Beidou PPP-RTK train full-scene positioning method, system and terminal

Abstract

A Beidou PPP-RTK train full-scene positioning method comprises the following steps that when a train is in an open or semi-shielded scene, a train positioning terminal positions the train in combination with Beidou PPP-RTK and an initialized INS, and train position information is obtained; when a train enters a full-shielding scene, after a train positioning terminal receives a UWB base station signal, a prior elevation is performed on the train, then an elevation constraint UWB model is established, and position information of the train is obtained through the elevation constraint UWB model and initialized INS calculation; when the train leaves the full-shielding scene, the train positioning terminal receives the PPP-RTK correction information and the UWB base station signal, an elevation constraint PPP-RTK model is established, the positioning result of the PPP-RTK is obtained, and therefore the position information of the train is obtained. The method is high in train positioning precision in a complex scene.

Description

Technical Field

[0001] The present invention relates to an improvement of Beidou train positioning technology, belongs to the field of train positioning, and in particular to a Beidou PPP-RTK train full-scene positioning method, system and terminal. Background Art

[0002] The precise positioning of high-speed trains is the key foundation for ensuring their safe operation. The Beidou system can not only provide real-time and high-precision positioning services, but also has the characteristics of high autonomy and low cost, which promotes the transformation of train positioning from automatic positioning to autonomous positioning technology. However, the accuracy of the traditional Beidou / GNSS satellite positioning technology pseudo-range single point positioning (SPP) is relatively low, which cannot meet the requirements of the lateral positioning accuracy of trains between different tracks. Real-Time Kinematic (RTK) can achieve centimeter-level positioning accuracy, but it depends on dense reference stations and needs to accurately control the distance between the train and the reference station. Precise single point positioning (PPP) can reduce the dependence on reference stations and achieve centimeter-level positioning accuracy, but the initialization time is long, which makes it difficult to quickly obtain high-precision positioning information when the train enters and exits tunnels or is blocked by canyons or buildings. Precise single point real-time dynamic positioning (Precise Point Positioning-Real-Time Kinematic, PPP-RTK) relies on sparsely distributed stations over nearly 100 kilometers, and can achieve instantaneous centimeter-level positioning accuracy in an open and unobstructed environment. It is especially suitable for large-scale and long-distance precision positioning in railway scenarios;

[0003] The train operating environment is complex. In scenes such as canyons and forests, satellite signals are blocked, and positioning accuracy and reliability will drop sharply. In fully shielded scenes such as tunnels, satellite positioning is completely ineffective. Satellite positioning alone cannot meet the needs of continuous high-precision positioning of trains in all scenes. The Inertial Navigation System (INS) is independent of external references and independently provides a variety of high-precision navigation parameters, which can effectively meet the shortcomings of Beidou satellite positioning in shielded areas. However, in long tunnel environments, INS accumulates navigation errors over time and cannot provide high-precision and high-reliability positioning results.

[0004] A Chinese patent application with application number CN202211424844.X and application date November 14, 2022 discloses a train positioning optimization method, system and rail transit vehicle, which corrects the original satellite observation data to obtain satellite corrected observation data; uses the satellite corrected observation data and the RTK differential data transmitted in real time by the ground base station system to calculate the train position information. This technology corrects the original satellite observation data and uses the corrected satellite observation data for positioning solution, but the above method does not solve the problem of insufficient train positioning accuracy in complex scenarios.

[0005] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of this patent application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to overcome the problem of insufficient train positioning accuracy in complex scenes in the prior art, and to provide a Beidou PPP-RTK train full-scene positioning method, system and terminal with high train positioning accuracy in complex scenes.

[0007] To achieve the above purpose, the technical solution of the present invention is: a Beidou PPP-RTK train full-scene positioning method, the Beidou PPP-RTK train full-scene positioning method comprises the following steps:

[0008] Step 1: The base station network first obtains GNSS raw data and UWB raw data, then sends the GNSS raw data to the computing center, and sends the UWB raw data to the train positioning terminal;

[0009] Step 2: The computing center first solves the GNSS raw data to obtain PPP-RTK correction information, and then sends the PPP-RTK correction information to the train positioning terminal;

[0010] Step 3: The train positioning terminal is stationary to initialize the INS zero bias and attitude to obtain the corrected INS. Then, the PPP-RTK correction information is combined with the UWB original data for filtering. The filtered data is then input into the corrected INS to obtain the initialized INS. The initialized INS is then used to locate the train to obtain the initial position of the train.

[0011] Step 4: When the train enters the running state, the method for positioning the train includes any one or more of the following in any combination, as follows:

[0012] The first type: When the train is in an open or semi-covered scene, the train positioning terminal combines Beidou PPP-RTK and the initialized INS to track the train's position, and then combines the train's initial position to recursively and correct it to obtain the train's real-time position information;

[0013] The second method: When the train enters a fully obscured scene, the train positioning terminal first receives the UWB base station signal in the UWB raw data, obtains the train’s prior elevation, and then establishes an elevation-constrained UWB model. The train’s position is tracked through the elevation-constrained UWB model and the initialized INS. Combined with the train’s initial position, the train’s real-time position information is obtained through recursion and correction.

[0014] The step 4 also includes a third scenario, which is as follows:

[0015] When the train leaves the fully shielded scene, the train positioning terminal first receives the PPP-RTK correction information and the UWB base station signal in the UWB original data, establishes the elevation-constrained PPP-RTK model, obtains the PPP-RTK positioning result, and then inputs the PPP-RTK positioning result into the INS for update, tracks the train position, and then combines the train's initial position to recursively and correct the train's real-time position information.

