STC-based graph optimization PPP / INS fusion positioning method and system

Abstract

This invention discloses a graph optimization PPP / INS fusion positioning method and system based on STC (Simultaneous Tunneling and Graph Control). The method includes the following steps: obtaining PPP floating-point solutions, single-difference floating-point ambiguities, and INS prior information; performing fixed updates on the PPP floating-point solutions and single-difference floating-point ambiguities based on the INS prior information; constructing PPP factors based on the fixed-updated PPP floating-point solutions and single-difference floating-point ambiguities; constructing IMU pre-integration factors based on the INS prior information; constructing an objective function based on factor graph optimization based on the PPP factors and IMU pre-integration factors; and solving the state vector of the dynamic target using the objective function. Therefore, this invention achieves an optimal balance between accuracy, robustness, and real-time performance through PPP / INS fusion positioning using STC technology, making it particularly suitable for high-precision positioning requirements in dynamically changing environments.

Description

Technical Field

[0001] This invention relates to the field of integrated navigation and high-precision positioning technology, and in particular to a graph-optimized PPP / INS fusion positioning method and system based on STC. Background Technology

[0002] Precise Point Positioning (PPP) utilizes precise ephemeris and clock correction data from the Global Navigation Satellite System (GNSS) to achieve high-precision positioning at the centimeter to decimeter level using a single receiver. However, its convergence time is relatively long, typically requiring tens of minutes, and its positioning performance deteriorates sharply in signal-blocked environments (such as urban canyons and tunnels), sometimes even failing to provide a valid solution. Furthermore, PPP observation noise is affected by factors such as satellite geometric distribution and atmospheric delay. Traditional methods employ fixed covariance, which struggles to adapt to error variations in dynamic environments, resulting in insufficient robustness of fused positioning.

[0003] INS (Inertial Navigation System) measures the angular velocity and linear acceleration of a vehicle using gyroscopes and accelerometers, and integrates the results to obtain position, velocity, and attitude. INS can provide high-precision attitude estimation in the short term, but its errors accumulate over time. In particular, gyroscope drift and accelerometer bias can lead to long-term positioning failure.

[0004] Traditional Kalman filtering methods assume that noise follows a fixed Gaussian distribution when estimating the state, which cannot adapt to the time-varying error characteristics of PPP / INS.

[0005] Existing loosely coupled PPP / INS fusion methods simply combine the position solutions from both, failing to fully utilize the tight coupling between raw IMU data and PPP observations. This leads to a sharp drop in positioning accuracy when GNSS signals are intermittent. Tightly coupled methods directly fuse raw GNSS observations and IMU data, utilizing the short-term stability of INS to assist in GNSS ambiguity resolution. However, this approach involves significant computational costs, requires real-time optimization of multi-source data, and the fixed covariance assumption is difficult to adapt to dynamic environments.

[0006] To address the aforementioned issues, Factor Graph Optimization (FGO) models the state estimation problem as a probabilistic graph and solves for the optimal solution through nonlinear optimization. Furthermore, using a semi-tightly coupled (STC) approach with factor graph optimization for PPP / INS fusion localization, STC inherits the advantages of both LC and TC while overcoming their shortcomings to some extent, achieving flexible expansion of multi-sensor fusion in the location domain. Summary of the Invention

[0007] This invention provides a graph-optimized PPP / INS fusion positioning method and system based on STC. While inheriting the advantages of LC and TC, STC overcomes their shortcomings to a certain extent. It realizes the elastic expansion of multi-sensor fusion in the position domain. By using STC technology to perform PPP / INS fusion positioning, the optimal balance can be achieved among accuracy, robustness and real-time performance, which is especially suitable for high-precision positioning requirements in dynamically changing environments.

[0008] Firstly, a graph optimization PPP / INS fusion positioning method based on STC is provided, including the following steps:

[0009] Obtain the PPP floating-point solution, single-difference floating-point ambiguity, and INS prior information;

[0010] The PPP floating-point solution and the single-difference floating-point ambiguity are updated in a fixed manner based on the INS prior information.

