ARAIM Method for GNSS PPP / IMU Fusion Positioning

Abstract

The embodiment of the application discloses a kind of GNSS PPP / IMU fusion positioning ARAIM methods, it is related to the integrity monitoring technical field of fusion navigation positioning, the method comprises: the observation model of precise point positioning and velocity measurement of multi-frequency multi-system global navigation satellite system under complex environment is constructed, state prediction model is established using the measurement information of inertial measurement unit;Observation value set is constructed, hypothesis test quantity is calculated and observation fault is detected, observation value subset is constructed, subset hypothesis test quantity is calculated and observation fault is identified and rejected;Integrity risk is optimally distributed, and the protection level of each direction of moving carrier is determined;If positioning result does not satisfy safety needs, real-time warning is given to user.The motion law of the application is obtained using IMU, and the observation fault of GNSS and IMU fusion is detected and rejected multiple times, effectively avoids the fault false alarm and missed detection of integrity monitoring, and improves the continuity and integrity of positioning result under complex environment.

Description

Technical Field

[0001] This application relates to the field of integrity monitoring technology for fused navigation and positioning, and in particular to an ARAIM method for GNSS PPP / IMU fused positioning, specifically a method for autonomous integrity monitoring of GNSS receivers using GNSS PPP / IMU fused positioning. Background Technology

[0002] A fusion positioning system based on multi-GNSS (Multi-Frequency Global Navigation Satellite System) precise point positioning (PPP) and inertial measurement unit (IMU) technologies can achieve high-precision positioning of various vehicles such as cars and drones in complex scenarios such as urban canyons, viaducts and tunnels.

[0003] However, in complex environments, GNSS signals are often interrupted or interfered with due to interference from tall buildings and multipath effects. Measurements may show non-line-of-sight errors or cycle slips, resulting in abnormal positioning results or outputting "false" high-precision information. The positioning results are not reliable, and the safety performance of users is difficult to guarantee. Therefore, it is necessary to monitor the integrity of the fusion navigation system.

[0004] Integrity refers to the system's ability to promptly alert users when it malfunctions for navigation or when positioning results fail to meet user safety requirements. However, in complex environments, both satellite-based and ground-based augmentation systems cannot detect misleading information caused by local environmental interference, nor do they support high-precision positioning, thus failing to meet the integrity monitoring requirements in complex environments.

[0005] While related technologies can detect, eliminate, and analyze the reliability of positioning results from fusion navigation systems, they do not rigorously monitor the integrity of the system from the perspective of user safety and continuity requirements. The mathematical models used for positioning calculations do not fully consider the positioning risks caused by multipath observation signals and colored noise in complex environments. Furthermore, they do not utilize attitude information to determine the reliable boundaries of the actual driving direction (forward / backward), lateral offset (lane keeping), and vertical height (slopes, bridges) of the moving vehicle. This creates potential collision hazards for operations such as dense vehicle platooning and drone swarms.

[0006] Therefore, there is currently a lack of a method to monitor the integrity of high-precision positioning users at the GNSS receiver end in complex environments. Summary of the Invention

[0007] This application provides an ARAIM method for GNSS PPP / IMU fusion positioning to overcome the shortcomings of the aforementioned related technologies. The technical solution is as follows: In a first aspect, embodiments of this application provide an ARAIM method for GNSS PPP / IMU fusion positioning, applied at the receiver end, the method comprising: A mathematical model of the GNSS PPP / IMU fusion positioning system is constructed by combining the colored noise of the environment in which the receiver is located. The mathematical model includes an observation model and a state prediction model. Based on the mathematical model, state prediction and measurement update are performed using Kalman filtering to obtain the complete set of observations, and the probability of the complete set of observations is calculated. The universal Kalman filter solution and the universal hypothesis test metric are obtained based on the universal set of observations and the universal set of observations. Each time, the observations of different hypothetical faulty satellites are isolated from the full set of observations to construct a subset of observations, and the probability of the subset of observations is calculated. Kalman filter estimation is performed based on subsets of observations and their probabilities to obtain subset Kalman filter solutions for each subset of observations. The observation faults in the whole set of observations are detected by using the global hypothesis test, and the partitioned solution in the ECEF coordinate system is obtained by using the difference between the solutions of the global Kalman filter and the subset Kalman filter. By transferring the partitioned solution to the carrier coordinate system, we obtain the partitioned solution and subset hypothesis test quantile in the carrier coordinate system. Using subset hypothesis testing, observational faults are identified and eliminated to obtain a new set of observations; The protection level of the moving vehicle in each direction is calculated based on the new set of observations. If the protection level in all directions is less than the alarm threshold in the corresponding direction, the positioning result is determined to be in good condition; otherwise, a good condition warning is issued. The directions of the moving carrier include radial, lateral, and vertical.

[0008] In one alternative to the first aspect, calculating the probability of the entire set of observations includes: Obtain the prior fault probability of each satellite's observations, and calculate the probability of the entire set of observations containing no faulty observations based on the prior fault probability, using the formula: ; in, Let H0 be the probability of the entire set of observations, where H0 denotes the fault-free hypothesis, and n is the total number of visible satellites at the receiver in the corresponding epoch. Let be the prior fault probability of the observation value of satellite s, and k represent the epoch.

