PPP-INS Integrated Navigation and Positioning Method and System Based on Low-Earth Orbit Navigation Enhancement

Abstract

The present invention discloses a PPP-INS integrated navigation positioning method and system based on low-orbit navigation enhancement, comprising: acquiring low-orbit satellite data and INS observation data in real time, and tightly combining the low-orbit satellite data and INS observation data to determine a positioning observation equation; solving the positioning observation equation using a random model to obtain preliminary terminal position information; acquiring low-orbit enhancement information, GNSS observation values, and INS observation data in real time, and tightly combining the low-orbit enhancement information, GNSS observation values, and INS observation data to determine a tightly combined observation equation; determining GNSS integer ambiguity based on the low-orbit enhancement information and preliminary position information; solving the tightly combined observation equation using a random model, and performing parameter estimation in combination with Kalman filtering to determine further terminal position information; and correcting the preliminary position information to obtain the terminal's final position information. The present invention achieves real-time high-precision positioning.

Description

Technical Field

[0001] The present invention relates to the field of low-orbit satellite navigation technology, and in particular to a PPP-INS integrated navigation and positioning method and system based on low-orbit navigation enhancement. Background Art

[0002] Precision Point Positioning-Inertial Navigation System (PPP-INS) integrated navigation is currently a hot research topic in the navigation field. It is widely used in autonomous driving, high-speed object positioning, and attitude measurement. INS systems can provide short-term, reliable, and highly accurate positioning results even when GNSS signals are intermittent or interfered with. However, INS positioning accuracy gradually degrades over time, requiring regular GNSS corrections. Currently, PPP navigation primarily relies on precise orbit and clock products, as well as atmospheric correction information, provided by GNSS data analysis centers such as IGS, CODE, and ESA to quickly fix GNSS observation ambiguities and achieve high-precision positioning. However, most data analysis centers produce precision data products post-process, with the fastest product update interval being 15 minutes. To meet the timeliness requirements of PPP-INS positioning technology for high-precision data products, data product information corresponding to the time period can only be obtained through extrapolation models, which reduces the accuracy and reliability of the data products obtained through extrapolation. As the value of low-orbit satellites in the fields of autonomous navigation and navigation enhancement gradually becomes apparent, using low-orbit satellites to provide continuous positioning enhancement information and enhanced signals to achieve rapidly converged PPP (Precise Point Positioning) positioning can effectively solve some of the current problems in PPP-INS positioning.

[0003] The public document with application number "CN202010584388.X" proposes a method of loosely combining GNSS and INS observations to form an observation equation, and using Kalman filtering to filter and estimate the observation equation to obtain terminal positioning information. The public document with application number "CN202010549660.0" proposes a method of adding a deep neural network model to the existing GNSS / INS tightly combined filter to correct the Kalman filter, thereby outputting a GNSS / INS tightly combined positioning result. The public document with application number "CN202010730262.9" proposes a method of using low-orbit satellite navigation enhancement signals and GNSS precision basic data products from data analysis centers such as IGS to compensate for the defect of satellite navigation systems being easily affected by the environment and losing signal lock, and to achieve GNSS / INS combined positioning. Publication number CN202010380332.2 proposes a method for fault detection and diagnosis of INS / GPS integrated navigation using a kernel Fisher discriminant for data processing and training with a one-class support vector machine (SVM) model. Based on this, the DS evidence theory is used to fuse the fault diagnosis of a BP neural network with an improved dynamic particle swarm optimization BP neural network. Another publication number CN201810052512.0 proposes a method for preprocessing radar / IMU output data using filtering methods such as wavelet transforms. Error models are then established based on the operating principles of different sensors to improve vehicle safety and driving performance.

[0004] However, the above existing PPP-INS positioning technology mainly achieves positioning by using high-precision navigation basic data products from data analysis centers such as IGS and CODE, which are all post-processing of observation data. Its positioning and attitude measurement accuracy are greatly affected by the timeliness of the data products, and there is a problem that it cannot achieve real-time and high-precision positioning. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that data analysis centers such as IGS are unable to provide the data products required for real-time high-precision positioning, and the current PPP-INS positioning system can only use the post-high-precision data products provided by data centers such as IGS to adopt an extrapolation model to obtain real-time positioning basic data products with attenuated accuracy, that is, there is a problem that real-time and high-precision positioning cannot be achieved.