[0016] In the fourth step, after the train positioning terminal receives the UWB base station signal, it also includes receiving the UWB base station elevation information U k The height H of the track surface from the UWB base station k .

[0017] The train priori altitude is specifically:

[0018] U=U k -H k +H0;

[0019] Among them, H0 is the height of the combined positioning terminal antenna installed on the train, U is taken as the elevation pseudo-observation value h, and the elevation above the earth ellipsoid is h<10km, satisfying the following relationship:

[0020]

[0021] Among them, a is the major semi-axis of the ellipsoid, b is the minor semi-axis of the ellipsoid, h is the elevation of this point, (x, y, z) are the coordinates of this point in the Earth-centered Earth-fixed coordinate system, and the partial derivative of h with respect to x, y, and z is obtained:

[0022]

[0023] So the differential dh is:

[0024]

[0025] The elevation constraint UWB model in step 4 is specifically:

[0026]

[0027] Among them, σ r is the standard deviation, is the standard deviation of the pseudo-observation elevation.

[0028] The elevation-constrained PPP-RTK model in step 4 is specifically:

[0029] The network receives GNSS pseudorange and phase observation data and constructs the original observation equation:

[0030]

[0031] Where E(·) represents the expectation operator, and They represent pseudorange and phase observation values, respectively. The station-satellite distance and tropospheric interference delay contained in them have been corrected in advance. c represents the speed of light, s represents the satellite number, r represents the receiver number, j represents the frequency band number, i represents the epoch number, and dt r and dt s is the receiver and satellite clock difference, τ r is the zenith tropospheric wet delay, τ r The projection function is represents the ionospheric delay, The coefficient of is the ratio between the ionospheric delay corresponding to frequency j and the first frequency, λ j is the wavelength corresponding to frequency j, d r，j and represents the pseudorange hardware delay between receiver r and satellite s corresponding to frequency j, δ r，j and represents the phase deviation between the receiver r and the satellite s corresponding to frequency j, is the integer ambiguity corresponding to the frequency j of the receiver r and the satellite s;

[0032] Taking the network-side reference station p as the reference station, the network-side ionosphere weighted pseudo-observation equation is constructed:

[0033]

[0034] Through the S-basis rank deficiency elimination method, the rank deficiency of the original observation equation and the pseudo observation equation is eliminated, and a new observation equation is constructed:

[0035]

[0036] The specific form of the new estimable parameters is:

[0037]

[0038] in,

[0039]

[0040] in, is the new estimable receiver clock error, is the satellite clock error, is the zenith tropospheric wet delay, is the ionospheric delay, is the receiver pseudorange hardware delay, is the satellite pseudorange hardware delay, is the receiver phase deviation, is the satellite phase deviation, is the fuzziness, is the hardware delay deviation between receiver r and receiver p.

[0041] The user terminal constructs the observation equation based on the received pseudorange, phase observation values ​​and the correction information broadcast by the network:

[0042]

[0043] Wherein, the subscript u represents the user terminal receiver, is the ionospheric delay value near the user station interpolated according to the network-side ionospheric delay information, as a pseudo-observation value, is the unit vector from the station to the satellite, Δx u (i) is the correction value of the coordinates, is the receiver clock error, is the tropospheric zenith wet delay, is the ambiguity parameter, is the pseudorange hardware delay, is the geometrically independent combination value of pseudorange hardware delay, is the ionospheric slant delay, is the receiver phase deviation, is the receiver phase deviation;

[0044] When entering or exiting a tunnel or other fully shielded area, you can impose elevation constraints based on the received elevation pseudo-observation Un:

[0045]

[0046] The corresponding random model is:

[0047]

[0048] Among them, σ p =0.3m,σ φ =0.003m,σ I=0.3m, d n is the distance value from the terminal to the UWB base station n.

[0049] The PPP-RTK uses a sparsely distributed reference station network on the ground to calculate precise correction information products of satellite deviations and atmospheric delays, which are broadcast to users through satellite communications / ground networks, etc., ultimately achieving fast ambiguity-fixed positioning.

[0050] A positioning system for a Beidou PPP-RTK train full-scene positioning method, the positioning system for the Beidou PPP-RTK train full-scene positioning method comprising a reference station network, a computing center and a train positioning terminal;

[0051] The reference station network includes a BeiDou / GNSS PPP-RTK reference station network and a UWB tunnel / platform base station network;

[0052] The BeiDou / GNSS PPP-RTK reference station network is deployed along the railway with an average station spacing of 100 km, receiving and sending BeiDou satellite data to the computing center;

[0053] The UWB tunnel / platform base station network provides UWB base station information and ranging information to the train terminal;

[0054] The computing center refers to the PPP-RTK network computing center, which collects Beidou satellite data observed by the PPP-RTK base station network, and calculates and broadcasts correction information of satellite phase deviation, satellite clock error, ionospheric delay, and tropospheric delay in real time;

[0055] The train positioning terminal is used to receive Beidou satellite observation data, Beidou correction information broadcast by the computing center, UWB ranging data, and its own INS observation data, and calculate the train position in real time through an adaptive fusion positioning algorithm.

