GNSS single-frequency precise single-point positioning method and related device

Abstract

The present application discloses a GNSS single-frequency precise single-point positioning method and related devices, which are used to solve the problem that the single-frequency PPP positioning method in the related art is affected by the condition number of the normal matrix, and has low positioning accuracy and unstable positioning results. The method comprises: determining the historical observation value of the preset observation quantity of the single-frequency receiver based on the GNSS satellite signal received by the single-frequency receiver; determining the predicted value of the preset observation quantity at the current epoch based on the regularized Kalman filter algorithm and the historical observation value of the preset observation quantity; determining the position of the single-frequency receiver at the current epoch based on the predicted value of the preset observation quantity at the current epoch.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation and positioning technology, and in particular to a GNSS single-frequency precision single-point positioning method and related devices. Background Art

[0002] Due to the low price of single-frequency GNSS receivers, single-frequency precision point positioning (PPP) technology is widely used in many civil fields for positioning. In addition, the single-frequency PPP model is simple, easy to analyze the error, and can be applied to multi-frequency PPP and other theoretical research fields, so single-frequency PPP still needs to be studied more deeply.

[0003] In the related art, the traditional Extended Kalman Filter (EKF) algorithm is usually used to perform single-frequency PPP solution. However, this solution method is affected by the excessively large condition number of the law matrix, thereby reducing the positioning accuracy and stability of the single-frequency PPP. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a GNSS single-frequency precise single-point positioning method and related devices, which are used to solve the problems of low positioning accuracy and unstable positioning results caused by the condition number of the method matrix in the single-frequency PPP positioning method in the related art.

[0005] In order to achieve the above objectives, the present application embodiment adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a GNSS single-frequency precise single-point positioning method, comprising:

[0007] Determining a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver;

[0008] Determining a predicted value of the preset observation value at a current epoch based on a Regularized Kalman Filter (RKF) algorithm and historical observation values ​​of the preset observation value;

[0009] Based on the predicted value of the preset observation quantity in the current epoch, the position of the single-frequency receiver in the current epoch is determined.

[0010] In a second aspect, an embodiment of the present application provides a GNSS single-frequency precision single-point positioning device, comprising:

[0011] A first determining unit, configured to determine a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver;

[0012] A prediction unit, configured to determine a predicted value of the preset observation value at a current epoch based on a regularized Kalman filter algorithm and a historical observation value of the preset observation value;

[0013] The second determination unit is used to determine the position of the single-frequency receiver at the current epoch based on the predicted value of the preset observation quantity at the current epoch.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0015] processor;

[0016] a memory for storing instructions executable by the processor;

[0017] The processor is configured to execute the instructions to implement the method as described in the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method described in the first aspect.

[0019] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0020] Based on the GNSS satellite signals received by the single-frequency receiver, the historical observation values ​​of the preset observation quantities of the single-frequency receiver are determined, and then the regularization method is used to help improve the advantages of most ill-conditioned problems. Based on the regularized Kalman filter algorithm and the historical observation values ​​of the preset observation quantities, the predicted values ​​of the preset observation quantities in the current epoch are determined. This can solve the ill-conditioning of the weight matrix of the observation parameters and the weight matrix of the state parameters in the single-frequency PPP solution process, reduce the condition number of the law matrix, and avoid the problem of a sudden increase in the condition number of the law matrix caused by cycle slips, changes in the number of satellites, and data missing, so that the condition number of the law matrix is ​​always maintained within a reasonable range, thereby improving the accuracy and reliability of the positioning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 A flowchart of a GNSS single-frequency precise single-point positioning method provided for one embodiment of the present application;

[0023] Figure 2 A flowchart of a GNSS single-frequency precise single-point positioning method provided for another embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of a GNSS single-frequency precise single-point positioning device provided for one embodiment of the present application;

[0025] Figure 4 A schematic diagram of the structure of an electronic device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0027] The terms "first", "second", etc. in this specification and claims are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, the term "and / or" in this specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0028] As mentioned above, the single-frequency PPP positioning method in the related art usually adopts the traditional extended Kalman filter algorithm to perform single-frequency PPP solution, but this solution method will be affected by the excessively large condition number of the law matrix, thereby reducing the positioning accuracy and stability of the single-frequency PPP.

