A precise point positioning method assisted by regional navigation system

Abstract

The present invention provides a method for precise point positioning assisted by a regional navigation system. The method comprises: obtaining original satellite observation files and establishing a precise point positioning model for a train; obtaining original observation files of a regional navigation system and establishing a regional navigation system positioning model for a train; establishing a tight combination positioning model of a regional navigation system and precise point positioning for a train, and estimating the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train online at each epoch by solving the tight combination positioning model of the regional navigation system and precise point positioning. The present invention is based on an extended Kalman filter algorithm, establishes a tight combination positioning model of a regional navigation system and precise point positioning, and estimates parameters such as position, velocity, acceleration and carrier phase integer ambiguity online at each epoch, thereby realizing a precise point positioning method assisted by a regional navigation system and expanding the positioning range of satellites.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a precise single-point positioning method assisted by a regional navigation system. Background Art

[0002] The Global Navigation Satellite System (GNSS) is an air-based radio navigation and positioning system that can provide services to users at any location on the Earth's surface or in near-Earth space. However, in obstructed environments such as tunnels, bridges and mountains, the positioning accuracy of GNSS will be reduced or even lose its effectiveness.

[0003] The regional navigation system can be regarded as a quasi-navigation satellite placed on the ground. As a system enhancement and supplement to GNSS, it can better overcome the inherent defects of the GNSS system, not only significantly improve the performance of the existing GNSS navigation system, but also expand the application scope of the navigation and positioning system. Therefore, in order to make up for the shortcomings of insufficient or inoperable GNSS signals in blocked environments, expand the system positioning range, and improve the geometric distribution of satellites, it is necessary to develop a precise single-point positioning method assisted by a regional navigation system to provide navigation and positioning services with high accuracy, high reliability, and high environmental adaptability within a certain range. Summary of the invention

[0004] The embodiment of the present invention provides a regional navigation system-assisted precise single-point positioning method to provide a navigation and positioning service with high precision, high reliability and high environmental adaptability to trains.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme.

[0006] A regional navigation system-assisted precise point positioning method, comprising:

[0007] Obtain original satellite observation files and establish a precise single-point positioning model for the train;

[0008] Obtain the original observation files of the regional navigation system and establish the regional navigation system positioning model of the train;

[0009] A tightly combined positioning model of a regional navigation system and a precise point positioning system for the train is established, and the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train are estimated online at each epoch by solving the tightly combined positioning model of the regional navigation system and the precise point positioning system.

[0010] Preferably, the obtaining of the original satellite observation file and establishing the precise point positioning model of the train includes:

[0011] Obtain the original satellite observation data file of the train, obtain the observation value, use the IGS precise satellite orbit and satellite clock error data to interpolate the required epoch satellite position, speed and clock error, calculate the correction parameters of each error according to the error correction model, correct the epoch satellite position, speed, clock error and receiver observation value, and establish the precise single-point positioning model of the train;

[0012] The precise single-point positioning model of the train is used to calculate the position of the train during movement based on the carrier phase observation value and the satellite position.

[0013] Preferably, the precise single-point positioning model of the train needs to correct various error terms existing in the positioning, including: ephemeris error, clock error, antenna phase center correction, relativistic effect, ionosphere error, troposphere error and earth rotation correction.

[0014] Preferably, the step of acquiring the original observation file of the regional navigation system and establishing the regional navigation system positioning model of the train includes:

[0015] The regional navigation system includes two observation quantities: carrier phase and pseudorange. The pseudorange and carrier phase observation equations between the receiver and base station i are:

[0016]

[0017]

[0018] Among them, ρ i is the pseudorange measurement, φ i is the carrier phase measurement, r i is the geometric distance between the receiver and the transmitting antenna, λ is the signal wavelength, is the tropospheric delay, c is the speed of light, δT is the receiver clock error, N i is the carrier phase ambiguity, is the residual for modeling;

[0019] Construct differential observations, eliminate receiver-related error terms by inter-satellite difference, select a pseudo-satellite as a reference satellite, make a difference between the observations of the two base stations, and establish a nonlinear single-difference observation model based on the basic pseudo-range and carrier phase observation equations of the pseudo-satellite for the receiver. Take j as the reference pseudo-satellite, and the pseudo-range and carrier phase observation equations after single difference are written as:

[0020]

[0021]

[0022] Preferably, the establishment of a tightly combined positioning model of a regional navigation system and a precise point positioning of a train, and online estimation of the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train at each epoch by solving the tightly combined positioning model of the regional navigation system and the precise point positioning, include:

[0023] The system state equation of Kalman filter is:

[0024]

[0025] In the system equation, X(k) is the system state vector, which is the object that needs to be optimally estimated. The error of each variable is estimated. F(k) is the system matrix, and Q(k) is the state noise covariance matrix of the system.