[0006] The present invention aims to provide a PPP-INS integrated navigation and positioning method and system based on low-orbit navigation enhancement. Based on the use of low-orbit satellite enhancement signals, the present invention adds low-orbit navigation enhancement information and uses a tight combination method to determine the observation equation, and ultimately solves the terminal position information through different rights confirmation methods. The present invention is a method that integrates the GNSS navigation system with low-orbit satellite (LEO) signal enhancement and information enhancement to meet the data requirements for positioning accuracy and attitude measurement accuracy for autonomous driving and high-speed moving objects.

[0007] The present invention is achieved through the following technical solutions:

[0008] In a first aspect, the present invention provides a PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement, the method comprising:

[0009] Acquire low-orbit satellite data and INS observation data in real time, and tightly combine the low-orbit satellite data and INS observation data to determine the positioning observation equation;

[0010] The random model is used to solve the positioning observation equation to obtain the preliminary position information of the terminal (i.e., rough position information);

[0011] Acquire low-orbit augmentation information, GNSS observations, and INS observation data in real time, and tightly combine these information, GNSS observations, and INS observation data to determine the tightly combined observation equation;

[0012] Determine the GNSS integer ambiguity based on the low-orbit augmentation information and preliminary position information. Use a stochastic model to solve the tightly combined observation equations and combine it with Kalman filtering for parameter estimation to determine the terminal's further position information.

[0013] Based on the further location information, the preliminary location information is fed back and corrected to obtain the complete and reliable final location information of the terminal.

[0014] Furthermore, the method further comprises:

[0015] The final location information is transmitted to the positioning terminal for other systems to judge and operate.

[0016] Furthermore, the low-orbit satellite data includes Doppler observations and low-orbit satellite ranging signal observations;

[0017] INS observation data includes INS acceleration observation values and INS angular velocity observation values.

[0018] Furthermore, the positioning observation equation refers to the equation after the linearization of the ionospheric-free combined observation values of the carrier and pseudorange in PPP. The expression of the positioning observation equation is:

[0019] PIF =αP1-βP2=|p r -p s |+μδp r -c(t r -t s )+T s +ε P (1)

[0020] L IF =αL1-βL2=|p r -p s |+μδp r -N IF -c(t r -t s )+T s +ε L (2)

[0021] Where P and L represent the GNSS pseudorange and carrier observation values, respectively; α and β represent the ionosphere-free (IF) coefficients, respectively; p r 、p s where represents the position of the receiver and satellite in the Earth-centered Earth-fixed coordinate system (including the GNSS high-precision satellite orbits broadcast by low-orbit satellites); || represents the modular operation; μ represents the direction cosine vector from the receiver to the satellite; δp r represents the receiver position correction vector; c represents the speed of light in vacuum; t r , t s Respectively represent the receiver and satellite clock errors (including the high-precision position clock error broadcast by low-orbit satellites); N IF represents the ionospheric combined ambiguity (unit is m); T s represents the tropospheric delay along the signal propagation path (in meters); ε represents the ionospheric combined pseudorange and carrier noise cancellation.

[0022] Furthermore, the expression of the compact combined observation equation is:

[0023]

[0024]

[0025] Where p INS represents the receiver position in the e-frame predicted by the INS; C1 represents the position correction number converted from the n-frame to the e-frame; δp INS represents the position correction relative to the IMU center; θ represents the attitude vector; δt r and δ wztd represent the receiver clock correction and the tropospheric wet delay residual error respectively.

[0026] Furthermore, the formula for parameter estimation combined with Kalman filtering is:

[0027]

[0028] Where, X k and I represent the gain matrix and the identity matrix respectively, R k represents the prior variance of the observation vector Z, where:

[0029]

[0030] Where Q represents the prior variance matrix of the state parameter dynamic noise; φ k,k-1 Represents the state transition matrix from epoch k-1 to epoch k.

[0031] Furthermore, the scenarios that this method is applicable to include:

[0032] a. Low-orbit navigation enhancement information enables high-precision and rapid convergence of PPP;

[0033] b. INS integrates low-orbit satellite ranging signal observations and Doppler observations to determine the terminal's positioning information;

[0034] c. PPP-INS integrates low-orbit satellite data sources in the fields of autonomous driving, high-speed moving object positioning, and attitude measurement.