[0056] A positioning terminal for a Beidou PPP-RTK train full-scenario positioning method, the positioning terminal for the Beidou PPP-RTK train full-scenario positioning method comprising a GNSS module, an INS module, a UWB module, a signal control module, a communication module and a core processing module;

[0057] The GNSS module is used to receive Beidou / GNSS raw observation data and send 1Hz pps pulses to the signal control module, providing GPS or BDS time for time calibration of the core processing module;

[0058] The INS module is used to generate high-frequency three-axis angular velocity and three-axis acceleration raw observation data;

[0059] The UWB module is used to receive UWB base station ranging information and achieve time synchronization through the core processing module time;

[0060] The signal control module is used to provide different frequency signals for the inertial navigation system to generate INS data of corresponding frequencies;

[0061] The communication module is used to transmit data via 4G / 5G / WiFi, receive PPP-RTK correction information, and output real-time combined positioning results or original observation data of each sensor;

[0062] The core processing module is used to receive the original observation data of each sensor, perform time synchronization, integrate the PPP-RTK / INS / UWB adaptive fusion algorithm, and calculate the positioning result in real time.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. In a Beidou PPP-RTK train full-scene positioning method, system and terminal of the present invention, an adaptive fusion positioning scheme is adopted in different scenes to realize the PPP-RTK technology, PPP-RTK / INS combination technology and UWB / INS combination technology method application of the Beidou PPP-B2b constraint in phased matching and switching in different scenes, to achieve full-scene decimeter-level positioning accuracy, and at the same time have the advantages of high efficiency and low cost. Therefore, the present invention has high positioning accuracy for trains in complex scenes.

[0065] 2. In a Beidou PPP-RTK train full-scene positioning method, system and terminal of the present invention, the terminal positioning result and other GNSS estimated parameters are obtained after filtering. Based on the prior track elevation constraint, the PPP-RTK convergence speed is accelerated, the positioning accuracy is improved, and a higher accuracy can be obtained under less satellite observation conditions when the train exits the tunnel. Therefore, the present invention has faster convergence speed and high positioning accuracy.

[0066] 3. In a Beidou PPP-RTK train full-scene positioning method, system and terminal of the present invention, the elevation deviation of the traditional method can reach 1m, while in this solution, the elevation direction accuracy is greatly improved, and the elevation deviation is reduced to within 0.5m based on the prior track elevation constraint. The elevation accuracy of the fully blocked scene such as the tunnel is improved by 90%, and the PPP-RTK ambiguity fixation speed out of the tunnel is accelerated, the ambiguity fixation success rate is improved, and the positioning accuracy is more accurate. Therefore, the elevation deviation of the present invention is small and the positioning accuracy is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a diagram of the horizontal positioning results of the PPP-RTK / INS combination in stages 1 and 2 of the experiment in the present invention.

[0068] Figure 2This is the PPP-RTK / INS combined elevation positioning result diagram for phases 1 and 2 of the experiment in the present invention.

[0069] Figure 3 It is the PPP-RTK / INS three-dimensional positioning error diagram of the experiments 1 and 2 in the present invention.

[0070] Figure 4 This is the horizontal positioning result of the PPP-RTK / INS combination + INS recursive positioning in the three-stage experiment of the present invention.

[0071] Figure 5 It is the result of the PPP-RTK / INS combination + INS recursive elevation positioning in the three-stage experiment of the present invention.

[0072] Figure 6 This is the three-dimensional positioning error diagram of the PPP-RTK / INS combination + INS recursion in the three stages of the experiment in the present invention.

[0073] Figure 7 This is a diagram of the horizontal positioning results of the UWB / INS combination in the fourth stage of the experiment in the present invention.

[0074] Figure 8 This is a diagram of the UWB / INS combined elevation positioning results in the fourth phase of the experiment in the present invention.

[0075] Fig. 9 It is the UWB / INS three-dimensional positioning error diagram of the fourth stage of the experiment in the present invention.

[0076] Fig.10 It is the combined positioning error of different scenes in all stages of the experiment in the present invention.

[0077] Fig.11 This is a map of the PPP-RTK distribution and experimental locations in the present invention. DETAILED DESCRIPTION

[0078] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0079] See also Figures 1 to 11 , a Beidou PPP-RTK train full-scene positioning method, the Beidou PPP-RTK train full-scene positioning method comprises the following steps:

[0080] Step 1: The base station network first obtains GNSS raw data and UWB raw data, then sends the GNSS raw data to the computing center, and sends the UWB raw data to the train positioning terminal;

[0081] Step 2: The computing center first solves the GNSS raw data to obtain PPP-RTK correction information, and then sends the PPP-RTK correction information to the train positioning terminal;

[0082] Step 3: The train positioning terminal is stationary to initialize the INS zero bias and attitude to obtain the corrected INS. Then, the PPP-RTK correction information is combined with the UWB original data for filtering. The filtered data is then input into the corrected INS to obtain the initialized INS. The initialized INS is then used to locate the train to obtain the initial position of the train.

[0083] Step 4: When the train enters the running state, the method for positioning the train includes any one or more of the following in any combination, as follows:

[0084] The first type: When the train is in an open or semi-covered scene, the train positioning terminal combines Beidou PPP-RTK and the initialized INS to track the train's position, and then combines the train's initial position to recursively and correct it to obtain the train's real-time position information;

[0085] The second method: When the train enters a fully obscured scene, the train positioning terminal first receives the UWB base station signal in the UWB raw data, obtains the train’s prior elevation, and then establishes an elevation-constrained UWB model. The train’s position is tracked through the elevation-constrained UWB model and the initialized INS. Combined with the train’s initial position, the train’s real-time position information is obtained through recursion and correction.

[0086] In the fourth step, after the train positioning terminal receives the UWB base station signal, it also includes receiving the UWB base station elevation information U k The height H of the track surface from the UWB base station k .