[0026] The system state vector is defined as:

[0027]

[0028] Including three-dimensional position, three-dimensional velocity, three-dimensional acceleration, tropospheric wet component, arm dη between regional navigation system and satellite antenna, ambiguity after regional navigation system inter-satellite single difference After the ionospheric error is eliminated by the dual-frequency signal, the ambiguity difference of the satellite can be observed;

[0029] System matrix F(t)

[0030]

[0031]

[0032] Before integrating the satellite navigation system and the regional navigation system, consider the arm between the satellite antenna and the regional navigation system;

[0033] The positional relationship between the system lever arms,

[0034]

[0035] The relationship between the three and the pseudorange is:

[0036]

[0037] Among them, ρ L is the pseudorange, (x, y, z) is the position coordinate of the receiver, (x i ,y i ,z i ) is the satellite position coordinate, (dη x ,dη y ,dη z ) is a three-dimensional lever arm;

[0038] The observation system equation of Kalman filter is:

[0039] Z(k)=H(k)X(k)+R(k)

[0040] Where Z(k) is the observation vector, H(k) is the observation matrix, X(k) is the state vector, and R(k) is the observation noise covariance matrix;

[0041] Establish a tightly combined positioning model of regional navigation system and precise single point positioning, select the satellite with the largest elevation angle as the reference satellite and make a difference with other satellites, and eliminate the error caused by the receiver clock error by the inter-satellite single difference method;

[0042]

[0043] in, represents the single difference of the carrier phase of each regional navigation system base station relative to the reference base station j, It represents the single difference of the carrier phase of each satellite observed at this time relative to the reference satellite k after the ionosphere is eliminated. The difference between the carrier phase observation value difference received by the receiver and the calculated carrier phase difference;

[0044] Correspondingly, the measurement matrix of the system is written as:

[0045]

[0046] in,

[0047]

[0048]

[0049]

[0050] Since the single differences of the carrier phase and pseudorange rate measurements are cross-correlated, the carrier phase or pseudorange rate portion of the measurement noise covariance matrix is ​​written as:

[0051]

[0052] in, Represents the prior noise variance of the carrier phase or pseudorange rate;

[0053] During the movement of the receiver, the original observation files of the satellite and regional navigation systems are obtained in real time. By solving the tight combination positioning model of the above-mentioned regional navigation system and precise single-point positioning, the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train are estimated online at each epoch.

[0054] It can be seen from the technical solution provided by the above-mentioned embodiments of the present invention that the embodiments of the present invention are based on the extended Kalman filter algorithm, establish a tightly combined positioning model of the regional navigation system and the precise single-point positioning, and online estimate parameters such as position, velocity, acceleration and carrier phase integer ambiguity at each epoch, thereby realizing a precise single-point positioning method assisted by a regional navigation system and expanding the positioning range of the satellite.

[0055] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0057] Figure 1 A flow chart of a regional navigation system-assisted precise point positioning method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0059] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0060] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0061] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0062] The embodiment of the present invention provides a regional navigation system assisted precise point positioning method, the specific processing flow of the method is as follows: Figure 1 As shown, the processing steps include the following:

[0063] Step S10: Obtain the original satellite observation file, establish a precise single-point positioning model for the train, and calculate the position of the train during movement.

[0064] The main process of precise point positioning includes obtaining the original satellite observation data file and obtaining the observation value. At the same time, the precise satellite orbit and satellite clock error data of IGS (International GNSS Service) are used to interpolate the position, speed and clock error of the required epoch satellite. In the next step, the correction parameters of each error are calculated according to the error correction model to correct the epoch satellite position, speed, clock error and receiver observation value, and establish a precise point positioning model for the train. After that, the precise point positioning model is established to calculate the position of the train when it is moving based on the carrier phase observation value, satellite position, etc.

