Positioning method and device

Abstract

The invention provides a positioning method and device, and belongs to the technical field of satellite positioning navigation. According to the method, fault processing is carried out on the observation vectors of the receiver based on the innovation vectors, and the accuracy of the observation vectors of the receiver is improved by eliminating the vectors with faults in the observation vectors, so that the precision of GNSS positioning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite positioning and navigation, and particularly to a positioning method and device. Background Art

[0002] As a space-based radio navigation and positioning system, the Global Navigation Satellite System (GNSS for short) can accurately locate most user devices on the Earth's surface or in near-Earth space; specifically, the user device realizes the positioning of the user device through the observation values (also called observation vectors) obtained by its built-in receiver for observing navigation satellites in the GNSS.

[0003] However, since the observation values of the receiver built into the user device are extremely vulnerable to hardware failures, satellite-side, signal propagation-side, and receiver-side errors, the observation values of the receiver are incorrect, which destroys the accuracy and reliability of GNSS positioning, and further results in insufficient accuracy of GNSS positioning. Summary of the Invention

[0004] The present invention provides a positioning method and device, which perform fault detection on the observation vector of the receiver based on the innovation vector, and eliminate the vectors with faults in the observation vector, protecting the accuracy and reliability of GNSS positioning, improving the accuracy of the observation vector of the receiver, and further improving the accuracy of GNSS positioning.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a positioning method, including:

[0007] S101. Determine the innovation vector of the receiver at the current epoch based on the observation vector of the receiver at the current epoch, the observation equation of the receiver, and the state prediction model; wherein, the observation equation is used to describe the relationship between the observation vector and the state vector of the receiver, and the state prediction model is used to predict the state vector of the receiver at the current epoch; the innovation vector is the difference between the observation vector of the receiver at the current epoch and the predicted value of the state vector of the receiver.

[0008] S102. Perform a fault determination on the observation vector of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch.

[0009] S103. In the case where it is determined that there is a fault in the observation vector of the receiver at the current epoch, eliminate the vectors in the observation vector of the receiver at the current epoch whose fault identification test quantity is greater than the fault test threshold; otherwise, execute S105.

[0010] S104. Determine the maximum number of faults of the receiver at the current epoch according to the preset integrity risk and the prior fault probability; when the number of vectors removed from the observation vector of the receiver at the current epoch is less than the maximum number of faults at the current epoch, update the observation vector of the receiver at the current epoch to the observation vector of the receiver after the vectors are removed, and then return to execute S101 - S104; otherwise, execute S105.

[0011] S105. Use the state update model and the innovation vector of the receiver at the current epoch to determine the positioning result of the receiver.

[0012] In a positioning method provided by the present invention, first, use the observation vector, the observation equation, and the state prediction model of the receiver at the current epoch to calculate the innovation vector of the receiver at the current epoch, and then perform fault analysis on the observation vector of the receiver at the current epoch through the innovation vector of the receiver at the current epoch; when it is determined that there are faults in the observation vector of the receiver at the current epoch, use the fault identification test quantity as the test index, and then remove the vectors in the observation vector whose fault identification test quantity is greater than the fault test threshold, and after removing the vectors, limit the number of removed vectors by the maximum number of faults, so that the finally retained vectors in the observation vector not only have a certain accuracy but also retain the redundancy of the observation vector, thereby improving the accuracy of GNSS positioning for positioning the receiver through the observation vector.

[0013] In one implementation manner of the first aspect, the observation equation satisfies the following formula;

[0014] z = H·X + v

[0015] where z represents the observation vector, and f represents the frequency, S represents the satellite, represents the pseudorange of satellite S at frequency f, represents the carrier observation value of satellite S at frequency f, represents the geometric distance between the receiver and the navigation satellite calculated based on the approximate coordinates of the receiver, represents the ionospheric delay, T S represents the tropospheric delay, D orb represents the satellite orbit error;

[0016] H represents the observation matrix, and represents the partial derivative symbol, λ f represents the wavelength of the carrier observation value at frequency f;

[0017] X represents the state vector, and X r represents the coordinate of the receiver on the x - axis of the ECEF coordinate system, Yr represents the coordinate of the receiver on the y-axis of the ECEF coordinate system, Z r represents the coordinate of the receiver on the z-axis of the ECEF coordinate system, Δt r represents the receiver clock error, c represents the speed of light, represents the integer ambiguity;

[0018] v represents the observation noise vector, and is the measurement noise of the pseudorange, is the measurement noise of the carrier wave.

[0019] In an implementation of the first aspect, the state prediction model satisfies the following formula;

[0020]

[0021] where k represents the current epoch, k - 1 represents an epoch before the current epoch, represents the predicted value of the change amount between the state vector of the receiver at k - 1 and the state vector of the receiver at k, Φ k|k-1 represents the state transition matrix of the receiver from k - 1 to k, represents the observed value of the state vector of the receiver at k - 1, P k|k-1 represents the predicted value of the state covariance matrix of the receiver at k, P k-1 represents the state covariance matrix of the receiver at k - 1, Q k represents the covariance matrix of the process noise of the receiver at k.