[0035] In a second aspect, the present invention provides a PPP-INS integrated navigation and positioning system based on low-orbit navigation enhancement, which supports a PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement; the system comprises:

[0036] The first real-time acquisition unit is used to acquire low-orbit satellite data and INS observation data in real time;

[0037] Positioning observation equation determination unit, used to tightly combine low-orbit satellite data and INS observation data to determine the positioning observation equation;

[0038] A preliminary position information solving unit is used to solve the positioning observation equation using a random model to obtain the preliminary position information (i.e., rough position information) of the terminal;

[0039] The second real-time acquisition unit is used to obtain low-orbit augmentation information, GNSS observation values and INS observation data in real time;

[0040] A tightly combined observation equation determination unit is used to tightly combine low-orbit augmentation information, GNSS observation values, and INS observation data to determine the tightly combined observation equation;

[0041] The further position information calculation unit is used to determine the GNSS integer ambiguity based on the low-orbit augmentation information and the preliminary position information; and adopts a stochastic model to solve the tightly combined observation equation and combines it with the Kalman filter to determine the further position information of the terminal;

[0042] The correction unit is used to provide feedback and correct the preliminary location information based on further location information to obtain complete and reliable final location information of the terminal.

[0043] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the PPP-INS combined navigation and positioning method based on low-orbit navigation enhancement is implemented.

[0044] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the PPP-INS combined navigation and positioning method based on low-orbit navigation enhancement.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] The present invention is based on a PPP-INS integrated navigation positioning method and system for low-orbit navigation enhancement. The present invention uses low-orbit satellites to provide high-precision, high-reliability real-time GNSS enhancement information and ranging signals with higher landing power, as well as Doppler observation data. Furthermore, the present invention combines INS observation data in different positioning and attitude measurement links to achieve a PPP-INS positioning effect with fixed ambiguity. This method overcomes the shortcomings of using an IGS data analysis center that cannot provide real-time data products, thereby reducing the positioning accuracy of the PPP-INS positioning system. This enables the PPP-INS positioning technology to achieve broader and deeper applications in the fields of autonomous driving, high-speed moving object positioning, speed measurement, and attitude measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0048] Figure 1 The figure is a flow chart of the PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement of the present invention.

[0049] Figure 2 This is a structural diagram of the PPP-INS integrated navigation and positioning system based on low-orbit navigation enhancement of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0051] Example 1

[0052] Based on the existing PPP-INS positioning technology in the background art, either achieves GNSS / INS integrated navigation positioning through the fusion of different filtering algorithms and different ground sensor data; or uses low-orbit satellite augmentation signals and performs GNSS / INS integrated navigation positioning through a loose combination. In other words, the existing technology mainly achieves positioning by using high-precision navigation basic data products from data analysis centers such as IGS and CODE. Both of these are post-processing of observation data. The positioning and attitude measurement accuracy of these technologies are significantly affected by the timeliness of the data products, and there is a problem that real-time and high-precision positioning cannot be achieved.

[0053] In response to the above problems in the prior art, the present invention designs a PPP-INS combined navigation and positioning method based on low-orbit navigation enhancement. On the basis of using low-orbit satellite enhancement signals, the present invention adds low-orbit navigation enhancement information and adopts a tight combination method to determine the observation equation, and finally solves the terminal position information through different rights confirmation methods. The scenarios adapted by the method of the present invention include: a. Low-orbit navigation enhancement information realizes high-precision and rapid convergence of PPP; b. INS integrates low-orbit satellite ranging signal observation values and Doppler observation values to determine the positioning information of the terminal; c. PPP-INS integrates low-orbit satellite data sources in the fields of autonomous driving, high-speed moving object positioning, and attitude measurement.

[0054] like Figure 1 As shown, the present invention is based on a PPP-INS integrated navigation and positioning method enhanced by low-orbit navigation. The low-orbit satellite can broadcast ranging signals similar to GNSS and has SPT functions. The method includes:

[0055] Step 1: Acquire low-orbit satellite data and INS observation data in real time, and tightly combine the low-orbit satellite data and INS observation data to determine the positioning observation equation; the low-orbit satellite data includes Doppler observation values and low-orbit satellite ranging signal observation values; the INS observation data includes INS acceleration observation values and INS angular velocity observation values.

[0056] Step 2: Use a random model to solve the positioning observation equation to obtain the preliminary location information of the terminal (i.e., rough location information);

[0057] Step 3: Acquire low-orbit augmentation information, GNSS observations, and INS observation data in real time, and tightly combine the low-orbit augmentation information, GNSS observations, and INS observation data to determine the tightly combined observation equation;

[0058] Step 4: Determine the GNSS integer ambiguity based on the LEO augmentation information and preliminary position information. Use a stochastic model to solve the tightly combined observation equations and perform parameter estimation in conjunction with the Kalman filter to determine the terminal's further position information.