[0065] Precision single-point positioning requires correction of various error terms in positioning:

[0066] 1) Ephemeris error

[0067] The satellite ephemeris error is mainly caused by the difference between the satellite position and velocity given by the ephemeris and the actual satellite position and velocity. In the final precise ephemeris provided by IGS, the three-dimensional coordinates and three-dimensional velocity of the observed satellite are given every 15 minutes. The precise ephemeris epochs can be interpolated to obtain the required epochs.

[0068] 2) Clock Difference

[0069] Clock errors include satellite clock errors and receiver clock errors. Due to clock frequency drift, there will be differences between the satellite clock and the receiver clock and the standard GPS time. In precise point positioning, similarly, the satellite clock error information provided by the precise ephemeris is interpolated to obtain the satellite clock error of the required frequency. Since the satellite clock error changes greatly in a short period of time, the shorter the interpolation data interval, the more accurate the result.

[0070] In precise point positioning, the GPS receiver clock error is estimated as an unknown parameter, or the carrier phase observation value obtained at the same time as the receiver is used for single difference to eliminate the receiver clock error and reduce the error parameter.

[0071] 3) Antenna phase center correction

[0072] The satellite position provided by the precise ephemeris is the position of the satellite's center of mass rather than the position of the satellite antenna phase center. The GPS receiver records the center of the antenna preamplifier chassis rather than the antenna center. In addition, changes in the antenna center will cause position calculation errors in precise single-point positioning. The satellite antenna phase center can be corrected using the ANTEX file provided by IGS.

[0073] 4) Relativistic Effect

[0074] Satellites and receivers at different locations are subject to different speeds and gravity, which results in a relativistic effect. The part that needs to be corrected by the user is:

[0075]

[0076] Among them, rela is the relativistic effect of the satellite and the receiver, r is the satellite position, v is the satellite velocity, and c is the speed of light.

[0077] 5) Ionospheric Error

[0078] The ionosphere is a layer of the atmosphere located 60 km above the ground. When GPS signals pass through it, they will be delayed due to the electromagnetic waves in it.

[0079] Using dual-frequency signals to eliminate errors is a simple and effective method. The combined observation value that eliminates the influence of the ionosphere is:

[0080]

[0081] Among them, f1 and f2 are GPS signal frequencies, L1 and L2 are carrier phase observation values ​​of frequencies f1 and f2, respectively, D1 and D2 are pseudorange rate observation values ​​of frequencies f1 and f2, respectively, and L3 and D3 are ionospheric-free combined observation values ​​of carrier phase and code pseudorange, respectively.

[0082] 6) Tropospheric Error

[0083] The atmosphere from the ground to 40km is the troposphere. Tropospheric errors cannot be eliminated by dual-frequency signals, so error models are used for correction. Tropospheric delay is one of the main error sources that affect the accuracy of precise single-point positioning. The size is related to the satellite altitude angle. Depending on the altitude angle, the error can reach 2.3m-20m.

[0084] Tropospheric delay can be divided into dry component delay and wet component delay, which are caused by dry gas and moist gas respectively. The former accounts for the majority and can be obtained with centimeter-level or even millimeter-level accuracy through the error model. It can be written as the product of the tropospheric delay in the zenith direction and the projection function:

[0085] δ trop =δ zpt M dry (θ)+δ zpw M wet (θ)

[0086] Among them, δ trop represents the total delay of GPS signal in the troposphere, δ zpt ,δ zpw The dry component delay and wet component delay of the troposphere in the zenith direction are respectively dry 、M wet are the projection functions of the tropospheric dry component and the wet component respectively, and θ is the altitude angle. In precise point positioning, a suitable model is usually used to eliminate the dry component delay, and the wet component delay is used as an unknown quantity in the estimation calculation.

[0087] The global projection function (GMF), which is easy to implement and sensitive to low altitude angles, is chosen to project the zenith tropospheric delay to an arbitrary altitude.

[0088] The dry component delay realization formula of the GMF model is as follows:

[0089]

[0090] Among them, a dry , b dry 、c dry The annual cumulative day, longitude, and latitude are used as parameters for interpolation. ht =2.53×10 -5 , b ht =5.49×10 -3 , c ht =1.14×10 -3 , H is the positive height.