[0009] In one alternative to the first aspect, obtaining the universal Kalman filter solution and the universal hypothesis test based on the universal set of observations and the universal set of observations probability includes: Based on the mathematical model, Kalman filtering is performed to predict the state, and the predicted state values ​​and state prediction variances of the entire set of observations are obtained. The state prediction values ​​and state prediction variance of the entire set of observations are measured and updated using Kalman filtering to obtain the complete set Kalman filter solution of the entire set of observations. The universal hypothesis test metric for the corresponding epoch is calculated based on the universal Kalman filter solution.

[0010] In one alternative to the first aspect, prior to the Kalman filter estimation based on a subset of observations and the probability of that subset, the method further includes: Adjust the mathematical model of the GNSS PPP / IMU fusion positioning system to construct the mathematical model corresponding to the subset of observations; The Kalman filter estimation based on subsets of observations and their probabilities yields a subset Kalman filter solution for each subset of observations, including: The mathematical model corresponding to the subset of observations is solved by Kalman filtering to obtain the subset Kalman filter solution of the subset of observations; wherein, the subset Kalman filter solution includes the state filter estimate and the state filter estimate variance matrix; The step of transferring the partition solution to the carrier coordinate system to obtain the partition solution and subset hypothesis test quantile in the carrier coordinate system includes: Based on the attitude information of the moving vehicle, determine the attitude rotation matrix from the ECEF coordinate system to the vehicle coordinate system. Then, use the attitude rotation matrix to transfer the partitioned solution of the observation subset to the vehicle coordinate system, and obtain the partitioned solution of the observation subset in the vehicle coordinate system. The corresponding subset hypothesis test quantity is calculated based on the partitioned solution of the observed subset in the carrier coordinate system.

[0011] In one alternative embodiment of the first aspect, the universal set test limit and the subset test limit are calculated based on the following steps: The continuity risk is assigned to the global hypothesis test and the subset hypothesis test respectively, resulting in the continuity risk assigned to the global hypothesis test and the continuity risk assigned to the subset hypothesis test. The global hypothesis test limit is calculated based on the continuity risk and distribution of the global hypothesis test values. The subset test limit for each subset of observations is calculated based on the continuity risk and distribution of the subset hypothesis test values.

[0012] In one alternative to the first aspect, the method of detecting observational faults in the universal set of observations using the universal hypothesis test includes: If the hypothesis test quantity of the whole set is less than the test limit of the whole set, it is determined that there is no observation fault in the whole set of observations, and the whole set of observations without observation faults and the Kalman filter solution of the whole set are output. Otherwise, if it is determined that there is an observation fault in the entire set of observations, the steps of identifying and removing observation faults using the subset hypothesis test metric are performed, including: If at least one subset hypothesis test value is greater than the corresponding subset test limit, obtain the maximum value of all subset hypothesis test values ​​and determine the subset of observations corresponding to the maximum value. Obtain the prediction residuals of the subset of observations corresponding to the maximum value, and establish a hypothesis test based on the chi-square distribution based on the prediction residuals; The method further includes comparing the hypothesis test value with the corresponding test limit. If the hypothesis test value is less than the test limit, the method also includes: The subset of observations that has the maximum value of the subset hypothesis test is updated to a new set of observations, and based on the new set of observations, the process proceeds to the step of calculating the probability of the entire set of observations. If it is confirmed that there are no observation faults in the updated set of observations, then output the set of observations without observation faults and the Kalman filter solution of the set. Otherwise, if it is determined that there are unremovable observation faults in the new set of observations, an integrity monitoring warning will be issued to the user.

[0013] In one alternative to the first aspect, the identification and removal of observational faults using subset hypothesis testing includes: If there is no subset hypothesis test case greater than the corresponding subset test limit, it is determined that there is an unidentifiable and unremovable observation fault in the entire set of observations, and an integrity monitoring warning is issued to the user.

[0014] Secondly, embodiments of this application also provide an ARAIM device for GNSS PPP / IMU fusion positioning, comprising: The observation set processing unit is used to construct a mathematical model of the GNSSPPP / IMU fusion positioning system by combining the colored noise of the environment in which the receiver is located. The mathematical model includes an observation model and a state prediction model. The observation set processing unit is also used to perform state prediction and measurement update based on the mathematical model using Kalman filtering to obtain the observation set and calculate the probability of the observation set. The observation set processing unit is also used to obtain the Kalman filter solution and the hypothesis test quantity of the whole set based on the probability of the whole set of observations; the observation set processing unit is used to isolate the observations of different assumed faulty satellites from the whole set of observations each time, construct the observation set, and calculate the probability of the observation set. The observation subset processing unit is also used to perform Kalman filter estimation based on the observation subset and the observation subset probability to obtain the subset Kalman filter solution corresponding to each observation subset. The observation subset processing unit is also used to obtain the partitioned solution in the ECEF coordinate system by utilizing the difference between the solutions of the full set Kalman filter and the subset Kalman filter. The observation subset processing unit is also used to transfer the partition solution to the carrier coordinate system to obtain the partition solution and subset hypothesis test quantity in the carrier coordinate system. The fault detection and elimination unit is used to detect observation faults using the full set hypothesis test and to identify and eliminate observation faults using the subset hypothesis test, thereby obtaining an updated full set of observations. The protection level calculation unit is used to calculate the protection level of the moving vehicle in each direction based on the updated set of observations. If the protection level in all directions is less than the alarm threshold in the corresponding direction, the positioning result is determined to be in good condition; otherwise, an integrity warning is issued. The directions of the moving carrier include radial, lateral, and vertical.