[0059] Step 5: Feedback and correction of the preliminary location information based on the further location information to obtain the complete and reliable final location information of the terminal;

[0060] Step 6: The final location information is transmitted to the positioning terminal for other systems to judge and operate.

[0061] Specifically, the positioning observation equation refers to the equation after the linearization of the ionospheric-free combined observation values of the carrier and pseudorange in PPP. The expression of the positioning observation equation is:

[0062] P IF =αP1-βP2=|p r -p s |+μδp r -c(t r -t s )+T s +ε P (1)

[0063] L IF =αL1-βL2=|p r -p s |+μδp r -N IF -c(t r -t s )+T s +ε L (2)

[0064] Where P and L represent the GNSS pseudorange and carrier observation values, respectively; α and β represent the ionosphere-free (IF) coefficients, respectively; p r 、p s where represents the position of the receiver and satellite in the Earth-centered Earth-fixed coordinate system (including the GNSS high-precision satellite orbits broadcast by low-orbit satellites); || represents the modular operation; μ represents the direction cosine vector from the receiver to the satellite; δp r represents the receiver position correction vector; c represents the speed of light in vacuum; t r , t s Respectively represent the receiver and satellite clock errors (including the high-precision position clock error broadcast by low-orbit satellites); N IF represents the ionospheric combined ambiguity (unit is m); T srepresents the tropospheric delay along the signal propagation path (in meters); ε represents the ionospheric combined pseudorange and carrier noise cancellation.

[0065] In addition, Doppler observations can effectively improve the IMU sensor error estimation accuracy in the PPP / INS tight combination, and the linearized observation equation is:

[0066]

[0067] Where, represents the pseudorange change rate; v r and v s Denote the satellite and receiver velocity vectors relative to the ground respectively; δv r represents the receiver velocity correction vector; and Represent the receiver clock rate and satellite clock rate respectively; ε D represents the Doppler observation noise.

[0068] When processing PPP / INS tightly combined data, the lever arm error caused by different reference centers needs to be considered. Parameter estimation methods and precise measurement methods can usually be used. The present invention uses precise measurement of the lever arm value (ι) from the IMU center to the receiver reference center in the carrier coordinate system (b, Forward-Right-Down). b ), the position of the IMU reference center in the navigation system (n, North-East-Down) solved by mechanical arrangement is respectively obtained by formula (4) and formula (5) and Translate to the position of the GNSS receiver center and speed

[0069]

[0070]

[0071] Where, represents the attitude rotation matrix from b system to n system; D R represents the rotation matrix that transforms the arm in the navigation system into geographic coordinates; represents the projection of the rotation of system n relative to the inertial system (i) on system n; × represents the cross product; Indicates the angular rate of the gyroscope.

[0072] According to formula (1)-formula (3), the observation model of the PPP / INS tight combination can be obtained as follows:

[0073]

[0074] Where Z represents the observation information vector obtained by taking the difference between the INS-predicted GNSS observation value and the actual GNSS observation value, R represents the prior variance matrix of Z; P IF,INS , L IF,INS They represent the predicted values of pseudorange and carrier-assisted ionospheric elimination combined with the position of INS and satellite respectively.

[0075] The linearized observation equation is obtained by combining the right-hand side of formula (1) and formula (2) with formula (4), that is, the expression of the tightly combined observation equation is:

[0076]

[0077]

[0078] Where p INS represents the receiver position in the e-frame predicted by the INS; C1 represents the position correction number converted from the n-frame to the e-frame; δp INS represents the position correction relative to the IMU center; θ represents the attitude vector; δt r and δ wztd represent the receiver clock correction and the tropospheric wet delay residual error respectively.

[0079] The pseudo-range change rate information calculated by INS velocity and satellite velocity information can be derived from formula (31) and formula (5): The linearized equation is expressed as:

[0080]

[0081] Where: represents the transfer matrix from n-system to e-system; and Indicates the receiver and satellite clock correction number.

[0082] According to formula (7) to formula (9), the final form of the design matrix H and the corresponding state vector X in formula (6) can be obtained:

[0083]

[0084] The formula for parameter estimation combined with Kalman filtering is:

[0085]

[0086] Where, X k and I represent the gain matrix and the identity matrix respectively, R k represents the prior variance of the observation vector Z, where:

[0087]

[0088] Where Q represents the prior variance matrix of the state parameter dynamic noise; φ k,k-1 Represents the state transition matrix from epoch k-1 to epoch k.