[0022] The innovation vector satisfies the following formula;

[0023]

[0024] where r k represents the innovation vector of the receiver at k, z k represents the observed value of the state vector of the receiver at k, H k represents the observation matrix of the receiver at k, represents the predicted value of the state vector of the receiver at k.

[0025] In an implementation of the first aspect, based on the innovation vector of the receiver at the current epoch, a fault determination is made on the observation vector of the receiver at the current epoch, including:

[0026] Calculating the fault detection test quantity of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch; the fault detection test quantity of the receiver satisfies the following formula;

[0027]

[0028] Among them, T g represents the fault detection test quantity of the receiver, and T g follows a chi-square distribution; k represents the current epoch, and r k represents the innovation vector of the receiver at time k, represents r k 's transpose matrix: H k represents the observation matrix of the receiver at time k, and P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, represents the transpose matrix of H k , and R k represents the measurement noise of the observed value of the receiver at time k.

[0029] When the fault detection test quantity is greater than the fault detection threshold, it is determined that there is a fault in the observation vector of the receiver at the current epoch; otherwise, it is determined that there is no fault in the observation vector of the receiver at the current epoch.

[0030] In an implementation manner of the first aspect, the fault identification test quantity satisfies the following formula;

[0031]

[0032] Among them, k represents the current epoch, i represents the i-th vector in the observation vector of the receiver at the current epoch, and T i represents the fault identification test quantity of vector i; c i is a vector with all elements equal to 0 except the i-th element equal to 1, represents the transpose matrix of c i , and R k represents the measurement noise of the observed value of the receiver at time k, represents the inverse vector of R k , and r k represents the innovation vector of the receiver at time k; P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, represents the transpose matrix of H k .

[0033] In an implementation manner of the first aspect, the state update model satisfies the following formula;

[0034]

[0035] P k =(I - K k H k)P k|k-1

[0036] Among them, k represents the current epoch, k - 1 represents an epoch before the current epoch, and K k represents the gain matrix of the receiver at time k, and P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, and H k represents the observation matrix of the receiver at time k, represents H k the transpose matrix of, R k represents the measurement noise of the observed value of the receiver at time k; represents the state vector of the receiver at updated time k, represents the predicted value of the change amount between the state vector of the receiver at time k - 1 and the state vector of the receiver at time k, and r k represents the innovation vector of the receiver at time k; P k represents the state covariance matrix of the receiver at updated time k.

[0037] In an implementation manner of the first aspect, the method further includes: S106. Construct an observation equation according to the pseudorange and carrier observations of the receiver; among them, the pseudorange is the pseudorange between the receiver and each of multiple navigation satellites; the carrier observation is the carrier observation between the receiver and each of multiple navigation satellites.

[0038] In a second aspect, the present invention provides a positioning device, including an innovation vector determination module, a fault determination module, an elimination module, an elimination quantity determination module, and a positioning module.

[0039] The innovation vector determination module is configured to determine the innovation vector of the receiver at the current epoch based on the observation vector of the receiver at the current epoch, the observation equation of the receiver, and the state prediction model; among them, the observation equation is used to describe the relationship between the observation vector and the state vector of the receiver, and the state prediction model is used to predict the state vector of the receiver at the current epoch; the innovation vector is the difference between the observation vector of the receiver at the current epoch and the predicted value of the state vector of the receiver.

[0040] The fault determination module is configured to perform a fault determination on the observation vector of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch.

[0041] The elimination module is configured to, when it is determined that there is a fault in the observation vector of the receiver at the current epoch, eliminate the vectors in the observation vector of the receiver at the current epoch whose fault identification test quantity is greater than the fault test threshold; otherwise, execute the processing steps of the positioning module.

[0042] The rejection quantity determination module is used to determine the maximum number of faults of the receiver at the current epoch according to the preset integrity risk and the prior fault probability; when the number of vectors rejected in the observation vector of the receiver at the current epoch is less than the maximum number of faults at the current epoch, update the observation vector of the receiver at the current epoch to the observation vector of the receiver after the rejected vectors, and then sequentially execute the processing steps of the innovation vector determination module, the fault determination module, the rejection module, and the rejection quantity determination module; otherwise, execute the processing steps of the positioning module.

[0043] The positioning module is used to determine the positioning result of the receiver by using the state update model and the innovation vector of the receiver at the current epoch.

[0044] In an implementation manner of the second aspect, the positioning device further includes an observation equation determination module; the observation equation determination module is used to construct an observation equation according to the pseudorange and carrier observations of the receiver; wherein, the pseudorange is the pseudorange between the receiver and each of the multiple navigation satellites; the carrier observation is the carrier observation between the receiver and each of the multiple navigation satellites.

[0045] In a third aspect, the present invention provides an electronic device, including a processor and a memory coupled to the processor; the memory is used to store computer instructions, and when the electronic device runs, the processor executes the computer instructions stored in the memory, so that the electronic device executes the method in the first aspect or any of its implementation manners as described above.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium, including computer program instructions, and when the computer program instructions are executed by a computer, the computer is caused to execute the method in the first aspect or any of its implementation manners as described above.

