Satellite positioning result verification method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of satellite positioning, and discloses a satellite positioning result verification method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a fault test statistic and a positioning result based on an observation equation and an obtained linearized observed quantity under the condition that an observation error in the observation equation obeys normal distribution; when the fault test statistic is lower than a detection threshold value, acquiring a first probability that a dangerous misleading value of each fault mode in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector under the condition that the multiple fault modes occur; on the basis of the first probability that the danger misleading value of each fault mode is the target value, the second probability that the multiple fault modes occur and the integrity risk budget, the integrity risk is obtained through calculation; and the positioning result is verified based on the integrity risk, so that the navigation and positioning reliability of the satellite navigation system under multiple frequencies and multiple satellites can be improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a satellite positioning result verification method, device, electronic equipment and storage medium. Background Art

[0002] With the development and improvement of the Global Navigation Satellite System (GNSS), GNSS has been widely used around the world. However, due to the fragility of the GNSS system itself, the integrity problem of its service performance has become increasingly prominent.

[0003] Traditional receiver autonomous integrity monitoring algorithms are mainly single-point positioning algorithms implemented in single-frequency and single-constellation systems. This single-point positioning algorithm assumes single-satellite failure observations and a limited number of satellites. However, with the successive construction and use of multiple satellite navigation systems in the world, multi-frequency and multi-constellation signals are constantly enriched, and the probability of a single satellite failure is low. Therefore, under the assumption that a single-satellite failure is an independent event, the probability of multiple satellites failing at the same time is easily overlooked, reducing the reliability of navigation and positioning under multi-frequency and multi-satellite conditions. Summary of the invention

[0004] The present application proposes a satellite positioning result verification method, device, electronic device and storage medium to improve the reliability of navigation positioning under multi-frequency and multi-satellite conditions.

[0005] In a first aspect, a satellite positioning result verification method is provided, comprising:

[0006] In the case where the observation error in the observation equation obeys a normal distribution, determining a fault test statistic and a positioning result based on the observation equation and the obtained linearized observation quantity;

[0007] When the fault test statistic is lower than the detection threshold, obtaining a first probability that the dangerous misleading value of each of the fault modes in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector when multiple fault modes occur;

[0008] Based on the occurrence of multiple failure modes, the first probability of the dangerous misleading value of each failure mode being the target value, the second probability of the occurrence of multiple failure modes and the integrity risk budget are calculated to obtain the integrity risk;

[0009] The positioning result is verified based on the integrity risk.

[0010] In a second aspect, a satellite positioning result verification device is provided, comprising:

[0011] A first determination module is used to determine a fault test statistic, a fault deviation vector and a positioning result based on the observation equation and the obtained linearized observation quantity when the observation error in the observation equation obeys a normal distribution;

[0012] an acquisition module, configured to acquire, when the fault test statistic is lower than a detection threshold, a first probability that a dangerous misleading value of each of the fault modes in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector when multiple fault modes occur;

[0013] A calculation module, configured to calculate the integrity risk based on the first probability that the dangerous misleading value of each failure mode is a target value, the second probability of the occurrence of the multiple failure modes, and the integrity risk budget when multiple failure modes occur;

[0014] A verification module is used to verify the positioning result based on the integrity risk.

[0015] Optionally, in some embodiments of the present application, the acquisition module includes:

[0016] an identification submodule, used to identify the number of fault elements from the fault deviation vector;

[0017] The first determination submodule is used to determine the second probability of occurrence of multiple failure modes based on the satellite failure probability, the number of failure elements and the total number of failure modes.

[0018] Optionally, in some embodiments of the present application, a second determining module is further included, and the second determining module is specifically configured to:

[0019] Determine the upper limit of the number of faulty elements based on the integrity risk budget;

[0020] If the number of faulty elements is greater than the upper limit of the number of faulty elements, the total number of fault modes is determined based on the upper limit of the number of faulty elements.

[0021] Optionally, in some embodiments of the present application, the acquisition module includes:

[0022] An acquisition submodule, used for acquiring a target direction of the fault deviation vector;

[0023] The search submodule is used to perform a modulo search on the fault deviation vector along the target direction when each fault mode occurs, so as to obtain a first probability that the dangerous misleading value of each fault mode in the fault deviation vector is a target value.

[0024] Optionally, in some embodiments of the present application, the acquisition submodule includes:

[0025] a processing unit, configured to perform whitening processing on a slope definition equation to obtain a fault element selection matrix, a parameter matrix to be decomposed, and a target vector, wherein the slope definition equation is a ratio of a mean square of a positioning error to a non-central parameter of a test statistic, and the target vector is a vector of the fault deviation vector composed of fault elements under each of the fault modes;

[0026] A decomposition unit, used for performing orthogonal decomposition on the parameter matrix to be decomposed to obtain a diagonal matrix and an orthogonal matrix;

[0027] An updating unit, configured to update the slope definition equation based on the diagonal matrix, the target vector and the orthogonal matrix;

[0028] A first determining unit, configured to determine a fault direction of the target vector based on the updated slope definition equation;

[0029] A second determining unit is configured to determine the target direction based on the fault element selection matrix, the diagonal matrix, the orthogonal matrix and the fault direction.