[0087] The train priori altitude is specifically:

[0088] U=U k -H k +H0;

[0089] Among them, H0 is the elevation of the combined positioning terminal antenna installed on the train, and U is taken as the elevation pseudo-observation value h. This approximate elevation value U is taken as a pseudo-observation value to constrain the PPP-RTK solution outside the tunnel and the UWB solution inside the tunnel. This will improve the PPP-RTK positioning speed and accuracy, and greatly improve the UWB elevation positioning accuracy. The elevation above the earth's ellipsoid is h<10km, satisfying the following relationship:

[0090]

[0091] Among them, a is the major semi-axis of the ellipsoid, b is the minor semi-axis of the ellipsoid, h is the elevation of this point, (x, y, z) are the coordinates of this point in the Earth-centered Earth-fixed coordinate system, and the partial derivative of h with respect to x, y, and z is obtained:

[0092]

[0093] So the differential dh is:

[0094]

[0095] The elevation constraint UWB model in step 5 is specifically:

[0096]

[0097] Among them, σ r is the standard deviation, is the standard deviation of the pseudo-observation elevation.

[0098] The elevation-constrained PPP-RTK model in step 4 is specifically:

[0099] The network receives GNSS pseudorange and phase observation data and constructs the original observation equation:

[0100]

[0101] Where E(·) represents the expectation operator, and They represent pseudorange and phase observation values, respectively. The station-satellite distance and tropospheric interference delay contained in them have been corrected in advance. c represents the speed of light, s represents the satellite number, r represents the receiver number, j represents the frequency band number, i represents the epoch number, and dt r and dt s is the receiver and satellite clock difference, τ r is the zenith tropospheric wet delay, τ r The projection function is represents the ionospheric delay, The coefficient of is the ratio between the ionospheric delay corresponding to frequency j and the first frequency, λ j is the wavelength corresponding to frequency j, d r,j and represents the pseudorange hardware delay between receiver r and satellite s corresponding to frequency j, δ r,j and represents the phase deviation between the receiver r and the satellite s corresponding to frequency j, is the integer ambiguity corresponding to the frequency j of the receiver r and the satellite s;

[0102] Taking the network-side reference station p as the reference station, the network-side ionosphere weighted pseudo-observation equation is constructed:

[0103]

[0104] Through the S-basis rank deficiency elimination method, the rank deficiency of the original observation equation and the pseudo observation equation is eliminated, and a new observation equation is constructed:

[0105]

[0106] The specific form of the new estimable parameters is:

[0107]

[0108] in,

[0109]

[0110] d r，DCB =d r，2 -d r，1 ;

[0111] in, is the new estimable receiver clock error, is the satellite clock error, is the zenith tropospheric wet delay, is the ionospheric delay, is the receiver pseudorange hardware delay, is the satellite pseudorange hardware delay, is the receiver phase deviation, is the satellite phase deviation, is the fuzziness, is the hardware delay deviation between receiver r and receiver p.

[0112] The user terminal constructs the observation equation based on the received pseudorange, phase observation values ​​and the correction information broadcast by the network:

[0113]

[0114] Wherein, the subscript u represents the user terminal receiver, is the ionospheric delay value near the user station interpolated according to the network-side ionospheric delay information, as a pseudo-observation value, is the unit vector from the station to the satellite, Δx u (i) is the correction value of the coordinates, is the receiver clock error, is the tropospheric zenith wet delay, is the ambiguity parameter, is the pseudorange hardware delay, is the geometrically independent combination value of pseudorange hardware delay, is the ionospheric slant delay, is the receiver phase deviation, is the receiver phase deviation;

[0115] When entering or exiting a tunnel or other fully shielded area, you can impose elevation constraints based on the received elevation pseudo-observation Un:

[0116]

[0117] The corresponding random model is:

[0118]

[0119] Among them, σ p =0.3m,σ φ =0.003m,σ I =0.3, d n is the distance value from the terminal to the UWB base station n.

[0120] The PPP-RTK uses a sparsely distributed reference station network on the ground to calculate precise correction information products of satellite deviations and atmospheric delays, which are broadcast to users through satellite communications / ground networks, etc., ultimately achieving fast ambiguity-fixed positioning.

[0121] The PPP-RTK correction information and UWB original data are filtered and input into the corrected INS, which also includes filtering the UWB positioning, wherein the UWB positioning is to measure the propagation time of the signal to calculate the distance between the UWB base station and the train positioning terminal, thereby determining the position of the train positioning terminal.

[0122] A positioning system for a Beidou PPP-RTK train full-scene positioning method, the positioning system for the Beidou PPP-RTK train full-scene positioning method comprising a reference station network, a computing center and a train positioning terminal;

[0123] The reference station network includes a BeiDou / GNSS PPP-RTK reference station network and a UWB tunnel / platform base station network;

[0124] The BeiDou / GNSS PPP-RTK reference station network is deployed along the railway with an average station spacing of 100 km, receiving and sending BeiDou satellite data to the computing center;

[0125] The UWB tunnel / platform base station network provides UWB base station information and ranging information to the train terminal;

[0126] The computing center refers to the PPP-RTK network computing center, which collects Beidou satellite data observed by the PPP-RTK base station network, and calculates and broadcasts correction information of satellite phase deviation, satellite clock error, ionospheric delay, and tropospheric delay in real time;

[0127] The train positioning terminal is used to receive Beidou satellite observation data, Beidou correction information broadcast by the computing center, UWB ranging data, and its own INS observation data, and calculate the train position in real time through an adaptive fusion positioning algorithm.