[0029] The applicant has conducted an in-depth analysis of each element in the law matrix and found that when using the extended Kalman filter for single-frequency PPP solution, the weight matrix of the state parameters will be ill-conditioned due to the large process noise of the parameters or the large initial variance of the newly added parameters, and the noise of the pseudo-range observations is larger than that of the phase observations, which will cause the observation noise weight matrix to be ill-conditioned. In addition, when cycle slips, changes in the number of satellites, and missing observation data occur, the condition number of the law matrix will also jump. These factors will cause the condition number of the law matrix to be too large, which will lead to low positioning accuracy and unstable positioning results of single-frequency PPP.

[0030] In view of this, an embodiment of the present application aims to propose a GNSS single-frequency precise single-point positioning method, which determines the historical observation value of the preset observation quantity of the single-frequency receiver based on the GNSS satellite signal received by the single-frequency receiver, and then uses the regularization method to help improve the advantages of most pathological problems. Based on the regularized Kalman filter algorithm (Regularized Kalman Filter, RKF) and the historical observation value of the preset observation quantity, the predicted value of the preset observation quantity in the current epoch is determined, which can solve the pathological problems of the weight matrix of the observation parameters and the weight matrix of the state parameters in the single-frequency PPP solution process, reduce the condition number of the law matrix, and avoid the problem of a sudden increase in the condition number of the law matrix due to cycle slips, changes in the number of satellites, and data missing, so that the condition number of the law matrix is ​​always maintained within a reasonable range, thereby improving the accuracy and reliability of the positioning results.

[0031] It should be understood that the GNSS single-frequency precise single-point positioning method provided in the embodiment of the present application can be executed by an electronic device or software installed in the electronic device. The electronic device referred to here may include a terminal device, such as a desktop computer, a laptop computer, a supercomputer, a vehicle-mounted terminal, etc.; or, the electronic device may also include a server, such as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0032] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0033] Please refer to Figure 1 , is a flow chart of a GNSS single-frequency precise single-point positioning method provided by an embodiment of the present application, the method comprising the following steps:

[0034] S102: Determine a historical observation value of a preset observation quantity of the single-frequency receiver based on the GNSS satellite signal received by the single-frequency receiver.

[0035] In the embodiment of the present application, the historical observation values ​​of the preset observation value include the historical observation values ​​of each epoch before the current epoch of the preset observation value. Among them, the preset observation value may specifically include, for example, but is not limited to: pseudorange, carrier phase, and deionospheric (also known as semi-combination) composed of pseudorange and carrier, etc.

[0036] Specifically, various commonly used analysis strategies in the art may be used to analyze the GNSS signal to obtain historical observation values ​​of preset observation quantities of the single-frequency receiver.

[0037] Optionally, to improve positioning accuracy, such as Figure 2As shown, after the above S102, the GNSS single-frequency precise single-point positioning method provided in the embodiment of the present application may also include: preprocessing the historical observation values ​​of the preset observation quantity, which may specifically include but is not limited to at least one of the following processes: detecting and repairing the single-frequency receiver clock jump in the historical observation values, detecting and eliminating gross errors in the historical observation values, detecting and repairing cycle slips in the historical observation values, etc.

[0038] S104, determining a predicted value of the preset observation value at the current epoch based on the regularized Kalman filter algorithm and the historical observation value of the preset observation value.

[0039] Taking into account the advantage that regularization method helps to improve most ill-conditioned problems, using the regularized Kalman filter algorithm instead of the conventional extended Kalman filter algorithm can improve the ill-conditioned weight matrix of the observation parameters and the weight matrix of the state parameters, reduce the condition number of the law matrix, and avoid the problem of a sudden increase in the condition number of the law matrix caused by cycle slips, changes in the number of satellites, and data missing, so that the condition number of the law matrix is ​​always maintained within a reasonable range, which is beneficial to improving the accuracy and reliability of the positioning results.

[0040] In an optional implementation, in order to accurately determine the currently used predicted value of the preset observation, the above S104 may include the following steps:

[0041] S141, constructing an objective function based on a regularized Kalman filter algorithm, a preset regularization matrix and an observation model.