[0091] 7) Earth rotation correction

[0092] During the time when the GPS signal reaches the ground receiver, the angle of the earth's rotation around its axis is ωΔt, where ω is the earth's rotation angular velocity and Δt is the satellite signal propagation time. When the GPS signal reaches the receiver, the satellite coordinates change from (X S ,Y S ,Z S ) becomes (X S' ,Y S' ,Z S' ):

[0093]

[0094] The resulting distance correction is:

[0095] Δρ=ω / c·[Y S (X R -X S )-X S (Y R -Y S )]

[0096] Where Δρ is the distance correction value, (X R ,Y R ,Z R ) is the coordinate value of the ground receiver.

[0097] The raw data obtained by the GPS receiver includes pseudorange and carrier phase observations. The latter is the difference between the phase value of the local reference signal generated by the GPS receiver at the time of reception and the phase value at the time of satellite transmission. Since the wavelength of the carrier is much smaller than the wavelength of the code, the accuracy of carrier phase measurement is 2 to 3 orders of magnitude higher than pseudorange measurement.

[0098] In precise point positioning, the carrier phase observation equation without considering UPD can be expressed as:

[0099]

[0100] Among them, Φ i represents the receiver carrier phase measurement value, ρ i represents the actual distance from the receiver to satellite i, represents the atmospheric ionospheric delay for satellite i signal, represents the atmospheric tropospheric delay of the satellite i signal, c represents the speed of light in vacuum, dt i represents the clock error of satellite i, dt r Represents the receiver clock error, N i represents the carrier phase ambiguity for the satellite, λ represents the wavelength of the GPS carrier phase, represents the unmodeled residual error in the carrier phase measurement from satellite i to the receiver.

[0101] In precise point positioning, the ionospheric elimination combination of dual-frequency observations can eliminate the influence of the first-order ionospheric term. It is approximately considered that the ionospheric delay is eliminated, so the observation equation of the carrier phase can be rewritten as:

[0102]

[0103] Among them, f1 and f2 correspond to the frequencies of L1 and L2 signals respectively, and λ1 and λ2 correspond to the wavelengths of L1 and L2 signals respectively. They correspond to the receiver acquisition and the accumulated number of carrier phase cycles of L1 and L2 signals, rela represents the relativistic effect, represents the carrier phase ambiguity of satellite i after eliminating ionospheric delay, represents the unmodeled residual error in the carrier phase measurement from satellite i to the receiver.

[0104] The satellite m with the best elevation angle is selected as the reference satellite, then the single-difference carrier phase observation equation of satellite i relative to satellite m is:

[0105]

[0106] Step S20: Acquire the original observation file of the regional navigation system and establish a regional navigation system positioning model for the train.

[0107] Like satellite navigation systems, regional navigation systems have two observations: carrier phase and pseudorange. Since the wavelength of carrier phase is much smaller than pseudorange, carrier positioning can achieve centimeter-level positioning accuracy.

[0108] The pseudorange and carrier phase observation equations between the receiver and base station i are:

[0109]

[0110]

[0111] Among them, ρ i is the pseudorange measurement, φ i is the carrier phase measurement, r i is the geometric distance between the receiver and the transmitting antenna, λ is the signal wavelength, is the tropospheric delay, c is the speed of light, δT is the receiver clock error, N i is the carrier phase ambiguity, is the residual for modeling.

[0112] Since the synchronization accuracy between regional navigation systems can reach nanosecond level, the base station clock difference can be ignored and not taken into consideration.

[0113] In order to obtain accurate positioning, differential observations are constructed, and receiver-related error terms are eliminated through inter-satellite differentials to reduce the errors caused by the receiver.

[0114] A pseudo-satellite is selected as a reference satellite, and the observation values ​​of the two base stations are subtracted to eliminate the receiver-related error terms in the observation equation, such as the receiver clock error, and eliminate its influence on the positioning solution. A nonlinear single-difference observation model is established based on the basic pseudo-range and carrier phase observation equations of the pseudo-satellite for the receiver. Taking j as the reference pseudo-satellite, the single-difference pseudo-range and carrier phase observation equations are written as:

[0115]

[0116]

[0117] Step S30: Establish a tightly combined positioning model of the regional navigation system and the precise point positioning of the train, and estimate the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train online at each epoch by solving the tightly combined positioning model of the regional navigation system and the precise point positioning.

[0118] The system state equation of Kalman filter is:

[0119]

[0120] In the system equation, X(k) is the system state vector, which is the object that needs to be optimally estimated. What is estimated here is the error of each variable. F(k) is the system matrix, and Q(k) is the state noise covariance matrix of the system.