[0015] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect or any implementation thereof of the embodiments of this application.

[0016] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiments of this application or any implementation thereof.

[0017] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following: The ARAIM method for GNSS PPP / IMU fusion positioning provided in this application embodiment can perform multiple detections and eliminations of observation faults in the entire set of observations by detecting the entire set of observations and the subset of observations with multiple fault assumptions. This can effectively avoid false alarms and missed detections of observation faults in integrity monitoring and improve the continuity and integrity in complex time-varying environments.

[0018] The embodiments of this application can perform integrity monitoring for high-precision positioning without the need to deploy integrity monitoring base stations. The operation is flexible and free, and it provides real-time alarms. It can provide stable and reliable integrity monitoring for carriers such as vehicles, drones and robots, and provide security for the positioning services of integrated navigation systems in complex environments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is one of the flowcharts illustrating an ARAIM method for GNSS PPP / IMU fusion positioning provided in an embodiment of this application; Figure 2 This is the second flowchart illustrating an ARAIM method for GNSS PPP / IMU fusion positioning provided in this application embodiment; Figure 3 This is a schematic diagram of the carrier protection level provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an ARAIM device for GNSS PPP / IMU fusion positioning provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.

[0023] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.

[0024] It should be noted that the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) method for GNSS PPP / IMU fusion positioning provided in this application is applied to a Multi-GNSS PPP / IMU fusion positioning system, specifically to the receiver end of the fusion navigation system. Specifically, this navigation system is a high-precision navigation system combining a receiver for precise point positioning using multiple global navigation satellite systems (specifically Multi-GNSS PPP) with inertial measurement unit (IMU) technology. It utilizes Multi-GNSS measurement information to provide users with high-precision positioning information, and uses high-frequency measurement information from the inertial measurement unit to enhance positioning accuracy and integrity, providing results for reliable high-precision positioning. The following detailed description of this application is provided in conjunction with specific embodiments.

[0025] It should be noted that the embodiments of this application are applied to complex time-varying observation environments. In such environments, GNSS receivers experience signal obstruction and loss, as well as significant multipath effects, leading to substantial signal deviations and the presence of noticeable colored noise. Complex time-varying observation environments can be understood to include scenarios not limited to those described above.

[0026] Next, combine Figure 1 This application introduces an ARAIM method for GNSS PPP / IMU fusion positioning, provided in an embodiment of the present application. This method is applied to the GNSS receiver end of a GNSS PPP / IMU fusion positioning system. For details, please refer to... Figure 1 , Figure 1 This illustration shows a flowchart of an ARAIM method for GNSS PPP / IMU fusion positioning provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S101, Construct a mathematical model of the GNSS PPP / IMU fusion positioning system by combining the colored noise of the environment in which the receiver is located; S102, based on the mathematical model, perform state prediction and measurement update using Kalman filtering to obtain the complete set of observations, and calculate the probability of the complete set of observations; S103, based on the entire set of observations and the probability of the entire set of observations, obtain the Kalman filter solution for the entire set and the hypothesis test quantile for the entire set; S104. Based on the multiple fault assumption, isolate the observations of different satellites with different assumed faults from the full set of observations to construct a subset of observations and calculate the probability of the subset of observations. S105, Kalman filter estimation is performed based on the subset of observations and the probability of the subset of observations to obtain the subset Kalman filter solution corresponding to each subset of observations; S106, using the universal hypothesis test to detect observation faults in the universal set of observations, and using the difference between the universal Kalman filter and the subset Kalman filter solutions to obtain the partitioned solution in the ECEF coordinate system; S107, transfer the partition solution to the carrier coordinate system to obtain the partition solution and subset hypothesis test quantile in the carrier coordinate system; S108, using subset hypothesis testing to identify and eliminate observation faults, to obtain a new set of observations; S109: Calculate the protection level of the moving vehicle in each direction based on the new set of observations. If the protection level in all directions is less than the alarm threshold in the corresponding direction, determine that the positioning result meets the integrity requirement; otherwise, issue an integrity warning.

[0027] In some embodiments, the mathematical model in S101 includes an observation model and a state prediction model, and includes the following steps: Establish The state prediction model for multi-GNSS PPP / IMU in a time-varying environment with complex timing is shown in the following formula: , ; in, for The state vector of an epoch, The state transition matrix is ​​established based on the measurement data of the inertial navigation unit. The system noise-driven array, Let k and k-1 be the system process noise vector, and k and k-1 represent the epoch numbers. This represents the epoch with index k.

[0028] State vector Includes: ; in, This refers to the error state quantity of the position in the ECEF (Earth Centered Earth Fixed) coordinate system. For the velocity error state quantity in the ECEF coordinate system; The angle of inaccuracy; This refers to the zero bias error of the accelerometer; This refers to the zero-bias error of the gyroscope; For GNSS receiver clock bias; For GNSS receiver clock speed; For zenith tropospheric wet delay; For ambiguity parameters; These are pseudorange multipath parameters; superscript symbols T This indicates the matrix transpose.