[0011] After the PPP floating-point solution and the single-difference floating-point ambiguity are fixed and updated, the PPP factor is constructed.

[0012] Construct the IMU pre-integration factor based on the INS prior information;

[0013] Construct an objective function based on factor graph optimization using the PPP factor and the IMU pre-integration factor;

[0014] The state vector of the dynamic objective is calculated using the objective function.

[0015] In some embodiments, the step of updating the PPP floating-point solution and the single-difference floating-point ambiguity based on the INS prior information includes:

[0016] The single-difference floating-point ambiguity is projected into multiple EWL ambiguities and multiple WL ambiguities;

[0017] The EWL ambiguity is fixed based on the rounding method;

[0018] The constraints of the PPP floating-point solution and the fixed EWL ambiguity are updated using INS prior information.

[0019] The WL ambiguity is fixed based on the LAMBDA method;

[0020] The constraints of the PPP floating-point solution and the WL ambiguity after fixation are updated using INS prior information.

[0021] In some embodiments, the method for projecting the single-difference floating-point ambiguity into multiple EWL ambiguities and multiple WL ambiguities is shown in the following formula:

[0022] ;

[0023] In the formula, This is an inter-satellite single difference symbol; For PPP inter-satellite single-difference ambiguity, subscripts 1-5 represent different frequencies;

[0024] The method for constraining and updating the PPP floating-point solution and the fixed EWL ambiguity using INS prior information is shown in the following formula:

[0025] ;

[0026] In the formula, The original floating-point coordinates and single-difference floating-point ambiguity; Updated floating-point coordinates and single-difference floating-point ambiguity for EWL; and These are the EWL ambiguity and the fixed ambiguity, respectively; and They are respectively and The corresponding covariance matrix, Associate with prior information from INS.

[0027] In some embodiments, the method for constructing the PPP factor is shown in the following formula:

[0028] ;

[0029] In the formula, For PPP factor, PPP location results; It is a state vector; Geodetic coordinates calculated for the GNSS positioning estimator; For lever arm, Let be the transformation matrix from the b-system to the w-system. This refers to the position within the w-system.

[0030] In some embodiments, the method for constructing the IMU pre-integration factor based on the INS prior information is shown in the following formula:

[0031] ;

[0032] in, ;

[0033] ;

[0034] In the formula, For IMU pre-integration factor, For state vectors, for Prior information about the relevant variables; and This is the pre-integration of the gravity and Coriolis force terms with respect to position and velocity, respectively; This is the projection of the gravity vector onto the w-frame; This is the projection of the Earth's rotation in the w-frame; , and These are the predicted components for position, velocity, and attitude, respectively. The zero bias is added to the table at time k; Zero bias for the gyroscope; , and These represent the position, velocity, and attitude in the w-frame, respectively. For the transformation quaternion from the b-system to the w-system; Let be the transformation matrix from the b-system to the w-system; is the time interval between adjacent epochs; k is the epoch; For time intervals; For the transformation quaternion from the b-system to the w-system; for Era to Quaternions for epochal world coordinate system transformation.

[0035] In some embodiments, the method for constructing an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor is as follows:

[0036] ;

[0037] In the formula, The objective function is... For marginalized residuals; It is a Jacobian matrix; PPP factor; This is the IMU pre-integration factor.

[0038] In some embodiments, the state vector of the dynamic target As shown in the following formula:

[0039] ;

[0040] in, ;

[0041] In the formula, , The state nodes are respectively in Three-dimensional position and velocity under the system; As the carrier's posture; for The set of state vectors for each epoch; The zero bias is added to the table at time k; is zero bias for the gyroscope; T is the matrix transpose.

[0042] Secondly, a graph-optimized PPP / INS fusion positioning system based on STC is provided, including:

[0043] The acquisition module is used to acquire the PPP floating-point solution and single-difference floating-point ambiguity, and to acquire INS prior information;

[0044] A fixed update module, which is communicatively connected to the acquisition module, is used to perform fixed updates on the PPP floating-point solution and the single-difference floating-point ambiguity based on the INS prior information.