[0047] In a fifth aspect, the present invention provides a computer program product, including computer program instructions, and when the computer program instructions run on a computer, the computer is caused to execute the method in the first aspect or any of its implementation manners as described above.

[0048] The technical effects corresponding to the second aspect to the fifth aspect and their possible implementation manners can refer to the description of the technical effects of the first aspect and its possible implementation manners as described above, and will not be elaborated here.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention can significantly improve the integrity and reliability of the observation vector during the precise positioning of user equipment, and is applicable to various positioning scenarios such as standard point positioning (SPP), precise point positioning (PPP), and real-time kinematic positioning (RTK). During the process of fault detection and rejection of the observation vector, the covariance information of the innovation vector is fully considered, reducing the false alarm rate. At the same time, through autonomous and real-time algorithm design, it meets the high-performance requirements of the next generation of precise and reliable positioning, providing an efficient and reliable positioning solution for user equipment. Brief Description of the Drawings

[0051] Figure 1 is one of the schematic diagrams of a positioning method provided by an embodiment of the present application;

[0052] Figure 2 is the schematic structural diagram of a positioning device provided by an embodiment of the present application. Detailed Embodiments

[0053] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0054] In the description of the present invention, unless otherwise specified, the meaning of "a plurality of" refers to two or more. For example, a plurality of navigation satellites refers to two or more navigation satellites.

[0055] The methods and devices provided by the embodiments of the present application relate to the technical field of satellite positioning and navigation, and can be used to position user equipment through the Global Navigation Satellite System (GNSS for short). Specifically, GNSS includes a plurality of navigation satellites, and the user equipment obtains observation values (also referred to as observation vectors) by observing a plurality of navigation satellites in GNSS through its built-in receiver, so as to realize the positioning of the user equipment. It can be understood that user equipment includes devices such as mobile phones, drones, and smart wearable devices.

[0056] To solve the problem in the background art that the observations of the receiver built into the user device are extremely vulnerable to hardware failures, satellite-side, signal propagation-side, and receiver-side errors, resulting in incorrect observations of the receiver, damaging the accuracy and reliability of GNSS positioning, and further leading to insufficient accuracy of GNSS positioning, the embodiments of the present application provide a positioning method and device. First, the innovation vector of the receiver at the current epoch is calculated using the observation vector, observation equation, and state prediction model of the receiver at the current epoch, and then the observation vector of the receiver at the current epoch is subjected to fault analysis through the innovation vector of the receiver at the current epoch; in the case where it is determined that the observation vector of the receiver at the current epoch has a fault, the fault identification test quantity is used as the test index, and then the vectors in the observation vector with the fault identification test quantity greater than the fault test threshold are removed. After removing the vectors, the number of removed vectors is limited by the maximum number of faults, so that the vectors finally retained in the observation vector not only have a certain degree of accuracy but also retain the redundancy of the observation vector, thereby improving the accuracy of GNSS positioning for positioning the receiver through the observation vector;

[0057] It should be understood that in the embodiments of the present application, the process of fault detection and fault removal for the observation vector of the receiver belongs to user-level integrity monitoring (ULIM); user-level integrity monitoring is an autonomous and independent algorithm based on hypothesis testing theory at the user side (such as a receiver), does not rely on external devices, has high flexibility, is suitable for real-time applications, and can meet the urgent practical and GNSS development needs;

[0058] Exemplarily, a positioning method provided by an embodiment of the present invention can be executed by an electronic device with processing capabilities. For example, the electronic device can be a computer, a server, etc.; taking the electronic device as a computer as an example, the hardware part of the computer can include: a processor, a memory, a network interface, a user interface, a communication bus, etc.;

[0059] Among them, the processor is used to control the electronic device to execute relevant processing and calculation tasks. For example, determining the innovation vector, fault determination, removing vectors, and determining the positioning result of the receiver, etc.; the processor can include a central processing unit (CPU) or other processors, and the processor can be single-core or multi-core. For example, the processor can include multiple CPUs;

[0060] The memory is used to store computer instructions and related data. For example, it stores the observation vector, innovation vector, fault identification test quantity, maximum number of faults, and positioning result of the current epoch receiver, etc. The memory can be a random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or optical memory, magnetic disk storage medium, or any other magnetic storage device, or any other medium capable of storing program code or data that can be accessed by a computer. Optionally, the memory can be integrated within the processor, or the memory can be independent of the processor.

[0061] The network interface is used for the computer to communicate with other devices or communication networks. The network interface can be a transceiver with transceiver functions. Optionally, the network interface can include standard wired interfaces, wireless interfaces (such as WI-FI interfaces, Bluetooth interfaces, 5G interfaces).

[0062] The communication bus is used to realize the connection and communication between different components. For example, the above-mentioned processor, memory, network interface, and user interface can be interconnected through the communication bus.