[0030] Optionally, in some embodiments of the present application, the first determining module includes:

[0031] An estimation submodule, used for performing position estimation on the linearized observation quantity based on the least square method to obtain a positioning result and a least square projection matrix;

[0032] A generating submodule, used for generating a pseudorange residual matrix according to the unit matrix, the least squares projection matrix, the design matrix in the observation equation and the linearized observation quantity;

[0033] The second determination submodule is used to determine the fault test statistic based on the pseudorange residual matrix and the variance covariance matrix.

[0034] Optionally, in some embodiments of the present application, a third determining submodule is further included, and the third determining submodule is used to:

[0035] Determining the number of observations and the number of parameters to be estimated based on the design matrix of the observation equation;

[0036] The detection threshold is determined based on the number of observations, the number of parameters to be estimated, and the continuity risk level.

[0037] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned satellite positioning result verification method when executing the computer program.

[0038] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned satellite positioning result verification method are implemented.

[0039] The present application provides a satellite positioning result verification method, device, electronic device and storage medium. The method comprises the following steps: determining a fault test statistic and a positioning result based on an observation equation and an acquired linearized observation quantity when an observation error in the observation equation obeys a normal distribution; obtaining, when the fault test statistic is lower than a detection threshold, a first probability that a dangerous misleading value of each of the fault modes in a fault deviation vector is a target value and a second probability that multiple fault modes occur in the fault deviation vector when multiple fault modes occur; calculating an integrity risk based on a first probability that a dangerous misleading value of each of the fault modes is a target value when multiple fault modes occur, a second probability that multiple fault modes occur and an integrity risk budget; and verifying the positioning result based on the integrity risk. In the satellite positioning result verification scheme provided in the present application, under the premise that the observation error obeys the normal distribution, the fault test statistic and the positioning result are determined by the observation equation and the obtained linearized observation quantity. When the fault test statistic is lower than the detection threshold, based on the occurrence of multiple fault modes, the first probability of the dangerous misleading value of each fault mode in the fault deviation vector being the target value and the second probability of the occurrence of multiple fault modes and the integrity risk budget are used to determine the integrity risk. The probability distribution of the fault deviation vector under multiple fault modes is quantified to determine the probability distribution of the positioning error under multiple fault modes, thereby determining the integrity risk of the positioning result under multiple fault modes, and then verifying the positioning result based on the integrity risk, which is beneficial to improving the reliability of the satellite navigation system in navigation and positioning under multi-frequency and multi-satellite conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention 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 only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flowchart of a satellite positioning result verification method provided in an embodiment of the present application;

[0042] Figure 2 A flowchart of a satellite positioning result verification method provided by another embodiment of the present application;

[0043] Figure 3A structural block diagram of a satellite positioning result verification device provided in an embodiment of the present application;

[0044] Figure 4 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the present application.

[0047] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0048] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0049] The satellite positioning result verification method provided in an embodiment of the present invention can be applied to a GNSS receiver, and the satellite positioning result verification method is embedded in the GNSS receiver, wherein the GNSS receiver can determine the fault test statistic and the positioning result based on the observation equation and the obtained linearized observation quantity when the observation error in the observation equation obeys the normal distribution; when the fault test statistic is lower than the detection threshold, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes in the fault deviation vector are obtained when multiple fault modes occur; the integrity risk is calculated based on the first probability that the dangerous misleading value of each fault mode in the case of the occurrence of multiple fault modes, the second probability of the occurrence of multiple fault modes and the integrity risk budget; the positioning result is verified based on the integrity risk. In the present application, under the premise that the observation error obeys the normal distribution, the fault test statistic and the positioning result are determined by the observation equation and the obtained linearized observation quantity. When the fault test statistic is lower than the detection threshold, based on the occurrence of multiple fault modes, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes and the integrity risk budget are used to determine the integrity risk. The probability distribution of the fault deviation vector under multiple fault modes is quantified to determine the probability distribution of the positioning error under multiple fault modes, thereby determining the integrity risk of the positioning result under multiple fault modes, and then verifying the positioning result based on the integrity risk, which is beneficial to improving the reliability of the satellite navigation system in navigation and positioning under multi-frequency and multi-satellite conditions.

[0050] See also Figure 1 As shown, Figure 1 The present invention provides a flow chart of a satellite positioning result verification method, which can be applied to a server. This embodiment is illustrated by applying it to a server. The satellite positioning result verification method includes the following steps:

[0051] 101: When the observation error in the observation equation obeys a normal distribution, determine a fault test statistic and a positioning result based on the observation equation and the obtained linearized observation quantity.