[0029] The system process noise vector can be determined based on the physical characteristics of the inertial navigation unit. Process noise variance matrix .

[0030] Construct an observation model for the entire set of observations. The observation model is shown in the following formula: ; in, This is the complete set of observations without ionospheric delay. for The design matrix; for The observation noise vector.

[0031] Specifically, in some embodiments, in S102, state prediction and measurement update based on the mathematical model of the GNSS PPP / IMU fusion positioning system using Kalman filtering include: S1021, Determine the complete set of observations The variance of each observation needs to take into account the errors of the spatial signal, the signal propagation path, and the receiver.

[0032] For the dual-frequency observations of satellite s, the variance of its observation noise is: ; in, Let be the variance of the spatial signal error of satellite s; Let be the variance of the signal propagation path error of satellite s; Let be the variance of the receiver end-to-end error corresponding to satellite s.

[0033] In addition, for single-frequency observations, the ionosphere can be corrected using IGS ionospheric grid products. In this case, the residual error variance after correction of the ionospheric products also needs to be considered.

[0034] S1022, Get the current epoch. The complete set of observations is obtained from the observation data of all corresponding receivers. The complete set of observations It can be represented as: ; Specifically, the observation data at the receiver end refers to the observation data of the visible satellites being observed, including: pseudorange observations. Phase observations and Doppler observations .

[0035] Furthermore, in S102, the probability of the entire set of observed values ​​is calculated based on the entire set of observed values, including: S1023, Obtain the prior fault probability for each satellite, and determine the complete set of observations based on the prior fault probability. The probability of the entire set of observations without observational faults. , The calculation method is as follows: ; in, For the calendar The number of visible satellites; 's' represents the satellite number; This represents the prior fault probability of the observation value corresponding to satellite s.

[0036] It should be noted that the phrase "no observation fault" mentioned in the embodiments of this application should be understood as meaning that no significant observation fault can be detected within the detection accuracy range of the equipment and / or the accuracy range set by the user.

[0037] In some embodiments, S103 includes the following steps: S1031, based on the mathematical model of the GNSS PPP / IMU fusion positioning system, Kalman filtering is used for state prediction to obtain the complete set of observations. State prediction value and state prediction variance Apply the formula: ; in, for The complete set of observations corresponding to an epoch The state filter estimate; for The variance matrix.

[0038] S1032, the state prediction value calculated from S1031 using Kalman filtering. and state prediction variance Perform measurement updates to obtain the complete Kalman filter solution, and apply the formula: ; in, For full-set Kalman filter state estimation; for The variance; This is the Kalman gain matrix; For the complete set of observations The predicted residual vector; It is an identity matrix.

[0039] Therefore, S1031-S1032 can obtain the complete set of observations based on the complete set of observations and the probability of the complete set of observations using Kalman filtering. The complete Kalman filter solution, including epochs The corresponding complete set of observations State filtering estimate and state filtering estimation variance matrix .

[0040] Among them, the complete set of observations The universal Kalman filter solution is expressed as: .

[0041] S1033, based on the complete set of observations The epochs are obtained by calculating the Kalman filter solution of the entire set. The corresponding universal hypothesis test metric is calculated using the following formula: ; in, For the calendar The universal hypothesis test metric; For the calendar The universal hypothesis test metric; for The weight matrix.

[0042] In some embodiments, S104 specifically includes the following steps: S1041, based on the multiple fault assumption, isolates the observations of different supposedly faulty satellites from the full set of observations each time to construct a subset of observations.

[0043] For example, let satellite i be a hypothetically faulty satellite, then from the complete set of observations... The observation value of the intermediate isolation satellite i is obtained by applying the formula: , ; in, A subset of observations that does not include observations of satellite i , For the calendar pseudorange observations of inner satellite i For the calendar Phase observations of inner satellite i For the calendar Doppler observations of inner satellite i This represents the total number of multiple fault assumptions, where i represents the satellite number of the assumed faulty satellite and is also used to represent the sequence number of the corresponding fault assumption.

[0044] S1042, Based on prior integrity information, determine the probability of each subset of observations by applying the formula: ; in, A subset of observations The probability, This represents the prior fault probability of assuming the observed value of faulty satellite i is the faulty observed value.

[0045] In some embodiments, before performing the Kalman filter estimation based on the subset of observations and the probability of the subset of observations in S105, S104 further includes the following step: S1043, Based on each subset of observations, adjust the mathematical model of the GNSS PPP / IMU fusion positioning system, and combine it with the measurement data from the inertial navigation unit to obtain the subset of observations. Corresponding state vector , is represented as: ; in, Assuming the pseudorange multipath parameters of faulty satellite i, Let be the phase ambiguity parameter of the assumed faulty satellite i.

[0046] Establish a subset of observed values The corresponding state prediction mathematical model, using the formula: ; in, for The state transition matrix; For the calendar state vector for System noise driving matrix for The process noise vector.