[0063] The user interface can include a display screen and an input unit (such as a keyboard). Optionally, the user interface can also include standard wired interfaces and wireless interfaces.

[0064] Those skilled in the art can understand that the above computer may further include more or fewer components, or combine certain components, or have different component arrangements. The embodiments of the present application do not limit this.

[0065] Exemplarily, as Figure 1 shown, a positioning method provided by an embodiment of the present application includes S101 - S105.

[0066] S101. Based on the observation vector of the current epoch receiver, the observation equation of the receiver, and the state prediction model, determine the innovation vector of the current epoch receiver.

[0067] It can be understood that the embodiments of the present application are used to position user equipment in an actual application scenario. The receiver in S101 is disposed inside the user equipment. Positioning the receiver is equivalent to positioning the user equipment, and the positioning result of the receiver is the positioning result of the user equipment.

[0068] The observation vector of the above current epoch receiver includes multiple vectors, where each vector corresponds to the observation value of the receiver for a navigation satellite included in GNSS; the above observation equation is used to describe the relationship between the observation vector of the receiver and the state vector, and the state prediction model is used to predict the state vector of the receiver at the current epoch; the innovation vector is the difference between the observation vector of the receiver at the current epoch and the predicted value of the state vector of the receiver.

[0069] Optionally, the state prediction model can be the time update module of the Kalman filter. Since the Kalman filter belongs to the related technologies commonly used in this technical field, the embodiments of this application will not further elaborate on the time update module of the Kalman filter here.

[0070] In one implementation, the above observation equation satisfies the following formula;

[0071] z = H·X + v

[0072] where z represents the observation vector, and f represents the frequency, S represents the satellite, represents the pseudorange of satellite S at frequency f, represents the carrier observation value of satellite S at frequency f, represents the geometric distance between the receiver and the navigation satellite calculated based on the approximate coordinates of the receiver, represents the ionospheric delay, T S represents the tropospheric delay, D orb represents the satellite orbit error;

[0073] H represents the observation matrix, and represents the partial derivative symbol, λ f represents the wavelength of the carrier observation value at frequency f;

[0074] X represents the state vector, and X r represents the coordinate of the receiver on the x-axis of the ECEF coordinate system, Y r represents the coordinate of the receiver on the y-axis of the ECEF coordinate system, Z r represents the coordinate of the receiver on the z-axis of the ECEF coordinate system, Δt r represents the receiver clock error, c represents the speed of light, represents the integer ambiguity; it can be understood that the above ECEF coordinate system (Earth-Centered, Earth-Fixed coordinate system), also known as the Earth-centered and Earth-fixed rectangular coordinate system, is a Cartesian coordinate system with the Earth's centroid as the origin, and is commonly used to describe the position and movement of objects on or near the Earth; since the ECEF coordinate system is a common coordinate system in the technical field of this application, the embodiments of this application will not be further described herein;

[0075] v represents the observation noise vector, and is the measurement noise of the pseudorange, is the measurement noise of the carrier;

[0076] It should be noted that the above observation equation is the observation equation in matrix form obtained by linearizing the pseudorange and carrier observation equations of the receiver; the pseudorange and carrier observation equations of the receiver satisfy the following formula;

[0077]

[0078] where ρ S represents the geometric distance from the receiver to the satellite; the geometric distance ρ from the receiver to the satellite S satisfies the following formula;

[0079]

[0080] where, (X r , Y r , Z r ) and (X S , Y S , Z S ) respectively represent the spatial position coordinates of the receiver and the satellite;

[0081] The above state prediction model satisfies the following formula;

[0082]

[0083] where, k represents the current epoch, k - 1 represents the epoch before the current epoch, represents the predicted value of the change amount between the state vector of the receiver at k - 1 and the state vector of the receiver at k, Φ k|k- 1 represents the state transition matrix of the receiver from k - 1 to k, represents the observed value of the state vector of the receiver at k - 1, P k|k-1 represents the predicted value of the state covariance matrix of the receiver at k, P k-1 represents the state covariance matrix of the receiver at k - 1, Q kDenote the covariance matrix of the process noise of the receiver at time k;

[0084] The above innovation vector satisfies the following formula;

[0085]

[0086] where, r k denotes the innovation vector of the receiver at time k, z k denotes the observed value of the state vector of the receiver at time k, H k denotes the observation matrix of the receiver at time k, denotes the predicted value of the state vector of the receiver at time k;

[0087] S102. Perform a fault determination on the observation vector of the receiver in the current epoch according to the innovation vector of the receiver in the current epoch;

[0088] Optionally, the above S102 includes S1021 - S1022;

[0089] S1021. Calculate the fault detection test quantity of the receiver in the current epoch according to the innovation vector of the receiver in the current epoch;

[0090] In the embodiments of the present application, the above fault determination refers to a global test of whether there are faulty values in all the observed values in the current epoch through a chi - square test. The sign of determining whether there is a fault lies in whether the fault detection test quantity exceeds the fault detection threshold;