[0089] The first-order Gauss-Markov model, random walk model and random constant model are used to describe the temporal variation of IMU sensor error, tropospheric zenith wet delay parameter and carrier ambiguity respectively:

[0090] x k =x k-1 e -Δt / T +ω k-1 ,ω k-1 ~N(0,2σ 2 Δt / T) (13)

[0091] x k =x k-1 +ω k-1 ,ω k-1 ~N(0,q 1 Δt k ) (14)

[0092] x k =x k-1 +ω k-1 ,ω k-1 ~N(0,0) (15)

[0093] Where, Δt k and T represent the IMU sampling interval and correlation time, respectively; σ represents the prior variance of the driving noise ω; q represents the spectral density of the tropospheric wet delay dynamic noise ω.

[0094] Currently, PPP-INS positioning is mainly achieved by using high-precision navigation basic data products from data analysis centers such as IGS and CODE. Its positioning and attitude measurement accuracy are greatly affected by the timeliness of the data products. Based on this, the present invention proposes three key points that are different:

[0095] (1) Based on the original determination of the terminal's preliminary position information based only on INS acceleration observations and angular velocity observations, multiple data types such as low-orbit satellite ranging signal observations and Doppler observations are added, and a random model is established to ultimately provide the terminal's preliminary position information;

[0096] (2) PPP ambiguity fixation is different from the post-data products used by the IGS and CODE data analysis centers. Instead, it uses GNSS augmentation information broadcast by low-orbit satellites with higher real-time performance. Combining augmentation information with the terminal's coarse position information, a right confirmation model is used to assist in achieving rapid GNSS ambiguity fixation.

[0097] (3) The GNSS observation values, low-orbit satellite enhancement information and INS observation data are combined into a tight combination observation equation, and the Kalman filter is used to determine the final positioning and attitude measurement results of the terminal.

[0098] The present invention uses low-orbit satellites to provide high-precision, high-reliability real-time GNSS augmentation information, ranging signals with higher landing power, and Doppler observation data. Furthermore, the invention combines INS observation data with different positioning and attitude measurement links to achieve a PPP-INS positioning effect with fixed ambiguity. This overcomes the shortcomings of using an IGS data analysis center, which cannot provide real-time data products and thus reduces the positioning accuracy of the PPP-INS positioning system. This enables PPP-INS positioning technology to achieve broader and deeper applications in the fields of autonomous driving, high-speed moving object positioning, speed measurement, and attitude measurement.

[0099] Example 2

[0100] like Figure 2 As shown, the difference between this embodiment and embodiment 1 is that this embodiment provides a PPP-INS integrated navigation and positioning system based on low-orbit navigation enhancement, which supports the PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement in embodiment 1; the system includes:

[0101] The first real-time acquisition unit is used to acquire low-orbit satellite data and INS observation data in real time;

[0102] Positioning observation equation determination unit, used to tightly combine low-orbit satellite data and INS observation data to determine the positioning observation equation;

[0103] A preliminary position information solving unit is used to solve the positioning observation equation using a random model to obtain the preliminary position information (i.e., rough position information) of the terminal;

[0104] The second real-time acquisition unit is used to obtain low-orbit augmentation information, GNSS observation values and INS observation data in real time;

[0105] A tightly combined observation equation determination unit is used to tightly combine low-orbit augmentation information, GNSS observation values, and INS observation data to determine the tightly combined observation equation;

[0106] The further position information calculation unit is used to determine the GNSS integer ambiguity based on the low-orbit augmentation information and the preliminary position information; and adopts a stochastic model to solve the tightly combined observation equation and combines it with the Kalman filter to determine the further position information of the terminal;

[0107] The correction unit is used to provide feedback and correct the preliminary location information based on further location information to obtain complete and reliable final location information of the terminal.

[0108] The execution process of each unit can be performed according to the process steps of the PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement described in Example 1, and will not be repeated in this embodiment.

[0109] At the same time, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the PPP-INS combined navigation and positioning method based on low-orbit navigation enhancement is implemented.