[0047] S1044, constructing a subset of observations The corresponding observation equation, applying the formula: ; in, A subset of observations The corresponding design matrix, A subset of observations The corresponding observation noise vector. From the complete set of observations. Observation noise vector Based on isolating the observation variance of the hypothetically faulty satellite i, we obtain... The variance matrix.

[0048] In some embodiments, S105 includes the following steps: The state prediction mathematical model and observation equation corresponding to the subset of observations are solved using Kalman filtering, including: S1051, using a subset of observations from S1043 The corresponding state prediction mathematical model is used for time prediction using Kalman filtering, applying the formula: ; in, State vector State prediction value, State vector Variance of state prediction; for Epochal Observation Subset State filtering estimate, for The variance; for The variance matrix.

[0049] S1052, using Kalman filtering to process the state vector State prediction value and state prediction variance To perform measurement updates, apply the following formula: ; in, A subset of observations The predicted residual vector, A subset of observations The gain matrix.

[0050] Calculate the subset of observations The subset of Kalman filter solutions includes: Era Corresponding subset of observations State estimate and the state estimation variance matrix ; Among them, the subset of observations The filtered solution is expressed as: .

[0051] In some embodiments, S106 specifically includes the following steps: Compare the complete set of observations separately The complete Kalman filter solution and each subset of observations The subset Kalman filter solution is used to calculate the subset of each observation based on the comparison results. The partitioned solution in the Earth-Centered, Earth-Fixed Coordinate System (ECEF) is expressed as: ; Where e represents the Earth-centered Earth-fixed coordinate system, hereinafter referred to as the ECEF coordinate system or the e system.

[0052] Furthermore, S107 is executed, specifically including: S1071, Determine the attitude rotation matrix from the ECEF coordinate system to the body frame coordinate system based on the carrier attitude information; S1072, the partitioned solution of the observation subset is transferred to the carrier coordinate system by using the attitude rotation matrix, applying the formula: ; The partitioned solution in the carrier coordinate system is obtained as follows: ; in, This is the attitude rotation matrix from the ECEF coordinate system to the body frame coordinate system.

[0053] Furthermore, executing S1073 includes: The state filtering estimation variance matrix of the entire set of observations in the ECEF coordinate system can be transformed to the carrier coordinate system using the attitude rotation matrix. This yields the state filtering estimation variance matrix of the entire Kalman filter solution in the carrier coordinate system, obtained by applying the formula: ; S1074, Separation solution based on the carrier coordinate system To calculate the subset hypothesis test metric, use the following formula: ; Each subset of observations was calculated. The corresponding subset hypothesis test measures specifically include the subset hypothesis test measures of fault hypothesis i in direction d. .

[0054] Where d represents the direction of the moving carrier, and d=1, 2, 3 correspond to the radial direction, the transverse direction, and the vertical direction of the carrier, respectively.

[0055] In some embodiments, such as Figure 2 As shown, S108 includes the following steps: First, assign continuous risk to calculate the test limit, including: S1081, regarding continuous risk The constraints for assigning hypothesis tests to the universal set and subsets are as follows: ; in, To allocate the hypothesis test metric to the entire set Continuing risks Hypothesis test values ​​to be assigned to each subset The risk of continuity.

[0056] S1082, calculate the universal set test limit and the subset test limit, including: Based on the hypothesis test metric of the entire set Continuity risk and total set hypothesis testing quantity Based on the distribution of the set, the universal set test limit is calculated. ; Based on subset hypothesis testing Continuity risk and subset hypothesis testing The distribution of the data is calculated to obtain a subset of each observation. The subset test limit in the corresponding carrier direction d ; Specifically, the calculation process for the test limit uses the following formula: ; ; in, Represents the chi-square distribution ( The quantile calculation function for the chi-square distribution, where nm is the degree of freedom of the chi-square distribution. The quantile approximation function represents the normal distribution.

[0057] Furthermore, observational faults in the entire set of observations are detected by comparing the results of the universal hypothesis test metric and the universal test limit, specifically including: S1083, Comparing hypothesis test measures across the entire set and the full set of test limits To detect observational faults in the entire set of observations.

[0058] Hypothesis testing quantity for the entire set Less than the universal set test limit In the case of accepting the complete set of observations The fault-free assumption, that is, determining the complete set of observations. There is no observation fault; proceed with step S1084.

[0059] S1084 outputs the complete set of observations for which there are no observation faults.

[0060] Hypothesis testing quantity for the entire set Greater than or equal to the global set test limit In cases where faulty observations are determined to exist in the full set of observations, it is necessary to identify and remove faulty observations by comparing the subset hypothesis test result and the subset test limit, and to perform the steps in S1085, including: S1085, Compare the hypothesis test measures for each subset. And the corresponding subset test limits, so as to further utilize the subset hypothesis test metric to identify and eliminate observational faults.

[0061] There exists at least one subset hypothesis test. Greater than the corresponding subset test limit If the condition is met, proceed with step S1086; otherwise, proceed with step S10810.

[0062] S1086: Obtain the maximum value of the hypothesis test value among all subsets, and select the subset of observations with the maximum value of the hypothesis test value as the candidate full set of observations. .

[0063] S1087, Obtain the complete set of alternative observations The predicted residuals are used to establish a chi-square-based system. Hypothesis test result of the distribution ; S1088, Comparing hypothesis test parameters and test limits In hypothesis testing quantity Less than the test limit If the condition is met, proceed with step S1089; otherwise, proceed with step S10810.