[0052] Among them, the fault test statistic is a numerical indicator used to evaluate the presence of anomalies or faults in the linearized observations.

[0053] In the global navigation satellite system, after determining the observation equation, the least squares residual (i.e., pseudorange residual) can be calculated to construct the fault test statistic. The least squares method can be used to estimate the position and obtain the positioning result.

[0054] For example, in the navigation and positioning application scenario, users use the global navigation satellite system for navigation and positioning. They can obtain the linearized observations from multiple satellites and estimate the current positioning results based on the pre-built observation equation. The pseudo-range observation equation of the global navigation satellite system is:

[0055] L=AX+ε

[0056] Where L is the n×1 dimensional linearized observation; A is the n×m dimensional design matrix; ε is the observation error, which is assumed to obey the normal distribution ε~N(b,Σ), Σ is the variance covariance matrix, which is an n-dimensional diagonal matrix; b is the fault bias vector. When there is a fault in the satellite, the corresponding fault element in the fault bias vector b is non-zero; otherwise, the fault bias vector b is a zero vector.

[0057] In one embodiment, the determining of the fault test statistic and the positioning result based on the observation equation and the linearized observation quantity includes:

[0058] Performing position estimation on the linearized observation quantity based on the least square method to obtain a positioning result and a least square projection matrix;

[0059] Generate a pseudorange residual matrix according to the unit matrix, the least squares projection matrix, the design matrix in the observation equation and the linearized observation quantity;

[0060] A fault test statistic is determined based on the pseudorange residual matrix and the variance covariance matrix.

[0061] The position estimation using weighted least squares method is:

[0062] X=SL

[0063] Where X is the positioning result, S is the least squares projection matrix, and P is the weight matrix. T PA) -1 A T P, P = Σ -1 , the superscript T is the symbol for matrix transposition;

[0064] The fault test statistic is further constructed as the weighted sum of squares of the residuals:

[0065] D=V T PV

[0066] Where D is the fault test statistic, V is the residual, superscript T is the matrix transposition symbol, and P is the weight matrix. Where V = (I-AS)L, I is the unit matrix.

[0067] In one embodiment, it further includes:

[0068] Determining the number of observations and the number of parameters to be estimated based on the design matrix of the observation equation;

[0069] The detection threshold is determined based on the number of observations, the number of parameters to be estimated, and the continuity risk level.

[0070] When the observation error follows a normal distribution, the failure test statistic follows a non-central chi-square distribution:

[0071] D~χ 2 (nm,λ)

[0072] Where n is the number of observations (i.e., the number of satellites), m is the number of parameters to be estimated (i.e., the number of satellites to be evaluated for integrity risk among n satellites), nm is the degree of freedom, λ is the non-centrality parameter, λ = b T Pb.

[0073] In the absence of faults, the fault test statistic follows a centralized chi-square distribution, i.e., D~χ 2 (nm,0). If the continuous risk level P is given fa , the detection threshold can be calculated as:

[0074] T=χ -2 [nm,1-P fa ]

[0075] In the formula, χ -2 [] is the cumulative distribution function of the inverse card distribution, and T is the test threshold.

[0076] 102: When the fault test statistic is lower than the detection threshold, obtaining a first probability that the dangerous misleading value of each fault mode in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector when multiple fault modes occur.

[0077] The first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value may represent a maximum value determined by searching the modulus of the fault element along the worst direction in the fault deviation vector.

[0078] The second probabilities of occurrence of multiple failure modes in the failure deviation vector may represent probabilities of failure modes involving multiple satellite failures.

[0079] When the fault test statistic exceeds the detection threshold, an alarm is issued to the relevant staff, indicating that the observed quantity contains a fault. Otherwise, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes in the fault deviation vector are obtained through the input of the relevant staff, and the integrity risk of the positioning result is evaluated.

[0080] 103: Based on the occurrence of multiple failure modes, the first probability that the dangerous misleading value of each failure mode is a target value, the second probability of the occurrence of multiple failure modes, and the integrity risk budget are calculated to obtain the integrity risk.

[0081] When the observation geometry is poor, the projection of the fault deviation in the residual domain will be much smaller than its projection in the positioning domain, resulting in the test statistic not exceeding the limit alarm, but causing the user positioning result (i.e. the positioning result obtained by the position estimation) to have a large deviation. The observation geometry is the relative spatial distribution and geometric relationship between the satellite and the receiver during observation. In this regard, integrity requires the risk quantification of such HMI events as a reliability indicator of position estimation. Since the fault test statistics of the least squares positioning error and the pseudorange residual are independent of each other, the integrity risk can be expressed as:

[0082]

[0083] Where P(HMI) is the integrity risk, P(HMI|H i ) is the first probability that the dangerous misleading value of the i-th failure mode is the target value, P(H i ) is the second probability of the occurrence of the i-th failure mode, P ob is the integrity risk budget, h max is the total number of failure modes, which may be a set constant.