[0128] A positioning terminal for a Beidou PPP-RTK train full-scenario positioning method, the positioning terminal for the Beidou PPP-RTK train full-scenario positioning method comprising a GNSS module, an INS module, a UWB module, a signal control module, a communication module and a core processing module;

[0129] The GNSS module is used to receive Beidou / GNSS raw observation data and send 1Hz pps pulses to the signal control module, providing GPS or BDS time for time calibration of the core processing module;

[0130] The INS module is used to generate high-frequency three-axis angular velocity and three-axis acceleration raw observation data;

[0131] The UWB module is used to receive UWB base station ranging information and achieve time synchronization through the core processing module time;

[0132] The signal control module is used to provide different frequency signals for the inertial navigation system to generate INS data of corresponding frequencies;

[0133] The communication module is used to transmit data via 4G / 5G / WiFi, receive PPP-RTK correction information, and output real-time combined positioning results or original observation data of each sensor;

[0134] The core processing module is used to receive the original observation data of each sensor, perform time synchronization, integrate the PPP-RTK / INS / UWB adaptive fusion algorithm, and calculate the positioning result in real time.

[0135] The supplementary description of the present invention is as follows:

[0136] The non-ionospheric differential PPP-RTK technology can freely select the reference station network within the region. Compared with the traditional ionospheric differential PPP-RTK technology that relies on the global reference station network, it is more suitable for building a nationwide high-precision satellite positioning system for trains based on domestic benchmarks using only Beidou, which meets the needs of modern railway safety application services.

[0137] Embodiment 1:

[0138] A Beidou PPP-RTK train full-scene positioning method, the Beidou PPP-RTK train full-scene positioning method comprising the following steps:

[0139] Step 1: The base station network first obtains GNSS raw data and UWB raw data, then sends the GNSS raw data to the computing center, and sends the UWB raw data to the train positioning terminal;

[0140] Step 2: The computing center first solves the GNSS raw data to obtain PPP-RTK correction information, and then sends the PPP-RTK correction information to the train positioning terminal;

[0141] Step 3: The train positioning terminal is stationary to initialize the INS zero bias and attitude to obtain the corrected INS. Then, the PPP-RTK correction information is combined with the UWB original data for filtering. The filtered data is then input into the corrected INS to obtain the initialized INS. The initialized INS is then used to locate the train to obtain the initial position of the train.

[0142] Step 4: When the train enters the running state, the method for positioning the train includes any one or more of the following in any combination, as follows:

[0143] The first type: When the train is in an open or semi-covered scene, the train positioning terminal combines Beidou PPP-RTK and the initialized INS to track the train's position, and then combines the train's initial position to recursively and correct it to obtain the train's real-time position information;

[0144] The second method: When the train enters a fully obscured scene, the train positioning terminal first receives the UWB base station signal in the UWB raw data, obtains the train’s prior elevation, and then establishes an elevation-constrained UWB model. The train’s position is tracked through the elevation-constrained UWB model and the initialized INS. Combined with the train’s initial position, the train’s real-time position information is obtained through recursion and correction.

[0145] Embodiment 2:

[0146] Embodiment 2 is substantially the same as Embodiment 1, except that:

[0147] Taking partial derivatives of h with respect to x, y, and z, we get:

[0148]

[0149] So the differential dh is:

[0150]

[0151] The above formula is the prior orbit elevation constraint equation, where dh = U-h0, U is the elevation pseudo-observation value formula, and h0 is the elevation corresponding to the approximate point (x0, y0, z0);

[0152] Using UWB observation value r n and the elevation pseudo observation value U n Combined to form the observation equation:

[0153]

[0154] Among them, (Xn, Yn, Zn) are the coordinates of the nth UWB base station, and the least squares adjustment method can be used to solve the position parameters (x, y, z) based on the prior track elevation constraints;

[0155] The UWB ranging accuracy is 0.2m, corresponding to the prior standard deviation:

[0156] σ r =0.2;

[0157] For the accuracy of pseudo-observation values ​​of elevation, the slope of the train track is usually less than 20‰. According to the distance d between the train positioning terminal and the UWB base station, n 20‰ is used as the accuracy of the elevation pseudo-observation value. The corresponding elevation change of 10m is less than 0.2m, and the corresponding accuracy is 0.2m. The following is the formula for the standard deviation of the elevation pseudo-observation value:

[0158]

[0159] The elevation constraint UWB model in step 5 is specifically:

[0160]

[0161] Among them, σ r is the standard deviation, is the standard deviation of the pseudo-observation elevation.

[0162] The elevation-constrained PPP-RTK model is as follows:

[0163] The network receives GNSS pseudorange and phase observation data and constructs the original observation equation:

[0164]

[0165] Where E(·) represents the expectation operator, and They represent pseudorange and phase observation values, respectively. The station-satellite distance and tropospheric interference delay contained in them have been corrected in advance. c represents the speed of light, s represents the satellite number, r represents the receiver number, j represents the frequency band number, i represents the epoch number, and dt r and dt s is the receiver and satellite clock difference, τ r is the zenith tropospheric wet delay, τ r The projection function is represents the ionospheric delay, The coefficient of is the ratio between the ionospheric delay corresponding to frequency j and the first frequency, λ j is the wavelength corresponding to frequency j, d r，j and represents the pseudorange hardware delay between receiver r and satellite s corresponding to frequency j, δ r，j and represents the phase deviation between the receiver r and the satellite s corresponding to frequency j, is the integer ambiguity corresponding to the frequency j of the receiver r and the satellite s;

[0166] Taking the network-side reference station p as the reference station, the network-side ionosphere weighted pseudo-observation equation is constructed:

[0167]

[0168] Through the S-basis rank deficiency elimination method, the rank deficiency of the original observation equation and the pseudo observation equation is eliminated, and a new observation equation is constructed:

[0169]

[0170] The specific form of the new estimable parameters is:

[0171]

[0172] in,

[0173]

[0174] d r，DCB =d r，2 -d r，1 ;

[0175] in, is the new estimable receiver clock error, is the satellite clock error, is the zenith tropospheric wet delay, is the ionospheric delay, is the receiver pseudorange hardware delay, is the satellite pseudorange hardware delay, is the receiver phase deviation, is the satellite phase deviation, is the fuzziness, is the hardware delay deviation between receiver r and receiver p.