[0064] Among them, the test limit It can be obtained based on the steps S1081-S1082.

[0065] S1089, the complete set of candidate observations Updated to a new set of observations.

[0066] This is equivalent to using the complete set of alternative observations. Replacement of the complete set of observations .

[0067] Understandably, if in the hypothesis test metric Less than the test limit Indicates the complete set of candidate observations It is the set of fault-free observations, that is, the subset of observations with the maximum value of the hypothesis test has removed the observations of the supposedly faulty satellite.

[0068] It should be noted that if S1085 determines the subset hypothesis test values ​​corresponding to all subsets of observations in each direction... All are less than or equal to the corresponding subset test limit. Or, in S1088, the hypothesis test metric Greater than the test limit Both scenarios indicate the presence of an observational fault that cannot be eliminated. In this case, the positioning information of the fused navigation system is unavailable and does not meet safety requirements. An integrity warning is issued to the user, and step S10810 is executed: S10810 issues an integrity monitoring warning to the user.

[0069] In some embodiments, after step S1089, the updated set of observations can be used as a basis. Perform steps S102 to S105.

[0070] In some embodiments, S109 specifically includes: S1091, the total integrity risk is allocated to each direction of the moving vehicle to obtain the components of the total integrity risk in each direction. .

[0071] Specifically, it includes the radial, lateral, and vertical components of the total integrity risk, using the following formula: ; in, For the radial component of total integrity risk, For the horizontal component of total integrity risk, The vertical component of the total risk to integrity; It should be noted that the subscript d represents the various directions of the moving carrier, d=1, 2, 3; where d=1 corresponds to the transverse direction of the carrier, which is the same as the direction represented by the subscript lat; d=2 corresponds to the radial direction of the carrier, which is the same as the direction represented by the subscript lon; and d=3 corresponds to the vertical direction of the carrier, which is the same as the direction represented by the subscript vert.

[0072] S1092, combining the components of the total integrity risk in each carrier direction obtained from S1091, calculates the protection level in each direction based on the updated full set filtered solution of observations, the subset separation solution of observations, and the subset verification limit, applying the formula: ; in, The cumulative distribution function represents the standard normal distribution. To be allocated to the carrier Risk to the integrity of the direction. For carrier direction The level of protection, , Calculated based on S107, Calculated based on S108.

[0073] S1093, using binary search The numerical solution yields the lateral protection level, radial protection level, and vertical protection level of the carrier, respectively. The formula is then applied: ; Understandably, d=1, 2, 3, where d=1 corresponds to the horizontal direction of the carrier, which is the same direction as indicated by the subscript lat. The horizontal protection direction for the carrier is d; d=2 corresponds to the radial direction of the carrier, which is the same as the direction indicated by the subscript lon. For radial protection of the carrier, d=3 corresponds to the vertical direction of the carrier, which is the same direction as indicated by the subscript vert. For the vertical protection of the carrier.

[0074] For example, taking a car carrier as an example, Figure 4 Examples of protection levels for the vehicle in various directions are provided. Understandably, the protection level (PL) for each direction calculated by S109 is dynamically obtained based on data from the vehicle's fusion navigation system. The protection level provides the upper limit of the positioning error of the vehicle's positioning result in the current epoch, such as... Figure 3 The circumscribed blue cuboid border shown is the protective horizontal boundary frame of the carrier.

[0075] S1094, obtain the alarm limit (AL) of the carrier in each direction, and compare the protection level of each carrier direction with the alarm limit of the corresponding direction.

[0076] If the protection level is less than the alarm threshold in all carrier directions, execute S1095, including: S1095, confirm that the positioning result of the fusion navigation system at the current epoch k meets the integrity and safety requirements.

[0077] Otherwise, execute S1096, S1096, and issue an integrity monitoring warning.

[0078] It should be noted that the alarm threshold is equivalent to the maximum positioning error threshold allowed by the carrier in the application scenario. If the protection level in any direction is greater than or equal to the alarm threshold in that direction, it indicates that the current positioning result of the carrier is too erroneous, and the actual position of the carrier may exceed the safety boundary determined by the alarm threshold. In other words, the current positioning result does not meet the integrity risk requirements, and an integrity monitoring warning needs to be issued to the user.