[0042] Among them, the observation model is used to represent the mapping relationship between the position-related parameters of the single-frequency receiver and the preset observation quantities. Specifically, different preset observation quantities can be combined in pairs to obtain a variety of observation models, and one or more observation models are further selected from these multiple observation models as observation models. For example, the position-related parameters of the single-frequency receiver are the parameters to be estimated of the single-frequency receiver, wherein the parameters to be estimated of the single-frequency reception may include, for example, but are not limited to: the clock error of the single-frequency receiver, the pseudorange hardware delay, the ionospheric delay, the phase ambiguity, etc. The above-mentioned multiple observation models may include a CP model based on a single-frequency code and phase, a CG model based on a single-frequency code and a semi-combination, and a GP model based on a semi-combination and phase, wherein the CP model is shown in the following formula (1), the CG model is shown in the following formula (2), and the GP model is shown in the following formula (3):

[0043]

[0044]

[0045]

[0046] Among them, Pi represents the pseudorange observation value of satellite i, L i represents the carrier phase observation value of satellite i, G i represents the observation value of the semi-composite combination of satellite i; ρ i represents the geometric distance from the single-frequency receiver to the satellite; c represents the speed of light; M i represents the tropospheric delay mapping function, d zpd represents the zenith tropospheric delay; represents the observation noise of pseudorange, The observation value noise of the phase is represented, and the receiver clock error parameter δt absorbs the hardware delay at the receiver. represents the noise of the observations of the semi-synthetic combination, Ionospheric path delay parameter I i Absorbs the satellite hardware delay; b i Represents the ambiguity parameters, which include integer ambiguities and the phase hardware delays of the satellite and the receiver.

[0047] Among the three observation models mentioned above, for the GP model and the CP model, the phase plays a major role, so their positioning results are almost the same. Based on this, the CG model and the CP model can be selected from the three observation models mentioned above as observation models, and both the CG model and the CP model are constrained by the ionospheric information provided by the global ionosphere maps (GIM).

[0048] In the embodiment of the present application, the regularized Kalman filter model can be filtered using the filter model shown in the following formula (4). Accordingly, in the above S141, based on the filter model and the observation model, an expression function of the predicted value of the preset observation quantity can be constructed.

[0049]

[0050] Where k represents the observation epoch; represents the predicted noise sequence; V k represents the observation noise sequence; Indicates the preset observation at epoch t k Observed value of Indicates the preset observation at epoch t k The predicted value of Φ k-1 is a non-singular state transfer matrix, H represents the observation value of the preset observation at epoch k-1; k represents the observation matrix, Z k It is an n×1 dimensional vector, which is the observed value of the preset observation at epoch k.

[0051] Furthermore, based on the expression function of the predicted value of the preset observation quantity and the preset regularization matrix, the objective function corresponding to the regularized Kalman filter algorithm can be constructed. The objective function is used to represent the mapping relationship between the value of the target optimization parameter and the predicted value of the preset observation quantity.

[0052] For example, the objective function is shown in the following formula (5):

[0053]

[0054] Among them, Ω(k) represents the target optimization parameter, represents the observed value of the preset observation at epoch k, V k represents the observation noise sequence, represents the predicted noise sequence, P k and Z k and The weight matrix of Σ k Z k The variance-covariance matrix of for The variance-covariance matrix of It is called the stabilization functional and plays a stabilizing role. Wherein R represents the preset regularization matrix, which is a symmetric non-negative definite matrix; α is the regularization parameter, which can be determined by the U-curve method.

[0055] S142, with the goal of minimizing the value of the target optimization parameter, based on the objective function, the historical observation value of the preset observation quantity and the preset parameter processing strategy for the position-related parameters, determine the predicted value of the preset observation quantity in the current epoch.

[0056] The preset parameter processing strategy is used to set the parameter value of the position-related parameter. The preset parameter processing strategy may include at least one of the following strategies:

[0057] Strategy 1: The calculation result of pseudorange single-point positioning is used as the initial coordinate value of the single-frequency receiver, the difference between the phase observation value and the pseudorange observation value is used as the initial value of the ambiguity, and the initial value of the ionospheric delay is calculated using the GIM grid ionosphere model to give a rough initial value to some parameters.

[0058] Strategy 2: Based on Strategy 1, an observation model with additional ionospheric constraints is used to perform calculations for a certain period of time to obtain the initial values ​​of position-related parameters such as coordinates, receiver clock errors, tropospheric delays, ionospheric delays, and ambiguities at the time of approximate convergence.

[0059] Strategy 3: Based on Strategy 2, select single-frequency receiver clock error parameters with strong constraints at the initial epoch to solve the model rank deficiency caused by the high correlation between location-related parameters, construct a full-rank single-frequency PPP model, and give all location-related parameters a more accurate initial value.

[0060] It should be noted that the epoch refers to the moment when the GNSS signal is received. In the time measurement system, in addition to determining the time unit, the starting point of time measurement must also be determined. This starting point is called the initial epoch of time measurement.