[0176] The user terminal constructs the observation equation based on the received pseudorange, phase observation values ​​and the correction information broadcast by the network:

[0177]

[0178] Wherein, the subscript u represents the user terminal receiver, is the ionospheric delay value near the user station interpolated according to the network-side ionospheric delay information, as a pseudo-observation value, is the unit vector from the station to the satellite, Δxu (i) is the correction value of the coordinates, is the receiver clock error, is the tropospheric zenith wet delay, is the ambiguity parameter, is the pseudorange hardware delay, is the geometrically independent combination value of pseudorange hardware delay, is the ionospheric slant delay, is the receiver phase deviation, is the receiver phase deviation;

[0179] When entering or exiting a tunnel or other fully shielded area, you can impose elevation constraints based on the received elevation pseudo-observation Un:

[0180]

[0181] The corresponding random model is:

[0182]

[0183] Among them, σ p =0.3m,σ φ =0.003m,σ I =0.3, d n is the distance value from the terminal to the UWB base station n.

[0184] Embodiment 3:

[0185] Embodiment 3 is substantially the same as Embodiment 1, except that:

[0186] A positioning system for a Beidou PPP-RTK train full-scene positioning method, the positioning system for the Beidou PPP-RTK train full-scene positioning method comprising a reference station network, a computing center and a train positioning terminal; the reference station network comprises a Beidou / GNSS PPP-RTK reference station network and a UWB tunnel / platform base station network; the Beidou / GNSS PPP-RTK reference station network is arranged along a railway, with an average station spacing of 100 km, and receives and sends Beidou satellite data to a computing center; the UWB tunnel / platform base station network provides UWB base station information and ranging information to a train terminal; the computing center refers to a PPP-RTK network end computing center, which collects Beidou satellite data observed by the PPP-RTK reference station network, and solves and broadcasts correction information of satellite phase deviation, satellite clock error, ionospheric delay and tropospheric delay in real time; the train positioning terminal is used to receive Beidou satellite observation data, Beidou correction information broadcast by the computing center, UWB ranging data, and INS observation data generated by itself, and calculates the train position in real time through an adaptive fusion positioning algorithm.

[0187] Embodiment 4:

[0188] Embodiment 4 is substantially the same as Embodiment 1, except that:

[0189] A positioning terminal for a Beidou PPP-RTK train full-scene positioning method, the positioning terminal for the Beidou PPP-RTK train full-scene positioning method comprising a GNSS module, an INS module, a UWB module, a signal control module, a communication module and a core processing module; the GNSS module is used to receive Beidou / GNSS original observation data, and send 1Hz pps second pulses to the signal control module, and provide GPS or BDS time to perform time calibration on the core processing module; the INS module is used to generate high-frequency three-axis angular velocity and three-axis acceleration original observation data; the UWB module is used to receive UWB base station ranging information, and realize time synchronization through the core processing module time; the signal control module is used to provide different frequency signals for inertial navigation to generate INS data of corresponding frequencies; the communication module is used to transmit data via 4G / 5G / WiFi, receive PPP-RTK correction information, and output real-time combined positioning results or original observation data of each sensor; the core processing The module is used to receive the original observation data of each sensor, perform time synchronization, integrate the PPP-RTK / INS / UWB adaptive fusion algorithm, and calculate the positioning results in real time. High-speed trains usually consist of 8 carriages to form a power unit group with a total length of about 210 meters. Beidou PPP-RTK / INS / UWB combination equipment can be deployed at the head and tail of the train. Multiple terminals jointly provide positioning results, thereby improving the redundancy of the system and the availability of positioning. At the same time, through the known distance constraints between terminals, multi-station collaborative positioning can be achieved, the error detection capability of the observation data can be improved, and the positioning accuracy and reliability of the system can be effectively improved.

[0190] Embodiment 6:

[0191] Embodiment 6 is substantially the same as Embodiment 1, except that:

[0192] A PPP-RTK / INS / UWB dynamic experimental scene for a rail vehicle is set up, covering a 15m*45m rooftop area. The GNSS / INS / UWB combined positioning terminal is fixed on the upper surface of the vehicle. The initial attitude of INS is (0, 0, 290°), the GNSS antenna arm error is (-0.18m, -0.04m, -0.51m), and the UWB antenna arm error is (-0.18m, -0.04m, -0.03m); Fig.11 As shown in the figure, the distribution map of PPP-RTK Hubei Wuhan network and experimental locations. The PPP-RTK base station uses the Hubei Wuhan reference network. Fig.11As shown, there are 11 base stations in total, distributed around Wuhan City, with an average station spacing of 38.24 km. The nearest reference station is 11.80 km away from the experimental site. The UWB base stations are distributed in a rectangular shape at 4 points outside the track, with an east-west spacing of about 45 m and a north-south spacing of about 15 m. Base stations No. 1 / 3 are at the same height as the UWB antenna of the combined positioning terminal, and base stations No. 2 / 4 increase the height by 1 m on this basis, and use RTK to accurately measure the position of the UWB base stations. The experiment started at 135470 seconds in the GPS week on October 14, and lasted for 1 hour until 139070 seconds. It was divided into four stages: 1. Keep still for the first 600 seconds to perform inertial navigation zero bias compensation and attitude calculation; 2600s-1600s use the PPP-RTK+INS combination to verify the combined positioning performance in open scenes; 31600s-2600s disconnect the GNSS data for 10s every 10s, use a single INS to guess for 10s, repeat 50 times, and verify the combined positioning performance in complex occlusion scenes; 42600s-3600s use the UWB+INS combination to verify the combined positioning performance in fully occluded scenes. In the experiment, the UBUC reference station with an average distance of 50m was used as the base station to calculate the millimeter-level RTK / INS combined positioning true value to compare the PPP-RTK / INS / UWB combined positioning errors at each stage;