[0091] In one implementation, calculate the fault detection test quantity of the receiver in the current epoch through the covariance matrix of the innovation vector of the receiver in the current epoch; the fault detection test quantity of the receiver satisfies the following formula;

[0092]

[0093] where, T g denotes the fault detection test quantity of the receiver, and T g follows a chi - square distribution. Specifically, T g follows a chi - square distribution with degrees of freedom (DOF) of q, where q is the number of measurements; k represents the current epoch, r k denotes the innovation vector of the receiver at time k, denotes the transpose matrix of r k ; W k denotes the covariance matrix of the innovation vector of the receiver at time k, H k denotes the observation matrix of the receiver at time k, P k|k-1 denotes the predicted value of the state covariance matrix of the receiver at time k, denotes H kThe transposed matrix of, R k represents the measurement noise of the receiver's observation value at time k;

[0094] S1022. When the fault detection test quantity is greater than the fault detection threshold, it is determined that there is a fault in the observation vector of the receiver at the current epoch; otherwise, it is determined that there is no fault in the observation vector of the receiver at the current epoch;

[0095] Optionally, the fault detection threshold can be determined according to the false alarm rate in the preset integrity risk requirement, or can be set or selected by other reasonable methods. The embodiments of the present application do not limit the setting method and selection range of the fault detection threshold;

[0096] In one implementation, the fault detection threshold is determined according to the false alarm rate in the preset integrity risk requirement and the number of vectors in the observation vector; among them, the false alarm rate satisfies the following formula;

[0097]

[0098] Among them, P fa represents the false alarm rate in the preset integrity risk; represents the alternative hypothesis. Specifically, each vector with a fault in the observation vector of the receiver at the current epoch corresponds to a fault mode. If the i-th vector in the observation vector of the receiver at the current epoch has a fault, then this hypothesis is called the alternative hypothesis represents the probability of occurrence of each fault mode, and m is the number of measurements; can be given by a preset method or calculated by an average distribution method is the recognition threshold under different fault modes and can be set according to the actual situation;

[0099] S103. In the case where it is determined that there is a fault in the observation vector of the receiver at the current epoch, the vectors in the observation vector of the receiver at the current epoch whose fault identification test quantity is greater than the fault test threshold are excluded; otherwise, S105 is executed;

[0100] Exemplarily, through the Barda data snooping method and the covariance matrix of the innovation vector of the receiver at the current epoch, the fault identification test quantity of each vector in the observation vector of the receiver at the current epoch is calculated; the fault identification test quantity satisfies the following formula;

[0101]

[0102] Among them, k represents the current epoch, i represents the i-th vector in the observation vector of the receiver at the current epoch, T iThe fault identification test quantity representing vector i; c i is a vector in which all elements are equal to 0 except that the i-th element is equal to 1, representing c i 's transpose matrix, R k represents the measurement noise of the receiver's observation value at time k, representing R k 's inverse vector, r k represents the innovation vector of the receiver at time k; P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, represents H k 's transpose matrix:

[0103] It should be understood that the above-mentioned fault identification test quantity takes into account the covariance information of the innovation vector, and thus can be applied to high-precision positioning models such as Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and PPP-RTK;

[0104] S104. Determine the maximum number of faults of the receiver at the current epoch according to the preset integrity risk and the prior fault probability; when the number of vectors excluded from the observation vector of the receiver at the current epoch is less than the maximum number of faults at the current epoch, update the observation vector of the receiver at the current epoch to the observation vector of the receiver after the excluded vectors, and then return to execute S101 - S104; otherwise, execute S105;

[0105] Specifically, the above S104 includes S1041 - S1044;

[0106] S1041. Determine various fault modes of the receiver at the current epoch;

[0107] Specifically, the situation of each vector in the observation vector of the receiver at the current epoch having a fault corresponds to a fault mode; a vector having a fault means that the receiver has a fault in the observation of the navigation satellite corresponding to the vector, resulting in an incorrect observation of the vector;

[0108] S1042. Under the preset integrity risk, allocate risks to each fault mode among various fault modes to obtain the prior occurrence probability of each fault mode among various fault modes;

[0109] In one implementation, the prior occurrence probability satisfies the following formula;

[0110]

[0111] where, P ap，iRepresents the prior occurrence probability of the i-th fault mode vector in the observation vector of the receiver. P obs，j Represents the prior fault probability of the j-th vector (i.e., the j-th fault mode) in the observation vector of the receiver; P obs，j Satisfies the following formula;

[0112]

[0113] Wherein, P ap,sat Represents the prior fault probability of the navigation satellite corresponding to the j-th vector in the observation vector of the receiver, N f 、N t Represent the number of frequencies and the number of observation types of the navigation satellite respectively;

[0114] S1043. Calculate the maximum number of faults of the receiver at the current epoch according to the prior occurrence probability of each fault mode in multiple fault modes;

[0115] In an application scenario, the maximum number of faults satisfies the following formula;

[0116]

[0117] Wherein, N max,fault Represents the maximum number of faults, P i，thres Represents the integrity risk threshold of the i-th fault mode that has not been monitored;