[0045] The PPP factor construction module is communicatively connected to the fixed update module and is used to construct the PPP factor after the PPP floating-point solution and the single-difference floating-point ambiguity are fixedly updated.

[0046] The IMU pre-integration factor construction module is communicatively connected to the acquisition module and is used to construct the IMU pre-integration factor based on the INS prior information.

[0047] The objective function construction module, communicatively connected to the PPP factor construction module and the IMU pre-integration factor construction module, is used to construct an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor; and...

[0048] The solution module is communicatively connected to the objective function construction module and is used to solve the state vector of the dynamic objective using the objective function.

[0049] Thirdly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the STC-based graph optimization PPP / INS fusion positioning method as described above.

[0050] Fourthly, an electronic device is provided, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor, when executing the computer program, implements the STC-based graph optimization PPP / INS fusion positioning method as described above.

[0051] Compared with existing technologies, the advantages of this invention are as follows: It uses a factor graph-optimized semi-tight combination (STC) method for PPP / INS fusion positioning. First, the PPP floating-point solution and single-difference floating-point ambiguity are fixedly updated based on INS prior information. Then, PPP factors are constructed based on the fixed-updated PPP floating-point solution and single-difference floating-point ambiguity. Next, IMU pre-integration factors are constructed based on INS prior information. Then, an objective function based on factor graph optimization is constructed based on the PPP factors and the IMU pre-integration factors. Finally, the state vector of the dynamic target is calculated using the objective function, yielding the position, velocity, and attitude navigation results of the dynamic target. Therefore, this invention's STC inherits the advantages of both loose and tight coupling while overcoming their shortcomings to some extent, achieving flexible expansion of multi-sensor fusion in the position domain, making it highly suitable for GNSS / INS integrated navigation of dynamic targets. Through STC technology, PPP / INS fusion can achieve an optimal balance between accuracy, robustness, and real-time performance, making it particularly suitable for high-precision positioning requirements in dynamically changing environments. Meanwhile, STC uses location domain information as a link to achieve mutual assistance and fusion between GNSS and INS. The solutions of each sensor remain relatively independent, which is beneficial to the stability of the algorithm and makes it easy to expand to more sensors for fusion. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating an embodiment of the graph optimization PPP / INS fusion positioning method based on STC according to the present invention.

[0053] Figure 2 This is a schematic diagram of the structure of a graph-optimized PPP / INS fusion positioning system based on STC according to the present invention. Detailed Implementation

[0054] Referring now to specific embodiments of the invention, examples of which are illustrated in the accompanying drawings. Although the invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. Rather, it is intended to cover variations, modifications, and equivalents included within the spirit and scope of the invention. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0055] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Note: The examples described below are merely specific examples and are not intended to limit the embodiments of the present invention to the specific steps, values, conditions, data, order, etc. Those skilled in the art can utilize the concept of the present invention to construct more embodiments not mentioned herein by reading this specification.

[0057] See Figure 1 As shown, this embodiment of the invention provides a graph optimization PPP / INS fusion positioning method based on STC, including the following steps:

[0058] S100, obtain PPP floating-point solution, single-difference floating-point ambiguity and INS prior information;

[0059] PPP floating-point solution refers to the precise single-point positioning solution result without fixed carrier phase integer ambiguity. Its core feature is that it directly retains the real number estimate of ambiguity and eventually achieves decimeter-level positioning accuracy through long-term convergence. It is a key initialization stage of the PPP / INS tightly coupled system.

[0060] PPP single-difference floating-point ambiguity refers to the non-integer ambiguity estimate after inter-station single-difference (SD) processing in precise single-point positioning (PPP). It is a key intermediate quantity in PPP-RTK or network PPP, used to eliminate satellite-end errors and assist in subsequent ambiguity fixing, ultimately achieving centimeter-level positioning.

[0061] Prior information in inertial navigation systems (INS) refers to the statistical description of the initial state and uncertainties of the INS at the start of INS initialization or integrated navigation filtering (such as GNSS / INS loose / tight combination). It is the basis for starting filters (such as Kalman filters) and directly affects the convergence speed and accuracy of navigation solutions.