[0121] Define the system state vector as,

[0122]

[0123] Including three-dimensional position, three-dimensional velocity, three-dimensional acceleration, tropospheric wet component, arm dη between regional navigation system and satellite antenna, ambiguity after regional navigation system inter-satellite single difference After the dual-frequency signal eliminates the ionospheric error, the satellite's ambiguity difference can be observed.

[0124] System matrix F(t)

[0125]

[0126]

[0127] In the actual experiment, the regional navigation system and the satellite antenna are installed in different positions. Therefore, before integrating the satellite navigation system and the regional navigation system, it is necessary to consider the arm between the satellite antenna and the regional navigation system.

[0128] The positional relationship between the system lever arms,

[0129]

[0130] The relationship between the three and the pseudorange is:

[0131]

[0132] Among them, ρ L is the pseudorange, (x, y, z) is the position coordinate of the receiver, (x i ,y i ,z i ) is the satellite position coordinate, (dη x ,dη y ,dη z ) is a three-dimensional lever arm.

[0133] The observation system equation of Kalman filter is:

[0134] Z(k)=H(k)X(k)+R(k)

[0135] Where Z(k) is the observation vector, H(k) is the observation matrix, X(k) is the state vector, and R(k) is the observation noise covariance matrix.

[0136] A tightly combined positioning model of regional navigation system and precise point positioning is established. In the measurement model of the tightly combined positioning model of regional navigation system and precise point positioning, the satellite with the largest elevation angle and good observation quality is selected as the reference satellite to make a difference with other satellites, that is, the error caused by the receiver clock error is eliminated by the inter-satellite single difference method.

[0137]

[0138] in, represents the single difference of the carrier phase of each regional navigation system base station relative to the reference base station j, It represents the single difference of the carrier phase of each satellite observed at this time relative to the reference satellite k after the ionosphere is eliminated. It is the difference between the carrier phase observation value difference received by the receiver and the calculated carrier phase difference.

[0139] Correspondingly, the measurement matrix of the system is written as:

[0140]

[0141] in,

[0142]

[0143]

[0144]

[0145] Since the single differences of the carrier phase and pseudorange rate measurements are cross-correlated, the carrier phase or pseudorange rate portion of the measurement noise covariance matrix is ​​written as:

[0146]

[0147] in, Represents the prior noise variance of the carrier phase or pseudorange rate.

[0148] During the movement of the receiver, the original observation files of the satellite and regional navigation system are obtained in real time. By solving the tight combination positioning model of the regional navigation system and precise point positioning, the position, velocity, acceleration and carrier phase integer ambiguity of the train can be estimated online at each epoch, realizing the precise point positioning method assisted by the regional navigation system and expanding the positioning range of the satellite.

[0149] In summary, the satellite of the embodiment of the present invention obtains the original observation file from the receiver, obtains the carrier phase, pseudorange rate and other observation values; uses the precise ephemeris, interpolates and calculates the required epoch satellite position, velocity and clock error; corrects the errors of the satellite end, propagation path and receiver end in positioning; and obtains the single-difference ionosphere-free combined observation value. At the same time, the regional navigation system receiver receives the base station transmission signal, performs capture and tracking, obtains the original carrier phase, pseudorange and other observation values ​​of the regional navigation system positioning, and eliminates the influence of the receiver end error through the inter-satellite single difference. Finally, based on the extended Kalman filter, the PPP single-difference ionosphere-free combined observation value and the regional navigation system single-difference carrier phase observation value are tightly combined and fused for positioning, and the position, velocity, acceleration, integer ambiguity and other parameters are estimated online for each epoch. The present invention can be used in scenarios where satellite signals are limited to improve the accuracy and reliability of satellite positioning and expand the scope of satellite positioning.