[0193] Open scene simulation:

[0194] like Figure 1 The PPP-RTK / INS combined positioning results of experimental phases 1 and 2 are shown. In phase 1, the car remains stationary; in phase 2, the 12 laps of the car are highly overlapped, and the elevation results fluctuate with the track elevation. Figure 2 The positioning error graphs of the PPP-RTK / INS combination in stages 1 and 2 are presented, and the results show that the horizontal positioning error is better than 2 cm, the elevation positioning error is better than 5 cm, and the root mean square errors (RMSE) in the north, east and sky directions are 0.51 cm, 0.90 cm and 2.47 cm respectively, verifying the ability of the combined system to achieve centimeter-level positioning performance in open scenes.

[0195] Semi-occluded scene simulation:

[0196] like Figure 3 In the experimental phase 3 shown in the figure, when the GNSS data is disconnected and restored every 10 seconds, the combined positioning results of PPP-RTK and INS show that the trajectory will be slightly offset relative to the orbit in the 10-second inertial navigation calculation, and the elevation will also deviate from the orbital plane. Figure 4From the error diagrams of the north, east and sky directions, it can be seen that the maximum offsets in the north and east directions are both within 35 cm, while the maximum offset in the elevation direction is 55 cm. The RMSEs in the north, east and sky directions are 4.77 cm, 3.40 cm and 7.19 cm respectively, which verifies that the combined system has decimeter-level positioning performance in semi-shaded scenes.

[0197] Full occlusion scene simulation:

[0198] like Figure 5 The results of UWB / INS combined positioning in experimental phase 4 are shown. On the horizontal positioning track, a small bend appeared, with a deviation range of less than 20 cm, which was mainly affected by the stone piers beside the track, resulting in obstruction and multipath effects of UWB observations. Figure 5 The elevation fluctuation in the image is within 1m, which is mainly due to the small height difference (1m) of the four UWB base stations, resulting in poor geometric configuration in the elevation direction, thus affecting the elevation positioning capability. Figure 6 The three-dimensional error of UWB / INS combined positioning, the RMSE in the north, east and sky directions are 5.96cm, 3.74cm and 43.36cm respectively, the horizontal error is less than 20cm, and the elevation error is less than 1m, which proves that the combined system has decimeter-level positioning performance in fully shielded scenarios.

[0199] like Figure 7 The three-dimensional positioning errors of all four stages of the experiment are shown. In the first stage, the car remained stationary. In the second stage, the positioning accuracy of the PPP-RTK / INS combination was better than 2 cm, proving that the system has centimeter-level positioning capability under open conditions. In the third stage, under the simulated complex short-term occlusion scenario, the horizontal offset calculated by INS was less than 35 cm within 10 seconds, and the RMSE in the north, east and sky directions were 4.77 cm, 3.40 cm and 7.19 cm respectively, indicating that the combined positioning system has centimeter to decimeter-level positioning capability in complex occlusion scenarios. In the fourth stage, the horizontal error of the UWB / INS combined positioning was less than 20 cm, and the RMSE in the north, east and sky directions were 5.96 cm, 3.74 cm and 43.36 cm respectively, proving that the combined system has decimeter-level positioning capability in fully occluded scenarios.

[0200] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A Beidou PPP-RTK train full-scene positioning method, characterized by: The Beidou PPP-RTK train full-scene positioning method comprises the following steps: Step 1: The base station network first obtains GNSS raw data and UWB raw data, then sends the GNSS raw data to the computing center, and sends the UWB raw data to the train positioning terminal; Step 2: The computing center first solves the GNSS raw data to obtain PPP-RTK correction information, and then sends the PPP-RTK correction information to the train positioning terminal; Step 3: The train positioning terminal is stationary to initialize the INS zero bias and attitude to obtain the corrected INS. Then, the PPP-RTK correction information is combined with the UWB original data for filtering. The filtered data is then input into the corrected INS to obtain the initialized INS. The initialized INS is then used to locate the train to obtain the initial position of the train. Step 4: When the train enters the running state, the method for positioning the train includes any one or more of the following in any combination, as follows: The first type: When the train is in an open or semi-covered scene, the train positioning terminal combines Beidou PPP-RTK and the initialized INS to track the train's position, and then combines the train's initial position to recursively and correct it to obtain the train's real-time position information; The second method: When the train enters a fully obscured scene, the train positioning terminal first receives the UWB base station signal in the UWB raw data, obtains the train’s prior elevation, and then establishes an elevation-constrained UWB model. The train’s position is tracked through the elevation-constrained UWB model and the initialized INS. Combined with the train’s initial position, the train’s real-time position information is obtained through recursion and correction.

2. A Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: The step 4 also includes a third scenario, which is as follows: When the train leaves the fully shielded scene, the train positioning terminal first receives the PPP-RTK correction information and the UWB base station signal in the UWB original data, establishes the elevation-constrained PPP-RTK model, obtains the PPP-RTK positioning result, and then inputs the PPP-RTK positioning result into the INS for update, tracks the train position, and then combines the train's initial position to recursively and correct the train's real-time position information.

3. A Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: In the step 4, after the train positioning terminal receives the UWB base station signal in the UWB original data, it also includes receiving the UWB base station elevation information U k The height H of the track surface from the UWB base station k .