[0062] S200, based on the INS prior information, the PPP floating-point solution and the single-difference floating-point ambiguity are updated in a fixed manner;

[0063] S300, after the PPP floating-point solution and the single-difference floating-point ambiguity are fixedly updated, the PPP factor is constructed;

[0064] S400, Construct the IMU pre-integration factor based on the INS prior information;

[0065] S500, construct an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor;

[0066] S600, use the objective function to calculate the state vector of the dynamic target.

[0067] Specifically, in this embodiment, PPP (Precise Point Positioning) and INS (Inertial Navigation System) are two commonly used positioning technologies. Existing loosely coupled PPP / INS fusion methods simply combine the position solutions of the two, failing to fully utilize the tight coupling between raw IMU data and PPP observations, leading to a sharp drop in positioning accuracy when GNSS signals are intermittent. Tightly coupled methods directly fuse raw GNSS observations and IMU data, utilizing the short-term stability of INS to assist in GNSS ambiguity resolution, but this involves high computational cost, requires real-time optimization of multi-source data, and the fixed covariance assumption is difficult to adapt to dynamic environments. To address these issues, FGO models the state estimation problem as a probabilistic graph and solves for the optimal solution through nonlinear optimization. The semi-tight combination STC method using factor graph optimization for PPP / INS fusion positioning first updates the PPP floating-point solution and single-difference floating-point ambiguity based on INS prior information; then constructs PPP factors based on the updated PPP floating-point solution and single-difference floating-point ambiguity; next, constructs IMU pre-integration factors based on INS prior information; then, constructs an objective function based on factor graph optimization using the PPP factors and the IMU pre-integration factors; finally, uses the objective function to calculate the state vector of the dynamic target, thus obtaining the position, velocity, and attitude navigation results of the dynamic target. Therefore, this invention's STC, while inheriting the advantages of loose coupling and tight coupling, overcomes their shortcomings to some extent, achieving flexible expansion of multi-sensor fusion in the position domain, making it highly suitable for GNSS / INS integrated navigation of dynamic targets. Through STC technology, the PPP / INS fusion system can achieve an optimal balance between accuracy, robustness, and real-time performance, especially suitable for high-precision positioning requirements in dynamically changing environments. Simultaneously, STC uses position domain information as a link to achieve mutual assistance and fusion between GNSS and INS, while maintaining relative independence in the solutions of each sensor, which is beneficial to the stability of the algorithm and facilitates the expansion to fuse more sensors.

[0068] Preferably, in another embodiment of this application, step S200, which involves a fixed update step of the PPP floating-point solution and the single-difference floating-point ambiguity based on the INS prior information, includes:

[0069] S210, the single-difference floating-point ambiguity is projected into multiple EWL ambiguities and multiple WL ambiguities;

[0070] S220, the EWL ambiguity is fixed based on the rounding method;

[0071] S230, Use INS prior information to update the constraints of the PPP floating-point solution and the EWL ambiguity after it has been fixed;

[0072] S240, The WL ambiguity is fixed based on the LAMBDA method;

[0073] S250, use INS prior information to update the constraints of the PPP floating-point solution and the WL ambiguity after it has been fixed.

[0074] Specifically, in this embodiment, without precise ionospheric constraints, the ionospheric parameters and ambiguities are strongly correlated, making them difficult to separate. To mitigate the influence of the ionosphere, EWL and WL ambiguities are typically constructed first in the parameter domain. Due to their longer wavelengths, ionospheric errors have a smaller impact on them compared to the original ambiguities, making them easier to fix.

[0075] The method for projecting the single-difference floating-point ambiguity into multiple EWL ambiguities and multiple WL ambiguities is shown in the following formula:

[0076] ;

[0077] In the formula, This is an inter-satellite single difference symbol; The values ​​represent the PPP inter-satellite single-difference ambiguity, with subscripts 1-5 indicating different frequencies.

[0078] This invention fixes ambiguity step by step from easy to difficult. First, it fixes the EWL and WL ambiguities with longer wavelengths. For EWL ambiguities, it uses the rounding method to fix them, and discards EWL ambiguities with a fractional part greater than 0.2 weeks to avoid incorrect fixing.