[0079] The following are apparatus embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0080] Please see below. Figure 4 This is a schematic diagram of a receiver autonomous integrity monitoring device for a fused navigation system, provided as an exemplary embodiment of this application. This device can be implemented as all or part of a terminal through software, hardware, or a combination of both. The receiver autonomous integrity monitoring device for a fused navigation system in this embodiment includes an observation set processing unit, an observation set processing unit, a fault detection and elimination unit, and a protection level calculation unit, wherein: The observation set processing unit is used to construct a mathematical model of the GNSSPPP / IMU fusion positioning system by combining the colored noise of the environment in which the receiver is located. The mathematical model includes an observation model and a state prediction model. The observation set processing unit is also used to perform state prediction and measurement update based on the mathematical model using Kalman filtering to obtain the observation set and calculate the probability of the observation set. The observation set processing unit is also used to obtain the whole set Kalman filter solution and the whole set hypothesis test quantity based on the whole set of observations and the probability of the whole set of observations. The observation subset processing unit is used to remove observations from different assumed faulty satellites from the full set of observations each time, construct an observation subset, and calculate the probability of the observation subset; The observation subset processing unit is also used to perform Kalman filter estimation based on the observation subset and the observation subset probability to obtain the subset Kalman filter solution corresponding to each observation subset. The observation subset processing unit also uses the difference between the solutions of the full set Kalman filter and the subset Kalman filter to obtain the partitioned solution in the ECEF coordinate system; The observation subset processing unit is also used to transfer the partition solution to the carrier coordinate system to obtain the partition solution and subset hypothesis test quantity in the carrier coordinate system. The fault detection and elimination unit is used to detect observation faults using the full set hypothesis test and to identify and eliminate observation faults using the subset hypothesis test, thereby obtaining an updated full set of observations. The protection level calculation unit is used to calculate the protection level of the moving vehicle in each direction based on the updated set of observations. If the protection level in all directions is less than the alarm threshold in the corresponding direction, the positioning result is determined to be in good condition; otherwise, an integrity warning is issued. The directions of the moving carrier include radial, lateral, and vertical.

[0081] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when executing the receiver autonomous integrity monitoring method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus provided in the above embodiments and the receiver autonomous integrity monitoring method embodiments belong to the same concept, and its implementation process is detailed in the method embodiments, which will not be repeated here.

[0082] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0083] Please see Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0084] like Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502.

[0085] In this embodiment, the processor 501 is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor 501 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 501 can be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0086] Processor 501 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake-up state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state.

[0087] Memory 502 may include one or more computer-readable storage media, which may be non-transitory. Memory 502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in memory 502 is used to store at least one instruction, which is executed by processor 501 to implement the method in the embodiments of this application.

[0088] In some embodiments, the electronic device 500 further includes a peripheral device interface 503 and at least one peripheral device 504. The processor 501, memory 502, and peripheral device interface 503 can be connected via a bus or signal line. Each peripheral device 504 can be connected to the peripheral device interface 503 via a bus, signal line, or circuit board. Specifically, the peripheral device 504 includes: a display screen, a camera, and audio circuitry. The peripheral device interface 503 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 501 and memory 502.

[0089] In some embodiments of this application, the processor 501, memory 502, and peripheral device interface 503 are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor 501, memory 502, and peripheral device interface 503 can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.

[0090] The block diagram of the electronic device shown in the embodiments of this application does not constitute a limitation on the electronic device 500. The electronic device 500 may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0091] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An ARAIM method for GNSS PPP / IMU fusion positioning, characterized in that, Applied to the receiver end, the method includes: A mathematical model of the GNSS PPP / IMU fusion positioning system is constructed by combining the colored noise of the environment in which the receiver is located. The mathematical model includes an observation model and a state prediction model. Based on the mathematical model, state prediction and measurement update are performed using Kalman filtering to obtain the complete set of observations, and the probability of the complete set of observations is calculated. The universal Kalman filter solution and the universal hypothesis test metric are obtained based on the universal set of observations and the universal set of observations. Each time, the observations of different hypothetical faulty satellites are isolated from the full set of observations to construct a subset of observations, and the probability of the subset of observations is calculated. Kalman filter estimation is performed based on subsets of observations and their probabilities to obtain subset Kalman filter solutions for each subset of observations. The observation faults in the whole set of observations are detected by using the global hypothesis test, and the partitioned solution in the ECEF coordinate system is obtained by using the difference between the solutions of the global Kalman filter and the subset Kalman filter. By transferring the partitioned solution to the carrier coordinate system, we obtain the partitioned solution and subset hypothesis test quantile in the carrier coordinate system. Using subset hypothesis testing, observational faults are identified and eliminated to obtain an updated set of observations; The protection level of the moving vehicle in each direction is calculated based on the updated set of observations. If the protection level in all directions is less than the alarm threshold in the corresponding direction, the positioning result is determined to be in good condition; otherwise, a good condition warning is issued. The directions of the moving carrier include radial, lateral, and vertical.

2. The ARAIM method for GNSS PPP / IMU fusion positioning according to claim 1, characterized in that, The calculation of the probability of the entire set of observations includes: Obtain the prior fault probability of the observations for each satellite, and calculate the total probability of the observations based on the prior fault probability using the following formula: ； in, Let H0 be the probability of the entire set of observations, where H0 denotes the fault-free hypothesis, and n is the total number of visible satellites at the receiver in the corresponding epoch. Let be the prior fault probability of the observation value of satellite s, and k represent the epoch.

3. The ARAIM method for GNSS PPP / IMU fusion positioning according to claim 2, characterized in that, The method for obtaining the universal Kalman filter solution and the universal hypothesis test based on the universal set of observations and the universal set of observations includes: Based on the mathematical model, Kalman filtering is performed to predict the state, and the predicted state values ​​and state prediction variances of the entire set of observations are obtained. The state prediction values ​​and state prediction variance of the entire set of observations are measured and updated using Kalman filtering to obtain the complete set Kalman filter solution of the entire set of observations. The universal hypothesis test metric for the corresponding epoch is calculated based on the universal Kalman filter solution.