[0084] 104: Verify the positioning result based on the integrity risk.

[0085] When the calculated integrity risk does not exceed the specified required value, the positioning result is considered safe and reliable and can be used by the user. Otherwise, the user will be warned that the current positioning result is unavailable.

[0086] In one embodiment, obtaining second probabilities of occurrence of multiple fault modes in the fault deviation vector includes:

[0087] identifying the number of faulty elements from the fault deviation vector;

[0088] A second probability of occurrence of the plurality of failure modes is determined based on the satellite failure probability, the number of the failure elements and the total number of failure modes.

[0089] When the observation geometry is poor, the projection of the fault deviation in the residual domain will be much smaller than its projection in the positioning domain, resulting in the test statistic not exceeding the limit alarm, but causing a large deviation in the user's positioning result. In this regard, integrity requires the risk quantification of such dangerous misleading information (HMI) events as a reliability indicator of position estimation. Since the least squares positioning error and the residual test statistic are independent of each other, the integrity risk can be expressed as:

[0090]

[0091] In the formula, |·| means taking the absolute value; is the parameter estimation error; α is the positioning error selection vector in a certain direction, such as α=[10…0] 1×m Indicates the selection of the first position parameter; l is the specified error alarm limit. Under the assumption that the observation error obeys the normal distribution, the positioning error also obeys the normal distribution:

[0092]

[0093] Where αSb is the mean positioning error, α(A T PA) -1 α T is the positioning error variance. and It can be seen that the probability distribution of the positioning error and the fault test statistic are both related to the fault deviation vector b.

[0094] When there is no fault (b = 0), the integrity risk is caused only by the nominal observation error, and there is a positioning error D~χ 2 (nm,0), the integrity risk can be directly obtained by taking the definite integral of the probability density function;

[0095] When there is a fault (b≠0), the mean of the positioning error and the non-centralized parameters of the test statistic cannot be determined, resulting in the inability to directly calculate the integrity risk. In this regard, considering that the number of failure modes (i.e., combinations of failure elements, combinations of failed satellites) is limited, this application indirectly calculates the integrity risk by traversing various possible failure modes. Based on the total probability formula, It can be further expressed as:

[0096]

[0097] Where h is the total number of failure modes; H i is the ith failure mode, in particular, H0 represents the no-failure mode. Assuming that the observations of different satellites are independent of each other, the probability of satellite failure is P fault , then the probability of a failure mode with r failures P(H i ) can be expressed as:

[0098]

[0099] In one embodiment, before determining the second probability of occurrence of multiple failure modes based on the satellite failure probability, the number of failure elements and the total number of failure modes, the method includes:

[0100] Determine the upper limit of the number of faulty elements based on the integrity risk budget;

[0101] If the number of faulty elements is greater than the upper limit of the number of faulty elements, the total number of fault modes is determined based on the upper limit of the number of faulty elements.

[0102] Since the total number of failure modes increases exponentially with the number of faulty elements, evaluating integrity risk by traversing all failure modes will result in a large amount of calculation and is not real-time. In this regard, this application draws on the ARAIM (time series prediction model) algorithm idea and only evaluates failures that do not exceed r max The integrity risk caused by the faulty elements can greatly reduce the amount of calculation. max The risk caused by the above failures is not evaluated, but the integrity risk budget P is allocated in advance. ob To envelop, that is, satisfy the following formula:

[0103]

[0104] Where r max The maximum integer that satisfies the above formula can be obtained. The total number of fault modes that need to be traversed is

[0105]

[0106] In one embodiment, obtaining, when multiple failure modes occur, a first probability that the dangerous misleading value of each failure mode in the failure deviation vector is a target value includes:

[0107] Obtaining a target direction of the fault deviation vector;

[0108] In the case where each fault mode occurs, a modulo search is performed on the fault deviation vector along the target direction to obtain a first probability that the dangerous misleading value of each fault mode in the fault deviation vector is a target value.

[0109] After the worst direction (i.e., target direction) of the fault deviation vector is determined by input from relevant technical personnel, that is, the target direction of the i-th fault element in the fault deviation vector, obtaining the worst integrity risk becomes a problem of finding the extreme value of a univariate function, that is, searching for the modulus || b along the worst direction i || to determine the maximum value of P(HMI|Hi):

[0110]

[0111] In the formula, g(x,u,σ 2 ) has a mean of u and a variance of σ 2is the probability density function of the normal distribution, and f(x,t,λ) is the probability density function of the noncentral chi-square distribution with t degrees of freedom and noncentral parameter λ.