[0079] The method for updating the constraints of the PPP floating-point solution and the fixed EWL ambiguity using INS prior information is shown in the following formula:

[0080] ;

[0081] In the formula, The original floating-point coordinates and single-difference floating-point ambiguity; Updated floating-point coordinates and single-difference floating-point ambiguity for EWL; and These are the EWL ambiguity and the fixed ambiguity, respectively; and They are respectively and The corresponding covariance matrix, Associate with prior information from INS.

[0082] After completing the EWL ambiguity update, the WL ambiguity is fixed using the LAMBDA method. Then, the coordinates and ambiguity parameters are updated using the same formula for constraining and updating the EWL ambiguity, which will not be elaborated here. After adding EWL and WL constraints, the positioning accuracy at the decimeter level can be basically met, and the single-difference ambiguity can theoretically be better separated from the ionospheric parameters.

[0083] High-precision INS prior position can improve floating-point ambiguity accuracy, thereby increasing the success rate of ambiguity fixation. The following will demonstrate the benefits of INS constraints for AR (ambiguity fixation) using formulas. For ease of derivation, this invention takes the first observation epoch after a GNSS interruption as an example. At this time, the ambiguity parameters need to be initialized. In the covariance matrix, the ambiguity parameters and other parameters are not correlated. Therefore, the prior covariance matrix can be written in block matrix form as follows:

[0084] ;

[0085] In the formula This represents the covariance matrix corresponding to the INS prediction location error; This is the covariance matrix corresponding to velocity, attitude, and zero bias error; This is the initial ambiguity covariance matrix.

[0086] Let matrix A = ;

[0087] ,but Let Q be a block matrix, then That is through It is derived from the block matrix form.

[0088] The updated ambiguity covariance matrix obtained according to the Kalman filter formula is as follows:

[0089] ;

[0090] In the formula The carrier wavelength; The measurement variance matrix representing the phase; after update Subject to satellite geometry configuration matrix and The stronger the INS prior information constraint, the greater the ambiguity covariance after the update. The smaller the value, the better the estimation accuracy of floating-point ambiguity can be, provided that the prior information provided by INS is more accurate than that of SPP.

[0091] Preferably, in another embodiment of this application, the method for constructing the PPP factor based on the fixed-updated PPP floating-point solution and the single-difference floating-point ambiguity in step S300 is as follows:

[0092] ;

[0093] In the formula, For PPP factor, PPP location results; It is a state vector; Geodetic coordinates calculated for the GNSS positioning estimator; For lever arm, Let be the transformation matrix from the b-system to the w-system. This refers to the position within the w-system.

[0094] Preferably, in another embodiment of this application, the influence of Earth's rotation is considered in the pre-integration to obtain the IMU pre-integration factor; in S400, the method for constructing the IMU pre-integration factor based on the INS prior information is shown in the following formula:

[0095] ;

[0096] in, ;

[0097] ;

[0098] In the formula, For IMU pre-integration factor, For state vectors, for Prior information about the relevant variables; and This is the pre-integration of the gravity and Coriolis force terms with respect to position and velocity, respectively; This is the projection of the gravity vector onto the w-frame; This is the projection of the Earth's rotation in the w-frame; , and These are the predicted components for position, velocity, and attitude, respectively. The zero bias is added to the table at time k; Zero bias for the gyroscope; , and These represent the position, velocity, and attitude in the w-frame, respectively. For the transformation quaternion from the b-system to the w-system; Let be the transformation matrix from the b-system to the w-system; is the time interval between adjacent epochs; k is the epoch; For time intervals; for Era to Quaternions for epochal world coordinate system transformation; It is a quaternion for the transformation from the b-system to the w-system.

[0099] Preferably, in another embodiment of this application, the method for constructing an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor in step S500 is as follows:

[0100] ;

[0101] In the formula, The objective function is... For marginalized residuals; It is a Jacobian matrix; PPP factor; This is the IMU pre-integration factor.