[0118] In the embodiments of the present application, the above prior fault probability and the above integrity risk threshold are provided by the integrity support information (Integrity Support Message, ISM) proposed and published by the GPS Lab team of Stanford University, which is generally recognized in the technical field;

[0119] S1044. When the number of vectors removed from the observation vector of the receiver at the current epoch is less than the maximum number of faults at the current epoch, update the observation vector of the receiver at the current epoch to the observation vector of the receiver after the removed vectors, and then return to execute S101-S104; otherwise, execute S105;

[0120] S105. Determine the positioning result of the receiver by using the state update model and the innovation vector of the receiver at the current epoch;

[0121] Optionally, the above state update model can be the measurement update module corresponding to the Kalman filter. Since the Kalman filter is a related technology commonly used in the technical field, the embodiments of the present application will not further elaborate on the measurement update module of the Kalman filter;

[0122] Specifically, the above state update model satisfies the following formula;

[0123]

[0124] P k = (I - K k H k )P k|k-1

[0125] where k represents the current epoch, k - 1 represents an epoch before the current epoch, K k represents the gain matrix of the receiver at time k, P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, H k represents the observation matrix of the receiver at time k, represents the transpose matrix of H k ; R k represents the measurement noise of the observed value of the receiver at time k; represents the state vector of the receiver at time k after update, represents the predicted value of the change amount between the state vector of the receiver at time k - 1 and the state vector of the receiver at time k, r k represents the innovation vector of the receiver at time k; P k represents the state covariance matrix of the receiver at time k after update;

[0126] It can be understood that since the state vector of the receiver at the current epoch obtained by the state update model receives the input of the observed vector after integrity monitoring and fault processing, its reliability increases. This process balances the information of the prior estimate and the actual observation, making the state vector of the receiver at the current epoch more reliable; specifically, the state vector of the receiver gives the accurate position of the receiver, thus enabling the accurate position of the user equipment to be given, and further realizing the precise positioning of the user equipment;

[0127] In summary, in a positioning method provided by an embodiment of the present application, first, the innovation vector of the receiver at the current epoch is calculated by using the observed vector, the observation equation, and the state prediction model of the receiver at the current epoch, and then the fault analysis of the observed vector of the receiver at the current epoch is performed through the innovation vector of the receiver at the current epoch; in the case of determining that the observed vector of the receiver at the current epoch has a fault, the fault identification test quantity is used as the test index, and then the vectors in the observed vector with the fault identification test quantity greater than the fault test threshold are removed. After removing the vectors, the number of removed vectors is limited by the maximum number of faults, so that the finally retained vectors in the observed vector not only have a certain accuracy but also retain the redundancy of the observed vector, thereby improving the accuracy of GNSS positioning for positioning the receiver through the observed vector.

[0128] In an application scenario, before S101, the above positioning method further includes S106.

[0129] S106. Construct an observation equation based on the pseudorange and carrier observation values of the receiver.

[0130] Wherein, the pseudorange is the pseudorange between the receiver and each of the multiple navigation satellites; the carrier observation value is the carrier observation value between the receiver and each of the multiple navigation satellites.

[0131] Specifically, in an application scenario, the time interval for the receiver to sample the observation vector is 1 s (i.e., 1 s is an epoch). When constructing the observation equation, the satellite cut-off elevation angle is recommended to be 10°. The constructed observation equation is applicable to various positioning services such as multi-frequency multi-mode standard single point positioning (SPP), precise point positioning (PPP), and real-time kinematic (RTK).

[0132] Correspondingly, an embodiment of the present application provides a positioning device, as Figure 2 shown, including an innovation vector determination module 501, a fault determination module 502, an elimination module 503, an elimination quantity determination module 504, and a positioning module 505.

[0133] Among them, the innovation vector determination module 501 is configured to determine the innovation vector of the receiver in the current epoch based on the observation vector of the receiver in the current epoch, the observation equation of the receiver, and the state prediction model. Wherein, the observation equation is used to describe the relationship between the observation vector and the state vector of the receiver, and the state prediction model is used to predict the state vector of the receiver in the current epoch. The innovation vector is the difference between the observation vector of the receiver in the current epoch and the predicted value of the state vector of the receiver. For example, the innovation vector determination module 501 is used to implement S101 of the above positioning method.

[0134] The fault determination module 502 is configured to perform fault determination on the observation vector of the receiver in the current epoch according to the innovation vector of the receiver in the current epoch. For example, the fault determination module 502 is used to implement S102 of the above positioning method.

[0135] The elimination module 503 is configured to, when it is determined that there is a fault in the observation vector of the receiver in the current epoch, eliminate the vectors in the observation vector of the receiver in the current epoch whose fault identification test quantity is greater than the fault test threshold; otherwise, execute the processing steps of the positioning module. For example, the elimination module 503 is used to implement S103 of the above positioning method.