[0061] In the above S142, based on the preset parameter processing scheme, the parameter value of the position-related parameter can be determined, and further based on the historical observation value of the preset observation quantity and the parameter value of the position-related parameter, the predicted value that minimizes the value of the target optimization parameter is searched within the preset value range of the predicted observation quantity as the predicted value of the preset observation quantity in the current epoch.

[0062] In practical applications, when the observation model is not full of rank, the condition number of the weight matrix of the preset observation will be very large, and the accuracy of the pseudorange observation is not high, the condition number of the observation weight matrix will also be very large, and when there is a cycle slip, the number of satellites changes, and data is missing, the corresponding part of the variance matrix of the state parameter will be very large, making it highly pathological. These will eventually lead to a sudden increase in the condition number of the law matrix, affecting the positioning accuracy and stability of the single-frequency PPP. For this reason, Figure 2 As shown, if the observation model is not full rank, the above strategy 1 or strategy 2 can be selected as the preset parameter processing strategy for position-related parameters; if the observation model is full rank, strategy 3 is selected as the preset parameter processing strategy for position-related parameters.

[0063] In addition, the regularization method is a biased estimation method, which inevitably introduces bias while improving the ill-conditionedness of the law matrix. When the ill-conditionedness of the law matrix is ​​effectively improved, the introduction of bias reduces the reliability of parameter estimation. A good regularization matrix can effectively improve the ill-conditionedness of the matrix, minimize the introduction of bias, and make the solution of ill-conditioned problems more reliable. Therefore, the selection of the regularization matrix is ​​of great significance to the solution of ill-conditioned problems. Based on this, before the above S142, the corresponding preset regularization matrix can be constructed according to three special situations: whether the current epoch is the initial epoch, the preset parameter processing strategy for position-related parameters, and whether the historical observation values ​​of the preset observation quantity have cycle slips, changes in the number of satellites, and data missing.

[0064] Alternatively, if Figure 2 As shown, if the current epoch is not the initial epoch, a preset regularization matrix is ​​constructed based on a preset parameter processing strategy.

[0065] For example, in order to observe the noise weight matrix Pk To provide additional support for observations with larger variance, the regularization matrix R1 can be established based on the following formula (6), where n is the total number of parameters to be solved, m is the number of parameters with smaller variance in the state parameter weight matrix, and combined with the observation vector to obtain R pk =A T R1A; In order to adjust the weight matrix of state parameters when the initial values ​​of position-related parameters are inaccurate The minimum weight (maximum variance) in the additional support can be used to construct the first regularization matrix R based on formula (6): pk1 ; In order to improve the correlation between parameters, the second regularization matrix R is constructed based on formula (7) using the eigenvector corresponding to the smaller singular value pk2 , G j is the eigenvector corresponding to the small singular value after the eigenvalue decomposition of the normal matrix; further, if the preset parameter processing strategy is strategy 1, the preset regularization matrix is ​​R = R pk +R pk1 +R pk2 , if the default parameter processing strategy is strategy 2, then the default regularization matrix is ​​R = R pk +R pk2 ; If the default parameter processing strategy is strategy 3, the default regularization matrix is ​​R = R pk .

[0066]

[0067]

[0068] Alternatively, if Figure 2 As shown, if the current epoch is not the initial epoch and the historical observations of the preset observation quantity do not meet the preset regularization filtering conditions, the preset regularization matrix can be constructed in the following manner: constructing a first regularization matrix based on the minimum weight in the weight matrix of the historical observations of the preset observation quantity, and constructing a preset regularization matrix based on the first regularization matrix. The preset regularization filtering conditions include at least one of the following: the number of GNSS satellites in the current epoch is greater than the number of GNSS satellites in the previous epoch of the current epoch, the GNSS satellite signal has a cycle slip, and the historical observations of the preset observation quantity have data missing.

[0069] For example, Figure 2 As shown, the weight matrix of the state parameters can be The minimum weight (maximum variance) in the additional support can be used to construct the first regularization matrix R based on formula (6): pk1 , accordingly, the preset regularization matrix R can be R = R pk +R pk1 .