[0112] In one embodiment, obtaining the target direction of the fault deviation vector includes:

[0113] Whitening the slope definition equation to obtain a fault element selection matrix, a parameter matrix to be decomposed, and a target vector, wherein the slope definition equation is the ratio of the mean square of the positioning error to the non-central parameter of the test statistic, and the target vector is a vector of the fault deviation vector composed of fault elements under each fault mode;

[0114] Performing orthogonal decomposition on the parameter matrix to be decomposed to obtain a diagonal matrix and an orthogonal matrix;

[0115] updating the slope definition equation based on the diagonal matrix, the target vector, and the orthogonal matrix;

[0116] Determining the fault direction of the target vector based on the updated slope definition equation;

[0117] The target direction is determined based on the fault element selection matrix, the diagonal matrix, the orthogonal matrix and the fault direction.

[0118] In the i-th fault mode, the location of the fault element is determined, but the fault vector b i The direction and magnitude of the fault vector are still unknown. To this end, this paper introduces the worst case concept commonly used in integrity monitoring. Then search for the worst case modulus || b i || to conservatively determine P(HMI|H i ). When the direction of the fault vector maximizes the integrity risk, it also maximizes the slope, which is defined as the ratio of the mean square of the location error to the non-central parameter of the test statistic:

[0119]

[0120] The above formula can be whitened: is the column vector consisting of only faulty elements in this mode, E i Select the matrix for the fault element of this mode. If the mode is that the first two observations have faults, then When b≠0, the denominator is positive, there is an orthogonal decomposition Where Λ is a diagonal matrix whose diagonal elements are eigenvalues, and C is an orthogonal matrix composed of eigenvectors. Slope iIt can be further expressed as:

[0121]

[0122] In the formula for The expression of slope i The maximum value of the matrix G i The maximum eigenvalue of , the direction of its eigenvector is The worst direction Then we can get b i The worst direction is:

[0123]

[0124] In one embodiment, if Figure 2 As shown, another specific embodiment of the satellite positioning result verification process is provided, which is as follows:

[0125] Positioning link:

[0126] Input the pseudorange observation values ​​(i.e. linearized observation values), variance-covariance matrix and satellite broadcast ephemeris of each GNSS satellite.

[0127] Perform least squares positioning to obtain the positioning result. Refer to the above formula X=SL.

[0128] Fault detection link:

[0129] The pseudo-range residual is further obtained to construct the chi-square test statistic (i.e., the fault test statistic). According to the continuity risk level p of the external input fa , the number of observed quantities and the number of parameters to be estimated determine the detection threshold. 2 (nm,λ) and T = χ -2 [nm,1-P fa ].

[0130] If the fault test statistic exceeds the detection threshold, a fault alarm is issued and the positioning result of the epoch is unavailable. Otherwise, the integrity risk assessment phase is entered. The epoch can represent a specific reference time point, such as GPS time, UTC time or other time standards.

[0131] Integrity risk assessment phase:

[0132] Determine multiple fault modes. Based on the externally input satellite failure prior probability and the unevaluated integrity risk threshold, determine the number of fault elements r that require integrity risk assessment max , and then determine the multiple failure modes h that need to be evaluated max . Refer to the above formula and

[0133] For the i-th fault mode, determine the worst direction under the worst conditions, i.e. the fault direction. See the above formula

[0134] For the ith fault mode, according to the external input positioning error limit, search for the mode that can maximize the integrity risk along the worst fault direction to determine the integrity risk of the ith mode. i ) max .

[0135] Loop through all the failure modes that need to be evaluated, accumulate the evaluated integrity risks, add the unevaluated integrity risks, and determine the final integrity risk output value. Refer to the above formula

[0136] The integrity risk output value is compared with the integrity risk requirement value input externally. If the risk exceeds the limit, the current positioning result is considered unreliable and unusable, otherwise the positioning result can be accepted.

[0137] By constructing a chi-square test statistic based on the least squares pseudorange residual for fault detection, and further based on the test statistic and the probability distribution of the positioning error, the calculation of the integrity risk is realized by searching for the worst-case fault vector under multiple fault modes, so as to achieve the purpose of quantifying the reliability of user positioning, and thus the integrity monitoring of multi-satellite fault conditions can be realized, and safe and reliable positioning results can be provided to users. Integrity refers to the measure of the confidence of the navigation information of the GNSS system, which is manifested as the ability of the system to issue a warning to the user in a timely manner when the navigation information is unreliable, reflecting the reliability of the navigation system. For users in the field of life safety (such as autonomous driving, drones, etc.), integrity becomes a key indicator and needs to be implemented through integrity monitoring. Receiver Autonomous Integrity Monitoring (RAIM) is the last link in the integrity monitoring system and is applied to navigation and positioning fields with high safety requirements such as aviation. The RAIM algorithm (i.e., the satellite positioning result verification method of this application) is embedded in the GNSS receiver and uses redundant observations to monitor the user positioning results. It mainly includes two parts: fault detection and integrity risk assessment. These two parts are interrelated and jointly guarantee the safety and reliability of the user's positioning results. Therefore, the satellite positioning result verification method of the present application can be applied to navigation and positioning application scenarios involving life safety fields such as autonomous driving.