[0136] The rejection quantity determination module 504 is configured to determine the maximum number of faults of the receiver at the current epoch according to the preset integrity risk and the prior fault probability; when the number of vectors to be rejected in the observation vector of the receiver at the current epoch is less than the maximum number of faults at the current epoch, update the observation vector of the receiver at the current epoch to the observation vector of the receiver after the rejected vectors, and then sequentially execute the processing steps of the innovation vector determination module, the fault determination module, the rejection module, and the rejection quantity determination module; otherwise, execute the processing steps of the positioning module. For example, the rejection quantity determination module 504 is configured to implement S104 of the above positioning method.

[0137] The positioning module 505 is configured to determine the positioning result of the receiver by using the state update model and the innovation vector of the receiver at the current epoch. For example, the positioning module 505 is configured to implement S105 of the above positioning method.

[0138] Optionally, the fault determination module 502 is specifically configured to: calculate the fault detection test quantity of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch. When the fault detection test quantity is greater than the fault detection threshold, it is determined that there is a fault in the observation vector of the receiver at the current epoch; otherwise, it is determined that there is no fault in the observation vector of the receiver at the current epoch. For example, the fault determination module 502 is specifically configured to implement S1021-S1022 of the above positioning method.

[0139] In one implementation manner, the above positioning device further includes an observation equation determination module 506.

[0140] The observation equation determination module 506 is configured to construct an observation equation according to the pseudorange and carrier observations of the receiver; where the pseudorange is the pseudorange between the receiver and each of the multiple navigation satellites; and the carrier observation is the carrier observation between the receiver and each of the multiple navigation satellites. For example, the observation equation determination module 506 is configured to implement S106 of the above positioning method.

[0141] Each module of the above positioning device can also be used to execute other steps in the above method embodiments. All relevant contents involved in the above method embodiments can be cited in the function descriptions of the corresponding functional modules, and will not be elaborated here.

[0142] An embodiment of the present application further provides an electronic device, including: a processor and a memory coupled to the processor; the memory is used to store computer instructions, and when the electronic device runs, the processor executes the computer instructions stored in the memory so that the electronic device executes the method described in the above embodiment. Among them, the processor can implement the above-mentioned innovation vector determination module 501, fault determination module 502, elimination module 503, elimination quantity determination module 504, positioning module 505, and observation equation determination module 506; the above-mentioned memory can also be used to store the observation vector, innovation vector, fault identification test quantity, maximum number of faults, and positioning results of the current epoch receiver, etc.

[0143] An embodiment of the present application further provides a computer-readable storage medium, which includes a computer program that, when running on a computer, executes the method described in the above embodiment.

[0144] An embodiment of the present application further provides a computer program product, which includes computer program instructions that, when running on a computer, execute the method described in the above embodiment.

[0145] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A positioning method, characterized in that: include: S101, based on the observation vector of the receiver at the current epoch, the observation equation of the receiver and the state prediction model, determine the innovation vector of the receiver at the current epoch; wherein the observation equation is used to describe the relationship between the observation vector and the state vector of the receiver, and the state prediction model is used to predict the state vector of the receiver at the current epoch; the innovation vector is the difference between the observation vector of the receiver at the current epoch and the predicted value of the state vector of the receiver; S102, performing fault determination on the observation vector of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch; S103, when it is determined that there is a fault in the observation vector of the receiver in the current epoch, the vectors whose fault identification verification amount in the observation vector of the receiver in the current epoch is greater than the fault verification threshold are eliminated; otherwise, S105 is executed; S104, determining the maximum number of failures of the receiver in the current epoch according to the preset integrity risk and the priori failure probability; when the number of vectors removed from the observation vector of the receiver in the current epoch is less than the maximum number of failures in the current epoch, updating the observation vector of the receiver in the current epoch to the observation vector of the receiver after the vectors are removed, and then returning to execute S101-S104; otherwise, executing S105; S105: Determine the positioning result of the receiver by using the state update model and the new information vector of the receiver at the current epoch.

2. The method according to claim 1, characterized in that The observation equation satisfies the following formula: z=H·X+ν Where z represents the observation vector, and f represents frequency, S represents satellite, represents the pseudorange of satellite S at frequency f, represents the carrier observation value of S satellite at frequency f, represents the geometric distance between the receiver and the navigation satellite calculated based on the approximate coordinates of the receiver, represents the ionospheric delay, T S represents the tropospheric delay, D orb represents the satellite orbit error; H represents the observation matrix, and represents the symbol of partial derivative, λ f represents the wavelength of the carrier observation at frequency f; X represents the state vector, and X r represents the coordinate of the receiver on the x-axis of the ECEF coordinate system, Y r represents the coordinate of the receiver on the y-axis of the ECEF coordinate system, Z r represents the coordinate of the receiver on the z-axis of the ECEF coordinate system, Δt r represents the receiver clock error, c represents the speed of light, represents the integer ambiguity; v represents the observation noise vector, and is the measurement noise of pseudorange, is the measured noise of the carrier.