[0070] Optionally, if the current epoch is not the initial epoch and the historical observation values ​​of the preset observation quantity satisfy the preset regularization filtering condition, the preset regularization matrix can be constructed in the following manner: construct a first regularization matrix based on the minimum weight in the weight matrix of the historical observation values ​​of the preset observation quantity; perform eigenvalue decomposition on the normal matrix corresponding to the historical observation values ​​of the preset observation quantity to obtain the target eigenvector, and construct a second regularization matrix based on the target eigenvector; construct a preset regularization matrix based on the first regularization matrix and the second regularization matrix.

[0071] For example, if the condition number of the normal matrix exceeds the preset condition number threshold, the regularization matrix R1 can be established based on formula (6), and combined with the observation vector to obtain R pk =A T R1A, k is equal to the number of satellites; then, in order to improve the correlation between parameters, the second regularization matrix R is constructed based on formula (7) using the eigenvector corresponding to the smaller singular value pk2 ; Then, the preset regularization matrix R is constructed as R = R pk +R pk2 If the condition number of the normal matrix is ​​less than the preset condition number threshold, the regularization matrix R1 can be established based on formula (6) and combined with the observation vector to obtain R pk =A T R1A, k is equal to the number of satellites, then the preset regularization matrix R is constructed as R = R pk .

[0072] In the above-mentioned construction method of the preset regularization matrix, through the study of the conventional extended Kalman filter algorithm, it is found that when the process noise of the parameter is large (generally as a form of white noise modeling) or the initial variance of the newly added parameter is large (generally lacking a suitable initial value), the variance matrix of the preset observation The corresponding part will have a larger value. Since the variance matrix is ​​positive definite, we know that Some eigenvalues ​​of will be larger. It will usually have large eigenvalues ​​and be close to pathological (this depends on H k In addition, due to H k Usually the number of rows is greater than the number of columns, so There are often 0 eigenvalues ​​and the pathological state directly changes to rank deficiency. Furthermore, if Σ k If the array value is small, no matter Is sick or rank-deficient, will remain highly morbid, which leads to The inversion of the conventional extended Kalman filter algorithm is unstable, so the update is prone to abnormality. The additional support of minimum weight (maximum variance) in can overcome the numerical calculation defects of the conventional extended Kalman filter algorithm and improve the quality of the solution results.

[0073] In order to improve the positioning accuracy and convergence speed at the same time, the extended Kalman filter algorithm and the regularized Kalman filter algorithm can be used to estimate parameters according to whether the current epoch is the initial epoch, the preset parameter processing strategy for the position-related parameters, and whether the historical observation value of the preset observation value has cycle slips, changes in the number of satellites, and data missing, to determine the predicted value of the preset observation value in the current epoch. Specifically, optionally, as Figure 2 As shown, before the above S142, the GNSS single-frequency precise single-point positioning method provided in the embodiment of the present application may further include: judging whether the current epoch is an initial epoch and whether the historical observation value of the preset observation quantity meets the preset regularization filtering condition. Accordingly, if the current epoch is an initial epoch, or the current epoch is not an initial epoch and the historical observation value of the preset observation quantity does not meet the preset regularization filtering condition, the above S142 is executed.

[0074] If the current epoch is not the initial epoch and the historical observation values ​​of the preset observation quantity meet the preset regularization conditions, then before the above S104, the GNSS single-frequency precise single-point positioning method provided in the embodiment of the present application may also include: repeatedly executing the following processing multiple times until the acquired condition number reaches the preset condition number threshold: based on the extended Kalman filter algorithm and the historical observation values ​​of the preset observation quantity, determine the predicted value of the preset observation quantity in the current epoch, and obtain the condition number of the normal matrix corresponding to the predicted value.

[0075] It is understandable that the use of regularized Kalman filter algorithm for solving the problem also greatly improves the dynamic positioning of single-frequency PPP. Compared with static positioning, the noise weight matrix P is observed under dynamic conditions. k The positioning result fluctuates more when the extended Kalman filter algorithm is used for solution, while the regularized Kalman filter algorithm improves the P caused by factors such as inaccurate initial value, cycle slip, and change in the number of satellites. k and the weight matrix of the state parameters After the pathology, a more stable and more accurate dm-level dynamic positioning result can be obtained; compared with the single-frequency PPP positioning that fully adopts the extended Kalman filter algorithm, the accuracy of the east component, north component and elevation component of the CG model under the regularized Kalman filter algorithm is improved, and the positioning accuracy of the CP model is also greatly improved; in addition, the convergence time of experimental statistics shows that the regularized Kalman filter algorithm can also shorten the convergence time of dynamic positioning and achieve rapid positioning.

