the next moment is predicted; The observation state model is used to provide observation information on the system state based on the observation data of GNSS and INS; based on the real-time observation data of the observation state model, the system error state model predicts the state correction state estimate at the next moment to obtain the train positioning.

[0060] like Figure 3 As shown, the present disclosure provides an electronic device, including a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304; Memory 303, storing computer programs; The processor 301 is used to implement the above-mentioned train positioning satellite failure integrity monitoring method, or the above-mentioned protection level-based train positioning integrity monitoring method, or the above-mentioned train positioning method when executing the computer program stored in the memory 303.

[0061] The present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for monitoring the integrity of a train positioning satellite failure, or the above-mentioned method for monitoring the integrity of a train positioning based on a protection level, or the above-mentioned method for train positioning.

[0062] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0063] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, including but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device.

[0064] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A train positioning system, characterized in that: include: System error state model and observation state model; The system error state model is used to describe the state of the system changing over time, including: position, speed and attitude; based on the system error state model, the state at the next moment is predicted; The observation state model is used to provide observation information on the system state based on the observation data of GNSS and INS; based on the real-time observation data of the observation state model, the system error state model predicts the state correction state estimate at the next moment to obtain the train positioning.

2. A train positioning system according to claim 1, characterized in that: The discrete time error state model of the system error state model is expressed as follows: in, represents the system state vector at time k; and Respectively and The transformation matrix; and Respectively and The process noise matrix; is the system process noise vector; The expression is as follows: in, Represent the position error vector, velocity error vector, attitude error vector, angular velocity error vector and acceleration error vector in turn; and are the GNSS clock error and clock drift respectively.

3. A train positioning system according to claim 1, characterized in that: Observation state model, including: Global Navigation Satellite System GNSS observation model and non-holonomic constrained NHC observation model.

4. A train positioning system according to claim 3, characterized in that: The global navigation satellite system GNSS observation model is expressed as follows: in, represents the measurement matrix of GNSS; represents the GNSS observation noise vector; and are the pseudorange vector predicted by INS and the pseudorange vector observed by GNSS respectively; and are the pseudorange rate vector predicted by INS and the pseudorange rate vector observed by GNSS respectively.

5. A train positioning system according to claim 3, characterized in that: The nonholonomic constrained NHC observation model is expressed as follows: in, represents the observation matrix of NHC; represents the observation noise vector of NHC; and They represent the speed predicted by INS and the actual speed vector of the train in the carrier b coordinate system respectively; and Respectively represent the INS predicted speed values ​​corresponding to the lateral and vertical directions of the train.

6. A method for monitoring the integrity of a train positioning satellite failure, characterized in that: A train positioning system based on any one of claims 1-5; Calculate the test statistic by using the innovation vector in the extended Kalman filter (EKF) algorithm and use it to detect GNSS satellite failures; By comparing the test statistic with the test threshold, it is determined whether the current satellite is faulty. It is expressed as follows: in, represents the i-th value in the GNSS observation update; represents the GNSS observation vector at time k; for The predicted value of represents the covariance of the i-th value in the GNSS observation update; is the system state covariance matrix; for The predicted value of for The covariance matrix of .

7. A train positioning integrity monitoring method based on protection level, characterized in that: A train positioning system based on any one of claims 1-5; Horizontal Protection Level HPL and Global Navigation Satellite System GNSS Measurement Matrix , measurement error And λ, expressed as follows: in, The characteristic slope The maximum value in is expressed as follows: in, and Respectively represent matrices The first row, i-th column element and the second row, i-th column element of ; Representation Matrix The element in the i-th row and i-th column of ; represents the unit matrix; the value of λ and the missed detection rate Related, expressed as follows: in, Indicates the inspection threshold corresponding to each satellite; represents the chi-square distribution density function of the variable x; Represents basic mathematical integral expressions; When the set horizontal alarm limit HAL, horizontal protection limit HPL and set horizontal position error HPE meet the following formula, the system is in a no-alarm state: When HPL > HAL, the train positioning unit issues an alarm to the driver within the alarm time threshold TTA, indicating that the current position output is unavailable.

8. A train positioning method, characterized in that: include: A train positioning system based on any one of claims 1-5; Train positioning is performed using system error state model and observation state model; The system error state model is used to describe the state of the system changing over time, including: position, speed and attitude; based on the system error state model, the state at the next moment is predicted; The observation state model is used to provide observation information on the system state based on the observation data of GNSS and INS; based on the real-time observation data of the observation state model, the system error state model predicts the state correction state estimate at the next moment to obtain the train positioning.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a memory storing a computer program; The processor is used to implement the method for monitoring the integrity of a train positioning satellite failure as described in claim 6, or the method for monitoring the integrity of a train positioning based on a protection level as described in claim 7, or the method for train positioning as described in claim 8 when executing a computer program stored in the memory.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the method for monitoring the integrity of a train positioning satellite failure as described in claim 6, or the method for monitoring the integrity of a train positioning based on a protection level as described in claim 7, or the method for train positioning as described in claim 8.

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