[0102] Preferably, in another embodiment of this application, the state vector of the dynamic target As shown in the following formula:

[0103] ;

[0104] in, ;

[0105] In the formula, , The state nodes are respectively in Three-dimensional position and velocity under the system; As the carrier's posture; for The set of state vectors for each epoch; The zero bias is added to the table at time k; is zero bias for the gyroscope; T is the matrix transpose.

[0106] See also Figure 2 As shown in the figure, an embodiment of the present invention provides a graph-optimized PPP / INS fusion positioning system based on STC, comprising:

[0107] The acquisition module is used to acquire the PPP floating-point solution and single-difference floating-point ambiguity, and to acquire INS prior information;

[0108] A fixed update module, which is communicatively connected to the acquisition module, is used to perform fixed updates on the PPP floating-point solution and the single-difference floating-point ambiguity based on the INS prior information.

[0109] The PPP factor construction module is communicatively connected to the fixed update module and is used to construct the PPP factor after the PPP floating-point solution and the single-difference floating-point ambiguity are fixedly updated.

[0110] The IMU pre-integration factor construction module is communicatively connected to the acquisition module and is used to construct the IMU pre-integration factor based on the INS prior information.

[0111] The objective function construction module, communicatively connected to the PPP factor construction module and the IMU pre-integration factor construction module, is used to construct an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor; and...

[0112] The solution module is communicatively connected to the objective function construction module and is used to solve the state vector of the dynamic objective using the objective function.

[0113] In summary, the beneficial effects of this invention are as follows: The PPP / INS fusion positioning is performed using a factor graph-optimized semi-compact combination (STC) method. First, the PPP floating-point solution and single-difference floating-point ambiguity are fixedly updated based on INS prior information. Then, PPP factors are constructed based on the fixed-updated PPP floating-point solution and single-difference floating-point ambiguity. Next, IMU pre-integration factors are constructed based on INS prior information. Then, an objective function based on factor graph optimization is constructed based on the PPP factors and the IMU pre-integration factors. Finally, the state vector of the dynamic target is calculated using the objective function, yielding the position, velocity, and attitude navigation results of the dynamic target. Therefore, this invention's STC method inherits the advantages of both loose and tight coupling while overcoming their shortcomings to some extent. It achieves flexible expansion of multi-sensor fusion in the position domain, making it highly suitable for GNSS / INS integrated navigation of dynamic targets. Through STC technology, the PPP / INS fusion system can achieve an optimal balance between accuracy, robustness, and real-time performance, making it particularly suitable for high-precision positioning requirements in dynamically changing environments. Meanwhile, STC uses location domain information as a link to achieve mutual assistance and fusion between GNSS and INS. The solutions of each sensor remain relatively independent, which is beneficial to the stability of the algorithm and makes it easy to expand to more sensors for fusion.

[0114] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0115] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements all or part of the method steps of the above method.

[0116] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0117] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor. When the processor executes the computer program, it implements all or part of the method steps described above.

[0118] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0119] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0120] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0121] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A graph optimization PPP / INS fusion positioning method based on STC, characterized in that, Includes the following steps: Obtain the PPP floating-point solution, single-difference floating-point ambiguity, and INS prior information; The PPP floating-point solution and the single-difference floating-point ambiguity are updated in a fixed manner based on the INS prior information. After the PPP floating-point solution and the single-difference floating-point ambiguity are fixed and updated, the PPP factor is constructed. Construct the IMU pre-integration factor based on the INS prior information; Construct an objective function based on factor graph optimization using the PPP factor and the IMU pre-integration factor; The state vector of the dynamic objective is calculated using the objective function.

2. The STC-based graph optimization PPP / INS fusion positioning method as described in claim 1, characterized in that, The step of updating the PPP floating-point solution and the single-difference floating-point ambiguity based on the INS prior information includes: The single-difference floating-point ambiguity is projected into multiple EWL ambiguities and multiple WL ambiguities; The EWL ambiguity is fixed based on the rounding method; The constraints of the PPP floating-point solution and the fixed EWL ambiguity are updated using INS prior information. The WL ambiguity is fixed based on the LAMBDA method; The constraints of the PPP floating-point solution and the WL ambiguity after fixation are updated using INS prior information.