[0150] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0151] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0152] 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 device or 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. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0153] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A regional navigation system-assisted precise point positioning method, characterized in that: include: Obtain original satellite observation files and establish a precise single-point positioning model for the train; Obtain the original observation files of the regional navigation system and establish the regional navigation system positioning model of the train; Establishing a regional navigation system and a precision point positioning tightly combined positioning model for the train, and estimating the train's position, velocity, acceleration, and carrier phase integer ambiguity parameters online at each epoch by solving the regional navigation system and the precision point positioning tightly combined positioning model; The process of obtaining the original satellite observation file and establishing the precise point positioning model of the train includes: Obtain the original satellite observation data file of the train, obtain the observation value, use the IGS precise satellite orbit and satellite clock error data to interpolate the required epoch satellite position, speed and clock error, calculate the correction parameters of each error according to the error correction model, correct the epoch satellite position, speed, clock error and receiver observation value, and establish the precise single-point positioning model of the train; The precise single-point positioning model of the train is used to calculate the position of the train during movement based on the carrier phase observation value and the satellite position; The method of obtaining the original observation file of the regional navigation system and establishing the regional navigation system positioning model of the train includes: The regional navigation system includes two observation quantities: carrier phase and pseudorange. The pseudorange and carrier phase observation equations between the receiver and base station i are: Among them, ρ i is the pseudorange measurement, φ i is the carrier phase measurement, r i is the geometric distance between the receiver and the transmitting antenna, λ is the signal wavelength, is the tropospheric delay, c is the speed of light, δT is the receiver clock error, N i is the carrier phase ambiguity, is the residual for modeling; Construct differential observations, eliminate receiver-related error terms by inter-satellite difference, select a pseudo-satellite as a reference satellite, make a difference between the observations of the two base stations, and establish a nonlinear single-difference observation model based on the basic pseudo-range and carrier phase observation equations of the pseudo-satellite for the receiver. Take j as the reference pseudo-satellite, and the pseudo-range and carrier phase observation equations after single difference are written as:

2. The method according to claim 1, characterized in that The precise single-point positioning model of the train needs to correct various error items existing in the positioning, including: ephemeris error, clock error, antenna phase center correction, relativistic effect, ionosphere error, troposphere error and earth rotation correction.

3. The method according to claim 1, characterized in that The method of establishing a regional navigation system and a precision point positioning tightly combined positioning model for a train, and estimating the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train online at each epoch by solving the regional navigation system and the precision point positioning tightly combined positioning model, includes: The system state equation of Kalman filter is: In the system equation, X(k) is the system state vector, which is the object that needs to be optimally estimated. The error of each variable is estimated. F(k) is the system matrix, and Q(k) is the state noise covariance matrix of the system. The system state vector is defined as: Including three-dimensional position, three-dimensional velocity, three-dimensional acceleration, tropospheric wet component, arm dη between regional navigation system and satellite antenna, ambiguity after regional navigation system inter-satellite single difference After the ionospheric error is eliminated by the dual-frequency signal, the ambiguity difference of the satellite can be observed; System matrix F(t) Before integrating the satellite navigation system and the regional navigation system, consider the arm between the satellite antenna and the regional navigation system; The positional relationship between the system lever arms, The relationship between the three and the pseudorange is: Among them, ρ L is the pseudorange, (x, y, z) is the position coordinate of the receiver, (x i ,y i ,z i ) is the satellite position coordinate, (dη x ,dη y ,dη z ) is a three-dimensional lever arm; The observation system equation of Kalman filter is: Z(k)=H(k)X(k)+R(k) Where Z(k) is the observation vector, H(k) is the observation matrix, X(k) is the state vector, and R(k) is the observation noise covariance matrix; Establish a tightly combined positioning model of regional navigation system and precise single point positioning, select the satellite with the largest elevation angle as the reference satellite and make a difference with other satellites, and eliminate the error caused by the receiver clock error by the inter-satellite single difference method; in, represents the single difference of the carrier phase of each regional navigation system base station relative to the reference base station j, It represents the single difference of the carrier phase of each satellite observed at this time relative to the reference satellite k after the ionosphere is eliminated. The difference between the carrier phase observation value difference received by the receiver and the calculated carrier phase difference; Correspondingly, the measurement matrix of the system is written as: in, Since the single differences of the carrier phase and pseudorange rate measurements are pairwise cross-correlated, the carrier phase or pseudorange rate portion of the measurement noise covariance matrix is ​​written as: in, Represents the prior noise variance of the carrier phase or pseudorange rate; During the movement of the receiver, the original observation files of the satellite and regional navigation systems are obtained in real time. By solving the tight combination positioning model of the above-mentioned regional navigation system and precise single-point positioning, the position, velocity, acceleration and carrier phase integer ambiguity parameters of the train are estimated online at each epoch.

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