3. The method according to claim 1 or 2, characterized in that The state prediction model satisfies the following formula: Among them, k represents the current epoch, k-1 represents the epoch before the current epoch, represents the predicted value of the change between the state vector of the receiver at time k-1 and the state vector of the receiver at time k, Φ k|k-1 represents the state transfer matrix of the receiver from k-1 to k, represents the observed value of the state vector of the receiver at time k-1, P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, P k-1 represents the state covariance matrix of the receiver at time k-1, Q k represents the covariance matrix of the process noise of the receiver at time k; The innovation vector satisfies the following formula: Among them, r k represents the receiver's innovation vector at time k, z k represents the observed value of the state vector of the receiver at time k, H k represents the observation matrix of the receiver at time k, represents the predicted value of the receiver's state vector at time k.

4. The method according to claim 1, characterized in that The performing fault determination on the observation vector of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch includes: Calculate the fault detection test quantity of the receiver in the current epoch according to the innovation vector of the receiver in the current epoch; the fault detection test quantity of the receiver satisfies the following formula; Among them, T g represents the fault detection verification quantity of the receiver, and T g It follows the chi-square distribution; k represents the current epoch, r k represents the receiver's innovation vector at time k, Represents r k The transposed matrix of H k represents the observation matrix of the receiver at time k, P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, Indicates H k The transposed matrix, R k represents the measurement noise of the observation value of the receiver at time k; When the fault detection verification amount is greater than the fault detection threshold, it is determined that there is a fault in the observation vector of the receiver in the current epoch; otherwise, it is determined that there is no fault in the observation vector of the receiver in the current epoch.

5. The method according to claim 1, characterized in that The fault identification test quantity satisfies the following formula: Where k represents the current epoch, i represents the i-th vector in the observation vector of the receiver at the current epoch, T i represents the fault identification test quantity of vector i; c i is a vector whose elements are all equal to 0 except the i-th element which is equal to 1. Indicates c i The transposed matrix, R k represents the measurement noise of the observation value of the receiver at time k, Represents R k The inverse vector, r k represents the innovation vector of the receiver at time k; P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, Indicates H k The transposed matrix of .

6. The method according to claim 1, characterized in that The state update model satisfies the following formula: P k =(I-K k H k )P k|k-1 Among them, k represents the current epoch, k-1 represents the epoch before the current epoch, and K k represents the gain matrix of the receiver at time k, P k|k-1 represents the predicted value of the state covariance matrix of the receiver at time k, H k represents the observation matrix of the receiver at time k, Indicates H k The transposed matrix of R k represents the measurement noise of the observation value of the receiver at time k; represents the state vector of the receiver at time k after update, represents the predicted value of the change between the state vector of the receiver at time k-1 and the state vector of the receiver at time k, r k represents the new information vector of the receiver at time k; P k represents the state covariance matrix of the receiver at time k after update.

7. The method according to claim 1, characterized in that Before S101, the method further includes: S106. Construct an observation equation based on the pseudorange and carrier observation value of the receiver; wherein the pseudorange is the pseudorange between the receiver and each of the multiple navigation satellites; and the carrier observation value is the carrier observation value between the receiver and each of the multiple navigation satellites.

8. A positioning device for implementing the method of claim 1, characterized in that: It includes an information vector determination module, a fault determination module, a rejection module, a rejection quantity determination module and a positioning module; The innovation vector determination module is used to determine the innovation vector of the receiver at the current epoch based on the observation vector of the receiver at the current epoch, the observation equation of the receiver and the state prediction model; wherein the observation equation is used to describe the relationship between the observation vector and the state vector of the receiver, and the state prediction model is used to predict the state vector of the receiver at the current epoch; the innovation vector is the difference between the observation vector of the receiver at the current epoch and the predicted value of the state vector of the receiver; The fault determination module is used to perform fault determination on the observation vector of the receiver at the current epoch according to the innovation vector of the receiver at the current epoch; The elimination module is used to, when it is determined that there is a fault in the observation vector of the receiver in the current epoch, eliminate the vector whose fault identification verification amount is greater than the fault verification threshold in the observation vector of the receiver in the current epoch; otherwise, execute the processing steps of the positioning module; The elimination number determination module is used to determine the maximum number of faults of the receiver in the current epoch according to the preset integrity risk and the priori fault probability; when the number of vectors eliminated in the observation vector of the receiver in the current epoch is less than the maximum number of faults in the current epoch, the observation vector of the receiver in the current epoch is updated to the observation vector of the receiver after the elimination vector, and then the processing steps of the new information vector determination module, the fault determination module, the elimination module and the elimination number determination module are executed in sequence; otherwise, the processing steps of the positioning module are executed; The positioning module is used to determine the positioning result of the receiver by using a state update model and an innovation vector of the receiver at a current epoch.

9. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor; the memory is used to store computer instructions, and when the electronic device is running, the processor executes the computer instructions stored in the memory, so that the electronic device executes the method as claimed in claim 1.

10. A computer-readable storage medium, characterized in that: The method comprises computer program instructions, which, when executed by a computer, cause the computer to perform the method according to any one of claims 1.

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