3. The STC-based graph optimization PPP / INS fusion positioning method as described in claim 2, characterized in that, The method for projecting the single-difference floating-point ambiguity into multiple EWL ambiguities and multiple WL ambiguities is shown in the following formula: ； In the formula, This is an inter-satellite single difference symbol; For PPP inter-satellite single-difference ambiguity, subscripts 1-5 represent different frequencies; The method for constraining and updating the PPP floating-point solution and the fixed EWL ambiguity using INS prior information is shown in the following formula: ； In the formula, The original floating-point coordinates and single-difference floating-point ambiguity; Updated floating-point coordinates and single-difference floating-point ambiguity for EWL; and These are the EWL ambiguity and the fixed ambiguity, respectively; and They are respectively and The corresponding covariance matrix, Associate with prior information from INS.

4. The STC-based graph optimization PPP / INS fusion positioning method as described in claim 1, characterized in that, The method for constructing the PPP factor is shown in the following formula: ； In the formula, For PPP factor, For PPP location results, It is a state vector; Geodetic coordinates calculated for the GNSS positioning estimator; For lever arm; Let be the transformation matrix from the b-system to the w-system; This refers to the position within the w-system.

5. The STC-based graph optimization PPP / INS fusion positioning method as described in claim 1, characterized in that, The method for constructing the IMU pre-integration factor based on the INS prior information is shown in the following formula: ； in, ; ； In the formula, For IMU pre-integration factor, For state vectors, for Prior information about the relevant variables; and This is the pre-integration of the gravity and Coriolis force terms with respect to position and velocity, respectively; This is the projection of the gravity vector onto the w-frame; This is the projection of the Earth's rotation in the w-frame; , and These are the predicted components for position, velocity, and attitude, respectively. The zero bias is added to the table at time k; Zero bias for the gyroscope; , and These represent the position, velocity, and attitude in the w-frame, respectively. For the transformation quaternion from the b-system to the w-system; Let be the transformation matrix from the b-system to the w-system; is the time interval between adjacent epochs; k is the epoch; For time intervals; for Era to Quaternions for epochal world coordinate system transformation; It is a quaternion for the transformation from the b-system to the w-system.

6. The STC-based graph optimization PPP / INS fusion positioning method as described in claim 1, characterized in that, The method for constructing an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor is shown in the following equation: ； In the formula, The objective function is... For marginalized residuals; It is a Jacobian matrix; PPP factor; This is the IMU pre-integration factor.

7. The STC-based graph optimization PPP / INS fusion positioning method as described in claim 1, characterized in that, The state vector of the dynamic target As shown in the following formula: ； in, ; In the formula, , The state nodes are respectively in Three-dimensional position and velocity under the system; As the carrier's posture; for The set of state vectors for each epoch; The zero bias is added to the table at time k; is zero bias for the gyroscope; T is the matrix transpose.

8. A graph optimization PPP / INS fusion positioning system based on STC, characterized in that, include: The acquisition module is used to acquire the PPP floating-point solution and single-difference floating-point ambiguity, and to acquire INS prior information; A fixed update module, which is communicatively connected to the acquisition module, is used to perform fixed updates on the PPP floating-point solution and the single-difference floating-point ambiguity based on the INS prior information. The PPP factor construction module is communicatively connected to the fixed update module and is used to construct the PPP factor after the PPP floating-point solution and the single-difference floating-point ambiguity are fixedly updated. The IMU pre-integration factor construction module is communicatively connected to the acquisition module and is used to construct the IMU pre-integration factor based on the INS prior information. The objective function construction module is communicatively connected to the PPP factor construction module and the IMU pre-integration factor construction module, and is used to construct an objective function based on factor graph optimization according to the PPP factor and the IMU pre-integration factor; as well as, The solution module is communicatively connected to the objective function construction module and is used to solve the state vector of the dynamic objective using the objective function.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the STC-based graph optimization PPP / INS fusion positioning method as described in any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that, When the processor runs the computer program, it implements the STC-based graph optimization PPP / INS fusion positioning method as described in any one of claims 1 to 7.

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