[0138] The above is the satellite positioning result verification process of this application.

[0139] As described above, the present application provides a satellite positioning result verification method, device, electronic device and storage medium, by determining the fault test statistic and the positioning result based on the observation equation and the obtained linearized observation quantity when the observation error in the observation equation obeys the normal distribution; when the fault test statistic is lower than the detection threshold, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes in the fault deviation vector are obtained when multiple fault modes occur; the integrity risk is calculated based on the first probability that the dangerous misleading value of each fault mode is the target value when multiple fault modes occur, the second probability of the occurrence of multiple fault modes and the integrity risk budget; the positioning result is verified based on the integrity risk. In the satellite positioning result verification scheme provided in the present application, under the premise that the observation error obeys the normal distribution, the fault test statistic and the positioning result are determined by the observation equation and the obtained linearized observation quantity. When the fault test statistic is lower than the detection threshold, based on the occurrence of multiple fault modes, the first probability of the dangerous misleading value of each fault mode in the fault deviation vector being the target value and the second probability of the occurrence of multiple fault modes and the integrity risk budget are used to determine the integrity risk. The probability distribution of the fault deviation vector under multiple fault modes is quantified to determine the probability distribution of the positioning error under multiple fault modes, thereby determining the integrity risk of the positioning result under multiple fault modes, and then verifying the positioning result based on the integrity risk, which is beneficial to improving the reliability of the satellite navigation system in navigation and positioning under multi-frequency and multi-satellite conditions.

[0140] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0141] In one embodiment, a satellite positioning result verification device is provided, and the satellite positioning result verification device corresponds to the satellite positioning result verification method in the above embodiment. Figure 3 As shown, the satellite positioning result verification device includes a first determination module 201, an acquisition module 202, a calculation module 203 and a verification module 204, and each functional module is described in detail as follows:

[0142] A first determination module 201 is used to determine a fault test statistic, a fault deviation vector and a positioning result based on the observation equation and the obtained linearized observation quantity when the observation error in the observation equation obeys a normal distribution;

[0143] An acquisition module 202 is configured to acquire, when the fault test statistic is lower than a detection threshold, a first probability that a dangerous misleading value of each of the fault modes in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector when multiple fault modes occur;

[0144] A calculation module 203 is used to calculate the integrity risk based on the first probability that the dangerous misleading value of each failure mode is a target value, the second probability of the occurrence of the multiple failure modes, and the integrity risk budget when multiple failure modes occur;

[0145] The verification module 204 is configured to verify the positioning result based on the integrity risk.

[0146] In this embodiment, the fault test statistic and the positioning result are determined based on the observation equation and the obtained linearized observation quantity when the observation error in the observation equation obeys the normal distribution; when the fault test statistic is lower than the detection threshold, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes in the fault deviation vector are obtained when multiple fault modes occur; the integrity risk is calculated based on the first probability that the dangerous misleading value of each fault mode is the target value when multiple fault modes occur, the second probability of the occurrence of multiple fault modes and the integrity risk budget; the positioning result is verified based on the integrity risk. In the satellite positioning result verification scheme provided in the present application, under the premise that the observation error obeys the normal distribution, the fault test statistic and the positioning result are determined by the observation equation and the obtained linearized observation quantity. When the fault test statistic is lower than the detection threshold, based on the occurrence of multiple fault modes, the first probability of the dangerous misleading value of each fault mode in the fault deviation vector being the target value and the second probability of the occurrence of multiple fault modes and the integrity risk budget are used to determine the integrity risk. The probability distribution of the fault deviation vector under multiple fault modes is quantified to determine the probability distribution of the positioning error under multiple fault modes, thereby determining the integrity risk of the positioning result under multiple fault modes, and then verifying the positioning result based on the integrity risk, which is beneficial to improving the reliability of the satellite navigation system in navigation and positioning under multi-frequency and multi-satellite conditions.

[0147] Optionally, in some embodiments of the present application, the acquisition module includes:

[0148] an identification submodule, used to identify the number of fault elements from the fault deviation vector;

[0149] The first determination submodule is used to determine the second probability of occurrence of multiple failure modes based on the satellite failure probability, the number of failure elements and the total number of failure modes.

[0150] Optionally, in some embodiments of the present application, a second determining module is further included, and the second determining module is specifically configured to:

[0151] Determine the upper limit of the number of faulty elements based on the integrity risk budget;

[0152] If the number of faulty elements is greater than the upper limit of the number of faulty elements, the total number of fault modes is determined based on the upper limit of the number of faulty elements.