[0076] The embodiment of the present application shows a specific implementation of the above S104. Of course, it should be understood that the above S104 can also be implemented in other ways, and the embodiment of the present application does not limit this.

[0077] S106, determining the position of the single-frequency receiver at the current epoch based on the predicted value of the preset observation quantity at the current epoch.

[0078] Specifically, various positioning analysis technologies commonly used in the field can be used to analyze the predicted value of the preset observation quantity at the current epoch to obtain the position of the single-frequency receiver at the current epoch. The embodiments of the present application will not be elaborated in detail here.

[0079] Through the GNSS single-frequency precise single-point positioning method of the embodiment of the present application, based on the GNSS satellite signal received by the single-frequency receiver, the historical observation value of the preset observation quantity of the single-frequency receiver is determined, and then the regularization method is used to help improve the advantage of most pathological problems. Based on the regularized Kalman filtering algorithm and the historical observation value of the preset observation quantity, the predicted value of the preset observation quantity at the current epoch is determined. The pathological conditions of the weight matrix of the observation parameters and the weight matrix of the state parameters in the single-frequency PPP solution process can be solved, the condition number of the method matrix can be reduced, and the problem of a sudden increase in the condition number of the method matrix due to cycle slips, changes in the number of satellites, and data missing can be avoided, so that the condition number of the method matrix is ​​always maintained within a reasonable range, thereby improving the accuracy and reliability of the positioning results.

[0080] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] In addition, Figure 1 Corresponding to the GNSS single-frequency precise single-point positioning method shown, the embodiment of the present application also provides a GNSS single-frequency precise single-point positioning device. Figure 3 : is a structural diagram of a GNSS single-frequency precise single-point positioning device 300 provided by an embodiment of the present application, the device 300 includes:

[0082] A first determining unit 310 is configured to determine a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver;

[0083] A prediction unit 320, configured to determine a predicted value of the preset observation value at a current epoch based on a regularized Kalman filter algorithm and a historical observation value of the preset observation value;

[0084] The second determination unit 330 is configured to determine the position of the single-frequency receiver at the current epoch based on the predicted value of the preset observation value at the current epoch.

[0085] Optionally, the prediction unit is specifically used for:

[0086] Based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, an objective function is constructed, wherein the objective function is used to represent the mapping relationship between the value of the target optimization parameter and the predicted value of the preset observation quantity, and the observation model is used to represent the mapping relationship between the position-related parameter of the single-frequency receiver and the preset observation quantity;

[0087] With the goal of minimizing the value of the target optimization parameter, based on the objective function, the historical observation value of the preset observation quantity and the preset parameter processing strategy for the position-related parameters, the predicted value of the preset observation quantity in the current epoch is determined.

[0088] Optionally, the prediction unit constructs an objective function based on the regularized Kalman filter algorithm, a preset regularization matrix and an observation model, including:

[0089] If the current epoch is an initial epoch, or the current epoch is not an initial epoch and the historical observation value of the preset observation quantity does not meet the preset regularization filtering condition, an objective function is constructed based on the regularized Kalman filtering algorithm, the preset regularization matrix and the observation model, wherein the preset regularization filtering condition includes at least one of the following: the number of GNSS satellites in the current epoch is greater than the number of GNSS satellites in the epoch before the current epoch, the GNSS satellite signal has a cycle slip, and the historical observation value of the preset observation quantity has missing data.

[0090] Optionally, if the current epoch is not an initial epoch and the historical observation value of the preset observation quantity does not satisfy the preset regularization filtering condition, the prediction unit is further used to:

[0091] Before constructing the objective function based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, constructing a first regularization matrix based on the minimum weight in the weight matrix of the historical observation values ​​of the preset observation quantity;

[0092] Based on the first regularization matrix, the preset regularization matrix is ​​constructed.

[0093] Optionally, if the current epoch is not an initial epoch and the historical observation value of the preset observation quantity satisfies the preset regularization filtering condition, the prediction unit is further used to:

[0094] Before determining the predicted value of the preset observation quantity at the current epoch based on the regularized Kalman filter algorithm and the historical observation values ​​of the preset observation quantity, the following processing is repeated multiple times until the acquired condition number reaches the preset condition number threshold: based on the extended Kalman filter algorithm and the historical observation values ​​of the preset observation quantity, the predicted value of the preset observation quantity at the current epoch is determined, and the condition number of the normal matrix corresponding to the predicted value is obtained.