4. A Beidou PPP-RTK train full-scene positioning method according to claim 3, characterized in that: The train priori altitude is specifically: U=U k -H k +H0; Among them, H0 is the height of the combined positioning terminal antenna installed on the train, U is taken as the elevation pseudo-observation value h, and the elevation above the earth ellipsoid is h<10km, satisfying the following relationship: Among them, a is the major semi-axis of the ellipsoid, b is the minor semi-axis of the ellipsoid, h is the elevation of this point, (x, y, z) are the coordinates of this point in the Earth-centered Earth-fixed coordinate system, and the partial derivative of h with respect to x, y, and z is obtained: So the differential dh is:

5. A Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: The elevation constraint UWB model in step 4 is specifically: Among them, σ r is the standard deviation, is the standard deviation of the pseudo-observation elevation.

6. A Beidou PPP-RTK train full-scene positioning method according to claim 5, characterized in that: The elevation-constrained PPP-RTK model in step 4 is specifically: The network receives GNSS pseudorange and phase observation data and constructs the original observation equation: Where E(·) represents the expectation operator, and They represent pseudorange and phase observation values, respectively. The station-satellite distance and tropospheric interference delay contained in them have been corrected in advance. c represents the speed of light, S represents the satellite number, r represents the receiver number, j represents the frequency band number, i represents the epoch number, and dt r and dt s is the receiver and satellite clock difference, τ r is the zenith tropospheric wet delay, τ r The projection function is represents the ionospheric delay, The coefficient of is the ratio between the ionospheric delay corresponding to frequency j and the first frequency, λ j is the wavelength corresponding to frequency j, d r，j and represents the pseudorange hardware delay between receiver r and satellite s corresponding to frequency j, δ r，j and represents the phase deviation between the receiver r and the satellite s corresponding to frequency j, is the integer ambiguity corresponding to the frequency j of the receiver r and the satellite s; Taking the network-side reference station p as the reference station, the network-side ionosphere weighted pseudo-observation equation is constructed: Through the S-basis rank deficiency elimination method, the rank deficiency of the original observation equation and the pseudo observation equation is eliminated, and a new observation equation is constructed: The specific form of the new estimable parameters is: in, in, is the new estimable receiver clock error, is the satellite clock error, is the zenith tropospheric wet delay, is the ionospheric delay, is the receiver pseudorange hardware delay, is the satellite pseudorange hardware delay, is the receiver phase deviation, is the satellite phase deviation, is the fuzziness, is the hardware delay deviation between receiver r and receiver p.

7. A Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: The user terminal constructs the observation equation based on the received pseudorange, phase observation values ​​and the correction information broadcast by the network: Wherein, the subscript u represents the user terminal receiver, is the ionospheric delay value near the user station interpolated according to the network-side ionospheric delay information, as a pseudo-observation value, is the unit vector from the station to the satellite, Δx u (i) is the correction value of the coordinates, is the receiver clock error, is the tropospheric zenith wet delay, is the fuzziness parameter, is the pseudorange hardware delay, is the geometrically independent combination value of pseudorange hardware delay, is the ionospheric slant delay, is the receiver phase deviation; When entering or exiting a tunnel or other fully shielded area, you can impose elevation constraints based on the received elevation pseudo-observation Un: The corresponding random model is: Among them, p p =0.3m,s φ =0.003m,s I =0.3m, d n is the distance value from the terminal to the UWB base station n.

8. A Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: The GNSS raw data includes satellite observation information and PPP-B2b information, and the UWB raw data includes UWB base station coordinates, ranging values ​​and UWB base station signals.

9. A positioning system for the Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: The positioning system of the Beidou PPP-RTK train full-scene positioning method includes a reference station network, a computing center and a train positioning terminal; The reference station network includes a BeiDou / GNSS PPP-RTK reference station network and a UWB tunnel / platform base station network; The BeiDou / GNSS PPP-RTK reference station network is deployed along the railway with an average station spacing of 100 km, receiving and sending BeiDou satellite data to the computing center; The UWB tunnel / platform base station network provides UWB base station information and ranging information to the train terminal; The computing center refers to the PPP-RTK network computing center, which collects Beidou satellite data observed by the PPP-RTK base station network, and calculates and broadcasts correction information of satellite phase deviation, satellite clock error, ionospheric delay, and tropospheric delay in real time; The train positioning terminal is used to receive Beidou satellite observation data, Beidou correction information broadcast by the computing center, UWB ranging data, and its own INS observation data, and calculate the train position in real time through an adaptive fusion positioning algorithm.

10. A positioning terminal for the Beidou PPP-RTK train full-scene positioning method according to claim 1, characterized in that: The positioning terminal of the Beidou PPP-RTK train full-scene positioning method includes a GNSS module, an INS module, a UWB module, a signal control module, a communication module and a core processing module; The GNSS module is used to receive Beidou / GNSS raw observation data and send 1Hz pps pulses to the signal control module, providing GPS or BDS time for time calibration of the core processing module; The INS module is used to generate high-frequency three-axis angular velocity and three-axis acceleration raw observation data; The UWB module is used to receive UWB base station ranging information and achieve time synchronization through the core processing module time; The signal control module is used to provide different frequency signals for the inertial navigation system to generate INS data of corresponding frequencies; The communication module is used to transmit data via 4G / 5G / WiFi, receive PPP-RTK correction information, and output real-time combined positioning results or original observation data of each sensor; The core processing module is used to receive the original observation data of each sensor, perform time synchronization, integrate the PPP-RTK / INS / UWB adaptive fusion algorithm, and calculate the positioning result in real time.

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