[0153] Optionally, in some embodiments of the present application, the acquisition module includes:

[0154] An acquisition submodule, used to acquire a target direction of the fault deviation vector;

[0155] The search submodule is used to perform a modulo search on the fault deviation vector along the target direction when each fault mode occurs, so as to obtain a first probability that the dangerous misleading value of each fault mode in the fault deviation vector is a target value.

[0156] Optionally, in some embodiments of the present application, the acquisition submodule includes:

[0157] a processing unit, configured to perform whitening processing on a slope definition equation to obtain a fault element selection matrix, a parameter matrix to be decomposed, and a target vector, wherein the slope definition equation is a ratio of a mean square of a positioning error to a non-central parameter of a test statistic, and the target vector is a vector of the fault deviation vector composed of fault elements under each of the fault modes;

[0158] A decomposition unit, used for performing orthogonal decomposition on the parameter matrix to be decomposed to obtain a diagonal matrix and an orthogonal matrix;

[0159] An updating unit, configured to update the slope definition equation based on the diagonal matrix, the target vector and the orthogonal matrix;

[0160] A first determining unit, configured to determine a fault direction of the target vector based on the updated slope definition equation;

[0161] A second determining unit is configured to determine the target direction based on the fault element selection matrix, the diagonal matrix, the orthogonal matrix and the fault direction.

[0162] Optionally, in some embodiments of the present application, the first determining module includes:

[0163] An estimation submodule, used for performing position estimation on the linearized observation quantity based on the least square method to obtain a positioning result and a least square projection matrix;

[0164] A generating submodule, used for generating a pseudorange residual matrix according to the unit matrix, the least squares projection matrix, the design matrix in the observation equation and the linearized observation quantity;

[0165] The second determination submodule is used to determine the fault test statistic based on the pseudorange residual matrix and the variance covariance matrix.

[0166] Optionally, in some embodiments of the present application, a third determining submodule is further included, and the third determining submodule is used to:

[0167] Determining the number of observations and the number of parameters to be estimated based on the design matrix of the observation equation;

[0168] The detection threshold is determined based on the number of observations, the number of parameters to be estimated, and the continuity risk level.

[0169] In one embodiment, an electronic device is provided, the internal structure diagram of the electronic device can be as follows: Figure 4 As shown. The electronic device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, the functions or steps of a satellite positioning result verification method are implemented.

[0170] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0171] Under the condition that the observation error in the observation equation obeys the normal distribution, the fault test statistic and the positioning result are determined based on the observation equation and the obtained linearized observation quantity; when the fault test statistic is lower than the detection threshold, the first probability of the dangerous misleading value of each fault mode in the fault deviation vector being the target value and the second probability of the occurrence of multiple fault modes in the fault deviation vector are obtained when multiple fault modes occur; the integrity risk is calculated based on the first probability of the dangerous misleading value of each fault mode being the target value when multiple fault modes occur, the second probability of the occurrence of multiple fault modes and the integrity risk budget; the positioning result is verified based on the integrity risk.

[0172] In this embodiment, under the premise that the observation error obeys the normal distribution, the fault test statistic and the positioning result are determined by the observation equation and the obtained linearized observation quantity. When the fault test statistic is lower than the detection threshold, based on the occurrence of multiple fault modes, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes and the integrity risk budget are used to determine the integrity risk. The probability distribution of the fault deviation vector under multiple fault modes is quantified to determine the probability distribution of the positioning error under multiple fault modes, thereby determining the integrity risk of the positioning result under multiple fault modes, and then verifying the positioning result based on the integrity risk, which is beneficial to improving the reliability of the satellite navigation system in navigation and positioning under multi-frequency and multi-satellite conditions.

[0173] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0174] Under the condition that the observation error in the observation equation obeys the normal distribution, the fault test statistic and the positioning result are determined based on the observation equation and the obtained linearized observation quantity; when the fault test statistic is lower than the detection threshold, the first probability of the dangerous misleading value of each fault mode in the fault deviation vector being the target value and the second probability of the occurrence of multiple fault modes in the fault deviation vector are obtained when multiple fault modes occur; the integrity risk is calculated based on the first probability of the dangerous misleading value of each fault mode being the target value when multiple fault modes occur, the second probability of the occurrence of multiple fault modes and the integrity risk budget; the positioning result is verified based on the integrity risk.