[0095] Optionally, the prediction unit is further used for:

[0096] Before constructing the objective function based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, constructing a first regularization matrix based on the minimum weight in the weight matrix of the historical observation values ​​of the preset observation quantity;

[0097] Performing eigenvalue decomposition on the normal matrix corresponding to the historical observation value of the preset observation quantity to obtain a target eigenvector;

[0098] Based on the target feature vector, construct a second regularization matrix;

[0099] The preset regularization matrix is ​​constructed based on the first regularization matrix and the second regularization matrix.

[0100] Optionally, if the current epoch is an initial epoch, the prediction unit is further configured to:

[0101] Before constructing the objective function based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, the preset regularization matrix is ​​constructed based on the preset parameter processing strategy.

[0102] Obviously, the GNSS single-frequency precision single-point positioning device provided in the embodiment of the present application can be used as Figure 1 The execution body of the GNSS single-frequency precise point positioning method shown, for example, Figure 1 In the GNSS single-frequency precise point positioning method shown in FIG. 1 , step S102 can be performed by Figure 3 The first determination unit in the GNSS single-frequency precise point positioning device shown in FIG. 1 is executed, and step S104 can be performed by Figure 3 The prediction unit in the GNSS single-frequency precise point positioning device shown in FIG. 1 is executed, and step S106 can be performed by Figure 3 The second determination unit in the GNSS single-frequency precise point positioning device is executed.

[0103] According to another embodiment of the present application, Figure 3The various units in the GNSS single-frequency precise point positioning device shown can be individually or completely combined into one or several other units for execution, or one (some) of the units can be further divided into multiple functionally smaller units for execution, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the function of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the GNSS single-frequency precise point positioning device can also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0104] According to another embodiment of the present application, the program can be executed on a general computing device (such as a computer) including a central processing unit (CPU), a random access memory medium (RAM), a read-only memory medium (ROM), and other processing elements and storage elements. Figure 2 A computer program (including program code) for each step involved in the corresponding method shown in FIG. Figure 3 The GNSS single-frequency precise single-point positioning device shown in the figure, and the GNSS single-frequency precise single-point positioning method for implementing the embodiment of the present application. The computer program can be recorded on, for example, a computer-readable storage medium, and transferred to an electronic device through the computer-readable storage medium, and run therein.

[0105] The GNSS single-frequency precise single-point positioning device provided in the embodiment of the present application determines the historical observation value of the preset observation quantity of the single-frequency receiver based on the GNSS satellite signal received by the single-frequency receiver, and then uses the regularization method to help improve the advantages of most pathological problems. Based on the regularized Kalman filtering algorithm and the historical observation value of the preset observation quantity, the predicted value of the preset observation quantity at the current epoch is determined, which can solve the pathological problems of the weight matrix of the observation parameters and the weight matrix of the state parameters in the single-frequency PPP solution process, reduce the condition number of the law matrix, and avoid the problem of a sudden increase in the condition number of the law matrix due to cycle slips, changes in the number of satellites, and data missing, so that the condition number of the law matrix is ​​always maintained within a reasonable range, thereby improving the accuracy and reliability of the positioning results.

[0106] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 4At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0107] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0108] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0109] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a GNSS single-frequency precision single-point positioning device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0110] Determining a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver;

[0111] Determine a predicted value of the preset observation value at a current epoch based on a regularized Kalman filter algorithm and historical observation values ​​of the preset observation value;

[0112] Based on the predicted value of the preset observation quantity in the current epoch, the position of the single-frequency receiver in the current epoch is determined.

[0113] The above application Figure 1The method performed by the GNSS single-frequency precision single-point positioning device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0114] The electronic device may also perform Figure 1 The method realizes the GNSS single-frequency precise single-point positioning device in Figure 1 , Figure 2 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0115] Of course, in addition to software implementation methods, the electronic device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0116] The embodiment of the present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 1 The method of the embodiment shown is specifically used to perform the following operations:

[0117] Determining a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver;

[0118] Determine a predicted value of the preset observation value at a current epoch based on a regularized Kalman filter algorithm and historical observation values ​​of the preset observation value;

[0119] Based on the predicted value of the preset observation quantity in the current epoch, the position of the single-frequency receiver in the current epoch is determined.

[0120] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0121] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a navigation device, or a combination of any of these devices.