4. The ARAIM method for GNSS PPP / IMU fusion positioning according to claim 3, characterized in that, Before performing Kalman filtering estimation based on subsets of observations and their probabilities, the method further includes: Adjust the mathematical model of the GNSS PPP / IMU fusion positioning system to construct the mathematical model corresponding to the subset of observations; The Kalman filter estimation based on subsets of observations and their probabilities yields a subset Kalman filter solution for each subset of observations, including: The mathematical model corresponding to the subset of observations is solved by Kalman filtering to obtain the subset Kalman filter solution of the subset of observations; wherein, the subset Kalman filter solution includes the state filter estimate and the state filter estimate variance matrix; The step of transferring the partition solution to the carrier coordinate system to obtain the partition solution and subset hypothesis test quantile in the carrier coordinate system includes: Based on the attitude information of the moving vehicle, determine the attitude rotation matrix from the ECEF coordinate system to the vehicle coordinate system. Then, use the attitude rotation matrix to transfer the partitioned solution of the observation subset to the vehicle coordinate system to obtain the partitioned solution of the observation subset in the vehicle coordinate system. The corresponding subset hypothesis test quantity is calculated based on the partitioned solution of the observed subset in the carrier coordinate system.

5. The ARAIM method for GNSS PPP / IMU fusion positioning according to claim 1, characterized in that, The method of using a universal hypothesis test to detect observational faults in the universal set of observations includes: If the hypothesis test quantity of the entire set is less than the test limit of the entire set, it can be used to determine that there is no observation fault in the entire set of observations and can be used for localization calculation. Otherwise, if it is determined that there is an observation fault in the entire set of observations, the steps of identifying and removing observation faults using the subset hypothesis test metric are performed, including: If at least one subset hypothesis test value is greater than the corresponding subset test limit, obtain the maximum value of all subset hypothesis test values ​​and determine the subset of observations corresponding to the maximum value. Obtain the prediction residuals of the subset of observations corresponding to the maximum value, and establish a hypothesis test based on the chi-square distribution based on the prediction residuals; The method further includes comparing the hypothesis test value with the corresponding test limit. If the hypothesis test value is less than the test limit, the method also includes: The subset of observations that has the maximum value of the subset hypothesis test is updated to a new set of observations, and based on the new set of observations, the process proceeds to the step of calculating the probability of the entire set of observations. If it is confirmed that there are no observation faults in the new set of observations, then the updated set of observations and the Kalman filter solution of the set are output. Otherwise, if it is determined that there are unremovable observation faults in the new set of observations, an integrity monitoring warning will be issued to the user; The universal set test limit and the subset test limit are calculated based on the following steps: The continuity risk is assigned to the global hypothesis test and the subset hypothesis test respectively, resulting in the continuity risk assigned to the global hypothesis test and the continuity risk assigned to the subset hypothesis test. The global hypothesis test limit is calculated based on the continuity risk and distribution of the global hypothesis test values. The subset test limit for each subset of observations is calculated based on the continuity risk and distribution of the subset hypothesis test values.

6. The ARAIM method for GNSS PPP / IMU fusion positioning according to claim 5, characterized in that, The method of identifying and eliminating observational faults using subset hypothesis testing includes: If there is no subset hypothesis test case greater than the corresponding subset test limit, it is determined that there is an unidentifiable and unremovable observation fault in the entire set of observations, and an integrity monitoring warning is issued to the user.

7. An apparatus for an ARAIM method based on GNSS PPP / IMU fusion positioning as described in any one of claims 1-6, characterized in that, The device includes: The observation set processing unit is used to construct a mathematical model of the GNSS PPP / IMU fusion positioning system by combining the colored noise of the environment in which the receiver is located. The mathematical model includes an observation model and a state prediction model. The observation set processing unit is also used to perform state prediction and measurement update based on the mathematical model using Kalman filtering to obtain the observation set and calculate the probability of the observation set. The observation set processing unit is also used to obtain the whole set Kalman filter solution and the whole set hypothesis test quantity based on the whole set of observations and the probability of the whole set of observations. The observation subset processing unit is used to isolate the observations of different hypothetical faulty satellites from the full set of observations each time, construct the observation subset, and calculate the probability of the observation subset; The observation subset processing unit is also used to perform Kalman filter estimation based on the observation subset and the observation subset probability to obtain the subset Kalman filter solution corresponding to each observation subset. The observation subset processing unit is also used to obtain the partitioned solution in the ECEF coordinate system by utilizing the difference between the solutions of the full set Kalman filter and the subset Kalman filter. The observation subset processing unit is also used to transfer the partitioned solution to the carrier coordinate system to obtain the partitioned solution and subset hypothesis test quantity in the carrier coordinate system. The fault detection and elimination unit is used to detect observation faults using the full set hypothesis test and to identify and eliminate observation faults using the subset hypothesis test, thereby obtaining an updated full set of observations. The protection level calculation unit is used to calculate the protection level of the moving vehicle in each direction based on the updated set of observations. If the protection level in all directions is less than the alarm threshold in the corresponding direction, the positioning result is determined to be in good condition; otherwise, an integrity warning is issued. The directions of the moving carrier include radial, lateral, and vertical.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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