[0175] In this embodiment, under the premise that the observation error obeys the normal distribution, the fault test statistic and the positioning result are determined by the observation equation and the obtained linearized observation quantity. When the fault test statistic is lower than the detection threshold, based on the occurrence of multiple fault modes, the first probability that the dangerous misleading value of each fault mode in the fault deviation vector is the target value and the second probability of the occurrence of multiple fault modes and the integrity risk budget are used to determine the integrity risk. The probability distribution of the fault deviation vector under multiple fault modes is quantified to determine the probability distribution of the positioning error under multiple fault modes, thereby determining the integrity risk of the positioning result under multiple fault modes, and then verifying the positioning result based on the integrity risk, which is beneficial to improving the reliability of the satellite navigation system in navigation and positioning under multi-frequency and multi-satellite conditions.

[0176] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0177] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0178] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0179] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A satellite positioning result verification method, characterized in that: include: In the case where the observation error in the observation equation obeys a normal distribution, determining a fault test statistic and a positioning result based on the observation equation and the obtained linearized observation quantity; When the fault test statistic is lower than the detection threshold, obtaining, in the case where multiple fault modes occur, a first probability that the dangerous misleading value of each of the fault modes in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector; Based on the occurrence of multiple failure modes, the first probability of the dangerous misleading value of each failure mode being the target value, the second probability of the occurrence of multiple failure modes and the integrity risk budget are calculated to obtain the integrity risk; The positioning result is verified based on the integrity risk.

2. The satellite positioning result verification method according to claim 1, characterized in that: The obtaining of second probabilities of occurrence of multiple fault modes in the fault deviation vector includes: identifying the number of faulty elements from the fault deviation vector; A second probability of occurrence of the plurality of failure modes is determined based on the satellite failure probability, the number of the failure elements and the total number of failure modes.

3. The satellite positioning result verification method according to claim 2, characterized in that: Before determining the second probability of occurrence of multiple failure modes based on the satellite failure probability, the number of failure elements and the total number of failure modes, the method includes: Determine the upper limit of the number of faulty elements based on the integrity risk budget; If the number of faulty elements is greater than the upper limit of the number of faulty elements, the total number of fault modes is determined based on the upper limit of the number of faulty elements.

4. The satellite positioning result verification method according to claim 1, characterized in that: The obtaining, when multiple failure modes occur, a first probability that the dangerous misleading value of each failure mode in the failure deviation vector is a target value comprises: Obtaining a target direction of the fault deviation vector; In the case where each fault mode occurs, a modulo search is performed on the fault deviation vector along the target direction to obtain a first probability that the dangerous misleading value of each fault mode in the fault deviation vector is a target value.

5. The satellite positioning result verification method according to claim 4, characterized in that: The obtaining the target direction of the fault deviation vector includes: Whitening the slope definition equation to obtain a fault element selection matrix, a parameter matrix to be decomposed, and a target vector, wherein the slope definition equation is the ratio of the mean square of the positioning error to the non-central parameter of the test statistic, and the target vector is a vector of the fault deviation vector composed of fault elements under each fault mode; Performing orthogonal decomposition on the parameter matrix to be decomposed to obtain a diagonal matrix and an orthogonal matrix; updating the slope definition equation based on the diagonal matrix, the target vector, and the orthogonal matrix; Determining the fault direction of the target vector based on the updated slope definition equation; The target direction is determined based on the fault element selection matrix, the diagonal matrix, the orthogonal matrix and the fault direction.

6. The satellite positioning result verification method according to claim 1, characterized in that: The method of determining the fault test statistics and the positioning result based on the observation equation and the linearized observation quantity includes: Performing position estimation on the linearized observation quantity based on the least square method to obtain a positioning result and a least square projection matrix; Generate a pseudorange residual matrix according to the unit matrix, the least squares projection matrix, the design matrix in the observation equation and the linearized observation quantity; A fault test statistic is determined based on the pseudorange residual matrix and the variance covariance matrix.

7. The satellite positioning result verification method according to claim 1, characterized in that: Also includes: Determining the number of observations and the number of parameters to be estimated based on the design matrix of the observation equation; The detection threshold is determined based on the number of observations, the number of parameters to be estimated, and the continuity risk level.

8. A satellite positioning result verification device, characterized in that: include: A first determination module is used to determine a fault test statistic, a fault deviation vector and a positioning result based on the observation equation and the obtained linearized observation quantity when the observation error in the observation equation obeys a normal distribution; an acquisition module, configured to acquire, when the fault test statistic is lower than a detection threshold, a first probability that a dangerous misleading value of each of the fault modes in the fault deviation vector is a target value and a second probability that the multiple fault modes occur in the fault deviation vector when multiple fault modes occur; A calculation module, configured to calculate the integrity risk based on the first probability that the dangerous misleading value of each failure mode is a target value, the second probability of the occurrence of the multiple failure modes, and the integrity risk budget when multiple failure modes occur; A verification module is used to verify the positioning result based on the integrity risk.

9. 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 computer program, the steps of the satellite positioning result verification method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the satellite positioning result verification method according to any one of claims 1 to 7 are implemented.

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