[0122] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0123] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0124] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A GNSS single-frequency precise single-point positioning method, characterized in that: include: Determining a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver; Determine a predicted value of the preset observation value at a current epoch based on a regularized Kalman filter algorithm and historical observation values ​​of the preset observation value; Based on the predicted value of the preset observation quantity at the current epoch, determining the position of the single-frequency receiver at the current epoch; wherein, The step of determining the predicted value of the preset observation value at the current epoch based on the regularized Kalman filter algorithm and the historical observation value of the preset observation value comprises: Based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, an objective function is constructed, wherein the objective function is used to represent the mapping relationship between the value of the target optimization parameter and the predicted value of the preset observation quantity, and the observation model is used to represent the mapping relationship between the position-related parameter of the single-frequency receiver and the preset observation quantity; With the goal of minimizing the value of the target optimization parameter, based on the objective function, the historical observation value of the preset observation quantity and the preset parameter processing strategy for the position-related parameters, the predicted value of the preset observation quantity in the current epoch is determined.

2. The method according to claim 1, characterized in that The objective function is constructed based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, including: If the current epoch is an initial epoch, or the current epoch is not an initial epoch and the historical observation value of the preset observation quantity does not meet the preset regularized filtering condition, an objective function is constructed based on the regularized Kalman filtering algorithm, the preset regularization matrix and the observation model, wherein the preset regularized filtering condition includes at least one of the following: the number of GNSS satellites in the current epoch is greater than the number of GNSS satellites in the epoch before the current epoch, a cycle slip occurs in the GNSS satellite signal, and there is missing data in the historical observation value of the preset observation quantity.

3. The method according to claim 2, characterized in that If the current epoch is not an initial epoch and the historical observation value of the preset observation quantity does not satisfy the preset regularized filtering condition, before constructing the objective function based on the regularized Kalman filter algorithm, the preset regularized matrix and the observation model, the method further includes: Constructing a first regularization matrix based on the minimum weight in the weight matrix of the historical observation values ​​of the preset observation quantity; Based on the first regularization matrix, the preset regularization matrix is ​​constructed.

4. The method according to claim 2, characterized in that: If the current epoch is not an initial epoch and the historical observation value of the preset observation quantity satisfies the preset regularized filtering condition, before determining the predicted value of the preset observation quantity at the current epoch based on the regularized Kalman filtering algorithm and the historical observation value of the preset observation quantity, the method further includes: Repeat the following process multiple times until the obtained condition number reaches the preset condition number threshold: based on the extended Kalman filter algorithm and the historical observation value of the preset observation quantity, determine the predicted value of the preset observation quantity at the current epoch, and obtain the condition number of the normal matrix corresponding to the predicted value.

5. The method according to claim 4, characterized in that Before constructing the objective function based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, the method further includes: Constructing a first regularization matrix based on the minimum weight in the weight matrix of the historical observation values ​​of the preset observation quantity; Performing eigenvalue decomposition on the normal matrix corresponding to the historical observation value of the preset observation quantity to obtain a target eigenvector; Based on the target feature vector, construct a second regularization matrix; The preset regularization matrix is ​​constructed based on the first regularization matrix and the second regularization matrix.

6. The method according to claim 1, characterized in that If the current epoch is an initial epoch, before constructing the objective function based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, the method further includes: Based on the preset parameter processing strategy, the preset regularization matrix is ​​constructed.

7. A GNSS single-frequency precision single-point positioning device, characterized in that: include: A first determining unit, configured to determine a historical observation value of a preset observation quantity of the single-frequency receiver based on a GNSS satellite signal received by the single-frequency receiver; A prediction unit, configured to determine a predicted value of the preset observation value at a current epoch based on a regularized Kalman filter algorithm and a historical observation value of the preset observation value; The second determination unit is used to determine the position of the single-frequency receiver at the current epoch based on the predicted value of the preset observation quantity at the current epoch; wherein, The step of determining the predicted value of the preset observation value at the current epoch based on the regularized Kalman filter algorithm and the historical observation value of the preset observation value comprises: Based on the regularized Kalman filter algorithm, the preset regularization matrix and the observation model, an objective function is constructed, wherein the objective function is used to represent the mapping relationship between the value of the target optimization parameter and the predicted value of the preset observation quantity, and the observation model is used to represent the mapping relationship between the position-related parameter of the single-frequency receiver and the preset observation quantity; With the goal of minimizing the value of the target optimization parameter, based on the objective function, the historical observation value of the preset observation quantity and the preset parameter processing strategy for the position-related parameters, the predicted value of the preset observation quantity in the current epoch is determined.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as claimed in any one of claims 1 to 6.

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