[0110] At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the PPP-INS combined navigation and positioning method based on low-orbit navigation enhancement.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement is characterized by: The method includes: Acquire low-orbit satellite data and INS observation data in real time, and tightly combine the low-orbit satellite data and INS observation data to determine the positioning observation equation; Solving the positioning observation equation using a random model to obtain preliminary location information of the terminal; acquiring low-orbit augmentation information, GNSS observations, and INS observation data in real time, and tightly combining the low-orbit augmentation information, GNSS observations, and INS observation data to determine a tightly combined observation equation; Determining GNSS integer ambiguity based on the LEO augmentation information and the preliminary position information; and solving the tightly combined observation equation using a stochastic model and performing parameter estimation in combination with a Kalman filter to determine further position information of the terminal; Based on the further location information, the preliminary location information is fed back and corrected to obtain the final location information of the terminal; The low-orbit satellite data includes Doppler observation values and low-orbit satellite ranging signal observation values; The INS observation data includes INS acceleration observation values and INS angular velocity observation values; The expression of the compact combined observation equation is: Where p INS represents the receiver position in the e-frame predicted by the INS; C1 represents the position correction number converted from the n-frame to the e-frame; δp INS represents the position correction relative to the IMU center; θ represents the attitude vector; δt r and δ wztd are the receiver clock correction and the tropospheric wet delay residual error respectively; P IF,INS , L IF,INS They represent the predicted values of pseudorange and carrier ionospheric elimination combination calculated using the position of INS and satellite position respectively; p s represents the position of the satellite in the Earth-centered Earth-fixed coordinate system; μ represents the direction cosine vector from the receiver to the satellite; represents the attitude rotation matrix from b system to n system; ι b represents the arm value of the receiver reference center; c represents the speed of light in vacuum; N IF represents the ionospheric-free combined ambiguity.

2. The PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement according to claim 1 is characterized in that: The method further includes: The final location information is transmitted to the positioning terminal for other systems to judge and operate.

3. The PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement according to claim 1 is characterized in that: The positioning observation equation refers to the equation after linearization of the ionospheric-free combined observation values of the carrier and pseudorange in PPP. The expression of the positioning observation equation is: P IF =αP1-βP2=|p r -p s |+msp r -c(t r -t s )+T s +e P L IF =αL1-βL2=|p r -p s |+msp r -N IF -c(t r -t s )+T s +e L Where, P IF and L IF denote the GNSS pseudorange and carrier observation values respectively; α and β denote the ionospheric elimination combination coefficients respectively; p r represents the position of the receiver in the Earth-centered Earth-fixed coordinate system, p s represents the position of the satellite in the Earth-centered Earth-fixed coordinate system; || represents the modular operation; μ represents the direction cosine vector from the receiver to the satellite; δp r represents the receiver position correction vector; c represents the speed of light in vacuum; t r represents the receiver clock error, t s represents the satellite clock error; N IF represents the ionospheric combined ambiguity; T s represents the tropospheric delay along the signal propagation path; δ represents the ionospheric combined pseudorange and carrier noise elimination.

4. The PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement according to claim 1 is characterized in that: The formula for parameter estimation combined with Kalman filtering is: Where, X k and I represent the gain matrix and the identity matrix respectively, R k represents the prior variance of the observation vector Z, where: Where Q represents the prior variance matrix of the state parameter dynamic noise; φ k,k-1 Represents the state transition matrix from epoch k-1 to epoch k.

5. The PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement according to claim 1 is characterized in that: The scenarios that this method is suitable for include: a. Low-orbit navigation enhancement information enables high-precision and rapid convergence of PPP; b. INS integrates low-orbit satellite ranging signal observations and Doppler observations to determine the terminal's positioning information; c. PPP-INS integrates low-orbit satellite data sources in the fields of autonomous driving, high-speed moving object positioning, and attitude measurement.

6. The PPP-INS integrated navigation and positioning system based on low-orbit navigation enhancement is characterized by: The system supports the PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement as described in any one of claims 1 to 5; the system includes: The first real-time acquisition unit is used to acquire low-orbit satellite data and INS observation data in real time; a positioning observation equation determination unit, configured to tightly combine the low-orbit satellite data and the INS observation data to determine a positioning observation equation; A preliminary position information solving unit, configured to solve the positioning observation equation using a random model to obtain preliminary position information of the terminal; The second real-time acquisition unit is used to obtain low-orbit augmentation information, GNSS observation values and INS observation data in real time; a tightly combined observation equation determining unit, configured to tightly combine the low-orbit augmentation information, the GNSS observation values, and the INS observation data to determine a tightly combined observation equation; a further position information solving unit, configured to determine GNSS integer ambiguity based on the LEO augmentation information and the preliminary position information; solve the compactly combined observation equation using a stochastic model, and determine further position information of the terminal in combination with a Kalman filter; The correction unit is used to provide feedback and correct the preliminary location information according to the further location information to obtain the final location information of the terminal.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement is implemented as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the PPP-INS integrated navigation and positioning method based on low-orbit navigation enhancement is implemented as described in any one of claims 1 to 5.

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